idx
int64 0
7.85k
| idx_lca
int64 0
223
| offset
int64 162
55k
| repo
stringclasses 62
values | commit_hash
stringclasses 113
values | target_file
stringclasses 134
values | line_type_lca
stringclasses 7
values | ground_truth
stringlengths 1
46
| in_completions
bool 1
class | completion_type
stringclasses 6
values | non_dunder_count_intellij
int64 0
529
| non_dunder_count_jedi
int64 0
128
| start_with_
bool 2
classes | first_occurrence
bool 2
classes | intellij_completions
listlengths 1
532
| jedi_completions
listlengths 3
148
| prefix
stringlengths 162
55k
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
7,666 | 216 | 4,013 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
Unknown
|
data
| true |
statement
| 98 | 102 | false | false |
[
"data",
"in_pd_df_by_series",
"ref_pd_series",
"test_add_df_with_series",
"assertEqual",
"test_add_df_with_empty_series",
"test_add_df_with_series_and_with_include_index",
"test_add_df_with_series_contains_na",
"asset_dir",
"in_pd_df_by_series_with_duplicated_popularity",
"in_pd_df_by_series_with_index",
"in_pd_df_by_series_with_nan",
"in_pd_series_dimension",
"in_pd_series_dimension_with_index",
"in_pd_series_dimension_with_nan",
"in_pd_series_measure",
"in_pd_series_measure_with_index",
"in_pd_series_measure_with_nan",
"ref_pd_df_by_series",
"ref_pd_df_by_series_max_rows",
"ref_pd_df_by_series_only_index",
"ref_pd_df_by_series_with_duplicated_popularity",
"ref_pd_df_by_series_with_index",
"ref_pd_df_by_series_with_nan",
"ref_pd_series_only_index",
"ref_pd_series_with_index",
"ref_pd_series_with_nan",
"set_up_pd_df",
"set_up_pd_series",
"setUp",
"setUpClass",
"addClassCleanup",
"addCleanup",
"addTypeEqualityFunc",
"assert_",
"assertAlmostEqual",
"assertAlmostEquals",
"assertCountEqual",
"assertDictContainsSubset",
"assertDictEqual",
"assertEquals",
"assertFalse",
"assertGreater",
"assertGreaterEqual",
"assertIn",
"assertIs",
"assertIsInstance",
"assertIsNone",
"assertIsNot",
"assertIsNotNone",
"assertLess",
"assertLessEqual",
"assertListEqual",
"assertLogs",
"assertMultiLineEqual",
"assertNoLogs",
"assertNotAlmostEqual",
"assertNotAlmostEquals",
"assertNotEqual",
"assertNotEquals",
"assertNotIn",
"assertNotIsInstance",
"assertNotRegex",
"assertNotRegexpMatches",
"assertRaises",
"assertRaisesRegex",
"assertRaisesRegexp",
"assertRegex",
"assertRegexpMatches",
"assertSequenceEqual",
"assertSetEqual",
"assertTrue",
"assertTupleEqual",
"assertWarns",
"assertWarnsRegex",
"countTestCases",
"debug",
"defaultTestResult",
"doClassCleanups",
"doCleanups",
"fail",
"failIf",
"failIfAlmostEqual",
"failIfEqual",
"failUnless",
"failUnlessAlmostEqual",
"failUnlessEqual",
"failUnlessRaises",
"failureException",
"id",
"longMessage",
"maxDiff",
"run",
"shortDescription",
"skipTest",
"subTest",
"tearDown",
"tearDownClass",
"__annotations__",
"__call__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "addClassCleanup",
"type": "function"
},
{
"name": "addCleanup",
"type": "function"
},
{
"name": "addTypeEqualityFunc",
"type": "function"
},
{
"name": "assert_",
"type": "function"
},
{
"name": "assertAlmostEqual",
"type": "function"
},
{
"name": "assertAlmostEquals",
"type": "function"
},
{
"name": "assertCountEqual",
"type": "function"
},
{
"name": "assertDictContainsSubset",
"type": "function"
},
{
"name": "assertDictEqual",
"type": "function"
},
{
"name": "assertEqual",
"type": "function"
},
{
"name": "assertEquals",
"type": "function"
},
{
"name": "assertFalse",
"type": "function"
},
{
"name": "assertGreater",
"type": "function"
},
{
"name": "assertGreaterEqual",
"type": "function"
},
{
"name": "assertIn",
"type": "function"
},
{
"name": "assertIs",
"type": "function"
},
{
"name": "assertIsInstance",
"type": "function"
},
{
"name": "assertIsNone",
"type": "function"
},
{
"name": "assertIsNot",
"type": "function"
},
{
"name": "assertIsNotNone",
"type": "function"
},
{
"name": "assertLess",
"type": "function"
},
{
"name": "assertLessEqual",
"type": "function"
},
{
"name": "assertListEqual",
"type": "function"
},
{
"name": "assertLogs",
"type": "function"
},
{
"name": "assertMultiLineEqual",
"type": "function"
},
{
"name": "assertNotAlmostEqual",
"type": "function"
},
{
"name": "assertNotAlmostEquals",
"type": "function"
},
{
"name": "assertNotEqual",
"type": "function"
},
{
"name": "assertNotEquals",
"type": "function"
},
{
"name": "assertNotIn",
"type": "function"
},
{
"name": "assertNotIsInstance",
"type": "function"
},
{
"name": "assertNotRegex",
"type": "function"
},
{
"name": "assertNotRegexpMatches",
"type": "function"
},
{
"name": "assertRaises",
"type": "function"
},
{
"name": "assertRaisesRegex",
"type": "function"
},
{
"name": "assertRaisesRegexp",
"type": "function"
},
{
"name": "assertRegex",
"type": "function"
},
{
"name": "assertRegexpMatches",
"type": "function"
},
{
"name": "assertSequenceEqual",
"type": "function"
},
{
"name": "assertSetEqual",
"type": "function"
},
{
"name": "assertTrue",
"type": "function"
},
{
"name": "assertTupleEqual",
"type": "function"
},
{
"name": "assertWarns",
"type": "function"
},
{
"name": "assertWarnsRegex",
"type": "function"
},
{
"name": "asset_dir",
"type": "statement"
},
{
"name": "countTestCases",
"type": "function"
},
{
"name": "data",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "defaultTestResult",
"type": "function"
},
{
"name": "doClassCleanups",
"type": "function"
},
{
"name": "doCleanups",
"type": "function"
},
{
"name": "fail",
"type": "function"
},
{
"name": "failIf",
"type": "function"
},
{
"name": "failIfAlmostEqual",
"type": "function"
},
{
"name": "failIfEqual",
"type": "function"
},
{
"name": "failUnless",
"type": "function"
},
{
"name": "failUnlessAlmostEqual",
"type": "function"
},
{
"name": "failUnlessEqual",
"type": "function"
},
{
"name": "failUnlessRaises",
"type": "function"
},
{
"name": "failureException",
"type": "statement"
},
{
"name": "id",
"type": "function"
},
{
"name": "in_pd_df_by_series",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_dimension",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_index",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_measure",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_index",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_nan",
"type": "statement"
},
{
"name": "longMessage",
"type": "statement"
},
{
"name": "maxDiff",
"type": "statement"
},
{
"name": "ref_pd_df_by_series",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_max_rows",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "ref_pd_series",
"type": "statement"
},
{
"name": "ref_pd_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_nan",
"type": "statement"
},
{
"name": "run",
"type": "function"
},
{
"name": "set_up_pd_df",
"type": "function"
},
{
"name": "set_up_pd_series",
"type": "function"
},
{
"name": "setUp",
"type": "function"
},
{
"name": "setUpClass",
"type": "function"
},
{
"name": "shortDescription",
"type": "function"
},
{
"name": "skipTest",
"type": "function"
},
{
"name": "subTest",
"type": "function"
},
{
"name": "tearDown",
"type": "function"
},
{
"name": "tearDownClass",
"type": "function"
},
{
"name": "test_add_df_with_empty_series",
"type": "function"
},
{
"name": "test_add_df_with_series",
"type": "function"
},
{
"name": "test_add_df_with_series_and_with_include_index",
"type": "function"
},
{
"name": "test_add_df_with_series_contains_na",
"type": "function"
},
{
"name": "_addSkip",
"type": "function"
},
{
"name": "_formatMessage",
"type": "function"
},
{
"name": "_getAssertEqualityFunc",
"type": "function"
},
{
"name": "_testMethodDoc",
"type": "statement"
},
{
"name": "_testMethodName",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__call__",
"type": "function"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.
|
7,667 | 216 | 4,018 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
Unknown
|
build
| true |
function
| 28 | 30 | false | false |
[
"add_df",
"add_data_frame",
"build",
"add_data_frame_index",
"add_df_index",
"add_dimension",
"add_measure",
"add_np_array",
"add_record",
"add_records",
"add_series",
"add_series_list",
"add_spark_df",
"dump",
"filter",
"from_json",
"set_filter",
"clear",
"copy",
"fromkeys",
"get",
"items",
"keys",
"pop",
"popitem",
"setdefault",
"update",
"values",
"__annotations__",
"__class__",
"__class_getitem__",
"__contains__",
"__delattr__",
"__delitem__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__getitem__",
"__hash__",
"__init__",
"__init_subclass__",
"__ior__",
"__iter__",
"__len__",
"__ne__",
"__new__",
"__or__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__reversed__",
"__ror__",
"__setattr__",
"__setitem__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "add_data_frame",
"type": "function"
},
{
"name": "add_data_frame_index",
"type": "function"
},
{
"name": "add_df",
"type": "function"
},
{
"name": "add_df_index",
"type": "function"
},
{
"name": "add_dimension",
"type": "function"
},
{
"name": "add_measure",
"type": "function"
},
{
"name": "add_np_array",
"type": "function"
},
{
"name": "add_record",
"type": "function"
},
{
"name": "add_records",
"type": "function"
},
{
"name": "add_series",
"type": "function"
},
{
"name": "add_series_list",
"type": "function"
},
{
"name": "add_spark_df",
"type": "function"
},
{
"name": "build",
"type": "function"
},
{
"name": "clear",
"type": "function"
},
{
"name": "copy",
"type": "function"
},
{
"name": "dump",
"type": "function"
},
{
"name": "filter",
"type": "function"
},
{
"name": "from_json",
"type": "function"
},
{
"name": "fromkeys",
"type": "function"
},
{
"name": "get",
"type": "function"
},
{
"name": "items",
"type": "function"
},
{
"name": "keys",
"type": "function"
},
{
"name": "pop",
"type": "function"
},
{
"name": "popitem",
"type": "function"
},
{
"name": "set_filter",
"type": "function"
},
{
"name": "setdefault",
"type": "function"
},
{
"name": "update",
"type": "function"
},
{
"name": "values",
"type": "function"
},
{
"name": "_add_named_value",
"type": "function"
},
{
"name": "_add_value",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__class_getitem__",
"type": "function"
},
{
"name": "__contains__",
"type": "function"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__delitem__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__getitem__",
"type": "function"
},
{
"name": "__hash__",
"type": "statement"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__ior__",
"type": "function"
},
{
"name": "__iter__",
"type": "function"
},
{
"name": "__len__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__or__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__reversed__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__setitem__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.
|
7,668 | 216 | 4,160 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
inproject
|
data
| true |
statement
| 97 | 101 | false | false |
[
"data",
"ref_pd_series",
"in_pd_df_by_series",
"test_add_data_frame_with_series",
"assertEqual",
"test_add_data_frame_with_empty_series",
"test_add_data_frame_with_series_contains_na",
"asset_dir",
"in_pd_df_by_series_with_duplicated_popularity",
"in_pd_df_by_series_with_index",
"in_pd_df_by_series_with_nan",
"in_pd_series_dimension",
"in_pd_series_dimension_with_index",
"in_pd_series_dimension_with_nan",
"in_pd_series_measure",
"in_pd_series_measure_with_index",
"in_pd_series_measure_with_nan",
"ref_pd_df_by_series",
"ref_pd_df_by_series_max_rows",
"ref_pd_df_by_series_only_index",
"ref_pd_df_by_series_with_duplicated_popularity",
"ref_pd_df_by_series_with_index",
"ref_pd_df_by_series_with_nan",
"ref_pd_series_only_index",
"ref_pd_series_with_index",
"ref_pd_series_with_nan",
"set_up_pd_df",
"set_up_pd_series",
"setUp",
"setUpClass",
"addClassCleanup",
"addCleanup",
"addTypeEqualityFunc",
"assert_",
"assertAlmostEqual",
"assertAlmostEquals",
"assertCountEqual",
"assertDictContainsSubset",
"assertDictEqual",
"assertEquals",
"assertFalse",
"assertGreater",
"assertGreaterEqual",
"assertIn",
"assertIs",
"assertIsInstance",
"assertIsNone",
"assertIsNot",
"assertIsNotNone",
"assertLess",
"assertLessEqual",
"assertListEqual",
"assertLogs",
"assertMultiLineEqual",
"assertNoLogs",
"assertNotAlmostEqual",
"assertNotAlmostEquals",
"assertNotEqual",
"assertNotEquals",
"assertNotIn",
"assertNotIsInstance",
"assertNotRegex",
"assertNotRegexpMatches",
"assertRaises",
"assertRaisesRegex",
"assertRaisesRegexp",
"assertRegex",
"assertRegexpMatches",
"assertSequenceEqual",
"assertSetEqual",
"assertTrue",
"assertTupleEqual",
"assertWarns",
"assertWarnsRegex",
"countTestCases",
"debug",
"defaultTestResult",
"doClassCleanups",
"doCleanups",
"fail",
"failIf",
"failIfAlmostEqual",
"failIfEqual",
"failUnless",
"failUnlessAlmostEqual",
"failUnlessEqual",
"failUnlessRaises",
"failureException",
"id",
"longMessage",
"maxDiff",
"run",
"shortDescription",
"skipTest",
"subTest",
"tearDown",
"tearDownClass",
"__annotations__",
"__call__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "addClassCleanup",
"type": "function"
},
{
"name": "addCleanup",
"type": "function"
},
{
"name": "addTypeEqualityFunc",
"type": "function"
},
{
"name": "assert_",
"type": "function"
},
{
"name": "assertAlmostEqual",
"type": "function"
},
{
"name": "assertAlmostEquals",
"type": "function"
},
{
"name": "assertCountEqual",
"type": "function"
},
{
"name": "assertDictContainsSubset",
"type": "function"
},
{
"name": "assertDictEqual",
"type": "function"
},
{
"name": "assertEqual",
"type": "function"
},
{
"name": "assertEquals",
"type": "function"
},
{
"name": "assertFalse",
"type": "function"
},
{
"name": "assertGreater",
"type": "function"
},
{
"name": "assertGreaterEqual",
"type": "function"
},
{
"name": "assertIn",
"type": "function"
},
{
"name": "assertIs",
"type": "function"
},
{
"name": "assertIsInstance",
"type": "function"
},
{
"name": "assertIsNone",
"type": "function"
},
{
"name": "assertIsNot",
"type": "function"
},
{
"name": "assertIsNotNone",
"type": "function"
},
{
"name": "assertLess",
"type": "function"
},
{
"name": "assertLessEqual",
"type": "function"
},
{
"name": "assertListEqual",
"type": "function"
},
{
"name": "assertLogs",
"type": "function"
},
{
"name": "assertMultiLineEqual",
"type": "function"
},
{
"name": "assertNotAlmostEqual",
"type": "function"
},
{
"name": "assertNotAlmostEquals",
"type": "function"
},
{
"name": "assertNotEqual",
"type": "function"
},
{
"name": "assertNotEquals",
"type": "function"
},
{
"name": "assertNotIn",
"type": "function"
},
{
"name": "assertNotIsInstance",
"type": "function"
},
{
"name": "assertNotRegex",
"type": "function"
},
{
"name": "assertNotRegexpMatches",
"type": "function"
},
{
"name": "assertRaises",
"type": "function"
},
{
"name": "assertRaisesRegex",
"type": "function"
},
{
"name": "assertRaisesRegexp",
"type": "function"
},
{
"name": "assertRegex",
"type": "function"
},
{
"name": "assertRegexpMatches",
"type": "function"
},
{
"name": "assertSequenceEqual",
"type": "function"
},
{
"name": "assertSetEqual",
"type": "function"
},
{
"name": "assertTrue",
"type": "function"
},
{
"name": "assertTupleEqual",
"type": "function"
},
{
"name": "assertWarns",
"type": "function"
},
{
"name": "assertWarnsRegex",
"type": "function"
},
{
"name": "asset_dir",
"type": "statement"
},
{
"name": "countTestCases",
"type": "function"
},
{
"name": "data",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "defaultTestResult",
"type": "function"
},
{
"name": "doClassCleanups",
"type": "function"
},
{
"name": "doCleanups",
"type": "function"
},
{
"name": "fail",
"type": "function"
},
{
"name": "failIf",
"type": "function"
},
{
"name": "failIfAlmostEqual",
"type": "function"
},
{
"name": "failIfEqual",
"type": "function"
},
{
"name": "failUnless",
"type": "function"
},
{
"name": "failUnlessAlmostEqual",
"type": "function"
},
{
"name": "failUnlessEqual",
"type": "function"
},
{
"name": "failUnlessRaises",
"type": "function"
},
{
"name": "failureException",
"type": "statement"
},
{
"name": "id",
"type": "function"
},
{
"name": "in_pd_df_by_series",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_dimension",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_index",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_measure",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_index",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_nan",
"type": "statement"
},
{
"name": "longMessage",
"type": "statement"
},
{
"name": "maxDiff",
"type": "statement"
},
{
"name": "ref_pd_df_by_series",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_max_rows",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "ref_pd_series",
"type": "statement"
},
{
"name": "ref_pd_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_nan",
"type": "statement"
},
{
"name": "run",
"type": "function"
},
{
"name": "set_up_pd_df",
"type": "function"
},
{
"name": "set_up_pd_series",
"type": "function"
},
{
"name": "setUp",
"type": "function"
},
{
"name": "setUpClass",
"type": "function"
},
{
"name": "shortDescription",
"type": "function"
},
{
"name": "skipTest",
"type": "function"
},
{
"name": "subTest",
"type": "function"
},
{
"name": "tearDown",
"type": "function"
},
{
"name": "tearDownClass",
"type": "function"
},
{
"name": "test_add_data_frame_with_empty_series",
"type": "function"
},
{
"name": "test_add_data_frame_with_series",
"type": "function"
},
{
"name": "test_add_data_frame_with_series_contains_na",
"type": "function"
},
{
"name": "_addSkip",
"type": "function"
},
{
"name": "_formatMessage",
"type": "function"
},
{
"name": "_getAssertEqualityFunc",
"type": "function"
},
{
"name": "_testMethodDoc",
"type": "statement"
},
{
"name": "_testMethodName",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__call__",
"type": "function"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.
|
7,669 | 216 | 4,165 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
inproject
|
add_data_frame
| true |
function
| 28 | 30 | false | false |
[
"add_df",
"add_data_frame",
"build",
"add_data_frame_index",
"add_df_index",
"add_dimension",
"add_measure",
"add_np_array",
"add_record",
"add_records",
"add_series",
"add_series_list",
"add_spark_df",
"dump",
"filter",
"from_json",
"set_filter",
"clear",
"copy",
"fromkeys",
"get",
"items",
"keys",
"pop",
"popitem",
"setdefault",
"update",
"values",
"__annotations__",
"__class__",
"__class_getitem__",
"__contains__",
"__delattr__",
"__delitem__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__getitem__",
"__hash__",
"__init__",
"__init_subclass__",
"__ior__",
"__iter__",
"__len__",
"__ne__",
"__new__",
"__or__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__reversed__",
"__ror__",
"__setattr__",
"__setitem__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "add_data_frame",
"type": "function"
},
{
"name": "add_data_frame_index",
"type": "function"
},
{
"name": "add_df",
"type": "function"
},
{
"name": "add_df_index",
"type": "function"
},
{
"name": "add_dimension",
"type": "function"
},
{
"name": "add_measure",
"type": "function"
},
{
"name": "add_np_array",
"type": "function"
},
{
"name": "add_record",
"type": "function"
},
{
"name": "add_records",
"type": "function"
},
{
"name": "add_series",
"type": "function"
},
{
"name": "add_series_list",
"type": "function"
},
{
"name": "add_spark_df",
"type": "function"
},
{
"name": "build",
"type": "function"
},
{
"name": "clear",
"type": "function"
},
{
"name": "copy",
"type": "function"
},
{
"name": "dump",
"type": "function"
},
{
"name": "filter",
"type": "function"
},
{
"name": "from_json",
"type": "function"
},
{
"name": "fromkeys",
"type": "function"
},
{
"name": "get",
"type": "function"
},
{
"name": "items",
"type": "function"
},
{
"name": "keys",
"type": "function"
},
{
"name": "pop",
"type": "function"
},
{
"name": "popitem",
"type": "function"
},
{
"name": "set_filter",
"type": "function"
},
{
"name": "setdefault",
"type": "function"
},
{
"name": "update",
"type": "function"
},
{
"name": "values",
"type": "function"
},
{
"name": "_add_named_value",
"type": "function"
},
{
"name": "_add_value",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__class_getitem__",
"type": "function"
},
{
"name": "__contains__",
"type": "function"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__delitem__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__getitem__",
"type": "function"
},
{
"name": "__hash__",
"type": "statement"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__ior__",
"type": "function"
},
{
"name": "__iter__",
"type": "function"
},
{
"name": "__len__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__or__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__reversed__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__setitem__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.
|
7,670 | 216 | 4,262 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
Unknown
|
data
| true |
statement
| 97 | 101 | false | false |
[
"data",
"test_add_data_frame_with_series",
"ref_pd_series",
"in_pd_df_by_series",
"assertEqual",
"test_add_data_frame_with_empty_series",
"test_add_data_frame_with_series_contains_na",
"asset_dir",
"in_pd_df_by_series_with_duplicated_popularity",
"in_pd_df_by_series_with_index",
"in_pd_df_by_series_with_nan",
"in_pd_series_dimension",
"in_pd_series_dimension_with_index",
"in_pd_series_dimension_with_nan",
"in_pd_series_measure",
"in_pd_series_measure_with_index",
"in_pd_series_measure_with_nan",
"ref_pd_df_by_series",
"ref_pd_df_by_series_max_rows",
"ref_pd_df_by_series_only_index",
"ref_pd_df_by_series_with_duplicated_popularity",
"ref_pd_df_by_series_with_index",
"ref_pd_df_by_series_with_nan",
"ref_pd_series_only_index",
"ref_pd_series_with_index",
"ref_pd_series_with_nan",
"set_up_pd_df",
"set_up_pd_series",
"setUp",
"setUpClass",
"addClassCleanup",
"addCleanup",
"addTypeEqualityFunc",
"assert_",
"assertAlmostEqual",
"assertAlmostEquals",
"assertCountEqual",
"assertDictContainsSubset",
"assertDictEqual",
"assertEquals",
"assertFalse",
"assertGreater",
"assertGreaterEqual",
"assertIn",
"assertIs",
"assertIsInstance",
"assertIsNone",
"assertIsNot",
"assertIsNotNone",
"assertLess",
"assertLessEqual",
"assertListEqual",
"assertLogs",
"assertMultiLineEqual",
"assertNoLogs",
"assertNotAlmostEqual",
"assertNotAlmostEquals",
"assertNotEqual",
"assertNotEquals",
"assertNotIn",
"assertNotIsInstance",
"assertNotRegex",
"assertNotRegexpMatches",
"assertRaises",
"assertRaisesRegex",
"assertRaisesRegexp",
"assertRegex",
"assertRegexpMatches",
"assertSequenceEqual",
"assertSetEqual",
"assertTrue",
"assertTupleEqual",
"assertWarns",
"assertWarnsRegex",
"countTestCases",
"debug",
"defaultTestResult",
"doClassCleanups",
"doCleanups",
"fail",
"failIf",
"failIfAlmostEqual",
"failIfEqual",
"failUnless",
"failUnlessAlmostEqual",
"failUnlessEqual",
"failUnlessRaises",
"failureException",
"id",
"longMessage",
"maxDiff",
"run",
"shortDescription",
"skipTest",
"subTest",
"tearDown",
"tearDownClass",
"__annotations__",
"__call__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "addClassCleanup",
"type": "function"
},
{
"name": "addCleanup",
"type": "function"
},
{
"name": "addTypeEqualityFunc",
"type": "function"
},
{
"name": "assert_",
"type": "function"
},
{
"name": "assertAlmostEqual",
"type": "function"
},
{
"name": "assertAlmostEquals",
"type": "function"
},
{
"name": "assertCountEqual",
"type": "function"
},
{
"name": "assertDictContainsSubset",
"type": "function"
},
{
"name": "assertDictEqual",
"type": "function"
},
{
"name": "assertEqual",
"type": "function"
},
{
"name": "assertEquals",
"type": "function"
},
{
"name": "assertFalse",
"type": "function"
},
{
"name": "assertGreater",
"type": "function"
},
{
"name": "assertGreaterEqual",
"type": "function"
},
{
"name": "assertIn",
"type": "function"
},
{
"name": "assertIs",
"type": "function"
},
{
"name": "assertIsInstance",
"type": "function"
},
{
"name": "assertIsNone",
"type": "function"
},
{
"name": "assertIsNot",
"type": "function"
},
{
"name": "assertIsNotNone",
"type": "function"
},
{
"name": "assertLess",
"type": "function"
},
{
"name": "assertLessEqual",
"type": "function"
},
{
"name": "assertListEqual",
"type": "function"
},
{
"name": "assertLogs",
"type": "function"
},
{
"name": "assertMultiLineEqual",
"type": "function"
},
{
"name": "assertNotAlmostEqual",
"type": "function"
},
{
"name": "assertNotAlmostEquals",
"type": "function"
},
{
"name": "assertNotEqual",
"type": "function"
},
{
"name": "assertNotEquals",
"type": "function"
},
{
"name": "assertNotIn",
"type": "function"
},
{
"name": "assertNotIsInstance",
"type": "function"
},
{
"name": "assertNotRegex",
"type": "function"
},
{
"name": "assertNotRegexpMatches",
"type": "function"
},
{
"name": "assertRaises",
"type": "function"
},
{
"name": "assertRaisesRegex",
"type": "function"
},
{
"name": "assertRaisesRegexp",
"type": "function"
},
{
"name": "assertRegex",
"type": "function"
},
{
"name": "assertRegexpMatches",
"type": "function"
},
{
"name": "assertSequenceEqual",
"type": "function"
},
{
"name": "assertSetEqual",
"type": "function"
},
{
"name": "assertTrue",
"type": "function"
},
{
"name": "assertTupleEqual",
"type": "function"
},
{
"name": "assertWarns",
"type": "function"
},
{
"name": "assertWarnsRegex",
"type": "function"
},
{
"name": "asset_dir",
"type": "statement"
},
{
"name": "countTestCases",
"type": "function"
},
{
"name": "data",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "defaultTestResult",
"type": "function"
},
{
"name": "doClassCleanups",
"type": "function"
},
{
"name": "doCleanups",
"type": "function"
},
{
"name": "fail",
"type": "function"
},
{
"name": "failIf",
"type": "function"
},
{
"name": "failIfAlmostEqual",
"type": "function"
},
{
"name": "failIfEqual",
"type": "function"
},
{
"name": "failUnless",
"type": "function"
},
{
"name": "failUnlessAlmostEqual",
"type": "function"
},
{
"name": "failUnlessEqual",
"type": "function"
},
{
"name": "failUnlessRaises",
"type": "function"
},
{
"name": "failureException",
"type": "statement"
},
{
"name": "id",
"type": "function"
},
{
"name": "in_pd_df_by_series",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_dimension",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_index",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_measure",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_index",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_nan",
"type": "statement"
},
{
"name": "longMessage",
"type": "statement"
},
{
"name": "maxDiff",
"type": "statement"
},
{
"name": "ref_pd_df_by_series",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_max_rows",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "ref_pd_series",
"type": "statement"
},
{
"name": "ref_pd_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_nan",
"type": "statement"
},
{
"name": "run",
"type": "function"
},
{
"name": "set_up_pd_df",
"type": "function"
},
{
"name": "set_up_pd_series",
"type": "function"
},
{
"name": "setUp",
"type": "function"
},
{
"name": "setUpClass",
"type": "function"
},
{
"name": "shortDescription",
"type": "function"
},
{
"name": "skipTest",
"type": "function"
},
{
"name": "subTest",
"type": "function"
},
{
"name": "tearDown",
"type": "function"
},
{
"name": "tearDownClass",
"type": "function"
},
{
"name": "test_add_data_frame_with_empty_series",
"type": "function"
},
{
"name": "test_add_data_frame_with_series",
"type": "function"
},
{
"name": "test_add_data_frame_with_series_contains_na",
"type": "function"
},
{
"name": "_addSkip",
"type": "function"
},
{
"name": "_formatMessage",
"type": "function"
},
{
"name": "_getAssertEqualityFunc",
"type": "function"
},
{
"name": "_testMethodDoc",
"type": "statement"
},
{
"name": "_testMethodName",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__call__",
"type": "function"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.
|
7,671 | 216 | 4,267 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
Unknown
|
build
| true |
function
| 28 | 30 | false | false |
[
"add_df",
"add_data_frame",
"build",
"add_data_frame_index",
"add_df_index",
"add_dimension",
"add_measure",
"add_np_array",
"add_record",
"add_records",
"add_series",
"add_series_list",
"add_spark_df",
"dump",
"filter",
"from_json",
"set_filter",
"clear",
"copy",
"fromkeys",
"get",
"items",
"keys",
"pop",
"popitem",
"setdefault",
"update",
"values",
"__annotations__",
"__class__",
"__class_getitem__",
"__contains__",
"__delattr__",
"__delitem__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__getitem__",
"__hash__",
"__init__",
"__init_subclass__",
"__ior__",
"__iter__",
"__len__",
"__ne__",
"__new__",
"__or__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__reversed__",
"__ror__",
"__setattr__",
"__setitem__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "add_data_frame",
"type": "function"
},
{
"name": "add_data_frame_index",
"type": "function"
},
{
"name": "add_df",
"type": "function"
},
{
"name": "add_df_index",
"type": "function"
},
{
"name": "add_dimension",
"type": "function"
},
{
"name": "add_measure",
"type": "function"
},
{
"name": "add_np_array",
"type": "function"
},
{
"name": "add_record",
"type": "function"
},
{
"name": "add_records",
"type": "function"
},
{
"name": "add_series",
"type": "function"
},
{
"name": "add_series_list",
"type": "function"
},
{
"name": "add_spark_df",
"type": "function"
},
{
"name": "build",
"type": "function"
},
{
"name": "clear",
"type": "function"
},
{
"name": "copy",
"type": "function"
},
{
"name": "dump",
"type": "function"
},
{
"name": "filter",
"type": "function"
},
{
"name": "from_json",
"type": "function"
},
{
"name": "fromkeys",
"type": "function"
},
{
"name": "get",
"type": "function"
},
{
"name": "items",
"type": "function"
},
{
"name": "keys",
"type": "function"
},
{
"name": "pop",
"type": "function"
},
{
"name": "popitem",
"type": "function"
},
{
"name": "set_filter",
"type": "function"
},
{
"name": "setdefault",
"type": "function"
},
{
"name": "update",
"type": "function"
},
{
"name": "values",
"type": "function"
},
{
"name": "_add_named_value",
"type": "function"
},
{
"name": "_add_value",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__class_getitem__",
"type": "function"
},
{
"name": "__contains__",
"type": "function"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__delitem__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__getitem__",
"type": "function"
},
{
"name": "__hash__",
"type": "statement"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__ior__",
"type": "function"
},
{
"name": "__iter__",
"type": "function"
},
{
"name": "__len__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__or__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__reversed__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__setitem__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.
|
7,672 | 216 | 4,355 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
inproject
|
data
| true |
statement
| 97 | 101 | false | false |
[
"data",
"ref_pd_series",
"in_pd_df_by_series",
"assertEqual",
"test_add_data_frame_with_empty_series",
"test_add_data_frame_with_series",
"test_add_data_frame_with_series_contains_na",
"asset_dir",
"in_pd_df_by_series_with_duplicated_popularity",
"in_pd_df_by_series_with_index",
"in_pd_df_by_series_with_nan",
"in_pd_series_dimension",
"in_pd_series_dimension_with_index",
"in_pd_series_dimension_with_nan",
"in_pd_series_measure",
"in_pd_series_measure_with_index",
"in_pd_series_measure_with_nan",
"ref_pd_df_by_series",
"ref_pd_df_by_series_max_rows",
"ref_pd_df_by_series_only_index",
"ref_pd_df_by_series_with_duplicated_popularity",
"ref_pd_df_by_series_with_index",
"ref_pd_df_by_series_with_nan",
"ref_pd_series_only_index",
"ref_pd_series_with_index",
"ref_pd_series_with_nan",
"set_up_pd_df",
"set_up_pd_series",
"setUp",
"setUpClass",
"addClassCleanup",
"addCleanup",
"addTypeEqualityFunc",
"assert_",
"assertAlmostEqual",
"assertAlmostEquals",
"assertCountEqual",
"assertDictContainsSubset",
"assertDictEqual",
"assertEquals",
"assertFalse",
"assertGreater",
"assertGreaterEqual",
"assertIn",
"assertIs",
"assertIsInstance",
"assertIsNone",
"assertIsNot",
"assertIsNotNone",
"assertLess",
"assertLessEqual",
"assertListEqual",
"assertLogs",
"assertMultiLineEqual",
"assertNoLogs",
"assertNotAlmostEqual",
"assertNotAlmostEquals",
"assertNotEqual",
"assertNotEquals",
"assertNotIn",
"assertNotIsInstance",
"assertNotRegex",
"assertNotRegexpMatches",
"assertRaises",
"assertRaisesRegex",
"assertRaisesRegexp",
"assertRegex",
"assertRegexpMatches",
"assertSequenceEqual",
"assertSetEqual",
"assertTrue",
"assertTupleEqual",
"assertWarns",
"assertWarnsRegex",
"countTestCases",
"debug",
"defaultTestResult",
"doClassCleanups",
"doCleanups",
"fail",
"failIf",
"failIfAlmostEqual",
"failIfEqual",
"failUnless",
"failUnlessAlmostEqual",
"failUnlessEqual",
"failUnlessRaises",
"failureException",
"id",
"longMessage",
"maxDiff",
"run",
"shortDescription",
"skipTest",
"subTest",
"tearDown",
"tearDownClass",
"__annotations__",
"__call__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "addClassCleanup",
"type": "function"
},
{
"name": "addCleanup",
"type": "function"
},
{
"name": "addTypeEqualityFunc",
"type": "function"
},
{
"name": "assert_",
"type": "function"
},
{
"name": "assertAlmostEqual",
"type": "function"
},
{
"name": "assertAlmostEquals",
"type": "function"
},
{
"name": "assertCountEqual",
"type": "function"
},
{
"name": "assertDictContainsSubset",
"type": "function"
},
{
"name": "assertDictEqual",
"type": "function"
},
{
"name": "assertEqual",
"type": "function"
},
{
"name": "assertEquals",
"type": "function"
},
{
"name": "assertFalse",
"type": "function"
},
{
"name": "assertGreater",
"type": "function"
},
{
"name": "assertGreaterEqual",
"type": "function"
},
{
"name": "assertIn",
"type": "function"
},
{
"name": "assertIs",
"type": "function"
},
{
"name": "assertIsInstance",
"type": "function"
},
{
"name": "assertIsNone",
"type": "function"
},
{
"name": "assertIsNot",
"type": "function"
},
{
"name": "assertIsNotNone",
"type": "function"
},
{
"name": "assertLess",
"type": "function"
},
{
"name": "assertLessEqual",
"type": "function"
},
{
"name": "assertListEqual",
"type": "function"
},
{
"name": "assertLogs",
"type": "function"
},
{
"name": "assertMultiLineEqual",
"type": "function"
},
{
"name": "assertNotAlmostEqual",
"type": "function"
},
{
"name": "assertNotAlmostEquals",
"type": "function"
},
{
"name": "assertNotEqual",
"type": "function"
},
{
"name": "assertNotEquals",
"type": "function"
},
{
"name": "assertNotIn",
"type": "function"
},
{
"name": "assertNotIsInstance",
"type": "function"
},
{
"name": "assertNotRegex",
"type": "function"
},
{
"name": "assertNotRegexpMatches",
"type": "function"
},
{
"name": "assertRaises",
"type": "function"
},
{
"name": "assertRaisesRegex",
"type": "function"
},
{
"name": "assertRaisesRegexp",
"type": "function"
},
{
"name": "assertRegex",
"type": "function"
},
{
"name": "assertRegexpMatches",
"type": "function"
},
{
"name": "assertSequenceEqual",
"type": "function"
},
{
"name": "assertSetEqual",
"type": "function"
},
{
"name": "assertTrue",
"type": "function"
},
{
"name": "assertTupleEqual",
"type": "function"
},
{
"name": "assertWarns",
"type": "function"
},
{
"name": "assertWarnsRegex",
"type": "function"
},
{
"name": "asset_dir",
"type": "statement"
},
{
"name": "countTestCases",
"type": "function"
},
{
"name": "data",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "defaultTestResult",
"type": "function"
},
{
"name": "doClassCleanups",
"type": "function"
},
{
"name": "doCleanups",
"type": "function"
},
{
"name": "fail",
"type": "function"
},
{
"name": "failIf",
"type": "function"
},
{
"name": "failIfAlmostEqual",
"type": "function"
},
{
"name": "failIfEqual",
"type": "function"
},
{
"name": "failUnless",
"type": "function"
},
{
"name": "failUnlessAlmostEqual",
"type": "function"
},
{
"name": "failUnlessEqual",
"type": "function"
},
{
"name": "failUnlessRaises",
"type": "function"
},
{
"name": "failureException",
"type": "statement"
},
{
"name": "id",
"type": "function"
},
{
"name": "in_pd_df_by_series",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_dimension",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_index",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_measure",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_index",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_nan",
"type": "statement"
},
{
"name": "longMessage",
"type": "statement"
},
{
"name": "maxDiff",
"type": "statement"
},
{
"name": "ref_pd_df_by_series",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_max_rows",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "ref_pd_series",
"type": "statement"
},
{
"name": "ref_pd_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_nan",
"type": "statement"
},
{
"name": "run",
"type": "function"
},
{
"name": "set_up_pd_df",
"type": "function"
},
{
"name": "set_up_pd_series",
"type": "function"
},
{
"name": "setUp",
"type": "function"
},
{
"name": "setUpClass",
"type": "function"
},
{
"name": "shortDescription",
"type": "function"
},
{
"name": "skipTest",
"type": "function"
},
{
"name": "subTest",
"type": "function"
},
{
"name": "tearDown",
"type": "function"
},
{
"name": "tearDownClass",
"type": "function"
},
{
"name": "test_add_data_frame_with_empty_series",
"type": "function"
},
{
"name": "test_add_data_frame_with_series",
"type": "function"
},
{
"name": "test_add_data_frame_with_series_contains_na",
"type": "function"
},
{
"name": "_addSkip",
"type": "function"
},
{
"name": "_formatMessage",
"type": "function"
},
{
"name": "_getAssertEqualityFunc",
"type": "function"
},
{
"name": "_testMethodDoc",
"type": "statement"
},
{
"name": "_testMethodName",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__call__",
"type": "function"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.
|
7,673 | 216 | 4,360 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
inproject
|
add_data_frame
| true |
function
| 28 | 30 | false | false |
[
"add_df",
"add_data_frame",
"build",
"add_data_frame_index",
"add_df_index",
"add_dimension",
"add_measure",
"add_np_array",
"add_record",
"add_records",
"add_series",
"add_series_list",
"add_spark_df",
"dump",
"filter",
"from_json",
"set_filter",
"clear",
"copy",
"fromkeys",
"get",
"items",
"keys",
"pop",
"popitem",
"setdefault",
"update",
"values",
"__annotations__",
"__class__",
"__class_getitem__",
"__contains__",
"__delattr__",
"__delitem__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__getitem__",
"__hash__",
"__init__",
"__init_subclass__",
"__ior__",
"__iter__",
"__len__",
"__ne__",
"__new__",
"__or__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__reversed__",
"__ror__",
"__setattr__",
"__setitem__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "add_data_frame",
"type": "function"
},
{
"name": "add_data_frame_index",
"type": "function"
},
{
"name": "add_df",
"type": "function"
},
{
"name": "add_df_index",
"type": "function"
},
{
"name": "add_dimension",
"type": "function"
},
{
"name": "add_measure",
"type": "function"
},
{
"name": "add_np_array",
"type": "function"
},
{
"name": "add_record",
"type": "function"
},
{
"name": "add_records",
"type": "function"
},
{
"name": "add_series",
"type": "function"
},
{
"name": "add_series_list",
"type": "function"
},
{
"name": "add_spark_df",
"type": "function"
},
{
"name": "build",
"type": "function"
},
{
"name": "clear",
"type": "function"
},
{
"name": "copy",
"type": "function"
},
{
"name": "dump",
"type": "function"
},
{
"name": "filter",
"type": "function"
},
{
"name": "from_json",
"type": "function"
},
{
"name": "fromkeys",
"type": "function"
},
{
"name": "get",
"type": "function"
},
{
"name": "items",
"type": "function"
},
{
"name": "keys",
"type": "function"
},
{
"name": "pop",
"type": "function"
},
{
"name": "popitem",
"type": "function"
},
{
"name": "set_filter",
"type": "function"
},
{
"name": "setdefault",
"type": "function"
},
{
"name": "update",
"type": "function"
},
{
"name": "values",
"type": "function"
},
{
"name": "_add_named_value",
"type": "function"
},
{
"name": "_add_value",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__class_getitem__",
"type": "function"
},
{
"name": "__contains__",
"type": "function"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__delitem__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__getitem__",
"type": "function"
},
{
"name": "__hash__",
"type": "statement"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__ior__",
"type": "function"
},
{
"name": "__iter__",
"type": "function"
},
{
"name": "__len__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__or__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__reversed__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__setitem__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.
|
7,674 | 216 | 4,380 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
inproject
|
in_pd_series_dimension
| true |
statement
| 97 | 101 | false | false |
[
"data",
"ref_pd_series",
"test_add_data_frame_with_empty_series",
"in_pd_df_by_series",
"test_add_data_frame_with_series_contains_na",
"test_add_data_frame_with_series",
"asset_dir",
"in_pd_df_by_series_with_duplicated_popularity",
"in_pd_df_by_series_with_index",
"in_pd_df_by_series_with_nan",
"in_pd_series_dimension",
"in_pd_series_dimension_with_index",
"in_pd_series_dimension_with_nan",
"in_pd_series_measure",
"in_pd_series_measure_with_index",
"in_pd_series_measure_with_nan",
"ref_pd_df_by_series",
"ref_pd_df_by_series_max_rows",
"ref_pd_df_by_series_only_index",
"ref_pd_df_by_series_with_duplicated_popularity",
"ref_pd_df_by_series_with_index",
"ref_pd_df_by_series_with_nan",
"ref_pd_series_only_index",
"ref_pd_series_with_index",
"ref_pd_series_with_nan",
"set_up_pd_df",
"set_up_pd_series",
"setUp",
"setUpClass",
"addClassCleanup",
"addCleanup",
"addTypeEqualityFunc",
"assert_",
"assertAlmostEqual",
"assertAlmostEquals",
"assertCountEqual",
"assertDictContainsSubset",
"assertDictEqual",
"assertEqual",
"assertEquals",
"assertFalse",
"assertGreater",
"assertGreaterEqual",
"assertIn",
"assertIs",
"assertIsInstance",
"assertIsNone",
"assertIsNot",
"assertIsNotNone",
"assertLess",
"assertLessEqual",
"assertListEqual",
"assertLogs",
"assertMultiLineEqual",
"assertNoLogs",
"assertNotAlmostEqual",
"assertNotAlmostEquals",
"assertNotEqual",
"assertNotEquals",
"assertNotIn",
"assertNotIsInstance",
"assertNotRegex",
"assertNotRegexpMatches",
"assertRaises",
"assertRaisesRegex",
"assertRaisesRegexp",
"assertRegex",
"assertRegexpMatches",
"assertSequenceEqual",
"assertSetEqual",
"assertTrue",
"assertTupleEqual",
"assertWarns",
"assertWarnsRegex",
"countTestCases",
"debug",
"defaultTestResult",
"doClassCleanups",
"doCleanups",
"fail",
"failIf",
"failIfAlmostEqual",
"failIfEqual",
"failUnless",
"failUnlessAlmostEqual",
"failUnlessEqual",
"failUnlessRaises",
"failureException",
"id",
"longMessage",
"maxDiff",
"run",
"shortDescription",
"skipTest",
"subTest",
"tearDown",
"tearDownClass",
"__annotations__",
"__call__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "addClassCleanup",
"type": "function"
},
{
"name": "addCleanup",
"type": "function"
},
{
"name": "addTypeEqualityFunc",
"type": "function"
},
{
"name": "assert_",
"type": "function"
},
{
"name": "assertAlmostEqual",
"type": "function"
},
{
"name": "assertAlmostEquals",
"type": "function"
},
{
"name": "assertCountEqual",
"type": "function"
},
{
"name": "assertDictContainsSubset",
"type": "function"
},
{
"name": "assertDictEqual",
"type": "function"
},
{
"name": "assertEqual",
"type": "function"
},
{
"name": "assertEquals",
"type": "function"
},
{
"name": "assertFalse",
"type": "function"
},
{
"name": "assertGreater",
"type": "function"
},
{
"name": "assertGreaterEqual",
"type": "function"
},
{
"name": "assertIn",
"type": "function"
},
{
"name": "assertIs",
"type": "function"
},
{
"name": "assertIsInstance",
"type": "function"
},
{
"name": "assertIsNone",
"type": "function"
},
{
"name": "assertIsNot",
"type": "function"
},
{
"name": "assertIsNotNone",
"type": "function"
},
{
"name": "assertLess",
"type": "function"
},
{
"name": "assertLessEqual",
"type": "function"
},
{
"name": "assertListEqual",
"type": "function"
},
{
"name": "assertLogs",
"type": "function"
},
{
"name": "assertMultiLineEqual",
"type": "function"
},
{
"name": "assertNotAlmostEqual",
"type": "function"
},
{
"name": "assertNotAlmostEquals",
"type": "function"
},
{
"name": "assertNotEqual",
"type": "function"
},
{
"name": "assertNotEquals",
"type": "function"
},
{
"name": "assertNotIn",
"type": "function"
},
{
"name": "assertNotIsInstance",
"type": "function"
},
{
"name": "assertNotRegex",
"type": "function"
},
{
"name": "assertNotRegexpMatches",
"type": "function"
},
{
"name": "assertRaises",
"type": "function"
},
{
"name": "assertRaisesRegex",
"type": "function"
},
{
"name": "assertRaisesRegexp",
"type": "function"
},
{
"name": "assertRegex",
"type": "function"
},
{
"name": "assertRegexpMatches",
"type": "function"
},
{
"name": "assertSequenceEqual",
"type": "function"
},
{
"name": "assertSetEqual",
"type": "function"
},
{
"name": "assertTrue",
"type": "function"
},
{
"name": "assertTupleEqual",
"type": "function"
},
{
"name": "assertWarns",
"type": "function"
},
{
"name": "assertWarnsRegex",
"type": "function"
},
{
"name": "asset_dir",
"type": "statement"
},
{
"name": "countTestCases",
"type": "function"
},
{
"name": "data",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "defaultTestResult",
"type": "function"
},
{
"name": "doClassCleanups",
"type": "function"
},
{
"name": "doCleanups",
"type": "function"
},
{
"name": "fail",
"type": "function"
},
{
"name": "failIf",
"type": "function"
},
{
"name": "failIfAlmostEqual",
"type": "function"
},
{
"name": "failIfEqual",
"type": "function"
},
{
"name": "failUnless",
"type": "function"
},
{
"name": "failUnlessAlmostEqual",
"type": "function"
},
{
"name": "failUnlessEqual",
"type": "function"
},
{
"name": "failUnlessRaises",
"type": "function"
},
{
"name": "failureException",
"type": "statement"
},
{
"name": "id",
"type": "function"
},
{
"name": "in_pd_df_by_series",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_dimension",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_index",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_measure",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_index",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_nan",
"type": "statement"
},
{
"name": "longMessage",
"type": "statement"
},
{
"name": "maxDiff",
"type": "statement"
},
{
"name": "ref_pd_df_by_series",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_max_rows",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "ref_pd_series",
"type": "statement"
},
{
"name": "ref_pd_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_nan",
"type": "statement"
},
{
"name": "run",
"type": "function"
},
{
"name": "set_up_pd_df",
"type": "function"
},
{
"name": "set_up_pd_series",
"type": "function"
},
{
"name": "setUp",
"type": "function"
},
{
"name": "setUpClass",
"type": "function"
},
{
"name": "shortDescription",
"type": "function"
},
{
"name": "skipTest",
"type": "function"
},
{
"name": "subTest",
"type": "function"
},
{
"name": "tearDown",
"type": "function"
},
{
"name": "tearDownClass",
"type": "function"
},
{
"name": "test_add_data_frame_with_empty_series",
"type": "function"
},
{
"name": "test_add_data_frame_with_series",
"type": "function"
},
{
"name": "test_add_data_frame_with_series_contains_na",
"type": "function"
},
{
"name": "_addSkip",
"type": "function"
},
{
"name": "_formatMessage",
"type": "function"
},
{
"name": "_getAssertEqualityFunc",
"type": "function"
},
{
"name": "_testMethodDoc",
"type": "statement"
},
{
"name": "_testMethodName",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__call__",
"type": "function"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.
|
7,675 | 216 | 4,417 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
inproject
|
data
| true |
statement
| 97 | 101 | false | false |
[
"data",
"ref_pd_series",
"in_pd_df_by_series",
"assertEqual",
"test_add_data_frame_with_empty_series",
"test_add_data_frame_with_series",
"test_add_data_frame_with_series_contains_na",
"asset_dir",
"in_pd_df_by_series_with_duplicated_popularity",
"in_pd_df_by_series_with_index",
"in_pd_df_by_series_with_nan",
"in_pd_series_dimension",
"in_pd_series_dimension_with_index",
"in_pd_series_dimension_with_nan",
"in_pd_series_measure",
"in_pd_series_measure_with_index",
"in_pd_series_measure_with_nan",
"ref_pd_df_by_series",
"ref_pd_df_by_series_max_rows",
"ref_pd_df_by_series_only_index",
"ref_pd_df_by_series_with_duplicated_popularity",
"ref_pd_df_by_series_with_index",
"ref_pd_df_by_series_with_nan",
"ref_pd_series_only_index",
"ref_pd_series_with_index",
"ref_pd_series_with_nan",
"set_up_pd_df",
"set_up_pd_series",
"setUp",
"setUpClass",
"addClassCleanup",
"addCleanup",
"addTypeEqualityFunc",
"assert_",
"assertAlmostEqual",
"assertAlmostEquals",
"assertCountEqual",
"assertDictContainsSubset",
"assertDictEqual",
"assertEquals",
"assertFalse",
"assertGreater",
"assertGreaterEqual",
"assertIn",
"assertIs",
"assertIsInstance",
"assertIsNone",
"assertIsNot",
"assertIsNotNone",
"assertLess",
"assertLessEqual",
"assertListEqual",
"assertLogs",
"assertMultiLineEqual",
"assertNoLogs",
"assertNotAlmostEqual",
"assertNotAlmostEquals",
"assertNotEqual",
"assertNotEquals",
"assertNotIn",
"assertNotIsInstance",
"assertNotRegex",
"assertNotRegexpMatches",
"assertRaises",
"assertRaisesRegex",
"assertRaisesRegexp",
"assertRegex",
"assertRegexpMatches",
"assertSequenceEqual",
"assertSetEqual",
"assertTrue",
"assertTupleEqual",
"assertWarns",
"assertWarnsRegex",
"countTestCases",
"debug",
"defaultTestResult",
"doClassCleanups",
"doCleanups",
"fail",
"failIf",
"failIfAlmostEqual",
"failIfEqual",
"failUnless",
"failUnlessAlmostEqual",
"failUnlessEqual",
"failUnlessRaises",
"failureException",
"id",
"longMessage",
"maxDiff",
"run",
"shortDescription",
"skipTest",
"subTest",
"tearDown",
"tearDownClass",
"__annotations__",
"__call__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "addClassCleanup",
"type": "function"
},
{
"name": "addCleanup",
"type": "function"
},
{
"name": "addTypeEqualityFunc",
"type": "function"
},
{
"name": "assert_",
"type": "function"
},
{
"name": "assertAlmostEqual",
"type": "function"
},
{
"name": "assertAlmostEquals",
"type": "function"
},
{
"name": "assertCountEqual",
"type": "function"
},
{
"name": "assertDictContainsSubset",
"type": "function"
},
{
"name": "assertDictEqual",
"type": "function"
},
{
"name": "assertEqual",
"type": "function"
},
{
"name": "assertEquals",
"type": "function"
},
{
"name": "assertFalse",
"type": "function"
},
{
"name": "assertGreater",
"type": "function"
},
{
"name": "assertGreaterEqual",
"type": "function"
},
{
"name": "assertIn",
"type": "function"
},
{
"name": "assertIs",
"type": "function"
},
{
"name": "assertIsInstance",
"type": "function"
},
{
"name": "assertIsNone",
"type": "function"
},
{
"name": "assertIsNot",
"type": "function"
},
{
"name": "assertIsNotNone",
"type": "function"
},
{
"name": "assertLess",
"type": "function"
},
{
"name": "assertLessEqual",
"type": "function"
},
{
"name": "assertListEqual",
"type": "function"
},
{
"name": "assertLogs",
"type": "function"
},
{
"name": "assertMultiLineEqual",
"type": "function"
},
{
"name": "assertNotAlmostEqual",
"type": "function"
},
{
"name": "assertNotAlmostEquals",
"type": "function"
},
{
"name": "assertNotEqual",
"type": "function"
},
{
"name": "assertNotEquals",
"type": "function"
},
{
"name": "assertNotIn",
"type": "function"
},
{
"name": "assertNotIsInstance",
"type": "function"
},
{
"name": "assertNotRegex",
"type": "function"
},
{
"name": "assertNotRegexpMatches",
"type": "function"
},
{
"name": "assertRaises",
"type": "function"
},
{
"name": "assertRaisesRegex",
"type": "function"
},
{
"name": "assertRaisesRegexp",
"type": "function"
},
{
"name": "assertRegex",
"type": "function"
},
{
"name": "assertRegexpMatches",
"type": "function"
},
{
"name": "assertSequenceEqual",
"type": "function"
},
{
"name": "assertSetEqual",
"type": "function"
},
{
"name": "assertTrue",
"type": "function"
},
{
"name": "assertTupleEqual",
"type": "function"
},
{
"name": "assertWarns",
"type": "function"
},
{
"name": "assertWarnsRegex",
"type": "function"
},
{
"name": "asset_dir",
"type": "statement"
},
{
"name": "countTestCases",
"type": "function"
},
{
"name": "data",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "defaultTestResult",
"type": "function"
},
{
"name": "doClassCleanups",
"type": "function"
},
{
"name": "doCleanups",
"type": "function"
},
{
"name": "fail",
"type": "function"
},
{
"name": "failIf",
"type": "function"
},
{
"name": "failIfAlmostEqual",
"type": "function"
},
{
"name": "failIfEqual",
"type": "function"
},
{
"name": "failUnless",
"type": "function"
},
{
"name": "failUnlessAlmostEqual",
"type": "function"
},
{
"name": "failUnlessEqual",
"type": "function"
},
{
"name": "failUnlessRaises",
"type": "function"
},
{
"name": "failureException",
"type": "statement"
},
{
"name": "id",
"type": "function"
},
{
"name": "in_pd_df_by_series",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_dimension",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_index",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_measure",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_index",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_nan",
"type": "statement"
},
{
"name": "longMessage",
"type": "statement"
},
{
"name": "maxDiff",
"type": "statement"
},
{
"name": "ref_pd_df_by_series",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_max_rows",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "ref_pd_series",
"type": "statement"
},
{
"name": "ref_pd_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_nan",
"type": "statement"
},
{
"name": "run",
"type": "function"
},
{
"name": "set_up_pd_df",
"type": "function"
},
{
"name": "set_up_pd_series",
"type": "function"
},
{
"name": "setUp",
"type": "function"
},
{
"name": "setUpClass",
"type": "function"
},
{
"name": "shortDescription",
"type": "function"
},
{
"name": "skipTest",
"type": "function"
},
{
"name": "subTest",
"type": "function"
},
{
"name": "tearDown",
"type": "function"
},
{
"name": "tearDownClass",
"type": "function"
},
{
"name": "test_add_data_frame_with_empty_series",
"type": "function"
},
{
"name": "test_add_data_frame_with_series",
"type": "function"
},
{
"name": "test_add_data_frame_with_series_contains_na",
"type": "function"
},
{
"name": "_addSkip",
"type": "function"
},
{
"name": "_formatMessage",
"type": "function"
},
{
"name": "_getAssertEqualityFunc",
"type": "function"
},
{
"name": "_testMethodDoc",
"type": "statement"
},
{
"name": "_testMethodName",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__call__",
"type": "function"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.
|
7,676 | 216 | 4,422 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
inproject
|
add_data_frame
| true |
function
| 28 | 30 | false | false |
[
"add_df",
"add_data_frame",
"build",
"add_data_frame_index",
"add_df_index",
"add_dimension",
"add_measure",
"add_np_array",
"add_record",
"add_records",
"add_series",
"add_series_list",
"add_spark_df",
"dump",
"filter",
"from_json",
"set_filter",
"clear",
"copy",
"fromkeys",
"get",
"items",
"keys",
"pop",
"popitem",
"setdefault",
"update",
"values",
"__annotations__",
"__class__",
"__class_getitem__",
"__contains__",
"__delattr__",
"__delitem__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__getitem__",
"__hash__",
"__init__",
"__init_subclass__",
"__ior__",
"__iter__",
"__len__",
"__ne__",
"__new__",
"__or__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__reversed__",
"__ror__",
"__setattr__",
"__setitem__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "add_data_frame",
"type": "function"
},
{
"name": "add_data_frame_index",
"type": "function"
},
{
"name": "add_df",
"type": "function"
},
{
"name": "add_df_index",
"type": "function"
},
{
"name": "add_dimension",
"type": "function"
},
{
"name": "add_measure",
"type": "function"
},
{
"name": "add_np_array",
"type": "function"
},
{
"name": "add_record",
"type": "function"
},
{
"name": "add_records",
"type": "function"
},
{
"name": "add_series",
"type": "function"
},
{
"name": "add_series_list",
"type": "function"
},
{
"name": "add_spark_df",
"type": "function"
},
{
"name": "build",
"type": "function"
},
{
"name": "clear",
"type": "function"
},
{
"name": "copy",
"type": "function"
},
{
"name": "dump",
"type": "function"
},
{
"name": "filter",
"type": "function"
},
{
"name": "from_json",
"type": "function"
},
{
"name": "fromkeys",
"type": "function"
},
{
"name": "get",
"type": "function"
},
{
"name": "items",
"type": "function"
},
{
"name": "keys",
"type": "function"
},
{
"name": "pop",
"type": "function"
},
{
"name": "popitem",
"type": "function"
},
{
"name": "set_filter",
"type": "function"
},
{
"name": "setdefault",
"type": "function"
},
{
"name": "update",
"type": "function"
},
{
"name": "values",
"type": "function"
},
{
"name": "_add_named_value",
"type": "function"
},
{
"name": "_add_value",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__class_getitem__",
"type": "function"
},
{
"name": "__contains__",
"type": "function"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__delitem__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__getitem__",
"type": "function"
},
{
"name": "__hash__",
"type": "statement"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__ior__",
"type": "function"
},
{
"name": "__iter__",
"type": "function"
},
{
"name": "__len__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__or__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__reversed__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__setitem__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.data.
|
7,677 | 216 | 4,442 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
inproject
|
in_pd_series_measure
| true |
statement
| 97 | 101 | false | false |
[
"data",
"ref_pd_series",
"test_add_data_frame_with_empty_series",
"in_pd_df_by_series",
"in_pd_series_dimension",
"test_add_data_frame_with_series",
"test_add_data_frame_with_series_contains_na",
"asset_dir",
"in_pd_df_by_series_with_duplicated_popularity",
"in_pd_df_by_series_with_index",
"in_pd_df_by_series_with_nan",
"in_pd_series_dimension_with_index",
"in_pd_series_dimension_with_nan",
"in_pd_series_measure",
"in_pd_series_measure_with_index",
"in_pd_series_measure_with_nan",
"ref_pd_df_by_series",
"ref_pd_df_by_series_max_rows",
"ref_pd_df_by_series_only_index",
"ref_pd_df_by_series_with_duplicated_popularity",
"ref_pd_df_by_series_with_index",
"ref_pd_df_by_series_with_nan",
"ref_pd_series_only_index",
"ref_pd_series_with_index",
"ref_pd_series_with_nan",
"set_up_pd_df",
"set_up_pd_series",
"setUp",
"setUpClass",
"addClassCleanup",
"addCleanup",
"addTypeEqualityFunc",
"assert_",
"assertAlmostEqual",
"assertAlmostEquals",
"assertCountEqual",
"assertDictContainsSubset",
"assertDictEqual",
"assertEqual",
"assertEquals",
"assertFalse",
"assertGreater",
"assertGreaterEqual",
"assertIn",
"assertIs",
"assertIsInstance",
"assertIsNone",
"assertIsNot",
"assertIsNotNone",
"assertLess",
"assertLessEqual",
"assertListEqual",
"assertLogs",
"assertMultiLineEqual",
"assertNoLogs",
"assertNotAlmostEqual",
"assertNotAlmostEquals",
"assertNotEqual",
"assertNotEquals",
"assertNotIn",
"assertNotIsInstance",
"assertNotRegex",
"assertNotRegexpMatches",
"assertRaises",
"assertRaisesRegex",
"assertRaisesRegexp",
"assertRegex",
"assertRegexpMatches",
"assertSequenceEqual",
"assertSetEqual",
"assertTrue",
"assertTupleEqual",
"assertWarns",
"assertWarnsRegex",
"countTestCases",
"debug",
"defaultTestResult",
"doClassCleanups",
"doCleanups",
"fail",
"failIf",
"failIfAlmostEqual",
"failIfEqual",
"failUnless",
"failUnlessAlmostEqual",
"failUnlessEqual",
"failUnlessRaises",
"failureException",
"id",
"longMessage",
"maxDiff",
"run",
"shortDescription",
"skipTest",
"subTest",
"tearDown",
"tearDownClass",
"__annotations__",
"__call__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "addClassCleanup",
"type": "function"
},
{
"name": "addCleanup",
"type": "function"
},
{
"name": "addTypeEqualityFunc",
"type": "function"
},
{
"name": "assert_",
"type": "function"
},
{
"name": "assertAlmostEqual",
"type": "function"
},
{
"name": "assertAlmostEquals",
"type": "function"
},
{
"name": "assertCountEqual",
"type": "function"
},
{
"name": "assertDictContainsSubset",
"type": "function"
},
{
"name": "assertDictEqual",
"type": "function"
},
{
"name": "assertEqual",
"type": "function"
},
{
"name": "assertEquals",
"type": "function"
},
{
"name": "assertFalse",
"type": "function"
},
{
"name": "assertGreater",
"type": "function"
},
{
"name": "assertGreaterEqual",
"type": "function"
},
{
"name": "assertIn",
"type": "function"
},
{
"name": "assertIs",
"type": "function"
},
{
"name": "assertIsInstance",
"type": "function"
},
{
"name": "assertIsNone",
"type": "function"
},
{
"name": "assertIsNot",
"type": "function"
},
{
"name": "assertIsNotNone",
"type": "function"
},
{
"name": "assertLess",
"type": "function"
},
{
"name": "assertLessEqual",
"type": "function"
},
{
"name": "assertListEqual",
"type": "function"
},
{
"name": "assertLogs",
"type": "function"
},
{
"name": "assertMultiLineEqual",
"type": "function"
},
{
"name": "assertNotAlmostEqual",
"type": "function"
},
{
"name": "assertNotAlmostEquals",
"type": "function"
},
{
"name": "assertNotEqual",
"type": "function"
},
{
"name": "assertNotEquals",
"type": "function"
},
{
"name": "assertNotIn",
"type": "function"
},
{
"name": "assertNotIsInstance",
"type": "function"
},
{
"name": "assertNotRegex",
"type": "function"
},
{
"name": "assertNotRegexpMatches",
"type": "function"
},
{
"name": "assertRaises",
"type": "function"
},
{
"name": "assertRaisesRegex",
"type": "function"
},
{
"name": "assertRaisesRegexp",
"type": "function"
},
{
"name": "assertRegex",
"type": "function"
},
{
"name": "assertRegexpMatches",
"type": "function"
},
{
"name": "assertSequenceEqual",
"type": "function"
},
{
"name": "assertSetEqual",
"type": "function"
},
{
"name": "assertTrue",
"type": "function"
},
{
"name": "assertTupleEqual",
"type": "function"
},
{
"name": "assertWarns",
"type": "function"
},
{
"name": "assertWarnsRegex",
"type": "function"
},
{
"name": "asset_dir",
"type": "statement"
},
{
"name": "countTestCases",
"type": "function"
},
{
"name": "data",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "defaultTestResult",
"type": "function"
},
{
"name": "doClassCleanups",
"type": "function"
},
{
"name": "doCleanups",
"type": "function"
},
{
"name": "fail",
"type": "function"
},
{
"name": "failIf",
"type": "function"
},
{
"name": "failIfAlmostEqual",
"type": "function"
},
{
"name": "failIfEqual",
"type": "function"
},
{
"name": "failUnless",
"type": "function"
},
{
"name": "failUnlessAlmostEqual",
"type": "function"
},
{
"name": "failUnlessEqual",
"type": "function"
},
{
"name": "failUnlessRaises",
"type": "function"
},
{
"name": "failureException",
"type": "statement"
},
{
"name": "id",
"type": "function"
},
{
"name": "in_pd_df_by_series",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_dimension",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_index",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_measure",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_index",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_nan",
"type": "statement"
},
{
"name": "longMessage",
"type": "statement"
},
{
"name": "maxDiff",
"type": "statement"
},
{
"name": "ref_pd_df_by_series",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_max_rows",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "ref_pd_series",
"type": "statement"
},
{
"name": "ref_pd_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_nan",
"type": "statement"
},
{
"name": "run",
"type": "function"
},
{
"name": "set_up_pd_df",
"type": "function"
},
{
"name": "set_up_pd_series",
"type": "function"
},
{
"name": "setUp",
"type": "function"
},
{
"name": "setUpClass",
"type": "function"
},
{
"name": "shortDescription",
"type": "function"
},
{
"name": "skipTest",
"type": "function"
},
{
"name": "subTest",
"type": "function"
},
{
"name": "tearDown",
"type": "function"
},
{
"name": "tearDownClass",
"type": "function"
},
{
"name": "test_add_data_frame_with_empty_series",
"type": "function"
},
{
"name": "test_add_data_frame_with_series",
"type": "function"
},
{
"name": "test_add_data_frame_with_series_contains_na",
"type": "function"
},
{
"name": "_addSkip",
"type": "function"
},
{
"name": "_formatMessage",
"type": "function"
},
{
"name": "_getAssertEqualityFunc",
"type": "function"
},
{
"name": "_testMethodDoc",
"type": "statement"
},
{
"name": "_testMethodName",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__call__",
"type": "function"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.data.add_data_frame(self.
|
7,678 | 216 | 4,507 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
Unknown
|
ref_pd_series
| true |
statement
| 97 | 101 | false | false |
[
"data",
"in_pd_df_by_series",
"in_pd_series_dimension",
"ref_pd_series",
"ref_pd_series_with_nan",
"test_add_data_frame_with_empty_series",
"test_add_data_frame_with_series",
"test_add_data_frame_with_series_contains_na",
"asset_dir",
"in_pd_df_by_series_with_duplicated_popularity",
"in_pd_df_by_series_with_index",
"in_pd_df_by_series_with_nan",
"in_pd_series_dimension_with_index",
"in_pd_series_dimension_with_nan",
"in_pd_series_measure",
"in_pd_series_measure_with_index",
"in_pd_series_measure_with_nan",
"ref_pd_df_by_series",
"ref_pd_df_by_series_max_rows",
"ref_pd_df_by_series_only_index",
"ref_pd_df_by_series_with_duplicated_popularity",
"ref_pd_df_by_series_with_index",
"ref_pd_df_by_series_with_nan",
"ref_pd_series_only_index",
"ref_pd_series_with_index",
"set_up_pd_df",
"set_up_pd_series",
"setUp",
"setUpClass",
"addClassCleanup",
"addCleanup",
"addTypeEqualityFunc",
"assert_",
"assertAlmostEqual",
"assertAlmostEquals",
"assertCountEqual",
"assertDictContainsSubset",
"assertDictEqual",
"assertEqual",
"assertEquals",
"assertFalse",
"assertGreater",
"assertGreaterEqual",
"assertIn",
"assertIs",
"assertIsInstance",
"assertIsNone",
"assertIsNot",
"assertIsNotNone",
"assertLess",
"assertLessEqual",
"assertListEqual",
"assertLogs",
"assertMultiLineEqual",
"assertNoLogs",
"assertNotAlmostEqual",
"assertNotAlmostEquals",
"assertNotEqual",
"assertNotEquals",
"assertNotIn",
"assertNotIsInstance",
"assertNotRegex",
"assertNotRegexpMatches",
"assertRaises",
"assertRaisesRegex",
"assertRaisesRegexp",
"assertRegex",
"assertRegexpMatches",
"assertSequenceEqual",
"assertSetEqual",
"assertTrue",
"assertTupleEqual",
"assertWarns",
"assertWarnsRegex",
"countTestCases",
"debug",
"defaultTestResult",
"doClassCleanups",
"doCleanups",
"fail",
"failIf",
"failIfAlmostEqual",
"failIfEqual",
"failUnless",
"failUnlessAlmostEqual",
"failUnlessEqual",
"failUnlessRaises",
"failureException",
"id",
"longMessage",
"maxDiff",
"run",
"shortDescription",
"skipTest",
"subTest",
"tearDown",
"tearDownClass",
"__annotations__",
"__call__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "addClassCleanup",
"type": "function"
},
{
"name": "addCleanup",
"type": "function"
},
{
"name": "addTypeEqualityFunc",
"type": "function"
},
{
"name": "assert_",
"type": "function"
},
{
"name": "assertAlmostEqual",
"type": "function"
},
{
"name": "assertAlmostEquals",
"type": "function"
},
{
"name": "assertCountEqual",
"type": "function"
},
{
"name": "assertDictContainsSubset",
"type": "function"
},
{
"name": "assertDictEqual",
"type": "function"
},
{
"name": "assertEqual",
"type": "function"
},
{
"name": "assertEquals",
"type": "function"
},
{
"name": "assertFalse",
"type": "function"
},
{
"name": "assertGreater",
"type": "function"
},
{
"name": "assertGreaterEqual",
"type": "function"
},
{
"name": "assertIn",
"type": "function"
},
{
"name": "assertIs",
"type": "function"
},
{
"name": "assertIsInstance",
"type": "function"
},
{
"name": "assertIsNone",
"type": "function"
},
{
"name": "assertIsNot",
"type": "function"
},
{
"name": "assertIsNotNone",
"type": "function"
},
{
"name": "assertLess",
"type": "function"
},
{
"name": "assertLessEqual",
"type": "function"
},
{
"name": "assertListEqual",
"type": "function"
},
{
"name": "assertLogs",
"type": "function"
},
{
"name": "assertMultiLineEqual",
"type": "function"
},
{
"name": "assertNotAlmostEqual",
"type": "function"
},
{
"name": "assertNotAlmostEquals",
"type": "function"
},
{
"name": "assertNotEqual",
"type": "function"
},
{
"name": "assertNotEquals",
"type": "function"
},
{
"name": "assertNotIn",
"type": "function"
},
{
"name": "assertNotIsInstance",
"type": "function"
},
{
"name": "assertNotRegex",
"type": "function"
},
{
"name": "assertNotRegexpMatches",
"type": "function"
},
{
"name": "assertRaises",
"type": "function"
},
{
"name": "assertRaisesRegex",
"type": "function"
},
{
"name": "assertRaisesRegexp",
"type": "function"
},
{
"name": "assertRegex",
"type": "function"
},
{
"name": "assertRegexpMatches",
"type": "function"
},
{
"name": "assertSequenceEqual",
"type": "function"
},
{
"name": "assertSetEqual",
"type": "function"
},
{
"name": "assertTrue",
"type": "function"
},
{
"name": "assertTupleEqual",
"type": "function"
},
{
"name": "assertWarns",
"type": "function"
},
{
"name": "assertWarnsRegex",
"type": "function"
},
{
"name": "asset_dir",
"type": "statement"
},
{
"name": "countTestCases",
"type": "function"
},
{
"name": "data",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "defaultTestResult",
"type": "function"
},
{
"name": "doClassCleanups",
"type": "function"
},
{
"name": "doCleanups",
"type": "function"
},
{
"name": "fail",
"type": "function"
},
{
"name": "failIf",
"type": "function"
},
{
"name": "failIfAlmostEqual",
"type": "function"
},
{
"name": "failIfEqual",
"type": "function"
},
{
"name": "failUnless",
"type": "function"
},
{
"name": "failUnlessAlmostEqual",
"type": "function"
},
{
"name": "failUnlessEqual",
"type": "function"
},
{
"name": "failUnlessRaises",
"type": "function"
},
{
"name": "failureException",
"type": "statement"
},
{
"name": "id",
"type": "function"
},
{
"name": "in_pd_df_by_series",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_dimension",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_index",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_measure",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_index",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_nan",
"type": "statement"
},
{
"name": "longMessage",
"type": "statement"
},
{
"name": "maxDiff",
"type": "statement"
},
{
"name": "ref_pd_df_by_series",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_max_rows",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "ref_pd_series",
"type": "statement"
},
{
"name": "ref_pd_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_nan",
"type": "statement"
},
{
"name": "run",
"type": "function"
},
{
"name": "set_up_pd_df",
"type": "function"
},
{
"name": "set_up_pd_series",
"type": "function"
},
{
"name": "setUp",
"type": "function"
},
{
"name": "setUpClass",
"type": "function"
},
{
"name": "shortDescription",
"type": "function"
},
{
"name": "skipTest",
"type": "function"
},
{
"name": "subTest",
"type": "function"
},
{
"name": "tearDown",
"type": "function"
},
{
"name": "tearDownClass",
"type": "function"
},
{
"name": "test_add_data_frame_with_empty_series",
"type": "function"
},
{
"name": "test_add_data_frame_with_series",
"type": "function"
},
{
"name": "test_add_data_frame_with_series_contains_na",
"type": "function"
},
{
"name": "_addSkip",
"type": "function"
},
{
"name": "_formatMessage",
"type": "function"
},
{
"name": "_getAssertEqualityFunc",
"type": "function"
},
{
"name": "_testMethodDoc",
"type": "statement"
},
{
"name": "_testMethodName",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__call__",
"type": "function"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.data.add_data_frame(self.in_pd_series_measure)
self.assertEqual(
self.
|
7,679 | 216 | 4,539 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
Unknown
|
data
| true |
statement
| 97 | 101 | false | false |
[
"data",
"ref_pd_series",
"in_pd_df_by_series",
"assertEqual",
"in_pd_series_dimension",
"test_add_data_frame_with_empty_series",
"test_add_data_frame_with_series",
"test_add_data_frame_with_series_contains_na",
"asset_dir",
"in_pd_df_by_series_with_duplicated_popularity",
"in_pd_df_by_series_with_index",
"in_pd_df_by_series_with_nan",
"in_pd_series_dimension_with_index",
"in_pd_series_dimension_with_nan",
"in_pd_series_measure",
"in_pd_series_measure_with_index",
"in_pd_series_measure_with_nan",
"ref_pd_df_by_series",
"ref_pd_df_by_series_max_rows",
"ref_pd_df_by_series_only_index",
"ref_pd_df_by_series_with_duplicated_popularity",
"ref_pd_df_by_series_with_index",
"ref_pd_df_by_series_with_nan",
"ref_pd_series_only_index",
"ref_pd_series_with_index",
"ref_pd_series_with_nan",
"set_up_pd_df",
"set_up_pd_series",
"setUp",
"setUpClass",
"addClassCleanup",
"addCleanup",
"addTypeEqualityFunc",
"assert_",
"assertAlmostEqual",
"assertAlmostEquals",
"assertCountEqual",
"assertDictContainsSubset",
"assertDictEqual",
"assertEquals",
"assertFalse",
"assertGreater",
"assertGreaterEqual",
"assertIn",
"assertIs",
"assertIsInstance",
"assertIsNone",
"assertIsNot",
"assertIsNotNone",
"assertLess",
"assertLessEqual",
"assertListEqual",
"assertLogs",
"assertMultiLineEqual",
"assertNoLogs",
"assertNotAlmostEqual",
"assertNotAlmostEquals",
"assertNotEqual",
"assertNotEquals",
"assertNotIn",
"assertNotIsInstance",
"assertNotRegex",
"assertNotRegexpMatches",
"assertRaises",
"assertRaisesRegex",
"assertRaisesRegexp",
"assertRegex",
"assertRegexpMatches",
"assertSequenceEqual",
"assertSetEqual",
"assertTrue",
"assertTupleEqual",
"assertWarns",
"assertWarnsRegex",
"countTestCases",
"debug",
"defaultTestResult",
"doClassCleanups",
"doCleanups",
"fail",
"failIf",
"failIfAlmostEqual",
"failIfEqual",
"failUnless",
"failUnlessAlmostEqual",
"failUnlessEqual",
"failUnlessRaises",
"failureException",
"id",
"longMessage",
"maxDiff",
"run",
"shortDescription",
"skipTest",
"subTest",
"tearDown",
"tearDownClass",
"__annotations__",
"__call__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "addClassCleanup",
"type": "function"
},
{
"name": "addCleanup",
"type": "function"
},
{
"name": "addTypeEqualityFunc",
"type": "function"
},
{
"name": "assert_",
"type": "function"
},
{
"name": "assertAlmostEqual",
"type": "function"
},
{
"name": "assertAlmostEquals",
"type": "function"
},
{
"name": "assertCountEqual",
"type": "function"
},
{
"name": "assertDictContainsSubset",
"type": "function"
},
{
"name": "assertDictEqual",
"type": "function"
},
{
"name": "assertEqual",
"type": "function"
},
{
"name": "assertEquals",
"type": "function"
},
{
"name": "assertFalse",
"type": "function"
},
{
"name": "assertGreater",
"type": "function"
},
{
"name": "assertGreaterEqual",
"type": "function"
},
{
"name": "assertIn",
"type": "function"
},
{
"name": "assertIs",
"type": "function"
},
{
"name": "assertIsInstance",
"type": "function"
},
{
"name": "assertIsNone",
"type": "function"
},
{
"name": "assertIsNot",
"type": "function"
},
{
"name": "assertIsNotNone",
"type": "function"
},
{
"name": "assertLess",
"type": "function"
},
{
"name": "assertLessEqual",
"type": "function"
},
{
"name": "assertListEqual",
"type": "function"
},
{
"name": "assertLogs",
"type": "function"
},
{
"name": "assertMultiLineEqual",
"type": "function"
},
{
"name": "assertNotAlmostEqual",
"type": "function"
},
{
"name": "assertNotAlmostEquals",
"type": "function"
},
{
"name": "assertNotEqual",
"type": "function"
},
{
"name": "assertNotEquals",
"type": "function"
},
{
"name": "assertNotIn",
"type": "function"
},
{
"name": "assertNotIsInstance",
"type": "function"
},
{
"name": "assertNotRegex",
"type": "function"
},
{
"name": "assertNotRegexpMatches",
"type": "function"
},
{
"name": "assertRaises",
"type": "function"
},
{
"name": "assertRaisesRegex",
"type": "function"
},
{
"name": "assertRaisesRegexp",
"type": "function"
},
{
"name": "assertRegex",
"type": "function"
},
{
"name": "assertRegexpMatches",
"type": "function"
},
{
"name": "assertSequenceEqual",
"type": "function"
},
{
"name": "assertSetEqual",
"type": "function"
},
{
"name": "assertTrue",
"type": "function"
},
{
"name": "assertTupleEqual",
"type": "function"
},
{
"name": "assertWarns",
"type": "function"
},
{
"name": "assertWarnsRegex",
"type": "function"
},
{
"name": "asset_dir",
"type": "statement"
},
{
"name": "countTestCases",
"type": "function"
},
{
"name": "data",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "defaultTestResult",
"type": "function"
},
{
"name": "doClassCleanups",
"type": "function"
},
{
"name": "doCleanups",
"type": "function"
},
{
"name": "fail",
"type": "function"
},
{
"name": "failIf",
"type": "function"
},
{
"name": "failIfAlmostEqual",
"type": "function"
},
{
"name": "failIfEqual",
"type": "function"
},
{
"name": "failUnless",
"type": "function"
},
{
"name": "failUnlessAlmostEqual",
"type": "function"
},
{
"name": "failUnlessEqual",
"type": "function"
},
{
"name": "failUnlessRaises",
"type": "function"
},
{
"name": "failureException",
"type": "statement"
},
{
"name": "id",
"type": "function"
},
{
"name": "in_pd_df_by_series",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_dimension",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_index",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_measure",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_index",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_nan",
"type": "statement"
},
{
"name": "longMessage",
"type": "statement"
},
{
"name": "maxDiff",
"type": "statement"
},
{
"name": "ref_pd_df_by_series",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_max_rows",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "ref_pd_series",
"type": "statement"
},
{
"name": "ref_pd_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_nan",
"type": "statement"
},
{
"name": "run",
"type": "function"
},
{
"name": "set_up_pd_df",
"type": "function"
},
{
"name": "set_up_pd_series",
"type": "function"
},
{
"name": "setUp",
"type": "function"
},
{
"name": "setUpClass",
"type": "function"
},
{
"name": "shortDescription",
"type": "function"
},
{
"name": "skipTest",
"type": "function"
},
{
"name": "subTest",
"type": "function"
},
{
"name": "tearDown",
"type": "function"
},
{
"name": "tearDownClass",
"type": "function"
},
{
"name": "test_add_data_frame_with_empty_series",
"type": "function"
},
{
"name": "test_add_data_frame_with_series",
"type": "function"
},
{
"name": "test_add_data_frame_with_series_contains_na",
"type": "function"
},
{
"name": "_addSkip",
"type": "function"
},
{
"name": "_formatMessage",
"type": "function"
},
{
"name": "_getAssertEqualityFunc",
"type": "function"
},
{
"name": "_testMethodDoc",
"type": "statement"
},
{
"name": "_testMethodName",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__call__",
"type": "function"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.data.add_data_frame(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.
|
7,680 | 216 | 4,544 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
Unknown
|
build
| true |
function
| 28 | 30 | false | false |
[
"add_data_frame",
"add_df",
"build",
"add_data_frame_index",
"add_df_index",
"add_dimension",
"add_measure",
"add_np_array",
"add_record",
"add_records",
"add_series",
"add_series_list",
"add_spark_df",
"dump",
"filter",
"from_json",
"set_filter",
"clear",
"copy",
"fromkeys",
"get",
"items",
"keys",
"pop",
"popitem",
"setdefault",
"update",
"values",
"__annotations__",
"__class__",
"__class_getitem__",
"__contains__",
"__delattr__",
"__delitem__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__getitem__",
"__hash__",
"__init__",
"__init_subclass__",
"__ior__",
"__iter__",
"__len__",
"__ne__",
"__new__",
"__or__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__reversed__",
"__ror__",
"__setattr__",
"__setitem__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "add_data_frame",
"type": "function"
},
{
"name": "add_data_frame_index",
"type": "function"
},
{
"name": "add_df",
"type": "function"
},
{
"name": "add_df_index",
"type": "function"
},
{
"name": "add_dimension",
"type": "function"
},
{
"name": "add_measure",
"type": "function"
},
{
"name": "add_np_array",
"type": "function"
},
{
"name": "add_record",
"type": "function"
},
{
"name": "add_records",
"type": "function"
},
{
"name": "add_series",
"type": "function"
},
{
"name": "add_series_list",
"type": "function"
},
{
"name": "add_spark_df",
"type": "function"
},
{
"name": "build",
"type": "function"
},
{
"name": "clear",
"type": "function"
},
{
"name": "copy",
"type": "function"
},
{
"name": "dump",
"type": "function"
},
{
"name": "filter",
"type": "function"
},
{
"name": "from_json",
"type": "function"
},
{
"name": "fromkeys",
"type": "function"
},
{
"name": "get",
"type": "function"
},
{
"name": "items",
"type": "function"
},
{
"name": "keys",
"type": "function"
},
{
"name": "pop",
"type": "function"
},
{
"name": "popitem",
"type": "function"
},
{
"name": "set_filter",
"type": "function"
},
{
"name": "setdefault",
"type": "function"
},
{
"name": "update",
"type": "function"
},
{
"name": "values",
"type": "function"
},
{
"name": "_add_named_value",
"type": "function"
},
{
"name": "_add_value",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__class_getitem__",
"type": "function"
},
{
"name": "__contains__",
"type": "function"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__delitem__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__getitem__",
"type": "function"
},
{
"name": "__hash__",
"type": "statement"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__ior__",
"type": "function"
},
{
"name": "__iter__",
"type": "function"
},
{
"name": "__len__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__or__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__reversed__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__setitem__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.data.add_data_frame(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.
|
7,681 | 216 | 4,644 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
inproject
|
data
| true |
statement
| 97 | 101 | false | false |
[
"data",
"test_add_data_frame_with_series",
"ref_pd_series",
"in_pd_df_by_series",
"assertEqual",
"test_add_data_frame_with_empty_series",
"test_add_data_frame_with_series_contains_na",
"asset_dir",
"in_pd_df_by_series_with_duplicated_popularity",
"in_pd_df_by_series_with_index",
"in_pd_df_by_series_with_nan",
"in_pd_series_dimension",
"in_pd_series_dimension_with_index",
"in_pd_series_dimension_with_nan",
"in_pd_series_measure",
"in_pd_series_measure_with_index",
"in_pd_series_measure_with_nan",
"ref_pd_df_by_series",
"ref_pd_df_by_series_max_rows",
"ref_pd_df_by_series_only_index",
"ref_pd_df_by_series_with_duplicated_popularity",
"ref_pd_df_by_series_with_index",
"ref_pd_df_by_series_with_nan",
"ref_pd_series_only_index",
"ref_pd_series_with_index",
"ref_pd_series_with_nan",
"set_up_pd_df",
"set_up_pd_series",
"setUp",
"setUpClass",
"addClassCleanup",
"addCleanup",
"addTypeEqualityFunc",
"assert_",
"assertAlmostEqual",
"assertAlmostEquals",
"assertCountEqual",
"assertDictContainsSubset",
"assertDictEqual",
"assertEquals",
"assertFalse",
"assertGreater",
"assertGreaterEqual",
"assertIn",
"assertIs",
"assertIsInstance",
"assertIsNone",
"assertIsNot",
"assertIsNotNone",
"assertLess",
"assertLessEqual",
"assertListEqual",
"assertLogs",
"assertMultiLineEqual",
"assertNoLogs",
"assertNotAlmostEqual",
"assertNotAlmostEquals",
"assertNotEqual",
"assertNotEquals",
"assertNotIn",
"assertNotIsInstance",
"assertNotRegex",
"assertNotRegexpMatches",
"assertRaises",
"assertRaisesRegex",
"assertRaisesRegexp",
"assertRegex",
"assertRegexpMatches",
"assertSequenceEqual",
"assertSetEqual",
"assertTrue",
"assertTupleEqual",
"assertWarns",
"assertWarnsRegex",
"countTestCases",
"debug",
"defaultTestResult",
"doClassCleanups",
"doCleanups",
"fail",
"failIf",
"failIfAlmostEqual",
"failIfEqual",
"failUnless",
"failUnlessAlmostEqual",
"failUnlessEqual",
"failUnlessRaises",
"failureException",
"id",
"longMessage",
"maxDiff",
"run",
"shortDescription",
"skipTest",
"subTest",
"tearDown",
"tearDownClass",
"__annotations__",
"__call__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "addClassCleanup",
"type": "function"
},
{
"name": "addCleanup",
"type": "function"
},
{
"name": "addTypeEqualityFunc",
"type": "function"
},
{
"name": "assert_",
"type": "function"
},
{
"name": "assertAlmostEqual",
"type": "function"
},
{
"name": "assertAlmostEquals",
"type": "function"
},
{
"name": "assertCountEqual",
"type": "function"
},
{
"name": "assertDictContainsSubset",
"type": "function"
},
{
"name": "assertDictEqual",
"type": "function"
},
{
"name": "assertEqual",
"type": "function"
},
{
"name": "assertEquals",
"type": "function"
},
{
"name": "assertFalse",
"type": "function"
},
{
"name": "assertGreater",
"type": "function"
},
{
"name": "assertGreaterEqual",
"type": "function"
},
{
"name": "assertIn",
"type": "function"
},
{
"name": "assertIs",
"type": "function"
},
{
"name": "assertIsInstance",
"type": "function"
},
{
"name": "assertIsNone",
"type": "function"
},
{
"name": "assertIsNot",
"type": "function"
},
{
"name": "assertIsNotNone",
"type": "function"
},
{
"name": "assertLess",
"type": "function"
},
{
"name": "assertLessEqual",
"type": "function"
},
{
"name": "assertListEqual",
"type": "function"
},
{
"name": "assertLogs",
"type": "function"
},
{
"name": "assertMultiLineEqual",
"type": "function"
},
{
"name": "assertNotAlmostEqual",
"type": "function"
},
{
"name": "assertNotAlmostEquals",
"type": "function"
},
{
"name": "assertNotEqual",
"type": "function"
},
{
"name": "assertNotEquals",
"type": "function"
},
{
"name": "assertNotIn",
"type": "function"
},
{
"name": "assertNotIsInstance",
"type": "function"
},
{
"name": "assertNotRegex",
"type": "function"
},
{
"name": "assertNotRegexpMatches",
"type": "function"
},
{
"name": "assertRaises",
"type": "function"
},
{
"name": "assertRaisesRegex",
"type": "function"
},
{
"name": "assertRaisesRegexp",
"type": "function"
},
{
"name": "assertRegex",
"type": "function"
},
{
"name": "assertRegexpMatches",
"type": "function"
},
{
"name": "assertSequenceEqual",
"type": "function"
},
{
"name": "assertSetEqual",
"type": "function"
},
{
"name": "assertTrue",
"type": "function"
},
{
"name": "assertTupleEqual",
"type": "function"
},
{
"name": "assertWarns",
"type": "function"
},
{
"name": "assertWarnsRegex",
"type": "function"
},
{
"name": "asset_dir",
"type": "statement"
},
{
"name": "countTestCases",
"type": "function"
},
{
"name": "data",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "defaultTestResult",
"type": "function"
},
{
"name": "doClassCleanups",
"type": "function"
},
{
"name": "doCleanups",
"type": "function"
},
{
"name": "fail",
"type": "function"
},
{
"name": "failIf",
"type": "function"
},
{
"name": "failIfAlmostEqual",
"type": "function"
},
{
"name": "failIfEqual",
"type": "function"
},
{
"name": "failUnless",
"type": "function"
},
{
"name": "failUnlessAlmostEqual",
"type": "function"
},
{
"name": "failUnlessEqual",
"type": "function"
},
{
"name": "failUnlessRaises",
"type": "function"
},
{
"name": "failureException",
"type": "statement"
},
{
"name": "id",
"type": "function"
},
{
"name": "in_pd_df_by_series",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_dimension",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_index",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_measure",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_index",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_nan",
"type": "statement"
},
{
"name": "longMessage",
"type": "statement"
},
{
"name": "maxDiff",
"type": "statement"
},
{
"name": "ref_pd_df_by_series",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_max_rows",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "ref_pd_series",
"type": "statement"
},
{
"name": "ref_pd_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_nan",
"type": "statement"
},
{
"name": "run",
"type": "function"
},
{
"name": "set_up_pd_df",
"type": "function"
},
{
"name": "set_up_pd_series",
"type": "function"
},
{
"name": "setUp",
"type": "function"
},
{
"name": "setUpClass",
"type": "function"
},
{
"name": "shortDescription",
"type": "function"
},
{
"name": "skipTest",
"type": "function"
},
{
"name": "subTest",
"type": "function"
},
{
"name": "tearDown",
"type": "function"
},
{
"name": "tearDownClass",
"type": "function"
},
{
"name": "test_add_data_frame_with_empty_series",
"type": "function"
},
{
"name": "test_add_data_frame_with_series",
"type": "function"
},
{
"name": "test_add_data_frame_with_series_contains_na",
"type": "function"
},
{
"name": "_addSkip",
"type": "function"
},
{
"name": "_formatMessage",
"type": "function"
},
{
"name": "_getAssertEqualityFunc",
"type": "function"
},
{
"name": "_testMethodDoc",
"type": "statement"
},
{
"name": "_testMethodName",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__call__",
"type": "function"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.data.add_data_frame(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_data_frame_with_series_contains_na(self) -> None:
self.
|
7,682 | 216 | 4,649 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
inproject
|
add_data_frame
| true |
function
| 28 | 30 | false | false |
[
"add_df",
"add_data_frame",
"build",
"add_data_frame_index",
"add_df_index",
"add_dimension",
"add_measure",
"add_np_array",
"add_record",
"add_records",
"add_series",
"add_series_list",
"add_spark_df",
"dump",
"filter",
"from_json",
"set_filter",
"clear",
"copy",
"fromkeys",
"get",
"items",
"keys",
"pop",
"popitem",
"setdefault",
"update",
"values",
"__annotations__",
"__class__",
"__class_getitem__",
"__contains__",
"__delattr__",
"__delitem__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__getitem__",
"__hash__",
"__init__",
"__init_subclass__",
"__ior__",
"__iter__",
"__len__",
"__ne__",
"__new__",
"__or__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__reversed__",
"__ror__",
"__setattr__",
"__setitem__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "add_data_frame",
"type": "function"
},
{
"name": "add_data_frame_index",
"type": "function"
},
{
"name": "add_df",
"type": "function"
},
{
"name": "add_df_index",
"type": "function"
},
{
"name": "add_dimension",
"type": "function"
},
{
"name": "add_measure",
"type": "function"
},
{
"name": "add_np_array",
"type": "function"
},
{
"name": "add_record",
"type": "function"
},
{
"name": "add_records",
"type": "function"
},
{
"name": "add_series",
"type": "function"
},
{
"name": "add_series_list",
"type": "function"
},
{
"name": "add_spark_df",
"type": "function"
},
{
"name": "build",
"type": "function"
},
{
"name": "clear",
"type": "function"
},
{
"name": "copy",
"type": "function"
},
{
"name": "dump",
"type": "function"
},
{
"name": "filter",
"type": "function"
},
{
"name": "from_json",
"type": "function"
},
{
"name": "fromkeys",
"type": "function"
},
{
"name": "get",
"type": "function"
},
{
"name": "items",
"type": "function"
},
{
"name": "keys",
"type": "function"
},
{
"name": "pop",
"type": "function"
},
{
"name": "popitem",
"type": "function"
},
{
"name": "set_filter",
"type": "function"
},
{
"name": "setdefault",
"type": "function"
},
{
"name": "update",
"type": "function"
},
{
"name": "values",
"type": "function"
},
{
"name": "_add_named_value",
"type": "function"
},
{
"name": "_add_value",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__class_getitem__",
"type": "function"
},
{
"name": "__contains__",
"type": "function"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__delitem__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__getitem__",
"type": "function"
},
{
"name": "__hash__",
"type": "statement"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__ior__",
"type": "function"
},
{
"name": "__iter__",
"type": "function"
},
{
"name": "__len__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__or__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__reversed__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__setitem__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.data.add_data_frame(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_data_frame_with_series_contains_na(self) -> None:
self.data.
|
7,683 | 216 | 4,669 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
inproject
|
in_pd_series_dimension_with_nan
| true |
statement
| 97 | 101 | false | false |
[
"data",
"test_add_data_frame_with_series",
"ref_pd_series",
"test_add_data_frame_with_empty_series",
"in_pd_df_by_series",
"test_add_data_frame_with_series_contains_na",
"asset_dir",
"in_pd_df_by_series_with_duplicated_popularity",
"in_pd_df_by_series_with_index",
"in_pd_df_by_series_with_nan",
"in_pd_series_dimension",
"in_pd_series_dimension_with_index",
"in_pd_series_dimension_with_nan",
"in_pd_series_measure",
"in_pd_series_measure_with_index",
"in_pd_series_measure_with_nan",
"ref_pd_df_by_series",
"ref_pd_df_by_series_max_rows",
"ref_pd_df_by_series_only_index",
"ref_pd_df_by_series_with_duplicated_popularity",
"ref_pd_df_by_series_with_index",
"ref_pd_df_by_series_with_nan",
"ref_pd_series_only_index",
"ref_pd_series_with_index",
"ref_pd_series_with_nan",
"set_up_pd_df",
"set_up_pd_series",
"setUp",
"setUpClass",
"addClassCleanup",
"addCleanup",
"addTypeEqualityFunc",
"assert_",
"assertAlmostEqual",
"assertAlmostEquals",
"assertCountEqual",
"assertDictContainsSubset",
"assertDictEqual",
"assertEqual",
"assertEquals",
"assertFalse",
"assertGreater",
"assertGreaterEqual",
"assertIn",
"assertIs",
"assertIsInstance",
"assertIsNone",
"assertIsNot",
"assertIsNotNone",
"assertLess",
"assertLessEqual",
"assertListEqual",
"assertLogs",
"assertMultiLineEqual",
"assertNoLogs",
"assertNotAlmostEqual",
"assertNotAlmostEquals",
"assertNotEqual",
"assertNotEquals",
"assertNotIn",
"assertNotIsInstance",
"assertNotRegex",
"assertNotRegexpMatches",
"assertRaises",
"assertRaisesRegex",
"assertRaisesRegexp",
"assertRegex",
"assertRegexpMatches",
"assertSequenceEqual",
"assertSetEqual",
"assertTrue",
"assertTupleEqual",
"assertWarns",
"assertWarnsRegex",
"countTestCases",
"debug",
"defaultTestResult",
"doClassCleanups",
"doCleanups",
"fail",
"failIf",
"failIfAlmostEqual",
"failIfEqual",
"failUnless",
"failUnlessAlmostEqual",
"failUnlessEqual",
"failUnlessRaises",
"failureException",
"id",
"longMessage",
"maxDiff",
"run",
"shortDescription",
"skipTest",
"subTest",
"tearDown",
"tearDownClass",
"__annotations__",
"__call__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "addClassCleanup",
"type": "function"
},
{
"name": "addCleanup",
"type": "function"
},
{
"name": "addTypeEqualityFunc",
"type": "function"
},
{
"name": "assert_",
"type": "function"
},
{
"name": "assertAlmostEqual",
"type": "function"
},
{
"name": "assertAlmostEquals",
"type": "function"
},
{
"name": "assertCountEqual",
"type": "function"
},
{
"name": "assertDictContainsSubset",
"type": "function"
},
{
"name": "assertDictEqual",
"type": "function"
},
{
"name": "assertEqual",
"type": "function"
},
{
"name": "assertEquals",
"type": "function"
},
{
"name": "assertFalse",
"type": "function"
},
{
"name": "assertGreater",
"type": "function"
},
{
"name": "assertGreaterEqual",
"type": "function"
},
{
"name": "assertIn",
"type": "function"
},
{
"name": "assertIs",
"type": "function"
},
{
"name": "assertIsInstance",
"type": "function"
},
{
"name": "assertIsNone",
"type": "function"
},
{
"name": "assertIsNot",
"type": "function"
},
{
"name": "assertIsNotNone",
"type": "function"
},
{
"name": "assertLess",
"type": "function"
},
{
"name": "assertLessEqual",
"type": "function"
},
{
"name": "assertListEqual",
"type": "function"
},
{
"name": "assertLogs",
"type": "function"
},
{
"name": "assertMultiLineEqual",
"type": "function"
},
{
"name": "assertNotAlmostEqual",
"type": "function"
},
{
"name": "assertNotAlmostEquals",
"type": "function"
},
{
"name": "assertNotEqual",
"type": "function"
},
{
"name": "assertNotEquals",
"type": "function"
},
{
"name": "assertNotIn",
"type": "function"
},
{
"name": "assertNotIsInstance",
"type": "function"
},
{
"name": "assertNotRegex",
"type": "function"
},
{
"name": "assertNotRegexpMatches",
"type": "function"
},
{
"name": "assertRaises",
"type": "function"
},
{
"name": "assertRaisesRegex",
"type": "function"
},
{
"name": "assertRaisesRegexp",
"type": "function"
},
{
"name": "assertRegex",
"type": "function"
},
{
"name": "assertRegexpMatches",
"type": "function"
},
{
"name": "assertSequenceEqual",
"type": "function"
},
{
"name": "assertSetEqual",
"type": "function"
},
{
"name": "assertTrue",
"type": "function"
},
{
"name": "assertTupleEqual",
"type": "function"
},
{
"name": "assertWarns",
"type": "function"
},
{
"name": "assertWarnsRegex",
"type": "function"
},
{
"name": "asset_dir",
"type": "statement"
},
{
"name": "countTestCases",
"type": "function"
},
{
"name": "data",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "defaultTestResult",
"type": "function"
},
{
"name": "doClassCleanups",
"type": "function"
},
{
"name": "doCleanups",
"type": "function"
},
{
"name": "fail",
"type": "function"
},
{
"name": "failIf",
"type": "function"
},
{
"name": "failIfAlmostEqual",
"type": "function"
},
{
"name": "failIfEqual",
"type": "function"
},
{
"name": "failUnless",
"type": "function"
},
{
"name": "failUnlessAlmostEqual",
"type": "function"
},
{
"name": "failUnlessEqual",
"type": "function"
},
{
"name": "failUnlessRaises",
"type": "function"
},
{
"name": "failureException",
"type": "statement"
},
{
"name": "id",
"type": "function"
},
{
"name": "in_pd_df_by_series",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_dimension",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_index",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_measure",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_index",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_nan",
"type": "statement"
},
{
"name": "longMessage",
"type": "statement"
},
{
"name": "maxDiff",
"type": "statement"
},
{
"name": "ref_pd_df_by_series",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_max_rows",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "ref_pd_series",
"type": "statement"
},
{
"name": "ref_pd_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_nan",
"type": "statement"
},
{
"name": "run",
"type": "function"
},
{
"name": "set_up_pd_df",
"type": "function"
},
{
"name": "set_up_pd_series",
"type": "function"
},
{
"name": "setUp",
"type": "function"
},
{
"name": "setUpClass",
"type": "function"
},
{
"name": "shortDescription",
"type": "function"
},
{
"name": "skipTest",
"type": "function"
},
{
"name": "subTest",
"type": "function"
},
{
"name": "tearDown",
"type": "function"
},
{
"name": "tearDownClass",
"type": "function"
},
{
"name": "test_add_data_frame_with_empty_series",
"type": "function"
},
{
"name": "test_add_data_frame_with_series",
"type": "function"
},
{
"name": "test_add_data_frame_with_series_contains_na",
"type": "function"
},
{
"name": "_addSkip",
"type": "function"
},
{
"name": "_formatMessage",
"type": "function"
},
{
"name": "_getAssertEqualityFunc",
"type": "function"
},
{
"name": "_testMethodDoc",
"type": "statement"
},
{
"name": "_testMethodName",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__call__",
"type": "function"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.data.add_data_frame(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_data_frame_with_series_contains_na(self) -> None:
self.data.add_data_frame(self.
|
7,684 | 216 | 4,715 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
inproject
|
data
| true |
statement
| 97 | 101 | false | false |
[
"data",
"test_add_data_frame_with_series",
"ref_pd_series",
"in_pd_df_by_series",
"assertEqual",
"test_add_data_frame_with_empty_series",
"test_add_data_frame_with_series_contains_na",
"asset_dir",
"in_pd_df_by_series_with_duplicated_popularity",
"in_pd_df_by_series_with_index",
"in_pd_df_by_series_with_nan",
"in_pd_series_dimension",
"in_pd_series_dimension_with_index",
"in_pd_series_dimension_with_nan",
"in_pd_series_measure",
"in_pd_series_measure_with_index",
"in_pd_series_measure_with_nan",
"ref_pd_df_by_series",
"ref_pd_df_by_series_max_rows",
"ref_pd_df_by_series_only_index",
"ref_pd_df_by_series_with_duplicated_popularity",
"ref_pd_df_by_series_with_index",
"ref_pd_df_by_series_with_nan",
"ref_pd_series_only_index",
"ref_pd_series_with_index",
"ref_pd_series_with_nan",
"set_up_pd_df",
"set_up_pd_series",
"setUp",
"setUpClass",
"addClassCleanup",
"addCleanup",
"addTypeEqualityFunc",
"assert_",
"assertAlmostEqual",
"assertAlmostEquals",
"assertCountEqual",
"assertDictContainsSubset",
"assertDictEqual",
"assertEquals",
"assertFalse",
"assertGreater",
"assertGreaterEqual",
"assertIn",
"assertIs",
"assertIsInstance",
"assertIsNone",
"assertIsNot",
"assertIsNotNone",
"assertLess",
"assertLessEqual",
"assertListEqual",
"assertLogs",
"assertMultiLineEqual",
"assertNoLogs",
"assertNotAlmostEqual",
"assertNotAlmostEquals",
"assertNotEqual",
"assertNotEquals",
"assertNotIn",
"assertNotIsInstance",
"assertNotRegex",
"assertNotRegexpMatches",
"assertRaises",
"assertRaisesRegex",
"assertRaisesRegexp",
"assertRegex",
"assertRegexpMatches",
"assertSequenceEqual",
"assertSetEqual",
"assertTrue",
"assertTupleEqual",
"assertWarns",
"assertWarnsRegex",
"countTestCases",
"debug",
"defaultTestResult",
"doClassCleanups",
"doCleanups",
"fail",
"failIf",
"failIfAlmostEqual",
"failIfEqual",
"failUnless",
"failUnlessAlmostEqual",
"failUnlessEqual",
"failUnlessRaises",
"failureException",
"id",
"longMessage",
"maxDiff",
"run",
"shortDescription",
"skipTest",
"subTest",
"tearDown",
"tearDownClass",
"__annotations__",
"__call__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "addClassCleanup",
"type": "function"
},
{
"name": "addCleanup",
"type": "function"
},
{
"name": "addTypeEqualityFunc",
"type": "function"
},
{
"name": "assert_",
"type": "function"
},
{
"name": "assertAlmostEqual",
"type": "function"
},
{
"name": "assertAlmostEquals",
"type": "function"
},
{
"name": "assertCountEqual",
"type": "function"
},
{
"name": "assertDictContainsSubset",
"type": "function"
},
{
"name": "assertDictEqual",
"type": "function"
},
{
"name": "assertEqual",
"type": "function"
},
{
"name": "assertEquals",
"type": "function"
},
{
"name": "assertFalse",
"type": "function"
},
{
"name": "assertGreater",
"type": "function"
},
{
"name": "assertGreaterEqual",
"type": "function"
},
{
"name": "assertIn",
"type": "function"
},
{
"name": "assertIs",
"type": "function"
},
{
"name": "assertIsInstance",
"type": "function"
},
{
"name": "assertIsNone",
"type": "function"
},
{
"name": "assertIsNot",
"type": "function"
},
{
"name": "assertIsNotNone",
"type": "function"
},
{
"name": "assertLess",
"type": "function"
},
{
"name": "assertLessEqual",
"type": "function"
},
{
"name": "assertListEqual",
"type": "function"
},
{
"name": "assertLogs",
"type": "function"
},
{
"name": "assertMultiLineEqual",
"type": "function"
},
{
"name": "assertNotAlmostEqual",
"type": "function"
},
{
"name": "assertNotAlmostEquals",
"type": "function"
},
{
"name": "assertNotEqual",
"type": "function"
},
{
"name": "assertNotEquals",
"type": "function"
},
{
"name": "assertNotIn",
"type": "function"
},
{
"name": "assertNotIsInstance",
"type": "function"
},
{
"name": "assertNotRegex",
"type": "function"
},
{
"name": "assertNotRegexpMatches",
"type": "function"
},
{
"name": "assertRaises",
"type": "function"
},
{
"name": "assertRaisesRegex",
"type": "function"
},
{
"name": "assertRaisesRegexp",
"type": "function"
},
{
"name": "assertRegex",
"type": "function"
},
{
"name": "assertRegexpMatches",
"type": "function"
},
{
"name": "assertSequenceEqual",
"type": "function"
},
{
"name": "assertSetEqual",
"type": "function"
},
{
"name": "assertTrue",
"type": "function"
},
{
"name": "assertTupleEqual",
"type": "function"
},
{
"name": "assertWarns",
"type": "function"
},
{
"name": "assertWarnsRegex",
"type": "function"
},
{
"name": "asset_dir",
"type": "statement"
},
{
"name": "countTestCases",
"type": "function"
},
{
"name": "data",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "defaultTestResult",
"type": "function"
},
{
"name": "doClassCleanups",
"type": "function"
},
{
"name": "doCleanups",
"type": "function"
},
{
"name": "fail",
"type": "function"
},
{
"name": "failIf",
"type": "function"
},
{
"name": "failIfAlmostEqual",
"type": "function"
},
{
"name": "failIfEqual",
"type": "function"
},
{
"name": "failUnless",
"type": "function"
},
{
"name": "failUnlessAlmostEqual",
"type": "function"
},
{
"name": "failUnlessEqual",
"type": "function"
},
{
"name": "failUnlessRaises",
"type": "function"
},
{
"name": "failureException",
"type": "statement"
},
{
"name": "id",
"type": "function"
},
{
"name": "in_pd_df_by_series",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_dimension",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_index",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_measure",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_index",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_nan",
"type": "statement"
},
{
"name": "longMessage",
"type": "statement"
},
{
"name": "maxDiff",
"type": "statement"
},
{
"name": "ref_pd_df_by_series",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_max_rows",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "ref_pd_series",
"type": "statement"
},
{
"name": "ref_pd_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_nan",
"type": "statement"
},
{
"name": "run",
"type": "function"
},
{
"name": "set_up_pd_df",
"type": "function"
},
{
"name": "set_up_pd_series",
"type": "function"
},
{
"name": "setUp",
"type": "function"
},
{
"name": "setUpClass",
"type": "function"
},
{
"name": "shortDescription",
"type": "function"
},
{
"name": "skipTest",
"type": "function"
},
{
"name": "subTest",
"type": "function"
},
{
"name": "tearDown",
"type": "function"
},
{
"name": "tearDownClass",
"type": "function"
},
{
"name": "test_add_data_frame_with_empty_series",
"type": "function"
},
{
"name": "test_add_data_frame_with_series",
"type": "function"
},
{
"name": "test_add_data_frame_with_series_contains_na",
"type": "function"
},
{
"name": "_addSkip",
"type": "function"
},
{
"name": "_formatMessage",
"type": "function"
},
{
"name": "_getAssertEqualityFunc",
"type": "function"
},
{
"name": "_testMethodDoc",
"type": "statement"
},
{
"name": "_testMethodName",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__call__",
"type": "function"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.data.add_data_frame(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_data_frame_with_series_contains_na(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension_with_nan)
self.
|
7,685 | 216 | 4,720 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
inproject
|
add_data_frame
| true |
function
| 28 | 30 | false | false |
[
"add_df",
"add_data_frame",
"build",
"add_data_frame_index",
"add_df_index",
"add_dimension",
"add_measure",
"add_np_array",
"add_record",
"add_records",
"add_series",
"add_series_list",
"add_spark_df",
"dump",
"filter",
"from_json",
"set_filter",
"clear",
"copy",
"fromkeys",
"get",
"items",
"keys",
"pop",
"popitem",
"setdefault",
"update",
"values",
"__annotations__",
"__class__",
"__class_getitem__",
"__contains__",
"__delattr__",
"__delitem__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__getitem__",
"__hash__",
"__init__",
"__init_subclass__",
"__ior__",
"__iter__",
"__len__",
"__ne__",
"__new__",
"__or__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__reversed__",
"__ror__",
"__setattr__",
"__setitem__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "add_data_frame",
"type": "function"
},
{
"name": "add_data_frame_index",
"type": "function"
},
{
"name": "add_df",
"type": "function"
},
{
"name": "add_df_index",
"type": "function"
},
{
"name": "add_dimension",
"type": "function"
},
{
"name": "add_measure",
"type": "function"
},
{
"name": "add_np_array",
"type": "function"
},
{
"name": "add_record",
"type": "function"
},
{
"name": "add_records",
"type": "function"
},
{
"name": "add_series",
"type": "function"
},
{
"name": "add_series_list",
"type": "function"
},
{
"name": "add_spark_df",
"type": "function"
},
{
"name": "build",
"type": "function"
},
{
"name": "clear",
"type": "function"
},
{
"name": "copy",
"type": "function"
},
{
"name": "dump",
"type": "function"
},
{
"name": "filter",
"type": "function"
},
{
"name": "from_json",
"type": "function"
},
{
"name": "fromkeys",
"type": "function"
},
{
"name": "get",
"type": "function"
},
{
"name": "items",
"type": "function"
},
{
"name": "keys",
"type": "function"
},
{
"name": "pop",
"type": "function"
},
{
"name": "popitem",
"type": "function"
},
{
"name": "set_filter",
"type": "function"
},
{
"name": "setdefault",
"type": "function"
},
{
"name": "update",
"type": "function"
},
{
"name": "values",
"type": "function"
},
{
"name": "_add_named_value",
"type": "function"
},
{
"name": "_add_value",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__class_getitem__",
"type": "function"
},
{
"name": "__contains__",
"type": "function"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__delitem__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__getitem__",
"type": "function"
},
{
"name": "__hash__",
"type": "statement"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__ior__",
"type": "function"
},
{
"name": "__iter__",
"type": "function"
},
{
"name": "__len__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__or__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__reversed__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__setitem__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.data.add_data_frame(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_data_frame_with_series_contains_na(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension_with_nan)
self.data.
|
7,686 | 216 | 4,740 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
inproject
|
in_pd_series_measure_with_nan
| true |
statement
| 97 | 101 | false | false |
[
"data",
"test_add_data_frame_with_series",
"ref_pd_series",
"test_add_data_frame_with_empty_series",
"in_pd_df_by_series",
"test_add_data_frame_with_series_contains_na",
"asset_dir",
"in_pd_df_by_series_with_duplicated_popularity",
"in_pd_df_by_series_with_index",
"in_pd_df_by_series_with_nan",
"in_pd_series_dimension",
"in_pd_series_dimension_with_index",
"in_pd_series_dimension_with_nan",
"in_pd_series_measure",
"in_pd_series_measure_with_index",
"in_pd_series_measure_with_nan",
"ref_pd_df_by_series",
"ref_pd_df_by_series_max_rows",
"ref_pd_df_by_series_only_index",
"ref_pd_df_by_series_with_duplicated_popularity",
"ref_pd_df_by_series_with_index",
"ref_pd_df_by_series_with_nan",
"ref_pd_series_only_index",
"ref_pd_series_with_index",
"ref_pd_series_with_nan",
"set_up_pd_df",
"set_up_pd_series",
"setUp",
"setUpClass",
"addClassCleanup",
"addCleanup",
"addTypeEqualityFunc",
"assert_",
"assertAlmostEqual",
"assertAlmostEquals",
"assertCountEqual",
"assertDictContainsSubset",
"assertDictEqual",
"assertEqual",
"assertEquals",
"assertFalse",
"assertGreater",
"assertGreaterEqual",
"assertIn",
"assertIs",
"assertIsInstance",
"assertIsNone",
"assertIsNot",
"assertIsNotNone",
"assertLess",
"assertLessEqual",
"assertListEqual",
"assertLogs",
"assertMultiLineEqual",
"assertNoLogs",
"assertNotAlmostEqual",
"assertNotAlmostEquals",
"assertNotEqual",
"assertNotEquals",
"assertNotIn",
"assertNotIsInstance",
"assertNotRegex",
"assertNotRegexpMatches",
"assertRaises",
"assertRaisesRegex",
"assertRaisesRegexp",
"assertRegex",
"assertRegexpMatches",
"assertSequenceEqual",
"assertSetEqual",
"assertTrue",
"assertTupleEqual",
"assertWarns",
"assertWarnsRegex",
"countTestCases",
"debug",
"defaultTestResult",
"doClassCleanups",
"doCleanups",
"fail",
"failIf",
"failIfAlmostEqual",
"failIfEqual",
"failUnless",
"failUnlessAlmostEqual",
"failUnlessEqual",
"failUnlessRaises",
"failureException",
"id",
"longMessage",
"maxDiff",
"run",
"shortDescription",
"skipTest",
"subTest",
"tearDown",
"tearDownClass",
"__annotations__",
"__call__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "addClassCleanup",
"type": "function"
},
{
"name": "addCleanup",
"type": "function"
},
{
"name": "addTypeEqualityFunc",
"type": "function"
},
{
"name": "assert_",
"type": "function"
},
{
"name": "assertAlmostEqual",
"type": "function"
},
{
"name": "assertAlmostEquals",
"type": "function"
},
{
"name": "assertCountEqual",
"type": "function"
},
{
"name": "assertDictContainsSubset",
"type": "function"
},
{
"name": "assertDictEqual",
"type": "function"
},
{
"name": "assertEqual",
"type": "function"
},
{
"name": "assertEquals",
"type": "function"
},
{
"name": "assertFalse",
"type": "function"
},
{
"name": "assertGreater",
"type": "function"
},
{
"name": "assertGreaterEqual",
"type": "function"
},
{
"name": "assertIn",
"type": "function"
},
{
"name": "assertIs",
"type": "function"
},
{
"name": "assertIsInstance",
"type": "function"
},
{
"name": "assertIsNone",
"type": "function"
},
{
"name": "assertIsNot",
"type": "function"
},
{
"name": "assertIsNotNone",
"type": "function"
},
{
"name": "assertLess",
"type": "function"
},
{
"name": "assertLessEqual",
"type": "function"
},
{
"name": "assertListEqual",
"type": "function"
},
{
"name": "assertLogs",
"type": "function"
},
{
"name": "assertMultiLineEqual",
"type": "function"
},
{
"name": "assertNotAlmostEqual",
"type": "function"
},
{
"name": "assertNotAlmostEquals",
"type": "function"
},
{
"name": "assertNotEqual",
"type": "function"
},
{
"name": "assertNotEquals",
"type": "function"
},
{
"name": "assertNotIn",
"type": "function"
},
{
"name": "assertNotIsInstance",
"type": "function"
},
{
"name": "assertNotRegex",
"type": "function"
},
{
"name": "assertNotRegexpMatches",
"type": "function"
},
{
"name": "assertRaises",
"type": "function"
},
{
"name": "assertRaisesRegex",
"type": "function"
},
{
"name": "assertRaisesRegexp",
"type": "function"
},
{
"name": "assertRegex",
"type": "function"
},
{
"name": "assertRegexpMatches",
"type": "function"
},
{
"name": "assertSequenceEqual",
"type": "function"
},
{
"name": "assertSetEqual",
"type": "function"
},
{
"name": "assertTrue",
"type": "function"
},
{
"name": "assertTupleEqual",
"type": "function"
},
{
"name": "assertWarns",
"type": "function"
},
{
"name": "assertWarnsRegex",
"type": "function"
},
{
"name": "asset_dir",
"type": "statement"
},
{
"name": "countTestCases",
"type": "function"
},
{
"name": "data",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "defaultTestResult",
"type": "function"
},
{
"name": "doClassCleanups",
"type": "function"
},
{
"name": "doCleanups",
"type": "function"
},
{
"name": "fail",
"type": "function"
},
{
"name": "failIf",
"type": "function"
},
{
"name": "failIfAlmostEqual",
"type": "function"
},
{
"name": "failIfEqual",
"type": "function"
},
{
"name": "failUnless",
"type": "function"
},
{
"name": "failUnlessAlmostEqual",
"type": "function"
},
{
"name": "failUnlessEqual",
"type": "function"
},
{
"name": "failUnlessRaises",
"type": "function"
},
{
"name": "failureException",
"type": "statement"
},
{
"name": "id",
"type": "function"
},
{
"name": "in_pd_df_by_series",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_dimension",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_index",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_measure",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_index",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_nan",
"type": "statement"
},
{
"name": "longMessage",
"type": "statement"
},
{
"name": "maxDiff",
"type": "statement"
},
{
"name": "ref_pd_df_by_series",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_max_rows",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "ref_pd_series",
"type": "statement"
},
{
"name": "ref_pd_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_nan",
"type": "statement"
},
{
"name": "run",
"type": "function"
},
{
"name": "set_up_pd_df",
"type": "function"
},
{
"name": "set_up_pd_series",
"type": "function"
},
{
"name": "setUp",
"type": "function"
},
{
"name": "setUpClass",
"type": "function"
},
{
"name": "shortDescription",
"type": "function"
},
{
"name": "skipTest",
"type": "function"
},
{
"name": "subTest",
"type": "function"
},
{
"name": "tearDown",
"type": "function"
},
{
"name": "tearDownClass",
"type": "function"
},
{
"name": "test_add_data_frame_with_empty_series",
"type": "function"
},
{
"name": "test_add_data_frame_with_series",
"type": "function"
},
{
"name": "test_add_data_frame_with_series_contains_na",
"type": "function"
},
{
"name": "_addSkip",
"type": "function"
},
{
"name": "_formatMessage",
"type": "function"
},
{
"name": "_getAssertEqualityFunc",
"type": "function"
},
{
"name": "_testMethodDoc",
"type": "statement"
},
{
"name": "_testMethodName",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__call__",
"type": "function"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.data.add_data_frame(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_data_frame_with_series_contains_na(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension_with_nan)
self.data.add_data_frame(self.
|
7,687 | 216 | 4,814 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
Unknown
|
ref_pd_series_with_nan
| true |
statement
| 97 | 101 | false | false |
[
"data",
"ref_pd_series",
"in_pd_df_by_series",
"in_pd_series_dimension",
"ref_pd_df_by_series_with_duplicated_popularity",
"test_add_data_frame_with_empty_series",
"test_add_data_frame_with_series",
"test_add_data_frame_with_series_contains_na",
"asset_dir",
"in_pd_df_by_series_with_duplicated_popularity",
"in_pd_df_by_series_with_index",
"in_pd_df_by_series_with_nan",
"in_pd_series_dimension_with_index",
"in_pd_series_dimension_with_nan",
"in_pd_series_measure",
"in_pd_series_measure_with_index",
"in_pd_series_measure_with_nan",
"ref_pd_df_by_series",
"ref_pd_df_by_series_max_rows",
"ref_pd_df_by_series_only_index",
"ref_pd_df_by_series_with_index",
"ref_pd_df_by_series_with_nan",
"ref_pd_series_only_index",
"ref_pd_series_with_index",
"ref_pd_series_with_nan",
"set_up_pd_df",
"set_up_pd_series",
"setUp",
"setUpClass",
"addClassCleanup",
"addCleanup",
"addTypeEqualityFunc",
"assert_",
"assertAlmostEqual",
"assertAlmostEquals",
"assertCountEqual",
"assertDictContainsSubset",
"assertDictEqual",
"assertEqual",
"assertEquals",
"assertFalse",
"assertGreater",
"assertGreaterEqual",
"assertIn",
"assertIs",
"assertIsInstance",
"assertIsNone",
"assertIsNot",
"assertIsNotNone",
"assertLess",
"assertLessEqual",
"assertListEqual",
"assertLogs",
"assertMultiLineEqual",
"assertNoLogs",
"assertNotAlmostEqual",
"assertNotAlmostEquals",
"assertNotEqual",
"assertNotEquals",
"assertNotIn",
"assertNotIsInstance",
"assertNotRegex",
"assertNotRegexpMatches",
"assertRaises",
"assertRaisesRegex",
"assertRaisesRegexp",
"assertRegex",
"assertRegexpMatches",
"assertSequenceEqual",
"assertSetEqual",
"assertTrue",
"assertTupleEqual",
"assertWarns",
"assertWarnsRegex",
"countTestCases",
"debug",
"defaultTestResult",
"doClassCleanups",
"doCleanups",
"fail",
"failIf",
"failIfAlmostEqual",
"failIfEqual",
"failUnless",
"failUnlessAlmostEqual",
"failUnlessEqual",
"failUnlessRaises",
"failureException",
"id",
"longMessage",
"maxDiff",
"run",
"shortDescription",
"skipTest",
"subTest",
"tearDown",
"tearDownClass",
"__annotations__",
"__call__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "addClassCleanup",
"type": "function"
},
{
"name": "addCleanup",
"type": "function"
},
{
"name": "addTypeEqualityFunc",
"type": "function"
},
{
"name": "assert_",
"type": "function"
},
{
"name": "assertAlmostEqual",
"type": "function"
},
{
"name": "assertAlmostEquals",
"type": "function"
},
{
"name": "assertCountEqual",
"type": "function"
},
{
"name": "assertDictContainsSubset",
"type": "function"
},
{
"name": "assertDictEqual",
"type": "function"
},
{
"name": "assertEqual",
"type": "function"
},
{
"name": "assertEquals",
"type": "function"
},
{
"name": "assertFalse",
"type": "function"
},
{
"name": "assertGreater",
"type": "function"
},
{
"name": "assertGreaterEqual",
"type": "function"
},
{
"name": "assertIn",
"type": "function"
},
{
"name": "assertIs",
"type": "function"
},
{
"name": "assertIsInstance",
"type": "function"
},
{
"name": "assertIsNone",
"type": "function"
},
{
"name": "assertIsNot",
"type": "function"
},
{
"name": "assertIsNotNone",
"type": "function"
},
{
"name": "assertLess",
"type": "function"
},
{
"name": "assertLessEqual",
"type": "function"
},
{
"name": "assertListEqual",
"type": "function"
},
{
"name": "assertLogs",
"type": "function"
},
{
"name": "assertMultiLineEqual",
"type": "function"
},
{
"name": "assertNotAlmostEqual",
"type": "function"
},
{
"name": "assertNotAlmostEquals",
"type": "function"
},
{
"name": "assertNotEqual",
"type": "function"
},
{
"name": "assertNotEquals",
"type": "function"
},
{
"name": "assertNotIn",
"type": "function"
},
{
"name": "assertNotIsInstance",
"type": "function"
},
{
"name": "assertNotRegex",
"type": "function"
},
{
"name": "assertNotRegexpMatches",
"type": "function"
},
{
"name": "assertRaises",
"type": "function"
},
{
"name": "assertRaisesRegex",
"type": "function"
},
{
"name": "assertRaisesRegexp",
"type": "function"
},
{
"name": "assertRegex",
"type": "function"
},
{
"name": "assertRegexpMatches",
"type": "function"
},
{
"name": "assertSequenceEqual",
"type": "function"
},
{
"name": "assertSetEqual",
"type": "function"
},
{
"name": "assertTrue",
"type": "function"
},
{
"name": "assertTupleEqual",
"type": "function"
},
{
"name": "assertWarns",
"type": "function"
},
{
"name": "assertWarnsRegex",
"type": "function"
},
{
"name": "asset_dir",
"type": "statement"
},
{
"name": "countTestCases",
"type": "function"
},
{
"name": "data",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "defaultTestResult",
"type": "function"
},
{
"name": "doClassCleanups",
"type": "function"
},
{
"name": "doCleanups",
"type": "function"
},
{
"name": "fail",
"type": "function"
},
{
"name": "failIf",
"type": "function"
},
{
"name": "failIfAlmostEqual",
"type": "function"
},
{
"name": "failIfEqual",
"type": "function"
},
{
"name": "failUnless",
"type": "function"
},
{
"name": "failUnlessAlmostEqual",
"type": "function"
},
{
"name": "failUnlessEqual",
"type": "function"
},
{
"name": "failUnlessRaises",
"type": "function"
},
{
"name": "failureException",
"type": "statement"
},
{
"name": "id",
"type": "function"
},
{
"name": "in_pd_df_by_series",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_dimension",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_index",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_measure",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_index",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_nan",
"type": "statement"
},
{
"name": "longMessage",
"type": "statement"
},
{
"name": "maxDiff",
"type": "statement"
},
{
"name": "ref_pd_df_by_series",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_max_rows",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "ref_pd_series",
"type": "statement"
},
{
"name": "ref_pd_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_nan",
"type": "statement"
},
{
"name": "run",
"type": "function"
},
{
"name": "set_up_pd_df",
"type": "function"
},
{
"name": "set_up_pd_series",
"type": "function"
},
{
"name": "setUp",
"type": "function"
},
{
"name": "setUpClass",
"type": "function"
},
{
"name": "shortDescription",
"type": "function"
},
{
"name": "skipTest",
"type": "function"
},
{
"name": "subTest",
"type": "function"
},
{
"name": "tearDown",
"type": "function"
},
{
"name": "tearDownClass",
"type": "function"
},
{
"name": "test_add_data_frame_with_empty_series",
"type": "function"
},
{
"name": "test_add_data_frame_with_series",
"type": "function"
},
{
"name": "test_add_data_frame_with_series_contains_na",
"type": "function"
},
{
"name": "_addSkip",
"type": "function"
},
{
"name": "_formatMessage",
"type": "function"
},
{
"name": "_getAssertEqualityFunc",
"type": "function"
},
{
"name": "_testMethodDoc",
"type": "statement"
},
{
"name": "_testMethodName",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__call__",
"type": "function"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.data.add_data_frame(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_data_frame_with_series_contains_na(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension_with_nan)
self.data.add_data_frame(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.
|
7,688 | 216 | 4,855 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
Unknown
|
data
| true |
statement
| 97 | 101 | false | false |
[
"data",
"test_add_data_frame_with_series",
"ref_pd_series",
"in_pd_df_by_series",
"assertEqual",
"test_add_data_frame_with_empty_series",
"test_add_data_frame_with_series_contains_na",
"asset_dir",
"in_pd_df_by_series_with_duplicated_popularity",
"in_pd_df_by_series_with_index",
"in_pd_df_by_series_with_nan",
"in_pd_series_dimension",
"in_pd_series_dimension_with_index",
"in_pd_series_dimension_with_nan",
"in_pd_series_measure",
"in_pd_series_measure_with_index",
"in_pd_series_measure_with_nan",
"ref_pd_df_by_series",
"ref_pd_df_by_series_max_rows",
"ref_pd_df_by_series_only_index",
"ref_pd_df_by_series_with_duplicated_popularity",
"ref_pd_df_by_series_with_index",
"ref_pd_df_by_series_with_nan",
"ref_pd_series_only_index",
"ref_pd_series_with_index",
"ref_pd_series_with_nan",
"set_up_pd_df",
"set_up_pd_series",
"setUp",
"setUpClass",
"addClassCleanup",
"addCleanup",
"addTypeEqualityFunc",
"assert_",
"assertAlmostEqual",
"assertAlmostEquals",
"assertCountEqual",
"assertDictContainsSubset",
"assertDictEqual",
"assertEquals",
"assertFalse",
"assertGreater",
"assertGreaterEqual",
"assertIn",
"assertIs",
"assertIsInstance",
"assertIsNone",
"assertIsNot",
"assertIsNotNone",
"assertLess",
"assertLessEqual",
"assertListEqual",
"assertLogs",
"assertMultiLineEqual",
"assertNoLogs",
"assertNotAlmostEqual",
"assertNotAlmostEquals",
"assertNotEqual",
"assertNotEquals",
"assertNotIn",
"assertNotIsInstance",
"assertNotRegex",
"assertNotRegexpMatches",
"assertRaises",
"assertRaisesRegex",
"assertRaisesRegexp",
"assertRegex",
"assertRegexpMatches",
"assertSequenceEqual",
"assertSetEqual",
"assertTrue",
"assertTupleEqual",
"assertWarns",
"assertWarnsRegex",
"countTestCases",
"debug",
"defaultTestResult",
"doClassCleanups",
"doCleanups",
"fail",
"failIf",
"failIfAlmostEqual",
"failIfEqual",
"failUnless",
"failUnlessAlmostEqual",
"failUnlessEqual",
"failUnlessRaises",
"failureException",
"id",
"longMessage",
"maxDiff",
"run",
"shortDescription",
"skipTest",
"subTest",
"tearDown",
"tearDownClass",
"__annotations__",
"__call__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "addClassCleanup",
"type": "function"
},
{
"name": "addCleanup",
"type": "function"
},
{
"name": "addTypeEqualityFunc",
"type": "function"
},
{
"name": "assert_",
"type": "function"
},
{
"name": "assertAlmostEqual",
"type": "function"
},
{
"name": "assertAlmostEquals",
"type": "function"
},
{
"name": "assertCountEqual",
"type": "function"
},
{
"name": "assertDictContainsSubset",
"type": "function"
},
{
"name": "assertDictEqual",
"type": "function"
},
{
"name": "assertEqual",
"type": "function"
},
{
"name": "assertEquals",
"type": "function"
},
{
"name": "assertFalse",
"type": "function"
},
{
"name": "assertGreater",
"type": "function"
},
{
"name": "assertGreaterEqual",
"type": "function"
},
{
"name": "assertIn",
"type": "function"
},
{
"name": "assertIs",
"type": "function"
},
{
"name": "assertIsInstance",
"type": "function"
},
{
"name": "assertIsNone",
"type": "function"
},
{
"name": "assertIsNot",
"type": "function"
},
{
"name": "assertIsNotNone",
"type": "function"
},
{
"name": "assertLess",
"type": "function"
},
{
"name": "assertLessEqual",
"type": "function"
},
{
"name": "assertListEqual",
"type": "function"
},
{
"name": "assertLogs",
"type": "function"
},
{
"name": "assertMultiLineEqual",
"type": "function"
},
{
"name": "assertNotAlmostEqual",
"type": "function"
},
{
"name": "assertNotAlmostEquals",
"type": "function"
},
{
"name": "assertNotEqual",
"type": "function"
},
{
"name": "assertNotEquals",
"type": "function"
},
{
"name": "assertNotIn",
"type": "function"
},
{
"name": "assertNotIsInstance",
"type": "function"
},
{
"name": "assertNotRegex",
"type": "function"
},
{
"name": "assertNotRegexpMatches",
"type": "function"
},
{
"name": "assertRaises",
"type": "function"
},
{
"name": "assertRaisesRegex",
"type": "function"
},
{
"name": "assertRaisesRegexp",
"type": "function"
},
{
"name": "assertRegex",
"type": "function"
},
{
"name": "assertRegexpMatches",
"type": "function"
},
{
"name": "assertSequenceEqual",
"type": "function"
},
{
"name": "assertSetEqual",
"type": "function"
},
{
"name": "assertTrue",
"type": "function"
},
{
"name": "assertTupleEqual",
"type": "function"
},
{
"name": "assertWarns",
"type": "function"
},
{
"name": "assertWarnsRegex",
"type": "function"
},
{
"name": "asset_dir",
"type": "statement"
},
{
"name": "countTestCases",
"type": "function"
},
{
"name": "data",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "defaultTestResult",
"type": "function"
},
{
"name": "doClassCleanups",
"type": "function"
},
{
"name": "doCleanups",
"type": "function"
},
{
"name": "fail",
"type": "function"
},
{
"name": "failIf",
"type": "function"
},
{
"name": "failIfAlmostEqual",
"type": "function"
},
{
"name": "failIfEqual",
"type": "function"
},
{
"name": "failUnless",
"type": "function"
},
{
"name": "failUnlessAlmostEqual",
"type": "function"
},
{
"name": "failUnlessEqual",
"type": "function"
},
{
"name": "failUnlessRaises",
"type": "function"
},
{
"name": "failureException",
"type": "statement"
},
{
"name": "id",
"type": "function"
},
{
"name": "in_pd_df_by_series",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_dimension",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_index",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_measure",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_index",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_nan",
"type": "statement"
},
{
"name": "longMessage",
"type": "statement"
},
{
"name": "maxDiff",
"type": "statement"
},
{
"name": "ref_pd_df_by_series",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_max_rows",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "ref_pd_series",
"type": "statement"
},
{
"name": "ref_pd_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_nan",
"type": "statement"
},
{
"name": "run",
"type": "function"
},
{
"name": "set_up_pd_df",
"type": "function"
},
{
"name": "set_up_pd_series",
"type": "function"
},
{
"name": "setUp",
"type": "function"
},
{
"name": "setUpClass",
"type": "function"
},
{
"name": "shortDescription",
"type": "function"
},
{
"name": "skipTest",
"type": "function"
},
{
"name": "subTest",
"type": "function"
},
{
"name": "tearDown",
"type": "function"
},
{
"name": "tearDownClass",
"type": "function"
},
{
"name": "test_add_data_frame_with_empty_series",
"type": "function"
},
{
"name": "test_add_data_frame_with_series",
"type": "function"
},
{
"name": "test_add_data_frame_with_series_contains_na",
"type": "function"
},
{
"name": "_addSkip",
"type": "function"
},
{
"name": "_formatMessage",
"type": "function"
},
{
"name": "_getAssertEqualityFunc",
"type": "function"
},
{
"name": "_testMethodDoc",
"type": "statement"
},
{
"name": "_testMethodName",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__call__",
"type": "function"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.data.add_data_frame(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_data_frame_with_series_contains_na(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension_with_nan)
self.data.add_data_frame(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.
|
7,689 | 216 | 4,860 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
Unknown
|
build
| true |
function
| 28 | 30 | false | false |
[
"add_data_frame",
"add_df",
"build",
"add_data_frame_index",
"add_df_index",
"add_dimension",
"add_measure",
"add_np_array",
"add_record",
"add_records",
"add_series",
"add_series_list",
"add_spark_df",
"dump",
"filter",
"from_json",
"set_filter",
"clear",
"copy",
"fromkeys",
"get",
"items",
"keys",
"pop",
"popitem",
"setdefault",
"update",
"values",
"__annotations__",
"__class__",
"__class_getitem__",
"__contains__",
"__delattr__",
"__delitem__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__getitem__",
"__hash__",
"__init__",
"__init_subclass__",
"__ior__",
"__iter__",
"__len__",
"__ne__",
"__new__",
"__or__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__reversed__",
"__ror__",
"__setattr__",
"__setitem__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "add_data_frame",
"type": "function"
},
{
"name": "add_data_frame_index",
"type": "function"
},
{
"name": "add_df",
"type": "function"
},
{
"name": "add_df_index",
"type": "function"
},
{
"name": "add_dimension",
"type": "function"
},
{
"name": "add_measure",
"type": "function"
},
{
"name": "add_np_array",
"type": "function"
},
{
"name": "add_record",
"type": "function"
},
{
"name": "add_records",
"type": "function"
},
{
"name": "add_series",
"type": "function"
},
{
"name": "add_series_list",
"type": "function"
},
{
"name": "add_spark_df",
"type": "function"
},
{
"name": "build",
"type": "function"
},
{
"name": "clear",
"type": "function"
},
{
"name": "copy",
"type": "function"
},
{
"name": "dump",
"type": "function"
},
{
"name": "filter",
"type": "function"
},
{
"name": "from_json",
"type": "function"
},
{
"name": "fromkeys",
"type": "function"
},
{
"name": "get",
"type": "function"
},
{
"name": "items",
"type": "function"
},
{
"name": "keys",
"type": "function"
},
{
"name": "pop",
"type": "function"
},
{
"name": "popitem",
"type": "function"
},
{
"name": "set_filter",
"type": "function"
},
{
"name": "setdefault",
"type": "function"
},
{
"name": "update",
"type": "function"
},
{
"name": "values",
"type": "function"
},
{
"name": "_add_named_value",
"type": "function"
},
{
"name": "_add_value",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__class_getitem__",
"type": "function"
},
{
"name": "__contains__",
"type": "function"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__delitem__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__getitem__",
"type": "function"
},
{
"name": "__hash__",
"type": "statement"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__ior__",
"type": "function"
},
{
"name": "__iter__",
"type": "function"
},
{
"name": "__len__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__or__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__reversed__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__setitem__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.data.add_data_frame(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_data_frame_with_series_contains_na(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension_with_nan)
self.data.add_data_frame(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.
|
7,690 | 216 | 4,980 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
inproject
|
data
| true |
statement
| 96 | 100 | false | false |
[
"data",
"assertEqual",
"test_add_df_index_with_df",
"in_pd_series_measure_with_index",
"in_pd_series_dimension_with_index",
"test_add_df_index_with_none",
"asset_dir",
"in_pd_df_by_series",
"in_pd_df_by_series_with_duplicated_popularity",
"in_pd_df_by_series_with_index",
"in_pd_df_by_series_with_nan",
"in_pd_series_dimension",
"in_pd_series_dimension_with_nan",
"in_pd_series_measure",
"in_pd_series_measure_with_nan",
"ref_pd_df_by_series",
"ref_pd_df_by_series_max_rows",
"ref_pd_df_by_series_only_index",
"ref_pd_df_by_series_with_duplicated_popularity",
"ref_pd_df_by_series_with_index",
"ref_pd_df_by_series_with_nan",
"ref_pd_series",
"ref_pd_series_only_index",
"ref_pd_series_with_index",
"ref_pd_series_with_nan",
"set_up_pd_df",
"set_up_pd_series",
"setUp",
"setUpClass",
"addClassCleanup",
"addCleanup",
"addTypeEqualityFunc",
"assert_",
"assertAlmostEqual",
"assertAlmostEquals",
"assertCountEqual",
"assertDictContainsSubset",
"assertDictEqual",
"assertEquals",
"assertFalse",
"assertGreater",
"assertGreaterEqual",
"assertIn",
"assertIs",
"assertIsInstance",
"assertIsNone",
"assertIsNot",
"assertIsNotNone",
"assertLess",
"assertLessEqual",
"assertListEqual",
"assertLogs",
"assertMultiLineEqual",
"assertNoLogs",
"assertNotAlmostEqual",
"assertNotAlmostEquals",
"assertNotEqual",
"assertNotEquals",
"assertNotIn",
"assertNotIsInstance",
"assertNotRegex",
"assertNotRegexpMatches",
"assertRaises",
"assertRaisesRegex",
"assertRaisesRegexp",
"assertRegex",
"assertRegexpMatches",
"assertSequenceEqual",
"assertSetEqual",
"assertTrue",
"assertTupleEqual",
"assertWarns",
"assertWarnsRegex",
"countTestCases",
"debug",
"defaultTestResult",
"doClassCleanups",
"doCleanups",
"fail",
"failIf",
"failIfAlmostEqual",
"failIfEqual",
"failUnless",
"failUnlessAlmostEqual",
"failUnlessEqual",
"failUnlessRaises",
"failureException",
"id",
"longMessage",
"maxDiff",
"run",
"shortDescription",
"skipTest",
"subTest",
"tearDown",
"tearDownClass",
"__annotations__",
"__call__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "addClassCleanup",
"type": "function"
},
{
"name": "addCleanup",
"type": "function"
},
{
"name": "addTypeEqualityFunc",
"type": "function"
},
{
"name": "assert_",
"type": "function"
},
{
"name": "assertAlmostEqual",
"type": "function"
},
{
"name": "assertAlmostEquals",
"type": "function"
},
{
"name": "assertCountEqual",
"type": "function"
},
{
"name": "assertDictContainsSubset",
"type": "function"
},
{
"name": "assertDictEqual",
"type": "function"
},
{
"name": "assertEqual",
"type": "function"
},
{
"name": "assertEquals",
"type": "function"
},
{
"name": "assertFalse",
"type": "function"
},
{
"name": "assertGreater",
"type": "function"
},
{
"name": "assertGreaterEqual",
"type": "function"
},
{
"name": "assertIn",
"type": "function"
},
{
"name": "assertIs",
"type": "function"
},
{
"name": "assertIsInstance",
"type": "function"
},
{
"name": "assertIsNone",
"type": "function"
},
{
"name": "assertIsNot",
"type": "function"
},
{
"name": "assertIsNotNone",
"type": "function"
},
{
"name": "assertLess",
"type": "function"
},
{
"name": "assertLessEqual",
"type": "function"
},
{
"name": "assertListEqual",
"type": "function"
},
{
"name": "assertLogs",
"type": "function"
},
{
"name": "assertMultiLineEqual",
"type": "function"
},
{
"name": "assertNotAlmostEqual",
"type": "function"
},
{
"name": "assertNotAlmostEquals",
"type": "function"
},
{
"name": "assertNotEqual",
"type": "function"
},
{
"name": "assertNotEquals",
"type": "function"
},
{
"name": "assertNotIn",
"type": "function"
},
{
"name": "assertNotIsInstance",
"type": "function"
},
{
"name": "assertNotRegex",
"type": "function"
},
{
"name": "assertNotRegexpMatches",
"type": "function"
},
{
"name": "assertRaises",
"type": "function"
},
{
"name": "assertRaisesRegex",
"type": "function"
},
{
"name": "assertRaisesRegexp",
"type": "function"
},
{
"name": "assertRegex",
"type": "function"
},
{
"name": "assertRegexpMatches",
"type": "function"
},
{
"name": "assertSequenceEqual",
"type": "function"
},
{
"name": "assertSetEqual",
"type": "function"
},
{
"name": "assertTrue",
"type": "function"
},
{
"name": "assertTupleEqual",
"type": "function"
},
{
"name": "assertWarns",
"type": "function"
},
{
"name": "assertWarnsRegex",
"type": "function"
},
{
"name": "asset_dir",
"type": "statement"
},
{
"name": "countTestCases",
"type": "function"
},
{
"name": "data",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "defaultTestResult",
"type": "function"
},
{
"name": "doClassCleanups",
"type": "function"
},
{
"name": "doCleanups",
"type": "function"
},
{
"name": "fail",
"type": "function"
},
{
"name": "failIf",
"type": "function"
},
{
"name": "failIfAlmostEqual",
"type": "function"
},
{
"name": "failIfEqual",
"type": "function"
},
{
"name": "failUnless",
"type": "function"
},
{
"name": "failUnlessAlmostEqual",
"type": "function"
},
{
"name": "failUnlessEqual",
"type": "function"
},
{
"name": "failUnlessRaises",
"type": "function"
},
{
"name": "failureException",
"type": "statement"
},
{
"name": "id",
"type": "function"
},
{
"name": "in_pd_df_by_series",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_dimension",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_index",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_measure",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_index",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_nan",
"type": "statement"
},
{
"name": "longMessage",
"type": "statement"
},
{
"name": "maxDiff",
"type": "statement"
},
{
"name": "ref_pd_df_by_series",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_max_rows",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "ref_pd_series",
"type": "statement"
},
{
"name": "ref_pd_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_nan",
"type": "statement"
},
{
"name": "run",
"type": "function"
},
{
"name": "set_up_pd_df",
"type": "function"
},
{
"name": "set_up_pd_series",
"type": "function"
},
{
"name": "setUp",
"type": "function"
},
{
"name": "setUpClass",
"type": "function"
},
{
"name": "shortDescription",
"type": "function"
},
{
"name": "skipTest",
"type": "function"
},
{
"name": "subTest",
"type": "function"
},
{
"name": "tearDown",
"type": "function"
},
{
"name": "tearDownClass",
"type": "function"
},
{
"name": "test_add_df_index_with_df",
"type": "function"
},
{
"name": "test_add_df_index_with_none",
"type": "function"
},
{
"name": "_addSkip",
"type": "function"
},
{
"name": "_formatMessage",
"type": "function"
},
{
"name": "_getAssertEqualityFunc",
"type": "function"
},
{
"name": "_testMethodDoc",
"type": "statement"
},
{
"name": "_testMethodName",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__call__",
"type": "function"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.data.add_data_frame(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_data_frame_with_series_contains_na(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension_with_nan)
self.data.add_data_frame(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
class TestDfIndex(DataWithAssets):
def test_add_df_index_with_none(self) -> None:
self.
|
7,691 | 216 | 4,985 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
inproject
|
add_df_index
| true |
function
| 28 | 30 | false | true |
[
"add_df",
"add_data_frame",
"build",
"add_data_frame_index",
"add_df_index",
"add_dimension",
"add_measure",
"add_np_array",
"add_record",
"add_records",
"add_series",
"add_series_list",
"add_spark_df",
"dump",
"filter",
"from_json",
"set_filter",
"clear",
"copy",
"fromkeys",
"get",
"items",
"keys",
"pop",
"popitem",
"setdefault",
"update",
"values",
"__annotations__",
"__class__",
"__class_getitem__",
"__contains__",
"__delattr__",
"__delitem__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__getitem__",
"__hash__",
"__init__",
"__init_subclass__",
"__ior__",
"__iter__",
"__len__",
"__ne__",
"__new__",
"__or__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__reversed__",
"__ror__",
"__setattr__",
"__setitem__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "add_data_frame",
"type": "function"
},
{
"name": "add_data_frame_index",
"type": "function"
},
{
"name": "add_df",
"type": "function"
},
{
"name": "add_df_index",
"type": "function"
},
{
"name": "add_dimension",
"type": "function"
},
{
"name": "add_measure",
"type": "function"
},
{
"name": "add_np_array",
"type": "function"
},
{
"name": "add_record",
"type": "function"
},
{
"name": "add_records",
"type": "function"
},
{
"name": "add_series",
"type": "function"
},
{
"name": "add_series_list",
"type": "function"
},
{
"name": "add_spark_df",
"type": "function"
},
{
"name": "build",
"type": "function"
},
{
"name": "clear",
"type": "function"
},
{
"name": "copy",
"type": "function"
},
{
"name": "dump",
"type": "function"
},
{
"name": "filter",
"type": "function"
},
{
"name": "from_json",
"type": "function"
},
{
"name": "fromkeys",
"type": "function"
},
{
"name": "get",
"type": "function"
},
{
"name": "items",
"type": "function"
},
{
"name": "keys",
"type": "function"
},
{
"name": "pop",
"type": "function"
},
{
"name": "popitem",
"type": "function"
},
{
"name": "set_filter",
"type": "function"
},
{
"name": "setdefault",
"type": "function"
},
{
"name": "update",
"type": "function"
},
{
"name": "values",
"type": "function"
},
{
"name": "_add_named_value",
"type": "function"
},
{
"name": "_add_value",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__class_getitem__",
"type": "function"
},
{
"name": "__contains__",
"type": "function"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__delitem__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__getitem__",
"type": "function"
},
{
"name": "__hash__",
"type": "statement"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__ior__",
"type": "function"
},
{
"name": "__iter__",
"type": "function"
},
{
"name": "__len__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__or__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__reversed__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__setitem__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.data.add_data_frame(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_data_frame_with_series_contains_na(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension_with_nan)
self.data.add_data_frame(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
class TestDfIndex(DataWithAssets):
def test_add_df_index_with_none(self) -> None:
self.data.
|
7,692 | 216 | 5,094 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
Unknown
|
data
| true |
statement
| 96 | 100 | false | false |
[
"data",
"assertEqual",
"test_add_df_index_with_df",
"in_pd_series_measure_with_index",
"in_pd_series_dimension_with_index",
"test_add_df_index_with_none",
"asset_dir",
"in_pd_df_by_series",
"in_pd_df_by_series_with_duplicated_popularity",
"in_pd_df_by_series_with_index",
"in_pd_df_by_series_with_nan",
"in_pd_series_dimension",
"in_pd_series_dimension_with_nan",
"in_pd_series_measure",
"in_pd_series_measure_with_nan",
"ref_pd_df_by_series",
"ref_pd_df_by_series_max_rows",
"ref_pd_df_by_series_only_index",
"ref_pd_df_by_series_with_duplicated_popularity",
"ref_pd_df_by_series_with_index",
"ref_pd_df_by_series_with_nan",
"ref_pd_series",
"ref_pd_series_only_index",
"ref_pd_series_with_index",
"ref_pd_series_with_nan",
"set_up_pd_df",
"set_up_pd_series",
"setUp",
"setUpClass",
"addClassCleanup",
"addCleanup",
"addTypeEqualityFunc",
"assert_",
"assertAlmostEqual",
"assertAlmostEquals",
"assertCountEqual",
"assertDictContainsSubset",
"assertDictEqual",
"assertEquals",
"assertFalse",
"assertGreater",
"assertGreaterEqual",
"assertIn",
"assertIs",
"assertIsInstance",
"assertIsNone",
"assertIsNot",
"assertIsNotNone",
"assertLess",
"assertLessEqual",
"assertListEqual",
"assertLogs",
"assertMultiLineEqual",
"assertNoLogs",
"assertNotAlmostEqual",
"assertNotAlmostEquals",
"assertNotEqual",
"assertNotEquals",
"assertNotIn",
"assertNotIsInstance",
"assertNotRegex",
"assertNotRegexpMatches",
"assertRaises",
"assertRaisesRegex",
"assertRaisesRegexp",
"assertRegex",
"assertRegexpMatches",
"assertSequenceEqual",
"assertSetEqual",
"assertTrue",
"assertTupleEqual",
"assertWarns",
"assertWarnsRegex",
"countTestCases",
"debug",
"defaultTestResult",
"doClassCleanups",
"doCleanups",
"fail",
"failIf",
"failIfAlmostEqual",
"failIfEqual",
"failUnless",
"failUnlessAlmostEqual",
"failUnlessEqual",
"failUnlessRaises",
"failureException",
"id",
"longMessage",
"maxDiff",
"run",
"shortDescription",
"skipTest",
"subTest",
"tearDown",
"tearDownClass",
"__annotations__",
"__call__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "addClassCleanup",
"type": "function"
},
{
"name": "addCleanup",
"type": "function"
},
{
"name": "addTypeEqualityFunc",
"type": "function"
},
{
"name": "assert_",
"type": "function"
},
{
"name": "assertAlmostEqual",
"type": "function"
},
{
"name": "assertAlmostEquals",
"type": "function"
},
{
"name": "assertCountEqual",
"type": "function"
},
{
"name": "assertDictContainsSubset",
"type": "function"
},
{
"name": "assertDictEqual",
"type": "function"
},
{
"name": "assertEqual",
"type": "function"
},
{
"name": "assertEquals",
"type": "function"
},
{
"name": "assertFalse",
"type": "function"
},
{
"name": "assertGreater",
"type": "function"
},
{
"name": "assertGreaterEqual",
"type": "function"
},
{
"name": "assertIn",
"type": "function"
},
{
"name": "assertIs",
"type": "function"
},
{
"name": "assertIsInstance",
"type": "function"
},
{
"name": "assertIsNone",
"type": "function"
},
{
"name": "assertIsNot",
"type": "function"
},
{
"name": "assertIsNotNone",
"type": "function"
},
{
"name": "assertLess",
"type": "function"
},
{
"name": "assertLessEqual",
"type": "function"
},
{
"name": "assertListEqual",
"type": "function"
},
{
"name": "assertLogs",
"type": "function"
},
{
"name": "assertMultiLineEqual",
"type": "function"
},
{
"name": "assertNotAlmostEqual",
"type": "function"
},
{
"name": "assertNotAlmostEquals",
"type": "function"
},
{
"name": "assertNotEqual",
"type": "function"
},
{
"name": "assertNotEquals",
"type": "function"
},
{
"name": "assertNotIn",
"type": "function"
},
{
"name": "assertNotIsInstance",
"type": "function"
},
{
"name": "assertNotRegex",
"type": "function"
},
{
"name": "assertNotRegexpMatches",
"type": "function"
},
{
"name": "assertRaises",
"type": "function"
},
{
"name": "assertRaisesRegex",
"type": "function"
},
{
"name": "assertRaisesRegexp",
"type": "function"
},
{
"name": "assertRegex",
"type": "function"
},
{
"name": "assertRegexpMatches",
"type": "function"
},
{
"name": "assertSequenceEqual",
"type": "function"
},
{
"name": "assertSetEqual",
"type": "function"
},
{
"name": "assertTrue",
"type": "function"
},
{
"name": "assertTupleEqual",
"type": "function"
},
{
"name": "assertWarns",
"type": "function"
},
{
"name": "assertWarnsRegex",
"type": "function"
},
{
"name": "asset_dir",
"type": "statement"
},
{
"name": "countTestCases",
"type": "function"
},
{
"name": "data",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "defaultTestResult",
"type": "function"
},
{
"name": "doClassCleanups",
"type": "function"
},
{
"name": "doCleanups",
"type": "function"
},
{
"name": "fail",
"type": "function"
},
{
"name": "failIf",
"type": "function"
},
{
"name": "failIfAlmostEqual",
"type": "function"
},
{
"name": "failIfEqual",
"type": "function"
},
{
"name": "failUnless",
"type": "function"
},
{
"name": "failUnlessAlmostEqual",
"type": "function"
},
{
"name": "failUnlessEqual",
"type": "function"
},
{
"name": "failUnlessRaises",
"type": "function"
},
{
"name": "failureException",
"type": "statement"
},
{
"name": "id",
"type": "function"
},
{
"name": "in_pd_df_by_series",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_dimension",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_index",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_measure",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_index",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_nan",
"type": "statement"
},
{
"name": "longMessage",
"type": "statement"
},
{
"name": "maxDiff",
"type": "statement"
},
{
"name": "ref_pd_df_by_series",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_max_rows",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "ref_pd_series",
"type": "statement"
},
{
"name": "ref_pd_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_nan",
"type": "statement"
},
{
"name": "run",
"type": "function"
},
{
"name": "set_up_pd_df",
"type": "function"
},
{
"name": "set_up_pd_series",
"type": "function"
},
{
"name": "setUp",
"type": "function"
},
{
"name": "setUpClass",
"type": "function"
},
{
"name": "shortDescription",
"type": "function"
},
{
"name": "skipTest",
"type": "function"
},
{
"name": "subTest",
"type": "function"
},
{
"name": "tearDown",
"type": "function"
},
{
"name": "tearDownClass",
"type": "function"
},
{
"name": "test_add_df_index_with_df",
"type": "function"
},
{
"name": "test_add_df_index_with_none",
"type": "function"
},
{
"name": "_addSkip",
"type": "function"
},
{
"name": "_formatMessage",
"type": "function"
},
{
"name": "_getAssertEqualityFunc",
"type": "function"
},
{
"name": "_testMethodDoc",
"type": "statement"
},
{
"name": "_testMethodName",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__call__",
"type": "function"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.data.add_data_frame(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_data_frame_with_series_contains_na(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension_with_nan)
self.data.add_data_frame(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
class TestDfIndex(DataWithAssets):
def test_add_df_index_with_none(self) -> None:
self.data.add_df_index(None, column_name="Index")
self.assertEqual(
{"data": {}},
self.
|
7,693 | 216 | 5,099 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
Unknown
|
build
| true |
function
| 28 | 30 | false | false |
[
"add_df",
"add_data_frame",
"build",
"add_data_frame_index",
"add_df_index",
"add_dimension",
"add_measure",
"add_np_array",
"add_record",
"add_records",
"add_series",
"add_series_list",
"add_spark_df",
"dump",
"filter",
"from_json",
"set_filter",
"clear",
"copy",
"fromkeys",
"get",
"items",
"keys",
"pop",
"popitem",
"setdefault",
"update",
"values",
"__annotations__",
"__class__",
"__class_getitem__",
"__contains__",
"__delattr__",
"__delitem__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__getitem__",
"__hash__",
"__init__",
"__init_subclass__",
"__ior__",
"__iter__",
"__len__",
"__ne__",
"__new__",
"__or__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__reversed__",
"__ror__",
"__setattr__",
"__setitem__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "add_data_frame",
"type": "function"
},
{
"name": "add_data_frame_index",
"type": "function"
},
{
"name": "add_df",
"type": "function"
},
{
"name": "add_df_index",
"type": "function"
},
{
"name": "add_dimension",
"type": "function"
},
{
"name": "add_measure",
"type": "function"
},
{
"name": "add_np_array",
"type": "function"
},
{
"name": "add_record",
"type": "function"
},
{
"name": "add_records",
"type": "function"
},
{
"name": "add_series",
"type": "function"
},
{
"name": "add_series_list",
"type": "function"
},
{
"name": "add_spark_df",
"type": "function"
},
{
"name": "build",
"type": "function"
},
{
"name": "clear",
"type": "function"
},
{
"name": "copy",
"type": "function"
},
{
"name": "dump",
"type": "function"
},
{
"name": "filter",
"type": "function"
},
{
"name": "from_json",
"type": "function"
},
{
"name": "fromkeys",
"type": "function"
},
{
"name": "get",
"type": "function"
},
{
"name": "items",
"type": "function"
},
{
"name": "keys",
"type": "function"
},
{
"name": "pop",
"type": "function"
},
{
"name": "popitem",
"type": "function"
},
{
"name": "set_filter",
"type": "function"
},
{
"name": "setdefault",
"type": "function"
},
{
"name": "update",
"type": "function"
},
{
"name": "values",
"type": "function"
},
{
"name": "_add_named_value",
"type": "function"
},
{
"name": "_add_value",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__class_getitem__",
"type": "function"
},
{
"name": "__contains__",
"type": "function"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__delitem__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__getitem__",
"type": "function"
},
{
"name": "__hash__",
"type": "statement"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__ior__",
"type": "function"
},
{
"name": "__iter__",
"type": "function"
},
{
"name": "__len__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__or__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__reversed__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__setitem__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.data.add_data_frame(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_data_frame_with_series_contains_na(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension_with_nan)
self.data.add_data_frame(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
class TestDfIndex(DataWithAssets):
def test_add_df_index_with_none(self) -> None:
self.data.add_df_index(None, column_name="Index")
self.assertEqual(
{"data": {}},
self.data.
|
7,694 | 216 | 5,186 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
Unknown
|
in_pd_df_by_series_with_index
| true |
statement
| 96 | 100 | false | false |
[
"in_pd_df_by_series_with_index",
"in_pd_df_by_series_with_nan",
"in_pd_df_by_series_with_duplicated_popularity",
"data",
"in_pd_df_by_series",
"test_add_df_index_with_df",
"test_add_df_index_with_none",
"asset_dir",
"in_pd_series_dimension",
"in_pd_series_dimension_with_index",
"in_pd_series_dimension_with_nan",
"in_pd_series_measure",
"in_pd_series_measure_with_index",
"in_pd_series_measure_with_nan",
"ref_pd_df_by_series",
"ref_pd_df_by_series_max_rows",
"ref_pd_df_by_series_only_index",
"ref_pd_df_by_series_with_duplicated_popularity",
"ref_pd_df_by_series_with_index",
"ref_pd_df_by_series_with_nan",
"ref_pd_series",
"ref_pd_series_only_index",
"ref_pd_series_with_index",
"ref_pd_series_with_nan",
"set_up_pd_df",
"set_up_pd_series",
"setUp",
"setUpClass",
"addClassCleanup",
"addCleanup",
"addTypeEqualityFunc",
"assert_",
"assertAlmostEqual",
"assertAlmostEquals",
"assertCountEqual",
"assertDictContainsSubset",
"assertDictEqual",
"assertEqual",
"assertEquals",
"assertFalse",
"assertGreater",
"assertGreaterEqual",
"assertIn",
"assertIs",
"assertIsInstance",
"assertIsNone",
"assertIsNot",
"assertIsNotNone",
"assertLess",
"assertLessEqual",
"assertListEqual",
"assertLogs",
"assertMultiLineEqual",
"assertNoLogs",
"assertNotAlmostEqual",
"assertNotAlmostEquals",
"assertNotEqual",
"assertNotEquals",
"assertNotIn",
"assertNotIsInstance",
"assertNotRegex",
"assertNotRegexpMatches",
"assertRaises",
"assertRaisesRegex",
"assertRaisesRegexp",
"assertRegex",
"assertRegexpMatches",
"assertSequenceEqual",
"assertSetEqual",
"assertTrue",
"assertTupleEqual",
"assertWarns",
"assertWarnsRegex",
"countTestCases",
"debug",
"defaultTestResult",
"doClassCleanups",
"doCleanups",
"fail",
"failIf",
"failIfAlmostEqual",
"failIfEqual",
"failUnless",
"failUnlessAlmostEqual",
"failUnlessEqual",
"failUnlessRaises",
"failureException",
"id",
"longMessage",
"maxDiff",
"run",
"shortDescription",
"skipTest",
"subTest",
"tearDown",
"tearDownClass",
"__annotations__",
"__call__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "addClassCleanup",
"type": "function"
},
{
"name": "addCleanup",
"type": "function"
},
{
"name": "addTypeEqualityFunc",
"type": "function"
},
{
"name": "assert_",
"type": "function"
},
{
"name": "assertAlmostEqual",
"type": "function"
},
{
"name": "assertAlmostEquals",
"type": "function"
},
{
"name": "assertCountEqual",
"type": "function"
},
{
"name": "assertDictContainsSubset",
"type": "function"
},
{
"name": "assertDictEqual",
"type": "function"
},
{
"name": "assertEqual",
"type": "function"
},
{
"name": "assertEquals",
"type": "function"
},
{
"name": "assertFalse",
"type": "function"
},
{
"name": "assertGreater",
"type": "function"
},
{
"name": "assertGreaterEqual",
"type": "function"
},
{
"name": "assertIn",
"type": "function"
},
{
"name": "assertIs",
"type": "function"
},
{
"name": "assertIsInstance",
"type": "function"
},
{
"name": "assertIsNone",
"type": "function"
},
{
"name": "assertIsNot",
"type": "function"
},
{
"name": "assertIsNotNone",
"type": "function"
},
{
"name": "assertLess",
"type": "function"
},
{
"name": "assertLessEqual",
"type": "function"
},
{
"name": "assertListEqual",
"type": "function"
},
{
"name": "assertLogs",
"type": "function"
},
{
"name": "assertMultiLineEqual",
"type": "function"
},
{
"name": "assertNotAlmostEqual",
"type": "function"
},
{
"name": "assertNotAlmostEquals",
"type": "function"
},
{
"name": "assertNotEqual",
"type": "function"
},
{
"name": "assertNotEquals",
"type": "function"
},
{
"name": "assertNotIn",
"type": "function"
},
{
"name": "assertNotIsInstance",
"type": "function"
},
{
"name": "assertNotRegex",
"type": "function"
},
{
"name": "assertNotRegexpMatches",
"type": "function"
},
{
"name": "assertRaises",
"type": "function"
},
{
"name": "assertRaisesRegex",
"type": "function"
},
{
"name": "assertRaisesRegexp",
"type": "function"
},
{
"name": "assertRegex",
"type": "function"
},
{
"name": "assertRegexpMatches",
"type": "function"
},
{
"name": "assertSequenceEqual",
"type": "function"
},
{
"name": "assertSetEqual",
"type": "function"
},
{
"name": "assertTrue",
"type": "function"
},
{
"name": "assertTupleEqual",
"type": "function"
},
{
"name": "assertWarns",
"type": "function"
},
{
"name": "assertWarnsRegex",
"type": "function"
},
{
"name": "asset_dir",
"type": "statement"
},
{
"name": "countTestCases",
"type": "function"
},
{
"name": "data",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "defaultTestResult",
"type": "function"
},
{
"name": "doClassCleanups",
"type": "function"
},
{
"name": "doCleanups",
"type": "function"
},
{
"name": "fail",
"type": "function"
},
{
"name": "failIf",
"type": "function"
},
{
"name": "failIfAlmostEqual",
"type": "function"
},
{
"name": "failIfEqual",
"type": "function"
},
{
"name": "failUnless",
"type": "function"
},
{
"name": "failUnlessAlmostEqual",
"type": "function"
},
{
"name": "failUnlessEqual",
"type": "function"
},
{
"name": "failUnlessRaises",
"type": "function"
},
{
"name": "failureException",
"type": "statement"
},
{
"name": "id",
"type": "function"
},
{
"name": "in_pd_df_by_series",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_dimension",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_index",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_measure",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_index",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_nan",
"type": "statement"
},
{
"name": "longMessage",
"type": "statement"
},
{
"name": "maxDiff",
"type": "statement"
},
{
"name": "ref_pd_df_by_series",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_max_rows",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "ref_pd_series",
"type": "statement"
},
{
"name": "ref_pd_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_nan",
"type": "statement"
},
{
"name": "run",
"type": "function"
},
{
"name": "set_up_pd_df",
"type": "function"
},
{
"name": "set_up_pd_series",
"type": "function"
},
{
"name": "setUp",
"type": "function"
},
{
"name": "setUpClass",
"type": "function"
},
{
"name": "shortDescription",
"type": "function"
},
{
"name": "skipTest",
"type": "function"
},
{
"name": "subTest",
"type": "function"
},
{
"name": "tearDown",
"type": "function"
},
{
"name": "tearDownClass",
"type": "function"
},
{
"name": "test_add_df_index_with_df",
"type": "function"
},
{
"name": "test_add_df_index_with_none",
"type": "function"
},
{
"name": "_addSkip",
"type": "function"
},
{
"name": "_formatMessage",
"type": "function"
},
{
"name": "_getAssertEqualityFunc",
"type": "function"
},
{
"name": "_testMethodDoc",
"type": "statement"
},
{
"name": "_testMethodName",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__call__",
"type": "function"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.data.add_data_frame(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_data_frame_with_series_contains_na(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension_with_nan)
self.data.add_data_frame(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
class TestDfIndex(DataWithAssets):
def test_add_df_index_with_none(self) -> None:
self.data.add_df_index(None, column_name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_index_with_df(self) -> None:
df = self.
|
7,695 | 216 | 5,229 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
inproject
|
data
| true |
statement
| 96 | 100 | false | false |
[
"data",
"assertEqual",
"test_add_df_index_with_none",
"in_pd_series_measure_with_index",
"in_pd_series_dimension_with_index",
"test_add_df_index_with_df",
"asset_dir",
"in_pd_df_by_series",
"in_pd_df_by_series_with_duplicated_popularity",
"in_pd_df_by_series_with_index",
"in_pd_df_by_series_with_nan",
"in_pd_series_dimension",
"in_pd_series_dimension_with_nan",
"in_pd_series_measure",
"in_pd_series_measure_with_nan",
"ref_pd_df_by_series",
"ref_pd_df_by_series_max_rows",
"ref_pd_df_by_series_only_index",
"ref_pd_df_by_series_with_duplicated_popularity",
"ref_pd_df_by_series_with_index",
"ref_pd_df_by_series_with_nan",
"ref_pd_series",
"ref_pd_series_only_index",
"ref_pd_series_with_index",
"ref_pd_series_with_nan",
"set_up_pd_df",
"set_up_pd_series",
"setUp",
"setUpClass",
"addClassCleanup",
"addCleanup",
"addTypeEqualityFunc",
"assert_",
"assertAlmostEqual",
"assertAlmostEquals",
"assertCountEqual",
"assertDictContainsSubset",
"assertDictEqual",
"assertEquals",
"assertFalse",
"assertGreater",
"assertGreaterEqual",
"assertIn",
"assertIs",
"assertIsInstance",
"assertIsNone",
"assertIsNot",
"assertIsNotNone",
"assertLess",
"assertLessEqual",
"assertListEqual",
"assertLogs",
"assertMultiLineEqual",
"assertNoLogs",
"assertNotAlmostEqual",
"assertNotAlmostEquals",
"assertNotEqual",
"assertNotEquals",
"assertNotIn",
"assertNotIsInstance",
"assertNotRegex",
"assertNotRegexpMatches",
"assertRaises",
"assertRaisesRegex",
"assertRaisesRegexp",
"assertRegex",
"assertRegexpMatches",
"assertSequenceEqual",
"assertSetEqual",
"assertTrue",
"assertTupleEqual",
"assertWarns",
"assertWarnsRegex",
"countTestCases",
"debug",
"defaultTestResult",
"doClassCleanups",
"doCleanups",
"fail",
"failIf",
"failIfAlmostEqual",
"failIfEqual",
"failUnless",
"failUnlessAlmostEqual",
"failUnlessEqual",
"failUnlessRaises",
"failureException",
"id",
"longMessage",
"maxDiff",
"run",
"shortDescription",
"skipTest",
"subTest",
"tearDown",
"tearDownClass",
"__annotations__",
"__call__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "addClassCleanup",
"type": "function"
},
{
"name": "addCleanup",
"type": "function"
},
{
"name": "addTypeEqualityFunc",
"type": "function"
},
{
"name": "assert_",
"type": "function"
},
{
"name": "assertAlmostEqual",
"type": "function"
},
{
"name": "assertAlmostEquals",
"type": "function"
},
{
"name": "assertCountEqual",
"type": "function"
},
{
"name": "assertDictContainsSubset",
"type": "function"
},
{
"name": "assertDictEqual",
"type": "function"
},
{
"name": "assertEqual",
"type": "function"
},
{
"name": "assertEquals",
"type": "function"
},
{
"name": "assertFalse",
"type": "function"
},
{
"name": "assertGreater",
"type": "function"
},
{
"name": "assertGreaterEqual",
"type": "function"
},
{
"name": "assertIn",
"type": "function"
},
{
"name": "assertIs",
"type": "function"
},
{
"name": "assertIsInstance",
"type": "function"
},
{
"name": "assertIsNone",
"type": "function"
},
{
"name": "assertIsNot",
"type": "function"
},
{
"name": "assertIsNotNone",
"type": "function"
},
{
"name": "assertLess",
"type": "function"
},
{
"name": "assertLessEqual",
"type": "function"
},
{
"name": "assertListEqual",
"type": "function"
},
{
"name": "assertLogs",
"type": "function"
},
{
"name": "assertMultiLineEqual",
"type": "function"
},
{
"name": "assertNotAlmostEqual",
"type": "function"
},
{
"name": "assertNotAlmostEquals",
"type": "function"
},
{
"name": "assertNotEqual",
"type": "function"
},
{
"name": "assertNotEquals",
"type": "function"
},
{
"name": "assertNotIn",
"type": "function"
},
{
"name": "assertNotIsInstance",
"type": "function"
},
{
"name": "assertNotRegex",
"type": "function"
},
{
"name": "assertNotRegexpMatches",
"type": "function"
},
{
"name": "assertRaises",
"type": "function"
},
{
"name": "assertRaisesRegex",
"type": "function"
},
{
"name": "assertRaisesRegexp",
"type": "function"
},
{
"name": "assertRegex",
"type": "function"
},
{
"name": "assertRegexpMatches",
"type": "function"
},
{
"name": "assertSequenceEqual",
"type": "function"
},
{
"name": "assertSetEqual",
"type": "function"
},
{
"name": "assertTrue",
"type": "function"
},
{
"name": "assertTupleEqual",
"type": "function"
},
{
"name": "assertWarns",
"type": "function"
},
{
"name": "assertWarnsRegex",
"type": "function"
},
{
"name": "asset_dir",
"type": "statement"
},
{
"name": "countTestCases",
"type": "function"
},
{
"name": "data",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "defaultTestResult",
"type": "function"
},
{
"name": "doClassCleanups",
"type": "function"
},
{
"name": "doCleanups",
"type": "function"
},
{
"name": "fail",
"type": "function"
},
{
"name": "failIf",
"type": "function"
},
{
"name": "failIfAlmostEqual",
"type": "function"
},
{
"name": "failIfEqual",
"type": "function"
},
{
"name": "failUnless",
"type": "function"
},
{
"name": "failUnlessAlmostEqual",
"type": "function"
},
{
"name": "failUnlessEqual",
"type": "function"
},
{
"name": "failUnlessRaises",
"type": "function"
},
{
"name": "failureException",
"type": "statement"
},
{
"name": "id",
"type": "function"
},
{
"name": "in_pd_df_by_series",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_dimension",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_index",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_measure",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_index",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_nan",
"type": "statement"
},
{
"name": "longMessage",
"type": "statement"
},
{
"name": "maxDiff",
"type": "statement"
},
{
"name": "ref_pd_df_by_series",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_max_rows",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "ref_pd_series",
"type": "statement"
},
{
"name": "ref_pd_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_nan",
"type": "statement"
},
{
"name": "run",
"type": "function"
},
{
"name": "set_up_pd_df",
"type": "function"
},
{
"name": "set_up_pd_series",
"type": "function"
},
{
"name": "setUp",
"type": "function"
},
{
"name": "setUpClass",
"type": "function"
},
{
"name": "shortDescription",
"type": "function"
},
{
"name": "skipTest",
"type": "function"
},
{
"name": "subTest",
"type": "function"
},
{
"name": "tearDown",
"type": "function"
},
{
"name": "tearDownClass",
"type": "function"
},
{
"name": "test_add_df_index_with_df",
"type": "function"
},
{
"name": "test_add_df_index_with_none",
"type": "function"
},
{
"name": "_addSkip",
"type": "function"
},
{
"name": "_formatMessage",
"type": "function"
},
{
"name": "_getAssertEqualityFunc",
"type": "function"
},
{
"name": "_testMethodDoc",
"type": "statement"
},
{
"name": "_testMethodName",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__call__",
"type": "function"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.data.add_data_frame(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_data_frame_with_series_contains_na(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension_with_nan)
self.data.add_data_frame(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
class TestDfIndex(DataWithAssets):
def test_add_df_index_with_none(self) -> None:
self.data.add_df_index(None, column_name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_index_with_df(self) -> None:
df = self.in_pd_df_by_series_with_index
self.
|
7,696 | 216 | 5,234 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
inproject
|
add_df_index
| true |
function
| 28 | 30 | false | false |
[
"add_df",
"add_data_frame",
"build",
"add_data_frame_index",
"add_df_index",
"add_dimension",
"add_measure",
"add_np_array",
"add_record",
"add_records",
"add_series",
"add_series_list",
"add_spark_df",
"dump",
"filter",
"from_json",
"set_filter",
"clear",
"copy",
"fromkeys",
"get",
"items",
"keys",
"pop",
"popitem",
"setdefault",
"update",
"values",
"__annotations__",
"__class__",
"__class_getitem__",
"__contains__",
"__delattr__",
"__delitem__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__getitem__",
"__hash__",
"__init__",
"__init_subclass__",
"__ior__",
"__iter__",
"__len__",
"__ne__",
"__new__",
"__or__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__reversed__",
"__ror__",
"__setattr__",
"__setitem__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "add_data_frame",
"type": "function"
},
{
"name": "add_data_frame_index",
"type": "function"
},
{
"name": "add_df",
"type": "function"
},
{
"name": "add_df_index",
"type": "function"
},
{
"name": "add_dimension",
"type": "function"
},
{
"name": "add_measure",
"type": "function"
},
{
"name": "add_np_array",
"type": "function"
},
{
"name": "add_record",
"type": "function"
},
{
"name": "add_records",
"type": "function"
},
{
"name": "add_series",
"type": "function"
},
{
"name": "add_series_list",
"type": "function"
},
{
"name": "add_spark_df",
"type": "function"
},
{
"name": "build",
"type": "function"
},
{
"name": "clear",
"type": "function"
},
{
"name": "copy",
"type": "function"
},
{
"name": "dump",
"type": "function"
},
{
"name": "filter",
"type": "function"
},
{
"name": "from_json",
"type": "function"
},
{
"name": "fromkeys",
"type": "function"
},
{
"name": "get",
"type": "function"
},
{
"name": "items",
"type": "function"
},
{
"name": "keys",
"type": "function"
},
{
"name": "pop",
"type": "function"
},
{
"name": "popitem",
"type": "function"
},
{
"name": "set_filter",
"type": "function"
},
{
"name": "setdefault",
"type": "function"
},
{
"name": "update",
"type": "function"
},
{
"name": "values",
"type": "function"
},
{
"name": "_add_named_value",
"type": "function"
},
{
"name": "_add_value",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__class_getitem__",
"type": "function"
},
{
"name": "__contains__",
"type": "function"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__delitem__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__getitem__",
"type": "function"
},
{
"name": "__hash__",
"type": "statement"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__ior__",
"type": "function"
},
{
"name": "__iter__",
"type": "function"
},
{
"name": "__len__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__or__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__reversed__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__setitem__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.data.add_data_frame(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_data_frame_with_series_contains_na(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension_with_nan)
self.data.add_data_frame(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
class TestDfIndex(DataWithAssets):
def test_add_df_index_with_none(self) -> None:
self.data.add_df_index(None, column_name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_index_with_df(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.
|
7,697 | 216 | 5,315 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
random
|
ref_pd_df_by_series_only_index
| true |
statement
| 96 | 100 | false | true |
[
"data",
"in_pd_series_measure_with_index",
"in_pd_series_dimension_with_index",
"in_pd_df_by_series_with_index",
"ref_pd_series_only_index",
"test_add_df_index_with_df",
"test_add_df_index_with_none",
"asset_dir",
"in_pd_df_by_series",
"in_pd_df_by_series_with_duplicated_popularity",
"in_pd_df_by_series_with_nan",
"in_pd_series_dimension",
"in_pd_series_dimension_with_nan",
"in_pd_series_measure",
"in_pd_series_measure_with_nan",
"ref_pd_df_by_series",
"ref_pd_df_by_series_max_rows",
"ref_pd_df_by_series_only_index",
"ref_pd_df_by_series_with_duplicated_popularity",
"ref_pd_df_by_series_with_index",
"ref_pd_df_by_series_with_nan",
"ref_pd_series",
"ref_pd_series_with_index",
"ref_pd_series_with_nan",
"set_up_pd_df",
"set_up_pd_series",
"setUp",
"setUpClass",
"addClassCleanup",
"addCleanup",
"addTypeEqualityFunc",
"assert_",
"assertAlmostEqual",
"assertAlmostEquals",
"assertCountEqual",
"assertDictContainsSubset",
"assertDictEqual",
"assertEqual",
"assertEquals",
"assertFalse",
"assertGreater",
"assertGreaterEqual",
"assertIn",
"assertIs",
"assertIsInstance",
"assertIsNone",
"assertIsNot",
"assertIsNotNone",
"assertLess",
"assertLessEqual",
"assertListEqual",
"assertLogs",
"assertMultiLineEqual",
"assertNoLogs",
"assertNotAlmostEqual",
"assertNotAlmostEquals",
"assertNotEqual",
"assertNotEquals",
"assertNotIn",
"assertNotIsInstance",
"assertNotRegex",
"assertNotRegexpMatches",
"assertRaises",
"assertRaisesRegex",
"assertRaisesRegexp",
"assertRegex",
"assertRegexpMatches",
"assertSequenceEqual",
"assertSetEqual",
"assertTrue",
"assertTupleEqual",
"assertWarns",
"assertWarnsRegex",
"countTestCases",
"debug",
"defaultTestResult",
"doClassCleanups",
"doCleanups",
"fail",
"failIf",
"failIfAlmostEqual",
"failIfEqual",
"failUnless",
"failUnlessAlmostEqual",
"failUnlessEqual",
"failUnlessRaises",
"failureException",
"id",
"longMessage",
"maxDiff",
"run",
"shortDescription",
"skipTest",
"subTest",
"tearDown",
"tearDownClass",
"__annotations__",
"__call__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "addClassCleanup",
"type": "function"
},
{
"name": "addCleanup",
"type": "function"
},
{
"name": "addTypeEqualityFunc",
"type": "function"
},
{
"name": "assert_",
"type": "function"
},
{
"name": "assertAlmostEqual",
"type": "function"
},
{
"name": "assertAlmostEquals",
"type": "function"
},
{
"name": "assertCountEqual",
"type": "function"
},
{
"name": "assertDictContainsSubset",
"type": "function"
},
{
"name": "assertDictEqual",
"type": "function"
},
{
"name": "assertEqual",
"type": "function"
},
{
"name": "assertEquals",
"type": "function"
},
{
"name": "assertFalse",
"type": "function"
},
{
"name": "assertGreater",
"type": "function"
},
{
"name": "assertGreaterEqual",
"type": "function"
},
{
"name": "assertIn",
"type": "function"
},
{
"name": "assertIs",
"type": "function"
},
{
"name": "assertIsInstance",
"type": "function"
},
{
"name": "assertIsNone",
"type": "function"
},
{
"name": "assertIsNot",
"type": "function"
},
{
"name": "assertIsNotNone",
"type": "function"
},
{
"name": "assertLess",
"type": "function"
},
{
"name": "assertLessEqual",
"type": "function"
},
{
"name": "assertListEqual",
"type": "function"
},
{
"name": "assertLogs",
"type": "function"
},
{
"name": "assertMultiLineEqual",
"type": "function"
},
{
"name": "assertNotAlmostEqual",
"type": "function"
},
{
"name": "assertNotAlmostEquals",
"type": "function"
},
{
"name": "assertNotEqual",
"type": "function"
},
{
"name": "assertNotEquals",
"type": "function"
},
{
"name": "assertNotIn",
"type": "function"
},
{
"name": "assertNotIsInstance",
"type": "function"
},
{
"name": "assertNotRegex",
"type": "function"
},
{
"name": "assertNotRegexpMatches",
"type": "function"
},
{
"name": "assertRaises",
"type": "function"
},
{
"name": "assertRaisesRegex",
"type": "function"
},
{
"name": "assertRaisesRegexp",
"type": "function"
},
{
"name": "assertRegex",
"type": "function"
},
{
"name": "assertRegexpMatches",
"type": "function"
},
{
"name": "assertSequenceEqual",
"type": "function"
},
{
"name": "assertSetEqual",
"type": "function"
},
{
"name": "assertTrue",
"type": "function"
},
{
"name": "assertTupleEqual",
"type": "function"
},
{
"name": "assertWarns",
"type": "function"
},
{
"name": "assertWarnsRegex",
"type": "function"
},
{
"name": "asset_dir",
"type": "statement"
},
{
"name": "countTestCases",
"type": "function"
},
{
"name": "data",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "defaultTestResult",
"type": "function"
},
{
"name": "doClassCleanups",
"type": "function"
},
{
"name": "doCleanups",
"type": "function"
},
{
"name": "fail",
"type": "function"
},
{
"name": "failIf",
"type": "function"
},
{
"name": "failIfAlmostEqual",
"type": "function"
},
{
"name": "failIfEqual",
"type": "function"
},
{
"name": "failUnless",
"type": "function"
},
{
"name": "failUnlessAlmostEqual",
"type": "function"
},
{
"name": "failUnlessEqual",
"type": "function"
},
{
"name": "failUnlessRaises",
"type": "function"
},
{
"name": "failureException",
"type": "statement"
},
{
"name": "id",
"type": "function"
},
{
"name": "in_pd_df_by_series",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_dimension",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_index",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_measure",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_index",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_nan",
"type": "statement"
},
{
"name": "longMessage",
"type": "statement"
},
{
"name": "maxDiff",
"type": "statement"
},
{
"name": "ref_pd_df_by_series",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_max_rows",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "ref_pd_series",
"type": "statement"
},
{
"name": "ref_pd_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_nan",
"type": "statement"
},
{
"name": "run",
"type": "function"
},
{
"name": "set_up_pd_df",
"type": "function"
},
{
"name": "set_up_pd_series",
"type": "function"
},
{
"name": "setUp",
"type": "function"
},
{
"name": "setUpClass",
"type": "function"
},
{
"name": "shortDescription",
"type": "function"
},
{
"name": "skipTest",
"type": "function"
},
{
"name": "subTest",
"type": "function"
},
{
"name": "tearDown",
"type": "function"
},
{
"name": "tearDownClass",
"type": "function"
},
{
"name": "test_add_df_index_with_df",
"type": "function"
},
{
"name": "test_add_df_index_with_none",
"type": "function"
},
{
"name": "_addSkip",
"type": "function"
},
{
"name": "_formatMessage",
"type": "function"
},
{
"name": "_getAssertEqualityFunc",
"type": "function"
},
{
"name": "_testMethodDoc",
"type": "statement"
},
{
"name": "_testMethodName",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__call__",
"type": "function"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.data.add_data_frame(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_data_frame_with_series_contains_na(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension_with_nan)
self.data.add_data_frame(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
class TestDfIndex(DataWithAssets):
def test_add_df_index_with_none(self) -> None:
self.data.add_df_index(None, column_name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_index_with_df(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df_index(df, column_name="Index")
self.assertEqual(
self.
|
7,698 | 216 | 5,364 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
Unknown
|
data
| true |
statement
| 96 | 100 | false | false |
[
"data",
"assertEqual",
"test_add_df_index_with_none",
"in_pd_df_by_series_with_index",
"in_pd_series_measure_with_index",
"test_add_df_index_with_df",
"asset_dir",
"in_pd_df_by_series",
"in_pd_df_by_series_with_duplicated_popularity",
"in_pd_df_by_series_with_nan",
"in_pd_series_dimension",
"in_pd_series_dimension_with_index",
"in_pd_series_dimension_with_nan",
"in_pd_series_measure",
"in_pd_series_measure_with_nan",
"ref_pd_df_by_series",
"ref_pd_df_by_series_max_rows",
"ref_pd_df_by_series_only_index",
"ref_pd_df_by_series_with_duplicated_popularity",
"ref_pd_df_by_series_with_index",
"ref_pd_df_by_series_with_nan",
"ref_pd_series",
"ref_pd_series_only_index",
"ref_pd_series_with_index",
"ref_pd_series_with_nan",
"set_up_pd_df",
"set_up_pd_series",
"setUp",
"setUpClass",
"addClassCleanup",
"addCleanup",
"addTypeEqualityFunc",
"assert_",
"assertAlmostEqual",
"assertAlmostEquals",
"assertCountEqual",
"assertDictContainsSubset",
"assertDictEqual",
"assertEquals",
"assertFalse",
"assertGreater",
"assertGreaterEqual",
"assertIn",
"assertIs",
"assertIsInstance",
"assertIsNone",
"assertIsNot",
"assertIsNotNone",
"assertLess",
"assertLessEqual",
"assertListEqual",
"assertLogs",
"assertMultiLineEqual",
"assertNoLogs",
"assertNotAlmostEqual",
"assertNotAlmostEquals",
"assertNotEqual",
"assertNotEquals",
"assertNotIn",
"assertNotIsInstance",
"assertNotRegex",
"assertNotRegexpMatches",
"assertRaises",
"assertRaisesRegex",
"assertRaisesRegexp",
"assertRegex",
"assertRegexpMatches",
"assertSequenceEqual",
"assertSetEqual",
"assertTrue",
"assertTupleEqual",
"assertWarns",
"assertWarnsRegex",
"countTestCases",
"debug",
"defaultTestResult",
"doClassCleanups",
"doCleanups",
"fail",
"failIf",
"failIfAlmostEqual",
"failIfEqual",
"failUnless",
"failUnlessAlmostEqual",
"failUnlessEqual",
"failUnlessRaises",
"failureException",
"id",
"longMessage",
"maxDiff",
"run",
"shortDescription",
"skipTest",
"subTest",
"tearDown",
"tearDownClass",
"__annotations__",
"__call__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "addClassCleanup",
"type": "function"
},
{
"name": "addCleanup",
"type": "function"
},
{
"name": "addTypeEqualityFunc",
"type": "function"
},
{
"name": "assert_",
"type": "function"
},
{
"name": "assertAlmostEqual",
"type": "function"
},
{
"name": "assertAlmostEquals",
"type": "function"
},
{
"name": "assertCountEqual",
"type": "function"
},
{
"name": "assertDictContainsSubset",
"type": "function"
},
{
"name": "assertDictEqual",
"type": "function"
},
{
"name": "assertEqual",
"type": "function"
},
{
"name": "assertEquals",
"type": "function"
},
{
"name": "assertFalse",
"type": "function"
},
{
"name": "assertGreater",
"type": "function"
},
{
"name": "assertGreaterEqual",
"type": "function"
},
{
"name": "assertIn",
"type": "function"
},
{
"name": "assertIs",
"type": "function"
},
{
"name": "assertIsInstance",
"type": "function"
},
{
"name": "assertIsNone",
"type": "function"
},
{
"name": "assertIsNot",
"type": "function"
},
{
"name": "assertIsNotNone",
"type": "function"
},
{
"name": "assertLess",
"type": "function"
},
{
"name": "assertLessEqual",
"type": "function"
},
{
"name": "assertListEqual",
"type": "function"
},
{
"name": "assertLogs",
"type": "function"
},
{
"name": "assertMultiLineEqual",
"type": "function"
},
{
"name": "assertNotAlmostEqual",
"type": "function"
},
{
"name": "assertNotAlmostEquals",
"type": "function"
},
{
"name": "assertNotEqual",
"type": "function"
},
{
"name": "assertNotEquals",
"type": "function"
},
{
"name": "assertNotIn",
"type": "function"
},
{
"name": "assertNotIsInstance",
"type": "function"
},
{
"name": "assertNotRegex",
"type": "function"
},
{
"name": "assertNotRegexpMatches",
"type": "function"
},
{
"name": "assertRaises",
"type": "function"
},
{
"name": "assertRaisesRegex",
"type": "function"
},
{
"name": "assertRaisesRegexp",
"type": "function"
},
{
"name": "assertRegex",
"type": "function"
},
{
"name": "assertRegexpMatches",
"type": "function"
},
{
"name": "assertSequenceEqual",
"type": "function"
},
{
"name": "assertSetEqual",
"type": "function"
},
{
"name": "assertTrue",
"type": "function"
},
{
"name": "assertTupleEqual",
"type": "function"
},
{
"name": "assertWarns",
"type": "function"
},
{
"name": "assertWarnsRegex",
"type": "function"
},
{
"name": "asset_dir",
"type": "statement"
},
{
"name": "countTestCases",
"type": "function"
},
{
"name": "data",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "defaultTestResult",
"type": "function"
},
{
"name": "doClassCleanups",
"type": "function"
},
{
"name": "doCleanups",
"type": "function"
},
{
"name": "fail",
"type": "function"
},
{
"name": "failIf",
"type": "function"
},
{
"name": "failIfAlmostEqual",
"type": "function"
},
{
"name": "failIfEqual",
"type": "function"
},
{
"name": "failUnless",
"type": "function"
},
{
"name": "failUnlessAlmostEqual",
"type": "function"
},
{
"name": "failUnlessEqual",
"type": "function"
},
{
"name": "failUnlessRaises",
"type": "function"
},
{
"name": "failureException",
"type": "statement"
},
{
"name": "id",
"type": "function"
},
{
"name": "in_pd_df_by_series",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_dimension",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_index",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_measure",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_index",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_nan",
"type": "statement"
},
{
"name": "longMessage",
"type": "statement"
},
{
"name": "maxDiff",
"type": "statement"
},
{
"name": "ref_pd_df_by_series",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_max_rows",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "ref_pd_series",
"type": "statement"
},
{
"name": "ref_pd_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_nan",
"type": "statement"
},
{
"name": "run",
"type": "function"
},
{
"name": "set_up_pd_df",
"type": "function"
},
{
"name": "set_up_pd_series",
"type": "function"
},
{
"name": "setUp",
"type": "function"
},
{
"name": "setUpClass",
"type": "function"
},
{
"name": "shortDescription",
"type": "function"
},
{
"name": "skipTest",
"type": "function"
},
{
"name": "subTest",
"type": "function"
},
{
"name": "tearDown",
"type": "function"
},
{
"name": "tearDownClass",
"type": "function"
},
{
"name": "test_add_df_index_with_df",
"type": "function"
},
{
"name": "test_add_df_index_with_none",
"type": "function"
},
{
"name": "_addSkip",
"type": "function"
},
{
"name": "_formatMessage",
"type": "function"
},
{
"name": "_getAssertEqualityFunc",
"type": "function"
},
{
"name": "_testMethodDoc",
"type": "statement"
},
{
"name": "_testMethodName",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__call__",
"type": "function"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.data.add_data_frame(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_data_frame_with_series_contains_na(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension_with_nan)
self.data.add_data_frame(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
class TestDfIndex(DataWithAssets):
def test_add_df_index_with_none(self) -> None:
self.data.add_df_index(None, column_name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_index_with_df(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df_index(df, column_name="Index")
self.assertEqual(
self.ref_pd_df_by_series_only_index,
self.
|
7,699 | 216 | 5,369 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
Unknown
|
build
| true |
function
| 28 | 30 | false | false |
[
"add_df",
"add_data_frame",
"build",
"add_data_frame_index",
"add_df_index",
"add_dimension",
"add_measure",
"add_np_array",
"add_record",
"add_records",
"add_series",
"add_series_list",
"add_spark_df",
"dump",
"filter",
"from_json",
"set_filter",
"clear",
"copy",
"fromkeys",
"get",
"items",
"keys",
"pop",
"popitem",
"setdefault",
"update",
"values",
"__annotations__",
"__class__",
"__class_getitem__",
"__contains__",
"__delattr__",
"__delitem__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__getitem__",
"__hash__",
"__init__",
"__init_subclass__",
"__ior__",
"__iter__",
"__len__",
"__ne__",
"__new__",
"__or__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__reversed__",
"__ror__",
"__setattr__",
"__setitem__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "add_data_frame",
"type": "function"
},
{
"name": "add_data_frame_index",
"type": "function"
},
{
"name": "add_df",
"type": "function"
},
{
"name": "add_df_index",
"type": "function"
},
{
"name": "add_dimension",
"type": "function"
},
{
"name": "add_measure",
"type": "function"
},
{
"name": "add_np_array",
"type": "function"
},
{
"name": "add_record",
"type": "function"
},
{
"name": "add_records",
"type": "function"
},
{
"name": "add_series",
"type": "function"
},
{
"name": "add_series_list",
"type": "function"
},
{
"name": "add_spark_df",
"type": "function"
},
{
"name": "build",
"type": "function"
},
{
"name": "clear",
"type": "function"
},
{
"name": "copy",
"type": "function"
},
{
"name": "dump",
"type": "function"
},
{
"name": "filter",
"type": "function"
},
{
"name": "from_json",
"type": "function"
},
{
"name": "fromkeys",
"type": "function"
},
{
"name": "get",
"type": "function"
},
{
"name": "items",
"type": "function"
},
{
"name": "keys",
"type": "function"
},
{
"name": "pop",
"type": "function"
},
{
"name": "popitem",
"type": "function"
},
{
"name": "set_filter",
"type": "function"
},
{
"name": "setdefault",
"type": "function"
},
{
"name": "update",
"type": "function"
},
{
"name": "values",
"type": "function"
},
{
"name": "_add_named_value",
"type": "function"
},
{
"name": "_add_value",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__class_getitem__",
"type": "function"
},
{
"name": "__contains__",
"type": "function"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__delitem__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__getitem__",
"type": "function"
},
{
"name": "__hash__",
"type": "statement"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__ior__",
"type": "function"
},
{
"name": "__iter__",
"type": "function"
},
{
"name": "__len__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__or__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__reversed__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__setitem__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.data.add_data_frame(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_data_frame_with_series_contains_na(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension_with_nan)
self.data.add_data_frame(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
class TestDfIndex(DataWithAssets):
def test_add_df_index_with_none(self) -> None:
self.data.add_df_index(None, column_name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_index_with_df(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df_index(df, column_name="Index")
self.assertEqual(
self.ref_pd_df_by_series_only_index,
self.data.
|
7,700 | 216 | 5,504 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
inproject
|
data
| true |
statement
| 96 | 100 | false | false |
[
"data",
"assertEqual",
"test_add_data_frame_index_with_df",
"in_pd_series_measure_with_index",
"in_pd_series_dimension_with_index",
"test_add_data_frame_index_with_none",
"asset_dir",
"in_pd_df_by_series",
"in_pd_df_by_series_with_duplicated_popularity",
"in_pd_df_by_series_with_index",
"in_pd_df_by_series_with_nan",
"in_pd_series_dimension",
"in_pd_series_dimension_with_nan",
"in_pd_series_measure",
"in_pd_series_measure_with_nan",
"ref_pd_df_by_series",
"ref_pd_df_by_series_max_rows",
"ref_pd_df_by_series_only_index",
"ref_pd_df_by_series_with_duplicated_popularity",
"ref_pd_df_by_series_with_index",
"ref_pd_df_by_series_with_nan",
"ref_pd_series",
"ref_pd_series_only_index",
"ref_pd_series_with_index",
"ref_pd_series_with_nan",
"set_up_pd_df",
"set_up_pd_series",
"setUp",
"setUpClass",
"addClassCleanup",
"addCleanup",
"addTypeEqualityFunc",
"assert_",
"assertAlmostEqual",
"assertAlmostEquals",
"assertCountEqual",
"assertDictContainsSubset",
"assertDictEqual",
"assertEquals",
"assertFalse",
"assertGreater",
"assertGreaterEqual",
"assertIn",
"assertIs",
"assertIsInstance",
"assertIsNone",
"assertIsNot",
"assertIsNotNone",
"assertLess",
"assertLessEqual",
"assertListEqual",
"assertLogs",
"assertMultiLineEqual",
"assertNoLogs",
"assertNotAlmostEqual",
"assertNotAlmostEquals",
"assertNotEqual",
"assertNotEquals",
"assertNotIn",
"assertNotIsInstance",
"assertNotRegex",
"assertNotRegexpMatches",
"assertRaises",
"assertRaisesRegex",
"assertRaisesRegexp",
"assertRegex",
"assertRegexpMatches",
"assertSequenceEqual",
"assertSetEqual",
"assertTrue",
"assertTupleEqual",
"assertWarns",
"assertWarnsRegex",
"countTestCases",
"debug",
"defaultTestResult",
"doClassCleanups",
"doCleanups",
"fail",
"failIf",
"failIfAlmostEqual",
"failIfEqual",
"failUnless",
"failUnlessAlmostEqual",
"failUnlessEqual",
"failUnlessRaises",
"failureException",
"id",
"longMessage",
"maxDiff",
"run",
"shortDescription",
"skipTest",
"subTest",
"tearDown",
"tearDownClass",
"__annotations__",
"__call__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "addClassCleanup",
"type": "function"
},
{
"name": "addCleanup",
"type": "function"
},
{
"name": "addTypeEqualityFunc",
"type": "function"
},
{
"name": "assert_",
"type": "function"
},
{
"name": "assertAlmostEqual",
"type": "function"
},
{
"name": "assertAlmostEquals",
"type": "function"
},
{
"name": "assertCountEqual",
"type": "function"
},
{
"name": "assertDictContainsSubset",
"type": "function"
},
{
"name": "assertDictEqual",
"type": "function"
},
{
"name": "assertEqual",
"type": "function"
},
{
"name": "assertEquals",
"type": "function"
},
{
"name": "assertFalse",
"type": "function"
},
{
"name": "assertGreater",
"type": "function"
},
{
"name": "assertGreaterEqual",
"type": "function"
},
{
"name": "assertIn",
"type": "function"
},
{
"name": "assertIs",
"type": "function"
},
{
"name": "assertIsInstance",
"type": "function"
},
{
"name": "assertIsNone",
"type": "function"
},
{
"name": "assertIsNot",
"type": "function"
},
{
"name": "assertIsNotNone",
"type": "function"
},
{
"name": "assertLess",
"type": "function"
},
{
"name": "assertLessEqual",
"type": "function"
},
{
"name": "assertListEqual",
"type": "function"
},
{
"name": "assertLogs",
"type": "function"
},
{
"name": "assertMultiLineEqual",
"type": "function"
},
{
"name": "assertNotAlmostEqual",
"type": "function"
},
{
"name": "assertNotAlmostEquals",
"type": "function"
},
{
"name": "assertNotEqual",
"type": "function"
},
{
"name": "assertNotEquals",
"type": "function"
},
{
"name": "assertNotIn",
"type": "function"
},
{
"name": "assertNotIsInstance",
"type": "function"
},
{
"name": "assertNotRegex",
"type": "function"
},
{
"name": "assertNotRegexpMatches",
"type": "function"
},
{
"name": "assertRaises",
"type": "function"
},
{
"name": "assertRaisesRegex",
"type": "function"
},
{
"name": "assertRaisesRegexp",
"type": "function"
},
{
"name": "assertRegex",
"type": "function"
},
{
"name": "assertRegexpMatches",
"type": "function"
},
{
"name": "assertSequenceEqual",
"type": "function"
},
{
"name": "assertSetEqual",
"type": "function"
},
{
"name": "assertTrue",
"type": "function"
},
{
"name": "assertTupleEqual",
"type": "function"
},
{
"name": "assertWarns",
"type": "function"
},
{
"name": "assertWarnsRegex",
"type": "function"
},
{
"name": "asset_dir",
"type": "statement"
},
{
"name": "countTestCases",
"type": "function"
},
{
"name": "data",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "defaultTestResult",
"type": "function"
},
{
"name": "doClassCleanups",
"type": "function"
},
{
"name": "doCleanups",
"type": "function"
},
{
"name": "fail",
"type": "function"
},
{
"name": "failIf",
"type": "function"
},
{
"name": "failIfAlmostEqual",
"type": "function"
},
{
"name": "failIfEqual",
"type": "function"
},
{
"name": "failUnless",
"type": "function"
},
{
"name": "failUnlessAlmostEqual",
"type": "function"
},
{
"name": "failUnlessEqual",
"type": "function"
},
{
"name": "failUnlessRaises",
"type": "function"
},
{
"name": "failureException",
"type": "statement"
},
{
"name": "id",
"type": "function"
},
{
"name": "in_pd_df_by_series",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_dimension",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_index",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_measure",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_index",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_nan",
"type": "statement"
},
{
"name": "longMessage",
"type": "statement"
},
{
"name": "maxDiff",
"type": "statement"
},
{
"name": "ref_pd_df_by_series",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_max_rows",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "ref_pd_series",
"type": "statement"
},
{
"name": "ref_pd_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_nan",
"type": "statement"
},
{
"name": "run",
"type": "function"
},
{
"name": "set_up_pd_df",
"type": "function"
},
{
"name": "set_up_pd_series",
"type": "function"
},
{
"name": "setUp",
"type": "function"
},
{
"name": "setUpClass",
"type": "function"
},
{
"name": "shortDescription",
"type": "function"
},
{
"name": "skipTest",
"type": "function"
},
{
"name": "subTest",
"type": "function"
},
{
"name": "tearDown",
"type": "function"
},
{
"name": "tearDownClass",
"type": "function"
},
{
"name": "test_add_data_frame_index_with_df",
"type": "function"
},
{
"name": "test_add_data_frame_index_with_none",
"type": "function"
},
{
"name": "_addSkip",
"type": "function"
},
{
"name": "_formatMessage",
"type": "function"
},
{
"name": "_getAssertEqualityFunc",
"type": "function"
},
{
"name": "_testMethodDoc",
"type": "statement"
},
{
"name": "_testMethodName",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__call__",
"type": "function"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.data.add_data_frame(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_data_frame_with_series_contains_na(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension_with_nan)
self.data.add_data_frame(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
class TestDfIndex(DataWithAssets):
def test_add_df_index_with_none(self) -> None:
self.data.add_df_index(None, column_name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_index_with_df(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df_index(df, column_name="Index")
self.assertEqual(
self.ref_pd_df_by_series_only_index,
self.data.build(),
)
class TestDataFrameIndex(DataWithAssets):
def test_add_data_frame_index_with_none(self) -> None:
self.
|
7,701 | 216 | 5,509 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
inproject
|
add_data_frame_index
| true |
function
| 28 | 30 | false | true |
[
"add_data_frame",
"add_df",
"build",
"add_data_frame_index",
"add_df_index",
"add_dimension",
"add_measure",
"add_np_array",
"add_record",
"add_records",
"add_series",
"add_series_list",
"add_spark_df",
"dump",
"filter",
"from_json",
"set_filter",
"clear",
"copy",
"fromkeys",
"get",
"items",
"keys",
"pop",
"popitem",
"setdefault",
"update",
"values",
"__annotations__",
"__class__",
"__class_getitem__",
"__contains__",
"__delattr__",
"__delitem__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__getitem__",
"__hash__",
"__init__",
"__init_subclass__",
"__ior__",
"__iter__",
"__len__",
"__ne__",
"__new__",
"__or__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__reversed__",
"__ror__",
"__setattr__",
"__setitem__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "add_data_frame",
"type": "function"
},
{
"name": "add_data_frame_index",
"type": "function"
},
{
"name": "add_df",
"type": "function"
},
{
"name": "add_df_index",
"type": "function"
},
{
"name": "add_dimension",
"type": "function"
},
{
"name": "add_measure",
"type": "function"
},
{
"name": "add_np_array",
"type": "function"
},
{
"name": "add_record",
"type": "function"
},
{
"name": "add_records",
"type": "function"
},
{
"name": "add_series",
"type": "function"
},
{
"name": "add_series_list",
"type": "function"
},
{
"name": "add_spark_df",
"type": "function"
},
{
"name": "build",
"type": "function"
},
{
"name": "clear",
"type": "function"
},
{
"name": "copy",
"type": "function"
},
{
"name": "dump",
"type": "function"
},
{
"name": "filter",
"type": "function"
},
{
"name": "from_json",
"type": "function"
},
{
"name": "fromkeys",
"type": "function"
},
{
"name": "get",
"type": "function"
},
{
"name": "items",
"type": "function"
},
{
"name": "keys",
"type": "function"
},
{
"name": "pop",
"type": "function"
},
{
"name": "popitem",
"type": "function"
},
{
"name": "set_filter",
"type": "function"
},
{
"name": "setdefault",
"type": "function"
},
{
"name": "update",
"type": "function"
},
{
"name": "values",
"type": "function"
},
{
"name": "_add_named_value",
"type": "function"
},
{
"name": "_add_value",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__class_getitem__",
"type": "function"
},
{
"name": "__contains__",
"type": "function"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__delitem__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__getitem__",
"type": "function"
},
{
"name": "__hash__",
"type": "statement"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__ior__",
"type": "function"
},
{
"name": "__iter__",
"type": "function"
},
{
"name": "__len__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__or__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__reversed__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__setitem__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.data.add_data_frame(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_data_frame_with_series_contains_na(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension_with_nan)
self.data.add_data_frame(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
class TestDfIndex(DataWithAssets):
def test_add_df_index_with_none(self) -> None:
self.data.add_df_index(None, column_name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_index_with_df(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df_index(df, column_name="Index")
self.assertEqual(
self.ref_pd_df_by_series_only_index,
self.data.build(),
)
class TestDataFrameIndex(DataWithAssets):
def test_add_data_frame_index_with_none(self) -> None:
self.data.
|
7,702 | 216 | 5,619 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
Unknown
|
data
| true |
statement
| 96 | 100 | false | false |
[
"data",
"assertEqual",
"test_add_data_frame_index_with_df",
"in_pd_series_measure_with_index",
"in_pd_series_dimension_with_index",
"test_add_data_frame_index_with_none",
"asset_dir",
"in_pd_df_by_series",
"in_pd_df_by_series_with_duplicated_popularity",
"in_pd_df_by_series_with_index",
"in_pd_df_by_series_with_nan",
"in_pd_series_dimension",
"in_pd_series_dimension_with_nan",
"in_pd_series_measure",
"in_pd_series_measure_with_nan",
"ref_pd_df_by_series",
"ref_pd_df_by_series_max_rows",
"ref_pd_df_by_series_only_index",
"ref_pd_df_by_series_with_duplicated_popularity",
"ref_pd_df_by_series_with_index",
"ref_pd_df_by_series_with_nan",
"ref_pd_series",
"ref_pd_series_only_index",
"ref_pd_series_with_index",
"ref_pd_series_with_nan",
"set_up_pd_df",
"set_up_pd_series",
"setUp",
"setUpClass",
"addClassCleanup",
"addCleanup",
"addTypeEqualityFunc",
"assert_",
"assertAlmostEqual",
"assertAlmostEquals",
"assertCountEqual",
"assertDictContainsSubset",
"assertDictEqual",
"assertEquals",
"assertFalse",
"assertGreater",
"assertGreaterEqual",
"assertIn",
"assertIs",
"assertIsInstance",
"assertIsNone",
"assertIsNot",
"assertIsNotNone",
"assertLess",
"assertLessEqual",
"assertListEqual",
"assertLogs",
"assertMultiLineEqual",
"assertNoLogs",
"assertNotAlmostEqual",
"assertNotAlmostEquals",
"assertNotEqual",
"assertNotEquals",
"assertNotIn",
"assertNotIsInstance",
"assertNotRegex",
"assertNotRegexpMatches",
"assertRaises",
"assertRaisesRegex",
"assertRaisesRegexp",
"assertRegex",
"assertRegexpMatches",
"assertSequenceEqual",
"assertSetEqual",
"assertTrue",
"assertTupleEqual",
"assertWarns",
"assertWarnsRegex",
"countTestCases",
"debug",
"defaultTestResult",
"doClassCleanups",
"doCleanups",
"fail",
"failIf",
"failIfAlmostEqual",
"failIfEqual",
"failUnless",
"failUnlessAlmostEqual",
"failUnlessEqual",
"failUnlessRaises",
"failureException",
"id",
"longMessage",
"maxDiff",
"run",
"shortDescription",
"skipTest",
"subTest",
"tearDown",
"tearDownClass",
"__annotations__",
"__call__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "addClassCleanup",
"type": "function"
},
{
"name": "addCleanup",
"type": "function"
},
{
"name": "addTypeEqualityFunc",
"type": "function"
},
{
"name": "assert_",
"type": "function"
},
{
"name": "assertAlmostEqual",
"type": "function"
},
{
"name": "assertAlmostEquals",
"type": "function"
},
{
"name": "assertCountEqual",
"type": "function"
},
{
"name": "assertDictContainsSubset",
"type": "function"
},
{
"name": "assertDictEqual",
"type": "function"
},
{
"name": "assertEqual",
"type": "function"
},
{
"name": "assertEquals",
"type": "function"
},
{
"name": "assertFalse",
"type": "function"
},
{
"name": "assertGreater",
"type": "function"
},
{
"name": "assertGreaterEqual",
"type": "function"
},
{
"name": "assertIn",
"type": "function"
},
{
"name": "assertIs",
"type": "function"
},
{
"name": "assertIsInstance",
"type": "function"
},
{
"name": "assertIsNone",
"type": "function"
},
{
"name": "assertIsNot",
"type": "function"
},
{
"name": "assertIsNotNone",
"type": "function"
},
{
"name": "assertLess",
"type": "function"
},
{
"name": "assertLessEqual",
"type": "function"
},
{
"name": "assertListEqual",
"type": "function"
},
{
"name": "assertLogs",
"type": "function"
},
{
"name": "assertMultiLineEqual",
"type": "function"
},
{
"name": "assertNotAlmostEqual",
"type": "function"
},
{
"name": "assertNotAlmostEquals",
"type": "function"
},
{
"name": "assertNotEqual",
"type": "function"
},
{
"name": "assertNotEquals",
"type": "function"
},
{
"name": "assertNotIn",
"type": "function"
},
{
"name": "assertNotIsInstance",
"type": "function"
},
{
"name": "assertNotRegex",
"type": "function"
},
{
"name": "assertNotRegexpMatches",
"type": "function"
},
{
"name": "assertRaises",
"type": "function"
},
{
"name": "assertRaisesRegex",
"type": "function"
},
{
"name": "assertRaisesRegexp",
"type": "function"
},
{
"name": "assertRegex",
"type": "function"
},
{
"name": "assertRegexpMatches",
"type": "function"
},
{
"name": "assertSequenceEqual",
"type": "function"
},
{
"name": "assertSetEqual",
"type": "function"
},
{
"name": "assertTrue",
"type": "function"
},
{
"name": "assertTupleEqual",
"type": "function"
},
{
"name": "assertWarns",
"type": "function"
},
{
"name": "assertWarnsRegex",
"type": "function"
},
{
"name": "asset_dir",
"type": "statement"
},
{
"name": "countTestCases",
"type": "function"
},
{
"name": "data",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "defaultTestResult",
"type": "function"
},
{
"name": "doClassCleanups",
"type": "function"
},
{
"name": "doCleanups",
"type": "function"
},
{
"name": "fail",
"type": "function"
},
{
"name": "failIf",
"type": "function"
},
{
"name": "failIfAlmostEqual",
"type": "function"
},
{
"name": "failIfEqual",
"type": "function"
},
{
"name": "failUnless",
"type": "function"
},
{
"name": "failUnlessAlmostEqual",
"type": "function"
},
{
"name": "failUnlessEqual",
"type": "function"
},
{
"name": "failUnlessRaises",
"type": "function"
},
{
"name": "failureException",
"type": "statement"
},
{
"name": "id",
"type": "function"
},
{
"name": "in_pd_df_by_series",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_dimension",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_index",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_measure",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_index",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_nan",
"type": "statement"
},
{
"name": "longMessage",
"type": "statement"
},
{
"name": "maxDiff",
"type": "statement"
},
{
"name": "ref_pd_df_by_series",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_max_rows",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "ref_pd_series",
"type": "statement"
},
{
"name": "ref_pd_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_nan",
"type": "statement"
},
{
"name": "run",
"type": "function"
},
{
"name": "set_up_pd_df",
"type": "function"
},
{
"name": "set_up_pd_series",
"type": "function"
},
{
"name": "setUp",
"type": "function"
},
{
"name": "setUpClass",
"type": "function"
},
{
"name": "shortDescription",
"type": "function"
},
{
"name": "skipTest",
"type": "function"
},
{
"name": "subTest",
"type": "function"
},
{
"name": "tearDown",
"type": "function"
},
{
"name": "tearDownClass",
"type": "function"
},
{
"name": "test_add_data_frame_index_with_df",
"type": "function"
},
{
"name": "test_add_data_frame_index_with_none",
"type": "function"
},
{
"name": "_addSkip",
"type": "function"
},
{
"name": "_formatMessage",
"type": "function"
},
{
"name": "_getAssertEqualityFunc",
"type": "function"
},
{
"name": "_testMethodDoc",
"type": "statement"
},
{
"name": "_testMethodName",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__call__",
"type": "function"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.data.add_data_frame(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_data_frame_with_series_contains_na(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension_with_nan)
self.data.add_data_frame(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
class TestDfIndex(DataWithAssets):
def test_add_df_index_with_none(self) -> None:
self.data.add_df_index(None, column_name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_index_with_df(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df_index(df, column_name="Index")
self.assertEqual(
self.ref_pd_df_by_series_only_index,
self.data.build(),
)
class TestDataFrameIndex(DataWithAssets):
def test_add_data_frame_index_with_none(self) -> None:
self.data.add_data_frame_index(None, name="Index")
self.assertEqual(
{"data": {}},
self.
|
7,703 | 216 | 5,624 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
Unknown
|
build
| true |
function
| 28 | 30 | false | false |
[
"add_df",
"add_data_frame",
"build",
"add_data_frame_index",
"add_df_index",
"add_dimension",
"add_measure",
"add_np_array",
"add_record",
"add_records",
"add_series",
"add_series_list",
"add_spark_df",
"dump",
"filter",
"from_json",
"set_filter",
"clear",
"copy",
"fromkeys",
"get",
"items",
"keys",
"pop",
"popitem",
"setdefault",
"update",
"values",
"__annotations__",
"__class__",
"__class_getitem__",
"__contains__",
"__delattr__",
"__delitem__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__getitem__",
"__hash__",
"__init__",
"__init_subclass__",
"__ior__",
"__iter__",
"__len__",
"__ne__",
"__new__",
"__or__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__reversed__",
"__ror__",
"__setattr__",
"__setitem__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "add_data_frame",
"type": "function"
},
{
"name": "add_data_frame_index",
"type": "function"
},
{
"name": "add_df",
"type": "function"
},
{
"name": "add_df_index",
"type": "function"
},
{
"name": "add_dimension",
"type": "function"
},
{
"name": "add_measure",
"type": "function"
},
{
"name": "add_np_array",
"type": "function"
},
{
"name": "add_record",
"type": "function"
},
{
"name": "add_records",
"type": "function"
},
{
"name": "add_series",
"type": "function"
},
{
"name": "add_series_list",
"type": "function"
},
{
"name": "add_spark_df",
"type": "function"
},
{
"name": "build",
"type": "function"
},
{
"name": "clear",
"type": "function"
},
{
"name": "copy",
"type": "function"
},
{
"name": "dump",
"type": "function"
},
{
"name": "filter",
"type": "function"
},
{
"name": "from_json",
"type": "function"
},
{
"name": "fromkeys",
"type": "function"
},
{
"name": "get",
"type": "function"
},
{
"name": "items",
"type": "function"
},
{
"name": "keys",
"type": "function"
},
{
"name": "pop",
"type": "function"
},
{
"name": "popitem",
"type": "function"
},
{
"name": "set_filter",
"type": "function"
},
{
"name": "setdefault",
"type": "function"
},
{
"name": "update",
"type": "function"
},
{
"name": "values",
"type": "function"
},
{
"name": "_add_named_value",
"type": "function"
},
{
"name": "_add_value",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__class_getitem__",
"type": "function"
},
{
"name": "__contains__",
"type": "function"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__delitem__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__getitem__",
"type": "function"
},
{
"name": "__hash__",
"type": "statement"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__ior__",
"type": "function"
},
{
"name": "__iter__",
"type": "function"
},
{
"name": "__len__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__or__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__reversed__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__setitem__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.data.add_data_frame(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_data_frame_with_series_contains_na(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension_with_nan)
self.data.add_data_frame(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
class TestDfIndex(DataWithAssets):
def test_add_df_index_with_none(self) -> None:
self.data.add_df_index(None, column_name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_index_with_df(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df_index(df, column_name="Index")
self.assertEqual(
self.ref_pd_df_by_series_only_index,
self.data.build(),
)
class TestDataFrameIndex(DataWithAssets):
def test_add_data_frame_index_with_none(self) -> None:
self.data.add_data_frame_index(None, name="Index")
self.assertEqual(
{"data": {}},
self.data.
|
7,704 | 216 | 5,719 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
Unknown
|
in_pd_df_by_series_with_index
| true |
statement
| 96 | 100 | false | false |
[
"data",
"in_pd_df_by_series_with_index",
"in_pd_df_by_series_with_nan",
"in_pd_df_by_series_with_duplicated_popularity",
"in_pd_df_by_series",
"test_add_data_frame_index_with_df",
"test_add_data_frame_index_with_none",
"asset_dir",
"in_pd_series_dimension",
"in_pd_series_dimension_with_index",
"in_pd_series_dimension_with_nan",
"in_pd_series_measure",
"in_pd_series_measure_with_index",
"in_pd_series_measure_with_nan",
"ref_pd_df_by_series",
"ref_pd_df_by_series_max_rows",
"ref_pd_df_by_series_only_index",
"ref_pd_df_by_series_with_duplicated_popularity",
"ref_pd_df_by_series_with_index",
"ref_pd_df_by_series_with_nan",
"ref_pd_series",
"ref_pd_series_only_index",
"ref_pd_series_with_index",
"ref_pd_series_with_nan",
"set_up_pd_df",
"set_up_pd_series",
"setUp",
"setUpClass",
"addClassCleanup",
"addCleanup",
"addTypeEqualityFunc",
"assert_",
"assertAlmostEqual",
"assertAlmostEquals",
"assertCountEqual",
"assertDictContainsSubset",
"assertDictEqual",
"assertEqual",
"assertEquals",
"assertFalse",
"assertGreater",
"assertGreaterEqual",
"assertIn",
"assertIs",
"assertIsInstance",
"assertIsNone",
"assertIsNot",
"assertIsNotNone",
"assertLess",
"assertLessEqual",
"assertListEqual",
"assertLogs",
"assertMultiLineEqual",
"assertNoLogs",
"assertNotAlmostEqual",
"assertNotAlmostEquals",
"assertNotEqual",
"assertNotEquals",
"assertNotIn",
"assertNotIsInstance",
"assertNotRegex",
"assertNotRegexpMatches",
"assertRaises",
"assertRaisesRegex",
"assertRaisesRegexp",
"assertRegex",
"assertRegexpMatches",
"assertSequenceEqual",
"assertSetEqual",
"assertTrue",
"assertTupleEqual",
"assertWarns",
"assertWarnsRegex",
"countTestCases",
"debug",
"defaultTestResult",
"doClassCleanups",
"doCleanups",
"fail",
"failIf",
"failIfAlmostEqual",
"failIfEqual",
"failUnless",
"failUnlessAlmostEqual",
"failUnlessEqual",
"failUnlessRaises",
"failureException",
"id",
"longMessage",
"maxDiff",
"run",
"shortDescription",
"skipTest",
"subTest",
"tearDown",
"tearDownClass",
"__annotations__",
"__call__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "addClassCleanup",
"type": "function"
},
{
"name": "addCleanup",
"type": "function"
},
{
"name": "addTypeEqualityFunc",
"type": "function"
},
{
"name": "assert_",
"type": "function"
},
{
"name": "assertAlmostEqual",
"type": "function"
},
{
"name": "assertAlmostEquals",
"type": "function"
},
{
"name": "assertCountEqual",
"type": "function"
},
{
"name": "assertDictContainsSubset",
"type": "function"
},
{
"name": "assertDictEqual",
"type": "function"
},
{
"name": "assertEqual",
"type": "function"
},
{
"name": "assertEquals",
"type": "function"
},
{
"name": "assertFalse",
"type": "function"
},
{
"name": "assertGreater",
"type": "function"
},
{
"name": "assertGreaterEqual",
"type": "function"
},
{
"name": "assertIn",
"type": "function"
},
{
"name": "assertIs",
"type": "function"
},
{
"name": "assertIsInstance",
"type": "function"
},
{
"name": "assertIsNone",
"type": "function"
},
{
"name": "assertIsNot",
"type": "function"
},
{
"name": "assertIsNotNone",
"type": "function"
},
{
"name": "assertLess",
"type": "function"
},
{
"name": "assertLessEqual",
"type": "function"
},
{
"name": "assertListEqual",
"type": "function"
},
{
"name": "assertLogs",
"type": "function"
},
{
"name": "assertMultiLineEqual",
"type": "function"
},
{
"name": "assertNotAlmostEqual",
"type": "function"
},
{
"name": "assertNotAlmostEquals",
"type": "function"
},
{
"name": "assertNotEqual",
"type": "function"
},
{
"name": "assertNotEquals",
"type": "function"
},
{
"name": "assertNotIn",
"type": "function"
},
{
"name": "assertNotIsInstance",
"type": "function"
},
{
"name": "assertNotRegex",
"type": "function"
},
{
"name": "assertNotRegexpMatches",
"type": "function"
},
{
"name": "assertRaises",
"type": "function"
},
{
"name": "assertRaisesRegex",
"type": "function"
},
{
"name": "assertRaisesRegexp",
"type": "function"
},
{
"name": "assertRegex",
"type": "function"
},
{
"name": "assertRegexpMatches",
"type": "function"
},
{
"name": "assertSequenceEqual",
"type": "function"
},
{
"name": "assertSetEqual",
"type": "function"
},
{
"name": "assertTrue",
"type": "function"
},
{
"name": "assertTupleEqual",
"type": "function"
},
{
"name": "assertWarns",
"type": "function"
},
{
"name": "assertWarnsRegex",
"type": "function"
},
{
"name": "asset_dir",
"type": "statement"
},
{
"name": "countTestCases",
"type": "function"
},
{
"name": "data",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "defaultTestResult",
"type": "function"
},
{
"name": "doClassCleanups",
"type": "function"
},
{
"name": "doCleanups",
"type": "function"
},
{
"name": "fail",
"type": "function"
},
{
"name": "failIf",
"type": "function"
},
{
"name": "failIfAlmostEqual",
"type": "function"
},
{
"name": "failIfEqual",
"type": "function"
},
{
"name": "failUnless",
"type": "function"
},
{
"name": "failUnlessAlmostEqual",
"type": "function"
},
{
"name": "failUnlessEqual",
"type": "function"
},
{
"name": "failUnlessRaises",
"type": "function"
},
{
"name": "failureException",
"type": "statement"
},
{
"name": "id",
"type": "function"
},
{
"name": "in_pd_df_by_series",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_dimension",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_index",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_measure",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_index",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_nan",
"type": "statement"
},
{
"name": "longMessage",
"type": "statement"
},
{
"name": "maxDiff",
"type": "statement"
},
{
"name": "ref_pd_df_by_series",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_max_rows",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "ref_pd_series",
"type": "statement"
},
{
"name": "ref_pd_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_nan",
"type": "statement"
},
{
"name": "run",
"type": "function"
},
{
"name": "set_up_pd_df",
"type": "function"
},
{
"name": "set_up_pd_series",
"type": "function"
},
{
"name": "setUp",
"type": "function"
},
{
"name": "setUpClass",
"type": "function"
},
{
"name": "shortDescription",
"type": "function"
},
{
"name": "skipTest",
"type": "function"
},
{
"name": "subTest",
"type": "function"
},
{
"name": "tearDown",
"type": "function"
},
{
"name": "tearDownClass",
"type": "function"
},
{
"name": "test_add_data_frame_index_with_df",
"type": "function"
},
{
"name": "test_add_data_frame_index_with_none",
"type": "function"
},
{
"name": "_addSkip",
"type": "function"
},
{
"name": "_formatMessage",
"type": "function"
},
{
"name": "_getAssertEqualityFunc",
"type": "function"
},
{
"name": "_testMethodDoc",
"type": "statement"
},
{
"name": "_testMethodName",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__call__",
"type": "function"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.data.add_data_frame(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_data_frame_with_series_contains_na(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension_with_nan)
self.data.add_data_frame(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
class TestDfIndex(DataWithAssets):
def test_add_df_index_with_none(self) -> None:
self.data.add_df_index(None, column_name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_index_with_df(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df_index(df, column_name="Index")
self.assertEqual(
self.ref_pd_df_by_series_only_index,
self.data.build(),
)
class TestDataFrameIndex(DataWithAssets):
def test_add_data_frame_index_with_none(self) -> None:
self.data.add_data_frame_index(None, name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_index_with_df(self) -> None:
df = self.
|
7,705 | 216 | 5,762 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
inproject
|
data
| true |
statement
| 96 | 100 | false | false |
[
"data",
"assertEqual",
"test_add_data_frame_index_with_none",
"in_pd_series_measure_with_index",
"in_pd_series_dimension_with_index",
"test_add_data_frame_index_with_df",
"asset_dir",
"in_pd_df_by_series",
"in_pd_df_by_series_with_duplicated_popularity",
"in_pd_df_by_series_with_index",
"in_pd_df_by_series_with_nan",
"in_pd_series_dimension",
"in_pd_series_dimension_with_nan",
"in_pd_series_measure",
"in_pd_series_measure_with_nan",
"ref_pd_df_by_series",
"ref_pd_df_by_series_max_rows",
"ref_pd_df_by_series_only_index",
"ref_pd_df_by_series_with_duplicated_popularity",
"ref_pd_df_by_series_with_index",
"ref_pd_df_by_series_with_nan",
"ref_pd_series",
"ref_pd_series_only_index",
"ref_pd_series_with_index",
"ref_pd_series_with_nan",
"set_up_pd_df",
"set_up_pd_series",
"setUp",
"setUpClass",
"addClassCleanup",
"addCleanup",
"addTypeEqualityFunc",
"assert_",
"assertAlmostEqual",
"assertAlmostEquals",
"assertCountEqual",
"assertDictContainsSubset",
"assertDictEqual",
"assertEquals",
"assertFalse",
"assertGreater",
"assertGreaterEqual",
"assertIn",
"assertIs",
"assertIsInstance",
"assertIsNone",
"assertIsNot",
"assertIsNotNone",
"assertLess",
"assertLessEqual",
"assertListEqual",
"assertLogs",
"assertMultiLineEqual",
"assertNoLogs",
"assertNotAlmostEqual",
"assertNotAlmostEquals",
"assertNotEqual",
"assertNotEquals",
"assertNotIn",
"assertNotIsInstance",
"assertNotRegex",
"assertNotRegexpMatches",
"assertRaises",
"assertRaisesRegex",
"assertRaisesRegexp",
"assertRegex",
"assertRegexpMatches",
"assertSequenceEqual",
"assertSetEqual",
"assertTrue",
"assertTupleEqual",
"assertWarns",
"assertWarnsRegex",
"countTestCases",
"debug",
"defaultTestResult",
"doClassCleanups",
"doCleanups",
"fail",
"failIf",
"failIfAlmostEqual",
"failIfEqual",
"failUnless",
"failUnlessAlmostEqual",
"failUnlessEqual",
"failUnlessRaises",
"failureException",
"id",
"longMessage",
"maxDiff",
"run",
"shortDescription",
"skipTest",
"subTest",
"tearDown",
"tearDownClass",
"__annotations__",
"__call__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "addClassCleanup",
"type": "function"
},
{
"name": "addCleanup",
"type": "function"
},
{
"name": "addTypeEqualityFunc",
"type": "function"
},
{
"name": "assert_",
"type": "function"
},
{
"name": "assertAlmostEqual",
"type": "function"
},
{
"name": "assertAlmostEquals",
"type": "function"
},
{
"name": "assertCountEqual",
"type": "function"
},
{
"name": "assertDictContainsSubset",
"type": "function"
},
{
"name": "assertDictEqual",
"type": "function"
},
{
"name": "assertEqual",
"type": "function"
},
{
"name": "assertEquals",
"type": "function"
},
{
"name": "assertFalse",
"type": "function"
},
{
"name": "assertGreater",
"type": "function"
},
{
"name": "assertGreaterEqual",
"type": "function"
},
{
"name": "assertIn",
"type": "function"
},
{
"name": "assertIs",
"type": "function"
},
{
"name": "assertIsInstance",
"type": "function"
},
{
"name": "assertIsNone",
"type": "function"
},
{
"name": "assertIsNot",
"type": "function"
},
{
"name": "assertIsNotNone",
"type": "function"
},
{
"name": "assertLess",
"type": "function"
},
{
"name": "assertLessEqual",
"type": "function"
},
{
"name": "assertListEqual",
"type": "function"
},
{
"name": "assertLogs",
"type": "function"
},
{
"name": "assertMultiLineEqual",
"type": "function"
},
{
"name": "assertNotAlmostEqual",
"type": "function"
},
{
"name": "assertNotAlmostEquals",
"type": "function"
},
{
"name": "assertNotEqual",
"type": "function"
},
{
"name": "assertNotEquals",
"type": "function"
},
{
"name": "assertNotIn",
"type": "function"
},
{
"name": "assertNotIsInstance",
"type": "function"
},
{
"name": "assertNotRegex",
"type": "function"
},
{
"name": "assertNotRegexpMatches",
"type": "function"
},
{
"name": "assertRaises",
"type": "function"
},
{
"name": "assertRaisesRegex",
"type": "function"
},
{
"name": "assertRaisesRegexp",
"type": "function"
},
{
"name": "assertRegex",
"type": "function"
},
{
"name": "assertRegexpMatches",
"type": "function"
},
{
"name": "assertSequenceEqual",
"type": "function"
},
{
"name": "assertSetEqual",
"type": "function"
},
{
"name": "assertTrue",
"type": "function"
},
{
"name": "assertTupleEqual",
"type": "function"
},
{
"name": "assertWarns",
"type": "function"
},
{
"name": "assertWarnsRegex",
"type": "function"
},
{
"name": "asset_dir",
"type": "statement"
},
{
"name": "countTestCases",
"type": "function"
},
{
"name": "data",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "defaultTestResult",
"type": "function"
},
{
"name": "doClassCleanups",
"type": "function"
},
{
"name": "doCleanups",
"type": "function"
},
{
"name": "fail",
"type": "function"
},
{
"name": "failIf",
"type": "function"
},
{
"name": "failIfAlmostEqual",
"type": "function"
},
{
"name": "failIfEqual",
"type": "function"
},
{
"name": "failUnless",
"type": "function"
},
{
"name": "failUnlessAlmostEqual",
"type": "function"
},
{
"name": "failUnlessEqual",
"type": "function"
},
{
"name": "failUnlessRaises",
"type": "function"
},
{
"name": "failureException",
"type": "statement"
},
{
"name": "id",
"type": "function"
},
{
"name": "in_pd_df_by_series",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_dimension",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_index",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_measure",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_index",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_nan",
"type": "statement"
},
{
"name": "longMessage",
"type": "statement"
},
{
"name": "maxDiff",
"type": "statement"
},
{
"name": "ref_pd_df_by_series",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_max_rows",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "ref_pd_series",
"type": "statement"
},
{
"name": "ref_pd_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_nan",
"type": "statement"
},
{
"name": "run",
"type": "function"
},
{
"name": "set_up_pd_df",
"type": "function"
},
{
"name": "set_up_pd_series",
"type": "function"
},
{
"name": "setUp",
"type": "function"
},
{
"name": "setUpClass",
"type": "function"
},
{
"name": "shortDescription",
"type": "function"
},
{
"name": "skipTest",
"type": "function"
},
{
"name": "subTest",
"type": "function"
},
{
"name": "tearDown",
"type": "function"
},
{
"name": "tearDownClass",
"type": "function"
},
{
"name": "test_add_data_frame_index_with_df",
"type": "function"
},
{
"name": "test_add_data_frame_index_with_none",
"type": "function"
},
{
"name": "_addSkip",
"type": "function"
},
{
"name": "_formatMessage",
"type": "function"
},
{
"name": "_getAssertEqualityFunc",
"type": "function"
},
{
"name": "_testMethodDoc",
"type": "statement"
},
{
"name": "_testMethodName",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__call__",
"type": "function"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.data.add_data_frame(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_data_frame_with_series_contains_na(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension_with_nan)
self.data.add_data_frame(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
class TestDfIndex(DataWithAssets):
def test_add_df_index_with_none(self) -> None:
self.data.add_df_index(None, column_name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_index_with_df(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df_index(df, column_name="Index")
self.assertEqual(
self.ref_pd_df_by_series_only_index,
self.data.build(),
)
class TestDataFrameIndex(DataWithAssets):
def test_add_data_frame_index_with_none(self) -> None:
self.data.add_data_frame_index(None, name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_index_with_df(self) -> None:
df = self.in_pd_df_by_series_with_index
self.
|
7,706 | 216 | 5,767 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
inproject
|
add_data_frame_index
| true |
function
| 28 | 30 | false | false |
[
"add_data_frame",
"add_df",
"build",
"add_data_frame_index",
"add_df_index",
"add_dimension",
"add_measure",
"add_np_array",
"add_record",
"add_records",
"add_series",
"add_series_list",
"add_spark_df",
"dump",
"filter",
"from_json",
"set_filter",
"clear",
"copy",
"fromkeys",
"get",
"items",
"keys",
"pop",
"popitem",
"setdefault",
"update",
"values",
"__annotations__",
"__class__",
"__class_getitem__",
"__contains__",
"__delattr__",
"__delitem__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__getitem__",
"__hash__",
"__init__",
"__init_subclass__",
"__ior__",
"__iter__",
"__len__",
"__ne__",
"__new__",
"__or__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__reversed__",
"__ror__",
"__setattr__",
"__setitem__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "add_data_frame",
"type": "function"
},
{
"name": "add_data_frame_index",
"type": "function"
},
{
"name": "add_df",
"type": "function"
},
{
"name": "add_df_index",
"type": "function"
},
{
"name": "add_dimension",
"type": "function"
},
{
"name": "add_measure",
"type": "function"
},
{
"name": "add_np_array",
"type": "function"
},
{
"name": "add_record",
"type": "function"
},
{
"name": "add_records",
"type": "function"
},
{
"name": "add_series",
"type": "function"
},
{
"name": "add_series_list",
"type": "function"
},
{
"name": "add_spark_df",
"type": "function"
},
{
"name": "build",
"type": "function"
},
{
"name": "clear",
"type": "function"
},
{
"name": "copy",
"type": "function"
},
{
"name": "dump",
"type": "function"
},
{
"name": "filter",
"type": "function"
},
{
"name": "from_json",
"type": "function"
},
{
"name": "fromkeys",
"type": "function"
},
{
"name": "get",
"type": "function"
},
{
"name": "items",
"type": "function"
},
{
"name": "keys",
"type": "function"
},
{
"name": "pop",
"type": "function"
},
{
"name": "popitem",
"type": "function"
},
{
"name": "set_filter",
"type": "function"
},
{
"name": "setdefault",
"type": "function"
},
{
"name": "update",
"type": "function"
},
{
"name": "values",
"type": "function"
},
{
"name": "_add_named_value",
"type": "function"
},
{
"name": "_add_value",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__class_getitem__",
"type": "function"
},
{
"name": "__contains__",
"type": "function"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__delitem__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__getitem__",
"type": "function"
},
{
"name": "__hash__",
"type": "statement"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__ior__",
"type": "function"
},
{
"name": "__iter__",
"type": "function"
},
{
"name": "__len__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__or__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__reversed__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__setitem__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.data.add_data_frame(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_data_frame_with_series_contains_na(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension_with_nan)
self.data.add_data_frame(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
class TestDfIndex(DataWithAssets):
def test_add_df_index_with_none(self) -> None:
self.data.add_df_index(None, column_name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_index_with_df(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df_index(df, column_name="Index")
self.assertEqual(
self.ref_pd_df_by_series_only_index,
self.data.build(),
)
class TestDataFrameIndex(DataWithAssets):
def test_add_data_frame_index_with_none(self) -> None:
self.data.add_data_frame_index(None, name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_index_with_df(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.
|
7,707 | 216 | 5,849 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
Unknown
|
ref_pd_df_by_series_only_index
| true |
statement
| 96 | 100 | false | false |
[
"data",
"in_pd_series_measure_with_index",
"in_pd_series_dimension_with_index",
"in_pd_df_by_series_with_index",
"ref_pd_series_only_index",
"test_add_data_frame_index_with_df",
"test_add_data_frame_index_with_none",
"asset_dir",
"in_pd_df_by_series",
"in_pd_df_by_series_with_duplicated_popularity",
"in_pd_df_by_series_with_nan",
"in_pd_series_dimension",
"in_pd_series_dimension_with_nan",
"in_pd_series_measure",
"in_pd_series_measure_with_nan",
"ref_pd_df_by_series",
"ref_pd_df_by_series_max_rows",
"ref_pd_df_by_series_only_index",
"ref_pd_df_by_series_with_duplicated_popularity",
"ref_pd_df_by_series_with_index",
"ref_pd_df_by_series_with_nan",
"ref_pd_series",
"ref_pd_series_with_index",
"ref_pd_series_with_nan",
"set_up_pd_df",
"set_up_pd_series",
"setUp",
"setUpClass",
"addClassCleanup",
"addCleanup",
"addTypeEqualityFunc",
"assert_",
"assertAlmostEqual",
"assertAlmostEquals",
"assertCountEqual",
"assertDictContainsSubset",
"assertDictEqual",
"assertEqual",
"assertEquals",
"assertFalse",
"assertGreater",
"assertGreaterEqual",
"assertIn",
"assertIs",
"assertIsInstance",
"assertIsNone",
"assertIsNot",
"assertIsNotNone",
"assertLess",
"assertLessEqual",
"assertListEqual",
"assertLogs",
"assertMultiLineEqual",
"assertNoLogs",
"assertNotAlmostEqual",
"assertNotAlmostEquals",
"assertNotEqual",
"assertNotEquals",
"assertNotIn",
"assertNotIsInstance",
"assertNotRegex",
"assertNotRegexpMatches",
"assertRaises",
"assertRaisesRegex",
"assertRaisesRegexp",
"assertRegex",
"assertRegexpMatches",
"assertSequenceEqual",
"assertSetEqual",
"assertTrue",
"assertTupleEqual",
"assertWarns",
"assertWarnsRegex",
"countTestCases",
"debug",
"defaultTestResult",
"doClassCleanups",
"doCleanups",
"fail",
"failIf",
"failIfAlmostEqual",
"failIfEqual",
"failUnless",
"failUnlessAlmostEqual",
"failUnlessEqual",
"failUnlessRaises",
"failureException",
"id",
"longMessage",
"maxDiff",
"run",
"shortDescription",
"skipTest",
"subTest",
"tearDown",
"tearDownClass",
"__annotations__",
"__call__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "addClassCleanup",
"type": "function"
},
{
"name": "addCleanup",
"type": "function"
},
{
"name": "addTypeEqualityFunc",
"type": "function"
},
{
"name": "assert_",
"type": "function"
},
{
"name": "assertAlmostEqual",
"type": "function"
},
{
"name": "assertAlmostEquals",
"type": "function"
},
{
"name": "assertCountEqual",
"type": "function"
},
{
"name": "assertDictContainsSubset",
"type": "function"
},
{
"name": "assertDictEqual",
"type": "function"
},
{
"name": "assertEqual",
"type": "function"
},
{
"name": "assertEquals",
"type": "function"
},
{
"name": "assertFalse",
"type": "function"
},
{
"name": "assertGreater",
"type": "function"
},
{
"name": "assertGreaterEqual",
"type": "function"
},
{
"name": "assertIn",
"type": "function"
},
{
"name": "assertIs",
"type": "function"
},
{
"name": "assertIsInstance",
"type": "function"
},
{
"name": "assertIsNone",
"type": "function"
},
{
"name": "assertIsNot",
"type": "function"
},
{
"name": "assertIsNotNone",
"type": "function"
},
{
"name": "assertLess",
"type": "function"
},
{
"name": "assertLessEqual",
"type": "function"
},
{
"name": "assertListEqual",
"type": "function"
},
{
"name": "assertLogs",
"type": "function"
},
{
"name": "assertMultiLineEqual",
"type": "function"
},
{
"name": "assertNotAlmostEqual",
"type": "function"
},
{
"name": "assertNotAlmostEquals",
"type": "function"
},
{
"name": "assertNotEqual",
"type": "function"
},
{
"name": "assertNotEquals",
"type": "function"
},
{
"name": "assertNotIn",
"type": "function"
},
{
"name": "assertNotIsInstance",
"type": "function"
},
{
"name": "assertNotRegex",
"type": "function"
},
{
"name": "assertNotRegexpMatches",
"type": "function"
},
{
"name": "assertRaises",
"type": "function"
},
{
"name": "assertRaisesRegex",
"type": "function"
},
{
"name": "assertRaisesRegexp",
"type": "function"
},
{
"name": "assertRegex",
"type": "function"
},
{
"name": "assertRegexpMatches",
"type": "function"
},
{
"name": "assertSequenceEqual",
"type": "function"
},
{
"name": "assertSetEqual",
"type": "function"
},
{
"name": "assertTrue",
"type": "function"
},
{
"name": "assertTupleEqual",
"type": "function"
},
{
"name": "assertWarns",
"type": "function"
},
{
"name": "assertWarnsRegex",
"type": "function"
},
{
"name": "asset_dir",
"type": "statement"
},
{
"name": "countTestCases",
"type": "function"
},
{
"name": "data",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "defaultTestResult",
"type": "function"
},
{
"name": "doClassCleanups",
"type": "function"
},
{
"name": "doCleanups",
"type": "function"
},
{
"name": "fail",
"type": "function"
},
{
"name": "failIf",
"type": "function"
},
{
"name": "failIfAlmostEqual",
"type": "function"
},
{
"name": "failIfEqual",
"type": "function"
},
{
"name": "failUnless",
"type": "function"
},
{
"name": "failUnlessAlmostEqual",
"type": "function"
},
{
"name": "failUnlessEqual",
"type": "function"
},
{
"name": "failUnlessRaises",
"type": "function"
},
{
"name": "failureException",
"type": "statement"
},
{
"name": "id",
"type": "function"
},
{
"name": "in_pd_df_by_series",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_dimension",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_index",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_measure",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_index",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_nan",
"type": "statement"
},
{
"name": "longMessage",
"type": "statement"
},
{
"name": "maxDiff",
"type": "statement"
},
{
"name": "ref_pd_df_by_series",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_max_rows",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "ref_pd_series",
"type": "statement"
},
{
"name": "ref_pd_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_nan",
"type": "statement"
},
{
"name": "run",
"type": "function"
},
{
"name": "set_up_pd_df",
"type": "function"
},
{
"name": "set_up_pd_series",
"type": "function"
},
{
"name": "setUp",
"type": "function"
},
{
"name": "setUpClass",
"type": "function"
},
{
"name": "shortDescription",
"type": "function"
},
{
"name": "skipTest",
"type": "function"
},
{
"name": "subTest",
"type": "function"
},
{
"name": "tearDown",
"type": "function"
},
{
"name": "tearDownClass",
"type": "function"
},
{
"name": "test_add_data_frame_index_with_df",
"type": "function"
},
{
"name": "test_add_data_frame_index_with_none",
"type": "function"
},
{
"name": "_addSkip",
"type": "function"
},
{
"name": "_formatMessage",
"type": "function"
},
{
"name": "_getAssertEqualityFunc",
"type": "function"
},
{
"name": "_testMethodDoc",
"type": "statement"
},
{
"name": "_testMethodName",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__call__",
"type": "function"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.data.add_data_frame(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_data_frame_with_series_contains_na(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension_with_nan)
self.data.add_data_frame(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
class TestDfIndex(DataWithAssets):
def test_add_df_index_with_none(self) -> None:
self.data.add_df_index(None, column_name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_index_with_df(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df_index(df, column_name="Index")
self.assertEqual(
self.ref_pd_df_by_series_only_index,
self.data.build(),
)
class TestDataFrameIndex(DataWithAssets):
def test_add_data_frame_index_with_none(self) -> None:
self.data.add_data_frame_index(None, name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_index_with_df(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_data_frame_index(df, name="Index")
self.assertEqual(
self.
|
7,708 | 216 | 5,898 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
Unknown
|
data
| true |
statement
| 96 | 100 | false | false |
[
"data",
"assertEqual",
"test_add_data_frame_index_with_none",
"in_pd_df_by_series_with_index",
"in_pd_series_measure_with_index",
"test_add_data_frame_index_with_df",
"asset_dir",
"in_pd_df_by_series",
"in_pd_df_by_series_with_duplicated_popularity",
"in_pd_df_by_series_with_nan",
"in_pd_series_dimension",
"in_pd_series_dimension_with_index",
"in_pd_series_dimension_with_nan",
"in_pd_series_measure",
"in_pd_series_measure_with_nan",
"ref_pd_df_by_series",
"ref_pd_df_by_series_max_rows",
"ref_pd_df_by_series_only_index",
"ref_pd_df_by_series_with_duplicated_popularity",
"ref_pd_df_by_series_with_index",
"ref_pd_df_by_series_with_nan",
"ref_pd_series",
"ref_pd_series_only_index",
"ref_pd_series_with_index",
"ref_pd_series_with_nan",
"set_up_pd_df",
"set_up_pd_series",
"setUp",
"setUpClass",
"addClassCleanup",
"addCleanup",
"addTypeEqualityFunc",
"assert_",
"assertAlmostEqual",
"assertAlmostEquals",
"assertCountEqual",
"assertDictContainsSubset",
"assertDictEqual",
"assertEquals",
"assertFalse",
"assertGreater",
"assertGreaterEqual",
"assertIn",
"assertIs",
"assertIsInstance",
"assertIsNone",
"assertIsNot",
"assertIsNotNone",
"assertLess",
"assertLessEqual",
"assertListEqual",
"assertLogs",
"assertMultiLineEqual",
"assertNoLogs",
"assertNotAlmostEqual",
"assertNotAlmostEquals",
"assertNotEqual",
"assertNotEquals",
"assertNotIn",
"assertNotIsInstance",
"assertNotRegex",
"assertNotRegexpMatches",
"assertRaises",
"assertRaisesRegex",
"assertRaisesRegexp",
"assertRegex",
"assertRegexpMatches",
"assertSequenceEqual",
"assertSetEqual",
"assertTrue",
"assertTupleEqual",
"assertWarns",
"assertWarnsRegex",
"countTestCases",
"debug",
"defaultTestResult",
"doClassCleanups",
"doCleanups",
"fail",
"failIf",
"failIfAlmostEqual",
"failIfEqual",
"failUnless",
"failUnlessAlmostEqual",
"failUnlessEqual",
"failUnlessRaises",
"failureException",
"id",
"longMessage",
"maxDiff",
"run",
"shortDescription",
"skipTest",
"subTest",
"tearDown",
"tearDownClass",
"__annotations__",
"__call__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "addClassCleanup",
"type": "function"
},
{
"name": "addCleanup",
"type": "function"
},
{
"name": "addTypeEqualityFunc",
"type": "function"
},
{
"name": "assert_",
"type": "function"
},
{
"name": "assertAlmostEqual",
"type": "function"
},
{
"name": "assertAlmostEquals",
"type": "function"
},
{
"name": "assertCountEqual",
"type": "function"
},
{
"name": "assertDictContainsSubset",
"type": "function"
},
{
"name": "assertDictEqual",
"type": "function"
},
{
"name": "assertEqual",
"type": "function"
},
{
"name": "assertEquals",
"type": "function"
},
{
"name": "assertFalse",
"type": "function"
},
{
"name": "assertGreater",
"type": "function"
},
{
"name": "assertGreaterEqual",
"type": "function"
},
{
"name": "assertIn",
"type": "function"
},
{
"name": "assertIs",
"type": "function"
},
{
"name": "assertIsInstance",
"type": "function"
},
{
"name": "assertIsNone",
"type": "function"
},
{
"name": "assertIsNot",
"type": "function"
},
{
"name": "assertIsNotNone",
"type": "function"
},
{
"name": "assertLess",
"type": "function"
},
{
"name": "assertLessEqual",
"type": "function"
},
{
"name": "assertListEqual",
"type": "function"
},
{
"name": "assertLogs",
"type": "function"
},
{
"name": "assertMultiLineEqual",
"type": "function"
},
{
"name": "assertNotAlmostEqual",
"type": "function"
},
{
"name": "assertNotAlmostEquals",
"type": "function"
},
{
"name": "assertNotEqual",
"type": "function"
},
{
"name": "assertNotEquals",
"type": "function"
},
{
"name": "assertNotIn",
"type": "function"
},
{
"name": "assertNotIsInstance",
"type": "function"
},
{
"name": "assertNotRegex",
"type": "function"
},
{
"name": "assertNotRegexpMatches",
"type": "function"
},
{
"name": "assertRaises",
"type": "function"
},
{
"name": "assertRaisesRegex",
"type": "function"
},
{
"name": "assertRaisesRegexp",
"type": "function"
},
{
"name": "assertRegex",
"type": "function"
},
{
"name": "assertRegexpMatches",
"type": "function"
},
{
"name": "assertSequenceEqual",
"type": "function"
},
{
"name": "assertSetEqual",
"type": "function"
},
{
"name": "assertTrue",
"type": "function"
},
{
"name": "assertTupleEqual",
"type": "function"
},
{
"name": "assertWarns",
"type": "function"
},
{
"name": "assertWarnsRegex",
"type": "function"
},
{
"name": "asset_dir",
"type": "statement"
},
{
"name": "countTestCases",
"type": "function"
},
{
"name": "data",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "defaultTestResult",
"type": "function"
},
{
"name": "doClassCleanups",
"type": "function"
},
{
"name": "doCleanups",
"type": "function"
},
{
"name": "fail",
"type": "function"
},
{
"name": "failIf",
"type": "function"
},
{
"name": "failIfAlmostEqual",
"type": "function"
},
{
"name": "failIfEqual",
"type": "function"
},
{
"name": "failUnless",
"type": "function"
},
{
"name": "failUnlessAlmostEqual",
"type": "function"
},
{
"name": "failUnlessEqual",
"type": "function"
},
{
"name": "failUnlessRaises",
"type": "function"
},
{
"name": "failureException",
"type": "statement"
},
{
"name": "id",
"type": "function"
},
{
"name": "in_pd_df_by_series",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_dimension",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_index",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_measure",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_index",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_nan",
"type": "statement"
},
{
"name": "longMessage",
"type": "statement"
},
{
"name": "maxDiff",
"type": "statement"
},
{
"name": "ref_pd_df_by_series",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_max_rows",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "ref_pd_series",
"type": "statement"
},
{
"name": "ref_pd_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_nan",
"type": "statement"
},
{
"name": "run",
"type": "function"
},
{
"name": "set_up_pd_df",
"type": "function"
},
{
"name": "set_up_pd_series",
"type": "function"
},
{
"name": "setUp",
"type": "function"
},
{
"name": "setUpClass",
"type": "function"
},
{
"name": "shortDescription",
"type": "function"
},
{
"name": "skipTest",
"type": "function"
},
{
"name": "subTest",
"type": "function"
},
{
"name": "tearDown",
"type": "function"
},
{
"name": "tearDownClass",
"type": "function"
},
{
"name": "test_add_data_frame_index_with_df",
"type": "function"
},
{
"name": "test_add_data_frame_index_with_none",
"type": "function"
},
{
"name": "_addSkip",
"type": "function"
},
{
"name": "_formatMessage",
"type": "function"
},
{
"name": "_getAssertEqualityFunc",
"type": "function"
},
{
"name": "_testMethodDoc",
"type": "statement"
},
{
"name": "_testMethodName",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__call__",
"type": "function"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.data.add_data_frame(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_data_frame_with_series_contains_na(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension_with_nan)
self.data.add_data_frame(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
class TestDfIndex(DataWithAssets):
def test_add_df_index_with_none(self) -> None:
self.data.add_df_index(None, column_name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_index_with_df(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df_index(df, column_name="Index")
self.assertEqual(
self.ref_pd_df_by_series_only_index,
self.data.build(),
)
class TestDataFrameIndex(DataWithAssets):
def test_add_data_frame_index_with_none(self) -> None:
self.data.add_data_frame_index(None, name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_index_with_df(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_data_frame_index(df, name="Index")
self.assertEqual(
self.ref_pd_df_by_series_only_index,
self.
|
7,709 | 216 | 5,903 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
Unknown
|
build
| true |
function
| 28 | 30 | false | false |
[
"add_data_frame",
"add_df",
"build",
"add_data_frame_index",
"add_df_index",
"add_dimension",
"add_measure",
"add_np_array",
"add_record",
"add_records",
"add_series",
"add_series_list",
"add_spark_df",
"dump",
"filter",
"from_json",
"set_filter",
"clear",
"copy",
"fromkeys",
"get",
"items",
"keys",
"pop",
"popitem",
"setdefault",
"update",
"values",
"__annotations__",
"__class__",
"__class_getitem__",
"__contains__",
"__delattr__",
"__delitem__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__getitem__",
"__hash__",
"__init__",
"__init_subclass__",
"__ior__",
"__iter__",
"__len__",
"__ne__",
"__new__",
"__or__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__reversed__",
"__ror__",
"__setattr__",
"__setitem__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "add_data_frame",
"type": "function"
},
{
"name": "add_data_frame_index",
"type": "function"
},
{
"name": "add_df",
"type": "function"
},
{
"name": "add_df_index",
"type": "function"
},
{
"name": "add_dimension",
"type": "function"
},
{
"name": "add_measure",
"type": "function"
},
{
"name": "add_np_array",
"type": "function"
},
{
"name": "add_record",
"type": "function"
},
{
"name": "add_records",
"type": "function"
},
{
"name": "add_series",
"type": "function"
},
{
"name": "add_series_list",
"type": "function"
},
{
"name": "add_spark_df",
"type": "function"
},
{
"name": "build",
"type": "function"
},
{
"name": "clear",
"type": "function"
},
{
"name": "copy",
"type": "function"
},
{
"name": "dump",
"type": "function"
},
{
"name": "filter",
"type": "function"
},
{
"name": "from_json",
"type": "function"
},
{
"name": "fromkeys",
"type": "function"
},
{
"name": "get",
"type": "function"
},
{
"name": "items",
"type": "function"
},
{
"name": "keys",
"type": "function"
},
{
"name": "pop",
"type": "function"
},
{
"name": "popitem",
"type": "function"
},
{
"name": "set_filter",
"type": "function"
},
{
"name": "setdefault",
"type": "function"
},
{
"name": "update",
"type": "function"
},
{
"name": "values",
"type": "function"
},
{
"name": "_add_named_value",
"type": "function"
},
{
"name": "_add_value",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__class_getitem__",
"type": "function"
},
{
"name": "__contains__",
"type": "function"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__delitem__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__getitem__",
"type": "function"
},
{
"name": "__hash__",
"type": "statement"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__ior__",
"type": "function"
},
{
"name": "__iter__",
"type": "function"
},
{
"name": "__len__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__or__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__reversed__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__setitem__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.data.add_data_frame(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_data_frame_with_series_contains_na(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension_with_nan)
self.data.add_data_frame(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
class TestDfIndex(DataWithAssets):
def test_add_df_index_with_none(self) -> None:
self.data.add_df_index(None, column_name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_index_with_df(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df_index(df, column_name="Index")
self.assertEqual(
self.ref_pd_df_by_series_only_index,
self.data.build(),
)
class TestDataFrameIndex(DataWithAssets):
def test_add_data_frame_index_with_none(self) -> None:
self.data.add_data_frame_index(None, name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_index_with_df(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_data_frame_index(df, name="Index")
self.assertEqual(
self.ref_pd_df_by_series_only_index,
self.data.
|
7,710 | 216 | 6,035 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
inproject
|
data
| true |
statement
| 95 | 99 | false | false |
[
"data",
"in_pd_df_by_series",
"ref_pd_series",
"assertEqual",
"in_pd_series_dimension",
"test_add_df_index_with_series",
"asset_dir",
"in_pd_df_by_series_with_duplicated_popularity",
"in_pd_df_by_series_with_index",
"in_pd_df_by_series_with_nan",
"in_pd_series_dimension_with_index",
"in_pd_series_dimension_with_nan",
"in_pd_series_measure",
"in_pd_series_measure_with_index",
"in_pd_series_measure_with_nan",
"ref_pd_df_by_series",
"ref_pd_df_by_series_max_rows",
"ref_pd_df_by_series_only_index",
"ref_pd_df_by_series_with_duplicated_popularity",
"ref_pd_df_by_series_with_index",
"ref_pd_df_by_series_with_nan",
"ref_pd_series_only_index",
"ref_pd_series_with_index",
"ref_pd_series_with_nan",
"set_up_pd_df",
"set_up_pd_series",
"setUp",
"setUpClass",
"addClassCleanup",
"addCleanup",
"addTypeEqualityFunc",
"assert_",
"assertAlmostEqual",
"assertAlmostEquals",
"assertCountEqual",
"assertDictContainsSubset",
"assertDictEqual",
"assertEquals",
"assertFalse",
"assertGreater",
"assertGreaterEqual",
"assertIn",
"assertIs",
"assertIsInstance",
"assertIsNone",
"assertIsNot",
"assertIsNotNone",
"assertLess",
"assertLessEqual",
"assertListEqual",
"assertLogs",
"assertMultiLineEqual",
"assertNoLogs",
"assertNotAlmostEqual",
"assertNotAlmostEquals",
"assertNotEqual",
"assertNotEquals",
"assertNotIn",
"assertNotIsInstance",
"assertNotRegex",
"assertNotRegexpMatches",
"assertRaises",
"assertRaisesRegex",
"assertRaisesRegexp",
"assertRegex",
"assertRegexpMatches",
"assertSequenceEqual",
"assertSetEqual",
"assertTrue",
"assertTupleEqual",
"assertWarns",
"assertWarnsRegex",
"countTestCases",
"debug",
"defaultTestResult",
"doClassCleanups",
"doCleanups",
"fail",
"failIf",
"failIfAlmostEqual",
"failIfEqual",
"failUnless",
"failUnlessAlmostEqual",
"failUnlessEqual",
"failUnlessRaises",
"failureException",
"id",
"longMessage",
"maxDiff",
"run",
"shortDescription",
"skipTest",
"subTest",
"tearDown",
"tearDownClass",
"__annotations__",
"__call__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "addClassCleanup",
"type": "function"
},
{
"name": "addCleanup",
"type": "function"
},
{
"name": "addTypeEqualityFunc",
"type": "function"
},
{
"name": "assert_",
"type": "function"
},
{
"name": "assertAlmostEqual",
"type": "function"
},
{
"name": "assertAlmostEquals",
"type": "function"
},
{
"name": "assertCountEqual",
"type": "function"
},
{
"name": "assertDictContainsSubset",
"type": "function"
},
{
"name": "assertDictEqual",
"type": "function"
},
{
"name": "assertEqual",
"type": "function"
},
{
"name": "assertEquals",
"type": "function"
},
{
"name": "assertFalse",
"type": "function"
},
{
"name": "assertGreater",
"type": "function"
},
{
"name": "assertGreaterEqual",
"type": "function"
},
{
"name": "assertIn",
"type": "function"
},
{
"name": "assertIs",
"type": "function"
},
{
"name": "assertIsInstance",
"type": "function"
},
{
"name": "assertIsNone",
"type": "function"
},
{
"name": "assertIsNot",
"type": "function"
},
{
"name": "assertIsNotNone",
"type": "function"
},
{
"name": "assertLess",
"type": "function"
},
{
"name": "assertLessEqual",
"type": "function"
},
{
"name": "assertListEqual",
"type": "function"
},
{
"name": "assertLogs",
"type": "function"
},
{
"name": "assertMultiLineEqual",
"type": "function"
},
{
"name": "assertNotAlmostEqual",
"type": "function"
},
{
"name": "assertNotAlmostEquals",
"type": "function"
},
{
"name": "assertNotEqual",
"type": "function"
},
{
"name": "assertNotEquals",
"type": "function"
},
{
"name": "assertNotIn",
"type": "function"
},
{
"name": "assertNotIsInstance",
"type": "function"
},
{
"name": "assertNotRegex",
"type": "function"
},
{
"name": "assertNotRegexpMatches",
"type": "function"
},
{
"name": "assertRaises",
"type": "function"
},
{
"name": "assertRaisesRegex",
"type": "function"
},
{
"name": "assertRaisesRegexp",
"type": "function"
},
{
"name": "assertRegex",
"type": "function"
},
{
"name": "assertRegexpMatches",
"type": "function"
},
{
"name": "assertSequenceEqual",
"type": "function"
},
{
"name": "assertSetEqual",
"type": "function"
},
{
"name": "assertTrue",
"type": "function"
},
{
"name": "assertTupleEqual",
"type": "function"
},
{
"name": "assertWarns",
"type": "function"
},
{
"name": "assertWarnsRegex",
"type": "function"
},
{
"name": "asset_dir",
"type": "statement"
},
{
"name": "countTestCases",
"type": "function"
},
{
"name": "data",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "defaultTestResult",
"type": "function"
},
{
"name": "doClassCleanups",
"type": "function"
},
{
"name": "doCleanups",
"type": "function"
},
{
"name": "fail",
"type": "function"
},
{
"name": "failIf",
"type": "function"
},
{
"name": "failIfAlmostEqual",
"type": "function"
},
{
"name": "failIfEqual",
"type": "function"
},
{
"name": "failUnless",
"type": "function"
},
{
"name": "failUnlessAlmostEqual",
"type": "function"
},
{
"name": "failUnlessEqual",
"type": "function"
},
{
"name": "failUnlessRaises",
"type": "function"
},
{
"name": "failureException",
"type": "statement"
},
{
"name": "id",
"type": "function"
},
{
"name": "in_pd_df_by_series",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_dimension",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_index",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_measure",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_index",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_nan",
"type": "statement"
},
{
"name": "longMessage",
"type": "statement"
},
{
"name": "maxDiff",
"type": "statement"
},
{
"name": "ref_pd_df_by_series",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_max_rows",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "ref_pd_series",
"type": "statement"
},
{
"name": "ref_pd_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_nan",
"type": "statement"
},
{
"name": "run",
"type": "function"
},
{
"name": "set_up_pd_df",
"type": "function"
},
{
"name": "set_up_pd_series",
"type": "function"
},
{
"name": "setUp",
"type": "function"
},
{
"name": "setUpClass",
"type": "function"
},
{
"name": "shortDescription",
"type": "function"
},
{
"name": "skipTest",
"type": "function"
},
{
"name": "subTest",
"type": "function"
},
{
"name": "tearDown",
"type": "function"
},
{
"name": "tearDownClass",
"type": "function"
},
{
"name": "test_add_df_index_with_series",
"type": "function"
},
{
"name": "_addSkip",
"type": "function"
},
{
"name": "_formatMessage",
"type": "function"
},
{
"name": "_getAssertEqualityFunc",
"type": "function"
},
{
"name": "_testMethodDoc",
"type": "statement"
},
{
"name": "_testMethodName",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__call__",
"type": "function"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.data.add_data_frame(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_data_frame_with_series_contains_na(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension_with_nan)
self.data.add_data_frame(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
class TestDfIndex(DataWithAssets):
def test_add_df_index_with_none(self) -> None:
self.data.add_df_index(None, column_name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_index_with_df(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df_index(df, column_name="Index")
self.assertEqual(
self.ref_pd_df_by_series_only_index,
self.data.build(),
)
class TestDataFrameIndex(DataWithAssets):
def test_add_data_frame_index_with_none(self) -> None:
self.data.add_data_frame_index(None, name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_index_with_df(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_data_frame_index(df, name="Index")
self.assertEqual(
self.ref_pd_df_by_series_only_index,
self.data.build(),
)
class TestDfIndexWithSeries(DataWithAssets):
def test_add_df_index_with_series(self) -> None:
self.
|
7,711 | 216 | 6,040 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
inproject
|
add_df_index
| true |
function
| 28 | 30 | false | false |
[
"add_df",
"add_data_frame",
"build",
"add_data_frame_index",
"add_df_index",
"add_dimension",
"add_measure",
"add_np_array",
"add_record",
"add_records",
"add_series",
"add_series_list",
"add_spark_df",
"dump",
"filter",
"from_json",
"set_filter",
"clear",
"copy",
"fromkeys",
"get",
"items",
"keys",
"pop",
"popitem",
"setdefault",
"update",
"values",
"__annotations__",
"__class__",
"__class_getitem__",
"__contains__",
"__delattr__",
"__delitem__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__getitem__",
"__hash__",
"__init__",
"__init_subclass__",
"__ior__",
"__iter__",
"__len__",
"__ne__",
"__new__",
"__or__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__reversed__",
"__ror__",
"__setattr__",
"__setitem__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "add_data_frame",
"type": "function"
},
{
"name": "add_data_frame_index",
"type": "function"
},
{
"name": "add_df",
"type": "function"
},
{
"name": "add_df_index",
"type": "function"
},
{
"name": "add_dimension",
"type": "function"
},
{
"name": "add_measure",
"type": "function"
},
{
"name": "add_np_array",
"type": "function"
},
{
"name": "add_record",
"type": "function"
},
{
"name": "add_records",
"type": "function"
},
{
"name": "add_series",
"type": "function"
},
{
"name": "add_series_list",
"type": "function"
},
{
"name": "add_spark_df",
"type": "function"
},
{
"name": "build",
"type": "function"
},
{
"name": "clear",
"type": "function"
},
{
"name": "copy",
"type": "function"
},
{
"name": "dump",
"type": "function"
},
{
"name": "filter",
"type": "function"
},
{
"name": "from_json",
"type": "function"
},
{
"name": "fromkeys",
"type": "function"
},
{
"name": "get",
"type": "function"
},
{
"name": "items",
"type": "function"
},
{
"name": "keys",
"type": "function"
},
{
"name": "pop",
"type": "function"
},
{
"name": "popitem",
"type": "function"
},
{
"name": "set_filter",
"type": "function"
},
{
"name": "setdefault",
"type": "function"
},
{
"name": "update",
"type": "function"
},
{
"name": "values",
"type": "function"
},
{
"name": "_add_named_value",
"type": "function"
},
{
"name": "_add_value",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__class_getitem__",
"type": "function"
},
{
"name": "__contains__",
"type": "function"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__delitem__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__getitem__",
"type": "function"
},
{
"name": "__hash__",
"type": "statement"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__ior__",
"type": "function"
},
{
"name": "__iter__",
"type": "function"
},
{
"name": "__len__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__or__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__reversed__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__setitem__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.data.add_data_frame(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_data_frame_with_series_contains_na(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension_with_nan)
self.data.add_data_frame(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
class TestDfIndex(DataWithAssets):
def test_add_df_index_with_none(self) -> None:
self.data.add_df_index(None, column_name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_index_with_df(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df_index(df, column_name="Index")
self.assertEqual(
self.ref_pd_df_by_series_only_index,
self.data.build(),
)
class TestDataFrameIndex(DataWithAssets):
def test_add_data_frame_index_with_none(self) -> None:
self.data.add_data_frame_index(None, name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_index_with_df(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_data_frame_index(df, name="Index")
self.assertEqual(
self.ref_pd_df_by_series_only_index,
self.data.build(),
)
class TestDfIndexWithSeries(DataWithAssets):
def test_add_df_index_with_series(self) -> None:
self.data.
|
7,712 | 216 | 6,071 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
random
|
in_pd_series_dimension_with_index
| true |
statement
| 95 | 99 | false | false |
[
"data",
"ref_pd_series",
"in_pd_series_dimension",
"ref_pd_series_with_nan",
"ref_pd_df_by_series_with_duplicated_popularity",
"test_add_df_index_with_series",
"asset_dir",
"in_pd_df_by_series",
"in_pd_df_by_series_with_duplicated_popularity",
"in_pd_df_by_series_with_index",
"in_pd_df_by_series_with_nan",
"in_pd_series_dimension_with_index",
"in_pd_series_dimension_with_nan",
"in_pd_series_measure",
"in_pd_series_measure_with_index",
"in_pd_series_measure_with_nan",
"ref_pd_df_by_series",
"ref_pd_df_by_series_max_rows",
"ref_pd_df_by_series_only_index",
"ref_pd_df_by_series_with_index",
"ref_pd_df_by_series_with_nan",
"ref_pd_series_only_index",
"ref_pd_series_with_index",
"set_up_pd_df",
"set_up_pd_series",
"setUp",
"setUpClass",
"addClassCleanup",
"addCleanup",
"addTypeEqualityFunc",
"assert_",
"assertAlmostEqual",
"assertAlmostEquals",
"assertCountEqual",
"assertDictContainsSubset",
"assertDictEqual",
"assertEqual",
"assertEquals",
"assertFalse",
"assertGreater",
"assertGreaterEqual",
"assertIn",
"assertIs",
"assertIsInstance",
"assertIsNone",
"assertIsNot",
"assertIsNotNone",
"assertLess",
"assertLessEqual",
"assertListEqual",
"assertLogs",
"assertMultiLineEqual",
"assertNoLogs",
"assertNotAlmostEqual",
"assertNotAlmostEquals",
"assertNotEqual",
"assertNotEquals",
"assertNotIn",
"assertNotIsInstance",
"assertNotRegex",
"assertNotRegexpMatches",
"assertRaises",
"assertRaisesRegex",
"assertRaisesRegexp",
"assertRegex",
"assertRegexpMatches",
"assertSequenceEqual",
"assertSetEqual",
"assertTrue",
"assertTupleEqual",
"assertWarns",
"assertWarnsRegex",
"countTestCases",
"debug",
"defaultTestResult",
"doClassCleanups",
"doCleanups",
"fail",
"failIf",
"failIfAlmostEqual",
"failIfEqual",
"failUnless",
"failUnlessAlmostEqual",
"failUnlessEqual",
"failUnlessRaises",
"failureException",
"id",
"longMessage",
"maxDiff",
"run",
"shortDescription",
"skipTest",
"subTest",
"tearDown",
"tearDownClass",
"__annotations__",
"__call__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "addClassCleanup",
"type": "function"
},
{
"name": "addCleanup",
"type": "function"
},
{
"name": "addTypeEqualityFunc",
"type": "function"
},
{
"name": "assert_",
"type": "function"
},
{
"name": "assertAlmostEqual",
"type": "function"
},
{
"name": "assertAlmostEquals",
"type": "function"
},
{
"name": "assertCountEqual",
"type": "function"
},
{
"name": "assertDictContainsSubset",
"type": "function"
},
{
"name": "assertDictEqual",
"type": "function"
},
{
"name": "assertEqual",
"type": "function"
},
{
"name": "assertEquals",
"type": "function"
},
{
"name": "assertFalse",
"type": "function"
},
{
"name": "assertGreater",
"type": "function"
},
{
"name": "assertGreaterEqual",
"type": "function"
},
{
"name": "assertIn",
"type": "function"
},
{
"name": "assertIs",
"type": "function"
},
{
"name": "assertIsInstance",
"type": "function"
},
{
"name": "assertIsNone",
"type": "function"
},
{
"name": "assertIsNot",
"type": "function"
},
{
"name": "assertIsNotNone",
"type": "function"
},
{
"name": "assertLess",
"type": "function"
},
{
"name": "assertLessEqual",
"type": "function"
},
{
"name": "assertListEqual",
"type": "function"
},
{
"name": "assertLogs",
"type": "function"
},
{
"name": "assertMultiLineEqual",
"type": "function"
},
{
"name": "assertNotAlmostEqual",
"type": "function"
},
{
"name": "assertNotAlmostEquals",
"type": "function"
},
{
"name": "assertNotEqual",
"type": "function"
},
{
"name": "assertNotEquals",
"type": "function"
},
{
"name": "assertNotIn",
"type": "function"
},
{
"name": "assertNotIsInstance",
"type": "function"
},
{
"name": "assertNotRegex",
"type": "function"
},
{
"name": "assertNotRegexpMatches",
"type": "function"
},
{
"name": "assertRaises",
"type": "function"
},
{
"name": "assertRaisesRegex",
"type": "function"
},
{
"name": "assertRaisesRegexp",
"type": "function"
},
{
"name": "assertRegex",
"type": "function"
},
{
"name": "assertRegexpMatches",
"type": "function"
},
{
"name": "assertSequenceEqual",
"type": "function"
},
{
"name": "assertSetEqual",
"type": "function"
},
{
"name": "assertTrue",
"type": "function"
},
{
"name": "assertTupleEqual",
"type": "function"
},
{
"name": "assertWarns",
"type": "function"
},
{
"name": "assertWarnsRegex",
"type": "function"
},
{
"name": "asset_dir",
"type": "statement"
},
{
"name": "countTestCases",
"type": "function"
},
{
"name": "data",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "defaultTestResult",
"type": "function"
},
{
"name": "doClassCleanups",
"type": "function"
},
{
"name": "doCleanups",
"type": "function"
},
{
"name": "fail",
"type": "function"
},
{
"name": "failIf",
"type": "function"
},
{
"name": "failIfAlmostEqual",
"type": "function"
},
{
"name": "failIfEqual",
"type": "function"
},
{
"name": "failUnless",
"type": "function"
},
{
"name": "failUnlessAlmostEqual",
"type": "function"
},
{
"name": "failUnlessEqual",
"type": "function"
},
{
"name": "failUnlessRaises",
"type": "function"
},
{
"name": "failureException",
"type": "statement"
},
{
"name": "id",
"type": "function"
},
{
"name": "in_pd_df_by_series",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_dimension",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_index",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_measure",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_index",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_nan",
"type": "statement"
},
{
"name": "longMessage",
"type": "statement"
},
{
"name": "maxDiff",
"type": "statement"
},
{
"name": "ref_pd_df_by_series",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_max_rows",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "ref_pd_series",
"type": "statement"
},
{
"name": "ref_pd_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_nan",
"type": "statement"
},
{
"name": "run",
"type": "function"
},
{
"name": "set_up_pd_df",
"type": "function"
},
{
"name": "set_up_pd_series",
"type": "function"
},
{
"name": "setUp",
"type": "function"
},
{
"name": "setUpClass",
"type": "function"
},
{
"name": "shortDescription",
"type": "function"
},
{
"name": "skipTest",
"type": "function"
},
{
"name": "subTest",
"type": "function"
},
{
"name": "tearDown",
"type": "function"
},
{
"name": "tearDownClass",
"type": "function"
},
{
"name": "test_add_df_index_with_series",
"type": "function"
},
{
"name": "_addSkip",
"type": "function"
},
{
"name": "_formatMessage",
"type": "function"
},
{
"name": "_getAssertEqualityFunc",
"type": "function"
},
{
"name": "_testMethodDoc",
"type": "statement"
},
{
"name": "_testMethodName",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__call__",
"type": "function"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.data.add_data_frame(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_data_frame_with_series_contains_na(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension_with_nan)
self.data.add_data_frame(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
class TestDfIndex(DataWithAssets):
def test_add_df_index_with_none(self) -> None:
self.data.add_df_index(None, column_name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_index_with_df(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df_index(df, column_name="Index")
self.assertEqual(
self.ref_pd_df_by_series_only_index,
self.data.build(),
)
class TestDataFrameIndex(DataWithAssets):
def test_add_data_frame_index_with_none(self) -> None:
self.data.add_data_frame_index(None, name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_index_with_df(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_data_frame_index(df, name="Index")
self.assertEqual(
self.ref_pd_df_by_series_only_index,
self.data.build(),
)
class TestDfIndexWithSeries(DataWithAssets):
def test_add_df_index_with_series(self) -> None:
self.data.add_df_index(
self.
|
7,713 | 216 | 6,171 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
Unknown
|
data
| true |
statement
| 95 | 99 | false | false |
[
"data",
"in_pd_df_by_series",
"ref_pd_series",
"assertEqual",
"in_pd_series_dimension",
"test_add_df_index_with_series",
"asset_dir",
"in_pd_df_by_series_with_duplicated_popularity",
"in_pd_df_by_series_with_index",
"in_pd_df_by_series_with_nan",
"in_pd_series_dimension_with_index",
"in_pd_series_dimension_with_nan",
"in_pd_series_measure",
"in_pd_series_measure_with_index",
"in_pd_series_measure_with_nan",
"ref_pd_df_by_series",
"ref_pd_df_by_series_max_rows",
"ref_pd_df_by_series_only_index",
"ref_pd_df_by_series_with_duplicated_popularity",
"ref_pd_df_by_series_with_index",
"ref_pd_df_by_series_with_nan",
"ref_pd_series_only_index",
"ref_pd_series_with_index",
"ref_pd_series_with_nan",
"set_up_pd_df",
"set_up_pd_series",
"setUp",
"setUpClass",
"addClassCleanup",
"addCleanup",
"addTypeEqualityFunc",
"assert_",
"assertAlmostEqual",
"assertAlmostEquals",
"assertCountEqual",
"assertDictContainsSubset",
"assertDictEqual",
"assertEquals",
"assertFalse",
"assertGreater",
"assertGreaterEqual",
"assertIn",
"assertIs",
"assertIsInstance",
"assertIsNone",
"assertIsNot",
"assertIsNotNone",
"assertLess",
"assertLessEqual",
"assertListEqual",
"assertLogs",
"assertMultiLineEqual",
"assertNoLogs",
"assertNotAlmostEqual",
"assertNotAlmostEquals",
"assertNotEqual",
"assertNotEquals",
"assertNotIn",
"assertNotIsInstance",
"assertNotRegex",
"assertNotRegexpMatches",
"assertRaises",
"assertRaisesRegex",
"assertRaisesRegexp",
"assertRegex",
"assertRegexpMatches",
"assertSequenceEqual",
"assertSetEqual",
"assertTrue",
"assertTupleEqual",
"assertWarns",
"assertWarnsRegex",
"countTestCases",
"debug",
"defaultTestResult",
"doClassCleanups",
"doCleanups",
"fail",
"failIf",
"failIfAlmostEqual",
"failIfEqual",
"failUnless",
"failUnlessAlmostEqual",
"failUnlessEqual",
"failUnlessRaises",
"failureException",
"id",
"longMessage",
"maxDiff",
"run",
"shortDescription",
"skipTest",
"subTest",
"tearDown",
"tearDownClass",
"__annotations__",
"__call__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "addClassCleanup",
"type": "function"
},
{
"name": "addCleanup",
"type": "function"
},
{
"name": "addTypeEqualityFunc",
"type": "function"
},
{
"name": "assert_",
"type": "function"
},
{
"name": "assertAlmostEqual",
"type": "function"
},
{
"name": "assertAlmostEquals",
"type": "function"
},
{
"name": "assertCountEqual",
"type": "function"
},
{
"name": "assertDictContainsSubset",
"type": "function"
},
{
"name": "assertDictEqual",
"type": "function"
},
{
"name": "assertEqual",
"type": "function"
},
{
"name": "assertEquals",
"type": "function"
},
{
"name": "assertFalse",
"type": "function"
},
{
"name": "assertGreater",
"type": "function"
},
{
"name": "assertGreaterEqual",
"type": "function"
},
{
"name": "assertIn",
"type": "function"
},
{
"name": "assertIs",
"type": "function"
},
{
"name": "assertIsInstance",
"type": "function"
},
{
"name": "assertIsNone",
"type": "function"
},
{
"name": "assertIsNot",
"type": "function"
},
{
"name": "assertIsNotNone",
"type": "function"
},
{
"name": "assertLess",
"type": "function"
},
{
"name": "assertLessEqual",
"type": "function"
},
{
"name": "assertListEqual",
"type": "function"
},
{
"name": "assertLogs",
"type": "function"
},
{
"name": "assertMultiLineEqual",
"type": "function"
},
{
"name": "assertNotAlmostEqual",
"type": "function"
},
{
"name": "assertNotAlmostEquals",
"type": "function"
},
{
"name": "assertNotEqual",
"type": "function"
},
{
"name": "assertNotEquals",
"type": "function"
},
{
"name": "assertNotIn",
"type": "function"
},
{
"name": "assertNotIsInstance",
"type": "function"
},
{
"name": "assertNotRegex",
"type": "function"
},
{
"name": "assertNotRegexpMatches",
"type": "function"
},
{
"name": "assertRaises",
"type": "function"
},
{
"name": "assertRaisesRegex",
"type": "function"
},
{
"name": "assertRaisesRegexp",
"type": "function"
},
{
"name": "assertRegex",
"type": "function"
},
{
"name": "assertRegexpMatches",
"type": "function"
},
{
"name": "assertSequenceEqual",
"type": "function"
},
{
"name": "assertSetEqual",
"type": "function"
},
{
"name": "assertTrue",
"type": "function"
},
{
"name": "assertTupleEqual",
"type": "function"
},
{
"name": "assertWarns",
"type": "function"
},
{
"name": "assertWarnsRegex",
"type": "function"
},
{
"name": "asset_dir",
"type": "statement"
},
{
"name": "countTestCases",
"type": "function"
},
{
"name": "data",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "defaultTestResult",
"type": "function"
},
{
"name": "doClassCleanups",
"type": "function"
},
{
"name": "doCleanups",
"type": "function"
},
{
"name": "fail",
"type": "function"
},
{
"name": "failIf",
"type": "function"
},
{
"name": "failIfAlmostEqual",
"type": "function"
},
{
"name": "failIfEqual",
"type": "function"
},
{
"name": "failUnless",
"type": "function"
},
{
"name": "failUnlessAlmostEqual",
"type": "function"
},
{
"name": "failUnlessEqual",
"type": "function"
},
{
"name": "failUnlessRaises",
"type": "function"
},
{
"name": "failureException",
"type": "statement"
},
{
"name": "id",
"type": "function"
},
{
"name": "in_pd_df_by_series",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_dimension",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_index",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_measure",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_index",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_nan",
"type": "statement"
},
{
"name": "longMessage",
"type": "statement"
},
{
"name": "maxDiff",
"type": "statement"
},
{
"name": "ref_pd_df_by_series",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_max_rows",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "ref_pd_series",
"type": "statement"
},
{
"name": "ref_pd_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_nan",
"type": "statement"
},
{
"name": "run",
"type": "function"
},
{
"name": "set_up_pd_df",
"type": "function"
},
{
"name": "set_up_pd_series",
"type": "function"
},
{
"name": "setUp",
"type": "function"
},
{
"name": "setUpClass",
"type": "function"
},
{
"name": "shortDescription",
"type": "function"
},
{
"name": "skipTest",
"type": "function"
},
{
"name": "subTest",
"type": "function"
},
{
"name": "tearDown",
"type": "function"
},
{
"name": "tearDownClass",
"type": "function"
},
{
"name": "test_add_df_index_with_series",
"type": "function"
},
{
"name": "_addSkip",
"type": "function"
},
{
"name": "_formatMessage",
"type": "function"
},
{
"name": "_getAssertEqualityFunc",
"type": "function"
},
{
"name": "_testMethodDoc",
"type": "statement"
},
{
"name": "_testMethodName",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__call__",
"type": "function"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.data.add_data_frame(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_data_frame_with_series_contains_na(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension_with_nan)
self.data.add_data_frame(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
class TestDfIndex(DataWithAssets):
def test_add_df_index_with_none(self) -> None:
self.data.add_df_index(None, column_name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_index_with_df(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df_index(df, column_name="Index")
self.assertEqual(
self.ref_pd_df_by_series_only_index,
self.data.build(),
)
class TestDataFrameIndex(DataWithAssets):
def test_add_data_frame_index_with_none(self) -> None:
self.data.add_data_frame_index(None, name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_index_with_df(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_data_frame_index(df, name="Index")
self.assertEqual(
self.ref_pd_df_by_series_only_index,
self.data.build(),
)
class TestDfIndexWithSeries(DataWithAssets):
def test_add_df_index_with_series(self) -> None:
self.data.add_df_index(
self.in_pd_series_dimension_with_index,
column_name="DimensionIndex",
)
self.
|
7,714 | 216 | 6,176 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
Unknown
|
add_df_index
| true |
function
| 28 | 30 | false | false |
[
"add_df",
"add_data_frame",
"build",
"add_data_frame_index",
"add_df_index",
"add_dimension",
"add_measure",
"add_np_array",
"add_record",
"add_records",
"add_series",
"add_series_list",
"add_spark_df",
"dump",
"filter",
"from_json",
"set_filter",
"clear",
"copy",
"fromkeys",
"get",
"items",
"keys",
"pop",
"popitem",
"setdefault",
"update",
"values",
"__annotations__",
"__class__",
"__class_getitem__",
"__contains__",
"__delattr__",
"__delitem__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__getitem__",
"__hash__",
"__init__",
"__init_subclass__",
"__ior__",
"__iter__",
"__len__",
"__ne__",
"__new__",
"__or__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__reversed__",
"__ror__",
"__setattr__",
"__setitem__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "add_data_frame",
"type": "function"
},
{
"name": "add_data_frame_index",
"type": "function"
},
{
"name": "add_df",
"type": "function"
},
{
"name": "add_df_index",
"type": "function"
},
{
"name": "add_dimension",
"type": "function"
},
{
"name": "add_measure",
"type": "function"
},
{
"name": "add_np_array",
"type": "function"
},
{
"name": "add_record",
"type": "function"
},
{
"name": "add_records",
"type": "function"
},
{
"name": "add_series",
"type": "function"
},
{
"name": "add_series_list",
"type": "function"
},
{
"name": "add_spark_df",
"type": "function"
},
{
"name": "build",
"type": "function"
},
{
"name": "clear",
"type": "function"
},
{
"name": "copy",
"type": "function"
},
{
"name": "dump",
"type": "function"
},
{
"name": "filter",
"type": "function"
},
{
"name": "from_json",
"type": "function"
},
{
"name": "fromkeys",
"type": "function"
},
{
"name": "get",
"type": "function"
},
{
"name": "items",
"type": "function"
},
{
"name": "keys",
"type": "function"
},
{
"name": "pop",
"type": "function"
},
{
"name": "popitem",
"type": "function"
},
{
"name": "set_filter",
"type": "function"
},
{
"name": "setdefault",
"type": "function"
},
{
"name": "update",
"type": "function"
},
{
"name": "values",
"type": "function"
},
{
"name": "_add_named_value",
"type": "function"
},
{
"name": "_add_value",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__class_getitem__",
"type": "function"
},
{
"name": "__contains__",
"type": "function"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__delitem__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__getitem__",
"type": "function"
},
{
"name": "__hash__",
"type": "statement"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__ior__",
"type": "function"
},
{
"name": "__iter__",
"type": "function"
},
{
"name": "__len__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__or__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__reversed__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__setitem__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.data.add_data_frame(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_data_frame_with_series_contains_na(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension_with_nan)
self.data.add_data_frame(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
class TestDfIndex(DataWithAssets):
def test_add_df_index_with_none(self) -> None:
self.data.add_df_index(None, column_name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_index_with_df(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df_index(df, column_name="Index")
self.assertEqual(
self.ref_pd_df_by_series_only_index,
self.data.build(),
)
class TestDataFrameIndex(DataWithAssets):
def test_add_data_frame_index_with_none(self) -> None:
self.data.add_data_frame_index(None, name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_index_with_df(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_data_frame_index(df, name="Index")
self.assertEqual(
self.ref_pd_df_by_series_only_index,
self.data.build(),
)
class TestDfIndexWithSeries(DataWithAssets):
def test_add_df_index_with_series(self) -> None:
self.data.add_df_index(
self.in_pd_series_dimension_with_index,
column_name="DimensionIndex",
)
self.data.
|
7,715 | 216 | 6,207 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
Unknown
|
in_pd_series_measure_with_index
| true |
statement
| 95 | 99 | false | false |
[
"data",
"ref_pd_series",
"in_pd_series_dimension",
"ref_pd_series_with_nan",
"ref_pd_df_by_series_with_duplicated_popularity",
"test_add_df_index_with_series",
"asset_dir",
"in_pd_df_by_series",
"in_pd_df_by_series_with_duplicated_popularity",
"in_pd_df_by_series_with_index",
"in_pd_df_by_series_with_nan",
"in_pd_series_dimension_with_index",
"in_pd_series_dimension_with_nan",
"in_pd_series_measure",
"in_pd_series_measure_with_index",
"in_pd_series_measure_with_nan",
"ref_pd_df_by_series",
"ref_pd_df_by_series_max_rows",
"ref_pd_df_by_series_only_index",
"ref_pd_df_by_series_with_index",
"ref_pd_df_by_series_with_nan",
"ref_pd_series_only_index",
"ref_pd_series_with_index",
"set_up_pd_df",
"set_up_pd_series",
"setUp",
"setUpClass",
"addClassCleanup",
"addCleanup",
"addTypeEqualityFunc",
"assert_",
"assertAlmostEqual",
"assertAlmostEquals",
"assertCountEqual",
"assertDictContainsSubset",
"assertDictEqual",
"assertEqual",
"assertEquals",
"assertFalse",
"assertGreater",
"assertGreaterEqual",
"assertIn",
"assertIs",
"assertIsInstance",
"assertIsNone",
"assertIsNot",
"assertIsNotNone",
"assertLess",
"assertLessEqual",
"assertListEqual",
"assertLogs",
"assertMultiLineEqual",
"assertNoLogs",
"assertNotAlmostEqual",
"assertNotAlmostEquals",
"assertNotEqual",
"assertNotEquals",
"assertNotIn",
"assertNotIsInstance",
"assertNotRegex",
"assertNotRegexpMatches",
"assertRaises",
"assertRaisesRegex",
"assertRaisesRegexp",
"assertRegex",
"assertRegexpMatches",
"assertSequenceEqual",
"assertSetEqual",
"assertTrue",
"assertTupleEqual",
"assertWarns",
"assertWarnsRegex",
"countTestCases",
"debug",
"defaultTestResult",
"doClassCleanups",
"doCleanups",
"fail",
"failIf",
"failIfAlmostEqual",
"failIfEqual",
"failUnless",
"failUnlessAlmostEqual",
"failUnlessEqual",
"failUnlessRaises",
"failureException",
"id",
"longMessage",
"maxDiff",
"run",
"shortDescription",
"skipTest",
"subTest",
"tearDown",
"tearDownClass",
"__annotations__",
"__call__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "addClassCleanup",
"type": "function"
},
{
"name": "addCleanup",
"type": "function"
},
{
"name": "addTypeEqualityFunc",
"type": "function"
},
{
"name": "assert_",
"type": "function"
},
{
"name": "assertAlmostEqual",
"type": "function"
},
{
"name": "assertAlmostEquals",
"type": "function"
},
{
"name": "assertCountEqual",
"type": "function"
},
{
"name": "assertDictContainsSubset",
"type": "function"
},
{
"name": "assertDictEqual",
"type": "function"
},
{
"name": "assertEqual",
"type": "function"
},
{
"name": "assertEquals",
"type": "function"
},
{
"name": "assertFalse",
"type": "function"
},
{
"name": "assertGreater",
"type": "function"
},
{
"name": "assertGreaterEqual",
"type": "function"
},
{
"name": "assertIn",
"type": "function"
},
{
"name": "assertIs",
"type": "function"
},
{
"name": "assertIsInstance",
"type": "function"
},
{
"name": "assertIsNone",
"type": "function"
},
{
"name": "assertIsNot",
"type": "function"
},
{
"name": "assertIsNotNone",
"type": "function"
},
{
"name": "assertLess",
"type": "function"
},
{
"name": "assertLessEqual",
"type": "function"
},
{
"name": "assertListEqual",
"type": "function"
},
{
"name": "assertLogs",
"type": "function"
},
{
"name": "assertMultiLineEqual",
"type": "function"
},
{
"name": "assertNotAlmostEqual",
"type": "function"
},
{
"name": "assertNotAlmostEquals",
"type": "function"
},
{
"name": "assertNotEqual",
"type": "function"
},
{
"name": "assertNotEquals",
"type": "function"
},
{
"name": "assertNotIn",
"type": "function"
},
{
"name": "assertNotIsInstance",
"type": "function"
},
{
"name": "assertNotRegex",
"type": "function"
},
{
"name": "assertNotRegexpMatches",
"type": "function"
},
{
"name": "assertRaises",
"type": "function"
},
{
"name": "assertRaisesRegex",
"type": "function"
},
{
"name": "assertRaisesRegexp",
"type": "function"
},
{
"name": "assertRegex",
"type": "function"
},
{
"name": "assertRegexpMatches",
"type": "function"
},
{
"name": "assertSequenceEqual",
"type": "function"
},
{
"name": "assertSetEqual",
"type": "function"
},
{
"name": "assertTrue",
"type": "function"
},
{
"name": "assertTupleEqual",
"type": "function"
},
{
"name": "assertWarns",
"type": "function"
},
{
"name": "assertWarnsRegex",
"type": "function"
},
{
"name": "asset_dir",
"type": "statement"
},
{
"name": "countTestCases",
"type": "function"
},
{
"name": "data",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "defaultTestResult",
"type": "function"
},
{
"name": "doClassCleanups",
"type": "function"
},
{
"name": "doCleanups",
"type": "function"
},
{
"name": "fail",
"type": "function"
},
{
"name": "failIf",
"type": "function"
},
{
"name": "failIfAlmostEqual",
"type": "function"
},
{
"name": "failIfEqual",
"type": "function"
},
{
"name": "failUnless",
"type": "function"
},
{
"name": "failUnlessAlmostEqual",
"type": "function"
},
{
"name": "failUnlessEqual",
"type": "function"
},
{
"name": "failUnlessRaises",
"type": "function"
},
{
"name": "failureException",
"type": "statement"
},
{
"name": "id",
"type": "function"
},
{
"name": "in_pd_df_by_series",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_dimension",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_index",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_measure",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_index",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_nan",
"type": "statement"
},
{
"name": "longMessage",
"type": "statement"
},
{
"name": "maxDiff",
"type": "statement"
},
{
"name": "ref_pd_df_by_series",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_max_rows",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "ref_pd_series",
"type": "statement"
},
{
"name": "ref_pd_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_nan",
"type": "statement"
},
{
"name": "run",
"type": "function"
},
{
"name": "set_up_pd_df",
"type": "function"
},
{
"name": "set_up_pd_series",
"type": "function"
},
{
"name": "setUp",
"type": "function"
},
{
"name": "setUpClass",
"type": "function"
},
{
"name": "shortDescription",
"type": "function"
},
{
"name": "skipTest",
"type": "function"
},
{
"name": "subTest",
"type": "function"
},
{
"name": "tearDown",
"type": "function"
},
{
"name": "tearDownClass",
"type": "function"
},
{
"name": "test_add_df_index_with_series",
"type": "function"
},
{
"name": "_addSkip",
"type": "function"
},
{
"name": "_formatMessage",
"type": "function"
},
{
"name": "_getAssertEqualityFunc",
"type": "function"
},
{
"name": "_testMethodDoc",
"type": "statement"
},
{
"name": "_testMethodName",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__call__",
"type": "function"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.data.add_data_frame(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_data_frame_with_series_contains_na(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension_with_nan)
self.data.add_data_frame(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
class TestDfIndex(DataWithAssets):
def test_add_df_index_with_none(self) -> None:
self.data.add_df_index(None, column_name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_index_with_df(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df_index(df, column_name="Index")
self.assertEqual(
self.ref_pd_df_by_series_only_index,
self.data.build(),
)
class TestDataFrameIndex(DataWithAssets):
def test_add_data_frame_index_with_none(self) -> None:
self.data.add_data_frame_index(None, name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_index_with_df(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_data_frame_index(df, name="Index")
self.assertEqual(
self.ref_pd_df_by_series_only_index,
self.data.build(),
)
class TestDfIndexWithSeries(DataWithAssets):
def test_add_df_index_with_series(self) -> None:
self.data.add_df_index(
self.in_pd_series_dimension_with_index,
column_name="DimensionIndex",
)
self.data.add_df_index(
self.
|
7,716 | 216 | 6,333 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
Unknown
|
ref_pd_series_only_index
| true |
statement
| 95 | 99 | false | true |
[
"data",
"ref_pd_series",
"in_pd_df_by_series_with_index",
"in_pd_series_dimension",
"in_pd_df_by_series",
"test_add_df_index_with_series",
"asset_dir",
"in_pd_df_by_series_with_duplicated_popularity",
"in_pd_df_by_series_with_nan",
"in_pd_series_dimension_with_index",
"in_pd_series_dimension_with_nan",
"in_pd_series_measure",
"in_pd_series_measure_with_index",
"in_pd_series_measure_with_nan",
"ref_pd_df_by_series",
"ref_pd_df_by_series_max_rows",
"ref_pd_df_by_series_only_index",
"ref_pd_df_by_series_with_duplicated_popularity",
"ref_pd_df_by_series_with_index",
"ref_pd_df_by_series_with_nan",
"ref_pd_series_only_index",
"ref_pd_series_with_index",
"ref_pd_series_with_nan",
"set_up_pd_df",
"set_up_pd_series",
"setUp",
"setUpClass",
"addClassCleanup",
"addCleanup",
"addTypeEqualityFunc",
"assert_",
"assertAlmostEqual",
"assertAlmostEquals",
"assertCountEqual",
"assertDictContainsSubset",
"assertDictEqual",
"assertEqual",
"assertEquals",
"assertFalse",
"assertGreater",
"assertGreaterEqual",
"assertIn",
"assertIs",
"assertIsInstance",
"assertIsNone",
"assertIsNot",
"assertIsNotNone",
"assertLess",
"assertLessEqual",
"assertListEqual",
"assertLogs",
"assertMultiLineEqual",
"assertNoLogs",
"assertNotAlmostEqual",
"assertNotAlmostEquals",
"assertNotEqual",
"assertNotEquals",
"assertNotIn",
"assertNotIsInstance",
"assertNotRegex",
"assertNotRegexpMatches",
"assertRaises",
"assertRaisesRegex",
"assertRaisesRegexp",
"assertRegex",
"assertRegexpMatches",
"assertSequenceEqual",
"assertSetEqual",
"assertTrue",
"assertTupleEqual",
"assertWarns",
"assertWarnsRegex",
"countTestCases",
"debug",
"defaultTestResult",
"doClassCleanups",
"doCleanups",
"fail",
"failIf",
"failIfAlmostEqual",
"failIfEqual",
"failUnless",
"failUnlessAlmostEqual",
"failUnlessEqual",
"failUnlessRaises",
"failureException",
"id",
"longMessage",
"maxDiff",
"run",
"shortDescription",
"skipTest",
"subTest",
"tearDown",
"tearDownClass",
"__annotations__",
"__call__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "addClassCleanup",
"type": "function"
},
{
"name": "addCleanup",
"type": "function"
},
{
"name": "addTypeEqualityFunc",
"type": "function"
},
{
"name": "assert_",
"type": "function"
},
{
"name": "assertAlmostEqual",
"type": "function"
},
{
"name": "assertAlmostEquals",
"type": "function"
},
{
"name": "assertCountEqual",
"type": "function"
},
{
"name": "assertDictContainsSubset",
"type": "function"
},
{
"name": "assertDictEqual",
"type": "function"
},
{
"name": "assertEqual",
"type": "function"
},
{
"name": "assertEquals",
"type": "function"
},
{
"name": "assertFalse",
"type": "function"
},
{
"name": "assertGreater",
"type": "function"
},
{
"name": "assertGreaterEqual",
"type": "function"
},
{
"name": "assertIn",
"type": "function"
},
{
"name": "assertIs",
"type": "function"
},
{
"name": "assertIsInstance",
"type": "function"
},
{
"name": "assertIsNone",
"type": "function"
},
{
"name": "assertIsNot",
"type": "function"
},
{
"name": "assertIsNotNone",
"type": "function"
},
{
"name": "assertLess",
"type": "function"
},
{
"name": "assertLessEqual",
"type": "function"
},
{
"name": "assertListEqual",
"type": "function"
},
{
"name": "assertLogs",
"type": "function"
},
{
"name": "assertMultiLineEqual",
"type": "function"
},
{
"name": "assertNotAlmostEqual",
"type": "function"
},
{
"name": "assertNotAlmostEquals",
"type": "function"
},
{
"name": "assertNotEqual",
"type": "function"
},
{
"name": "assertNotEquals",
"type": "function"
},
{
"name": "assertNotIn",
"type": "function"
},
{
"name": "assertNotIsInstance",
"type": "function"
},
{
"name": "assertNotRegex",
"type": "function"
},
{
"name": "assertNotRegexpMatches",
"type": "function"
},
{
"name": "assertRaises",
"type": "function"
},
{
"name": "assertRaisesRegex",
"type": "function"
},
{
"name": "assertRaisesRegexp",
"type": "function"
},
{
"name": "assertRegex",
"type": "function"
},
{
"name": "assertRegexpMatches",
"type": "function"
},
{
"name": "assertSequenceEqual",
"type": "function"
},
{
"name": "assertSetEqual",
"type": "function"
},
{
"name": "assertTrue",
"type": "function"
},
{
"name": "assertTupleEqual",
"type": "function"
},
{
"name": "assertWarns",
"type": "function"
},
{
"name": "assertWarnsRegex",
"type": "function"
},
{
"name": "asset_dir",
"type": "statement"
},
{
"name": "countTestCases",
"type": "function"
},
{
"name": "data",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "defaultTestResult",
"type": "function"
},
{
"name": "doClassCleanups",
"type": "function"
},
{
"name": "doCleanups",
"type": "function"
},
{
"name": "fail",
"type": "function"
},
{
"name": "failIf",
"type": "function"
},
{
"name": "failIfAlmostEqual",
"type": "function"
},
{
"name": "failIfEqual",
"type": "function"
},
{
"name": "failUnless",
"type": "function"
},
{
"name": "failUnlessAlmostEqual",
"type": "function"
},
{
"name": "failUnlessEqual",
"type": "function"
},
{
"name": "failUnlessRaises",
"type": "function"
},
{
"name": "failureException",
"type": "statement"
},
{
"name": "id",
"type": "function"
},
{
"name": "in_pd_df_by_series",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_dimension",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_index",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_measure",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_index",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_nan",
"type": "statement"
},
{
"name": "longMessage",
"type": "statement"
},
{
"name": "maxDiff",
"type": "statement"
},
{
"name": "ref_pd_df_by_series",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_max_rows",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "ref_pd_series",
"type": "statement"
},
{
"name": "ref_pd_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_nan",
"type": "statement"
},
{
"name": "run",
"type": "function"
},
{
"name": "set_up_pd_df",
"type": "function"
},
{
"name": "set_up_pd_series",
"type": "function"
},
{
"name": "setUp",
"type": "function"
},
{
"name": "setUpClass",
"type": "function"
},
{
"name": "shortDescription",
"type": "function"
},
{
"name": "skipTest",
"type": "function"
},
{
"name": "subTest",
"type": "function"
},
{
"name": "tearDown",
"type": "function"
},
{
"name": "tearDownClass",
"type": "function"
},
{
"name": "test_add_df_index_with_series",
"type": "function"
},
{
"name": "_addSkip",
"type": "function"
},
{
"name": "_formatMessage",
"type": "function"
},
{
"name": "_getAssertEqualityFunc",
"type": "function"
},
{
"name": "_testMethodDoc",
"type": "statement"
},
{
"name": "_testMethodName",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__call__",
"type": "function"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.data.add_data_frame(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_data_frame_with_series_contains_na(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension_with_nan)
self.data.add_data_frame(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
class TestDfIndex(DataWithAssets):
def test_add_df_index_with_none(self) -> None:
self.data.add_df_index(None, column_name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_index_with_df(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df_index(df, column_name="Index")
self.assertEqual(
self.ref_pd_df_by_series_only_index,
self.data.build(),
)
class TestDataFrameIndex(DataWithAssets):
def test_add_data_frame_index_with_none(self) -> None:
self.data.add_data_frame_index(None, name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_index_with_df(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_data_frame_index(df, name="Index")
self.assertEqual(
self.ref_pd_df_by_series_only_index,
self.data.build(),
)
class TestDfIndexWithSeries(DataWithAssets):
def test_add_df_index_with_series(self) -> None:
self.data.add_df_index(
self.in_pd_series_dimension_with_index,
column_name="DimensionIndex",
)
self.data.add_df_index(
self.in_pd_series_measure_with_index,
column_name="MeasureIndex",
)
self.assertEqual(
self.
|
7,717 | 216 | 6,376 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
inproject
|
data
| true |
statement
| 95 | 99 | false | false |
[
"data",
"in_pd_df_by_series",
"ref_pd_series",
"assertEqual",
"in_pd_series_dimension",
"test_add_df_index_with_series",
"asset_dir",
"in_pd_df_by_series_with_duplicated_popularity",
"in_pd_df_by_series_with_index",
"in_pd_df_by_series_with_nan",
"in_pd_series_dimension_with_index",
"in_pd_series_dimension_with_nan",
"in_pd_series_measure",
"in_pd_series_measure_with_index",
"in_pd_series_measure_with_nan",
"ref_pd_df_by_series",
"ref_pd_df_by_series_max_rows",
"ref_pd_df_by_series_only_index",
"ref_pd_df_by_series_with_duplicated_popularity",
"ref_pd_df_by_series_with_index",
"ref_pd_df_by_series_with_nan",
"ref_pd_series_only_index",
"ref_pd_series_with_index",
"ref_pd_series_with_nan",
"set_up_pd_df",
"set_up_pd_series",
"setUp",
"setUpClass",
"addClassCleanup",
"addCleanup",
"addTypeEqualityFunc",
"assert_",
"assertAlmostEqual",
"assertAlmostEquals",
"assertCountEqual",
"assertDictContainsSubset",
"assertDictEqual",
"assertEquals",
"assertFalse",
"assertGreater",
"assertGreaterEqual",
"assertIn",
"assertIs",
"assertIsInstance",
"assertIsNone",
"assertIsNot",
"assertIsNotNone",
"assertLess",
"assertLessEqual",
"assertListEqual",
"assertLogs",
"assertMultiLineEqual",
"assertNoLogs",
"assertNotAlmostEqual",
"assertNotAlmostEquals",
"assertNotEqual",
"assertNotEquals",
"assertNotIn",
"assertNotIsInstance",
"assertNotRegex",
"assertNotRegexpMatches",
"assertRaises",
"assertRaisesRegex",
"assertRaisesRegexp",
"assertRegex",
"assertRegexpMatches",
"assertSequenceEqual",
"assertSetEqual",
"assertTrue",
"assertTupleEqual",
"assertWarns",
"assertWarnsRegex",
"countTestCases",
"debug",
"defaultTestResult",
"doClassCleanups",
"doCleanups",
"fail",
"failIf",
"failIfAlmostEqual",
"failIfEqual",
"failUnless",
"failUnlessAlmostEqual",
"failUnlessEqual",
"failUnlessRaises",
"failureException",
"id",
"longMessage",
"maxDiff",
"run",
"shortDescription",
"skipTest",
"subTest",
"tearDown",
"tearDownClass",
"__annotations__",
"__call__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "addClassCleanup",
"type": "function"
},
{
"name": "addCleanup",
"type": "function"
},
{
"name": "addTypeEqualityFunc",
"type": "function"
},
{
"name": "assert_",
"type": "function"
},
{
"name": "assertAlmostEqual",
"type": "function"
},
{
"name": "assertAlmostEquals",
"type": "function"
},
{
"name": "assertCountEqual",
"type": "function"
},
{
"name": "assertDictContainsSubset",
"type": "function"
},
{
"name": "assertDictEqual",
"type": "function"
},
{
"name": "assertEqual",
"type": "function"
},
{
"name": "assertEquals",
"type": "function"
},
{
"name": "assertFalse",
"type": "function"
},
{
"name": "assertGreater",
"type": "function"
},
{
"name": "assertGreaterEqual",
"type": "function"
},
{
"name": "assertIn",
"type": "function"
},
{
"name": "assertIs",
"type": "function"
},
{
"name": "assertIsInstance",
"type": "function"
},
{
"name": "assertIsNone",
"type": "function"
},
{
"name": "assertIsNot",
"type": "function"
},
{
"name": "assertIsNotNone",
"type": "function"
},
{
"name": "assertLess",
"type": "function"
},
{
"name": "assertLessEqual",
"type": "function"
},
{
"name": "assertListEqual",
"type": "function"
},
{
"name": "assertLogs",
"type": "function"
},
{
"name": "assertMultiLineEqual",
"type": "function"
},
{
"name": "assertNotAlmostEqual",
"type": "function"
},
{
"name": "assertNotAlmostEquals",
"type": "function"
},
{
"name": "assertNotEqual",
"type": "function"
},
{
"name": "assertNotEquals",
"type": "function"
},
{
"name": "assertNotIn",
"type": "function"
},
{
"name": "assertNotIsInstance",
"type": "function"
},
{
"name": "assertNotRegex",
"type": "function"
},
{
"name": "assertNotRegexpMatches",
"type": "function"
},
{
"name": "assertRaises",
"type": "function"
},
{
"name": "assertRaisesRegex",
"type": "function"
},
{
"name": "assertRaisesRegexp",
"type": "function"
},
{
"name": "assertRegex",
"type": "function"
},
{
"name": "assertRegexpMatches",
"type": "function"
},
{
"name": "assertSequenceEqual",
"type": "function"
},
{
"name": "assertSetEqual",
"type": "function"
},
{
"name": "assertTrue",
"type": "function"
},
{
"name": "assertTupleEqual",
"type": "function"
},
{
"name": "assertWarns",
"type": "function"
},
{
"name": "assertWarnsRegex",
"type": "function"
},
{
"name": "asset_dir",
"type": "statement"
},
{
"name": "countTestCases",
"type": "function"
},
{
"name": "data",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "defaultTestResult",
"type": "function"
},
{
"name": "doClassCleanups",
"type": "function"
},
{
"name": "doCleanups",
"type": "function"
},
{
"name": "fail",
"type": "function"
},
{
"name": "failIf",
"type": "function"
},
{
"name": "failIfAlmostEqual",
"type": "function"
},
{
"name": "failIfEqual",
"type": "function"
},
{
"name": "failUnless",
"type": "function"
},
{
"name": "failUnlessAlmostEqual",
"type": "function"
},
{
"name": "failUnlessEqual",
"type": "function"
},
{
"name": "failUnlessRaises",
"type": "function"
},
{
"name": "failureException",
"type": "statement"
},
{
"name": "id",
"type": "function"
},
{
"name": "in_pd_df_by_series",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_dimension",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_index",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_measure",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_index",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_nan",
"type": "statement"
},
{
"name": "longMessage",
"type": "statement"
},
{
"name": "maxDiff",
"type": "statement"
},
{
"name": "ref_pd_df_by_series",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_max_rows",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "ref_pd_series",
"type": "statement"
},
{
"name": "ref_pd_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_nan",
"type": "statement"
},
{
"name": "run",
"type": "function"
},
{
"name": "set_up_pd_df",
"type": "function"
},
{
"name": "set_up_pd_series",
"type": "function"
},
{
"name": "setUp",
"type": "function"
},
{
"name": "setUpClass",
"type": "function"
},
{
"name": "shortDescription",
"type": "function"
},
{
"name": "skipTest",
"type": "function"
},
{
"name": "subTest",
"type": "function"
},
{
"name": "tearDown",
"type": "function"
},
{
"name": "tearDownClass",
"type": "function"
},
{
"name": "test_add_df_index_with_series",
"type": "function"
},
{
"name": "_addSkip",
"type": "function"
},
{
"name": "_formatMessage",
"type": "function"
},
{
"name": "_getAssertEqualityFunc",
"type": "function"
},
{
"name": "_testMethodDoc",
"type": "statement"
},
{
"name": "_testMethodName",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__call__",
"type": "function"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.data.add_data_frame(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_data_frame_with_series_contains_na(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension_with_nan)
self.data.add_data_frame(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
class TestDfIndex(DataWithAssets):
def test_add_df_index_with_none(self) -> None:
self.data.add_df_index(None, column_name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_index_with_df(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df_index(df, column_name="Index")
self.assertEqual(
self.ref_pd_df_by_series_only_index,
self.data.build(),
)
class TestDataFrameIndex(DataWithAssets):
def test_add_data_frame_index_with_none(self) -> None:
self.data.add_data_frame_index(None, name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_index_with_df(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_data_frame_index(df, name="Index")
self.assertEqual(
self.ref_pd_df_by_series_only_index,
self.data.build(),
)
class TestDfIndexWithSeries(DataWithAssets):
def test_add_df_index_with_series(self) -> None:
self.data.add_df_index(
self.in_pd_series_dimension_with_index,
column_name="DimensionIndex",
)
self.data.add_df_index(
self.in_pd_series_measure_with_index,
column_name="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_only_index,
self.
|
7,719 | 216 | 6,528 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
inproject
|
data
| true |
statement
| 95 | 99 | false | false |
[
"data",
"ref_pd_series",
"in_pd_df_by_series",
"assertEqual",
"in_pd_series_dimension",
"test_add_data_frame_index_with_series",
"asset_dir",
"in_pd_df_by_series_with_duplicated_popularity",
"in_pd_df_by_series_with_index",
"in_pd_df_by_series_with_nan",
"in_pd_series_dimension_with_index",
"in_pd_series_dimension_with_nan",
"in_pd_series_measure",
"in_pd_series_measure_with_index",
"in_pd_series_measure_with_nan",
"ref_pd_df_by_series",
"ref_pd_df_by_series_max_rows",
"ref_pd_df_by_series_only_index",
"ref_pd_df_by_series_with_duplicated_popularity",
"ref_pd_df_by_series_with_index",
"ref_pd_df_by_series_with_nan",
"ref_pd_series_only_index",
"ref_pd_series_with_index",
"ref_pd_series_with_nan",
"set_up_pd_df",
"set_up_pd_series",
"setUp",
"setUpClass",
"addClassCleanup",
"addCleanup",
"addTypeEqualityFunc",
"assert_",
"assertAlmostEqual",
"assertAlmostEquals",
"assertCountEqual",
"assertDictContainsSubset",
"assertDictEqual",
"assertEquals",
"assertFalse",
"assertGreater",
"assertGreaterEqual",
"assertIn",
"assertIs",
"assertIsInstance",
"assertIsNone",
"assertIsNot",
"assertIsNotNone",
"assertLess",
"assertLessEqual",
"assertListEqual",
"assertLogs",
"assertMultiLineEqual",
"assertNoLogs",
"assertNotAlmostEqual",
"assertNotAlmostEquals",
"assertNotEqual",
"assertNotEquals",
"assertNotIn",
"assertNotIsInstance",
"assertNotRegex",
"assertNotRegexpMatches",
"assertRaises",
"assertRaisesRegex",
"assertRaisesRegexp",
"assertRegex",
"assertRegexpMatches",
"assertSequenceEqual",
"assertSetEqual",
"assertTrue",
"assertTupleEqual",
"assertWarns",
"assertWarnsRegex",
"countTestCases",
"debug",
"defaultTestResult",
"doClassCleanups",
"doCleanups",
"fail",
"failIf",
"failIfAlmostEqual",
"failIfEqual",
"failUnless",
"failUnlessAlmostEqual",
"failUnlessEqual",
"failUnlessRaises",
"failureException",
"id",
"longMessage",
"maxDiff",
"run",
"shortDescription",
"skipTest",
"subTest",
"tearDown",
"tearDownClass",
"__annotations__",
"__call__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "addClassCleanup",
"type": "function"
},
{
"name": "addCleanup",
"type": "function"
},
{
"name": "addTypeEqualityFunc",
"type": "function"
},
{
"name": "assert_",
"type": "function"
},
{
"name": "assertAlmostEqual",
"type": "function"
},
{
"name": "assertAlmostEquals",
"type": "function"
},
{
"name": "assertCountEqual",
"type": "function"
},
{
"name": "assertDictContainsSubset",
"type": "function"
},
{
"name": "assertDictEqual",
"type": "function"
},
{
"name": "assertEqual",
"type": "function"
},
{
"name": "assertEquals",
"type": "function"
},
{
"name": "assertFalse",
"type": "function"
},
{
"name": "assertGreater",
"type": "function"
},
{
"name": "assertGreaterEqual",
"type": "function"
},
{
"name": "assertIn",
"type": "function"
},
{
"name": "assertIs",
"type": "function"
},
{
"name": "assertIsInstance",
"type": "function"
},
{
"name": "assertIsNone",
"type": "function"
},
{
"name": "assertIsNot",
"type": "function"
},
{
"name": "assertIsNotNone",
"type": "function"
},
{
"name": "assertLess",
"type": "function"
},
{
"name": "assertLessEqual",
"type": "function"
},
{
"name": "assertListEqual",
"type": "function"
},
{
"name": "assertLogs",
"type": "function"
},
{
"name": "assertMultiLineEqual",
"type": "function"
},
{
"name": "assertNotAlmostEqual",
"type": "function"
},
{
"name": "assertNotAlmostEquals",
"type": "function"
},
{
"name": "assertNotEqual",
"type": "function"
},
{
"name": "assertNotEquals",
"type": "function"
},
{
"name": "assertNotIn",
"type": "function"
},
{
"name": "assertNotIsInstance",
"type": "function"
},
{
"name": "assertNotRegex",
"type": "function"
},
{
"name": "assertNotRegexpMatches",
"type": "function"
},
{
"name": "assertRaises",
"type": "function"
},
{
"name": "assertRaisesRegex",
"type": "function"
},
{
"name": "assertRaisesRegexp",
"type": "function"
},
{
"name": "assertRegex",
"type": "function"
},
{
"name": "assertRegexpMatches",
"type": "function"
},
{
"name": "assertSequenceEqual",
"type": "function"
},
{
"name": "assertSetEqual",
"type": "function"
},
{
"name": "assertTrue",
"type": "function"
},
{
"name": "assertTupleEqual",
"type": "function"
},
{
"name": "assertWarns",
"type": "function"
},
{
"name": "assertWarnsRegex",
"type": "function"
},
{
"name": "asset_dir",
"type": "statement"
},
{
"name": "countTestCases",
"type": "function"
},
{
"name": "data",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "defaultTestResult",
"type": "function"
},
{
"name": "doClassCleanups",
"type": "function"
},
{
"name": "doCleanups",
"type": "function"
},
{
"name": "fail",
"type": "function"
},
{
"name": "failIf",
"type": "function"
},
{
"name": "failIfAlmostEqual",
"type": "function"
},
{
"name": "failIfEqual",
"type": "function"
},
{
"name": "failUnless",
"type": "function"
},
{
"name": "failUnlessAlmostEqual",
"type": "function"
},
{
"name": "failUnlessEqual",
"type": "function"
},
{
"name": "failUnlessRaises",
"type": "function"
},
{
"name": "failureException",
"type": "statement"
},
{
"name": "id",
"type": "function"
},
{
"name": "in_pd_df_by_series",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_dimension",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_index",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_measure",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_index",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_nan",
"type": "statement"
},
{
"name": "longMessage",
"type": "statement"
},
{
"name": "maxDiff",
"type": "statement"
},
{
"name": "ref_pd_df_by_series",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_max_rows",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "ref_pd_series",
"type": "statement"
},
{
"name": "ref_pd_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_nan",
"type": "statement"
},
{
"name": "run",
"type": "function"
},
{
"name": "set_up_pd_df",
"type": "function"
},
{
"name": "set_up_pd_series",
"type": "function"
},
{
"name": "setUp",
"type": "function"
},
{
"name": "setUpClass",
"type": "function"
},
{
"name": "shortDescription",
"type": "function"
},
{
"name": "skipTest",
"type": "function"
},
{
"name": "subTest",
"type": "function"
},
{
"name": "tearDown",
"type": "function"
},
{
"name": "tearDownClass",
"type": "function"
},
{
"name": "test_add_data_frame_index_with_series",
"type": "function"
},
{
"name": "_addSkip",
"type": "function"
},
{
"name": "_formatMessage",
"type": "function"
},
{
"name": "_getAssertEqualityFunc",
"type": "function"
},
{
"name": "_testMethodDoc",
"type": "statement"
},
{
"name": "_testMethodName",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__call__",
"type": "function"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.data.add_data_frame(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_data_frame_with_series_contains_na(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension_with_nan)
self.data.add_data_frame(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
class TestDfIndex(DataWithAssets):
def test_add_df_index_with_none(self) -> None:
self.data.add_df_index(None, column_name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_index_with_df(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df_index(df, column_name="Index")
self.assertEqual(
self.ref_pd_df_by_series_only_index,
self.data.build(),
)
class TestDataFrameIndex(DataWithAssets):
def test_add_data_frame_index_with_none(self) -> None:
self.data.add_data_frame_index(None, name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_index_with_df(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_data_frame_index(df, name="Index")
self.assertEqual(
self.ref_pd_df_by_series_only_index,
self.data.build(),
)
class TestDfIndexWithSeries(DataWithAssets):
def test_add_df_index_with_series(self) -> None:
self.data.add_df_index(
self.in_pd_series_dimension_with_index,
column_name="DimensionIndex",
)
self.data.add_df_index(
self.in_pd_series_measure_with_index,
column_name="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_only_index,
self.data.build(),
)
class TestDataFrameIndexWithSeries(DataWithAssets):
def test_add_data_frame_index_with_series(self) -> None:
self.
|
7,721 | 216 | 6,572 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
Unknown
|
in_pd_series_dimension_with_index
| true |
statement
| 95 | 99 | false | false |
[
"data",
"ref_pd_series",
"in_pd_series_dimension",
"ref_pd_series_with_nan",
"in_pd_series_dimension_with_index",
"test_add_data_frame_index_with_series",
"asset_dir",
"in_pd_df_by_series",
"in_pd_df_by_series_with_duplicated_popularity",
"in_pd_df_by_series_with_index",
"in_pd_df_by_series_with_nan",
"in_pd_series_dimension_with_nan",
"in_pd_series_measure",
"in_pd_series_measure_with_index",
"in_pd_series_measure_with_nan",
"ref_pd_df_by_series",
"ref_pd_df_by_series_max_rows",
"ref_pd_df_by_series_only_index",
"ref_pd_df_by_series_with_duplicated_popularity",
"ref_pd_df_by_series_with_index",
"ref_pd_df_by_series_with_nan",
"ref_pd_series_only_index",
"ref_pd_series_with_index",
"set_up_pd_df",
"set_up_pd_series",
"setUp",
"setUpClass",
"addClassCleanup",
"addCleanup",
"addTypeEqualityFunc",
"assert_",
"assertAlmostEqual",
"assertAlmostEquals",
"assertCountEqual",
"assertDictContainsSubset",
"assertDictEqual",
"assertEqual",
"assertEquals",
"assertFalse",
"assertGreater",
"assertGreaterEqual",
"assertIn",
"assertIs",
"assertIsInstance",
"assertIsNone",
"assertIsNot",
"assertIsNotNone",
"assertLess",
"assertLessEqual",
"assertListEqual",
"assertLogs",
"assertMultiLineEqual",
"assertNoLogs",
"assertNotAlmostEqual",
"assertNotAlmostEquals",
"assertNotEqual",
"assertNotEquals",
"assertNotIn",
"assertNotIsInstance",
"assertNotRegex",
"assertNotRegexpMatches",
"assertRaises",
"assertRaisesRegex",
"assertRaisesRegexp",
"assertRegex",
"assertRegexpMatches",
"assertSequenceEqual",
"assertSetEqual",
"assertTrue",
"assertTupleEqual",
"assertWarns",
"assertWarnsRegex",
"countTestCases",
"debug",
"defaultTestResult",
"doClassCleanups",
"doCleanups",
"fail",
"failIf",
"failIfAlmostEqual",
"failIfEqual",
"failUnless",
"failUnlessAlmostEqual",
"failUnlessEqual",
"failUnlessRaises",
"failureException",
"id",
"longMessage",
"maxDiff",
"run",
"shortDescription",
"skipTest",
"subTest",
"tearDown",
"tearDownClass",
"__annotations__",
"__call__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "addClassCleanup",
"type": "function"
},
{
"name": "addCleanup",
"type": "function"
},
{
"name": "addTypeEqualityFunc",
"type": "function"
},
{
"name": "assert_",
"type": "function"
},
{
"name": "assertAlmostEqual",
"type": "function"
},
{
"name": "assertAlmostEquals",
"type": "function"
},
{
"name": "assertCountEqual",
"type": "function"
},
{
"name": "assertDictContainsSubset",
"type": "function"
},
{
"name": "assertDictEqual",
"type": "function"
},
{
"name": "assertEqual",
"type": "function"
},
{
"name": "assertEquals",
"type": "function"
},
{
"name": "assertFalse",
"type": "function"
},
{
"name": "assertGreater",
"type": "function"
},
{
"name": "assertGreaterEqual",
"type": "function"
},
{
"name": "assertIn",
"type": "function"
},
{
"name": "assertIs",
"type": "function"
},
{
"name": "assertIsInstance",
"type": "function"
},
{
"name": "assertIsNone",
"type": "function"
},
{
"name": "assertIsNot",
"type": "function"
},
{
"name": "assertIsNotNone",
"type": "function"
},
{
"name": "assertLess",
"type": "function"
},
{
"name": "assertLessEqual",
"type": "function"
},
{
"name": "assertListEqual",
"type": "function"
},
{
"name": "assertLogs",
"type": "function"
},
{
"name": "assertMultiLineEqual",
"type": "function"
},
{
"name": "assertNotAlmostEqual",
"type": "function"
},
{
"name": "assertNotAlmostEquals",
"type": "function"
},
{
"name": "assertNotEqual",
"type": "function"
},
{
"name": "assertNotEquals",
"type": "function"
},
{
"name": "assertNotIn",
"type": "function"
},
{
"name": "assertNotIsInstance",
"type": "function"
},
{
"name": "assertNotRegex",
"type": "function"
},
{
"name": "assertNotRegexpMatches",
"type": "function"
},
{
"name": "assertRaises",
"type": "function"
},
{
"name": "assertRaisesRegex",
"type": "function"
},
{
"name": "assertRaisesRegexp",
"type": "function"
},
{
"name": "assertRegex",
"type": "function"
},
{
"name": "assertRegexpMatches",
"type": "function"
},
{
"name": "assertSequenceEqual",
"type": "function"
},
{
"name": "assertSetEqual",
"type": "function"
},
{
"name": "assertTrue",
"type": "function"
},
{
"name": "assertTupleEqual",
"type": "function"
},
{
"name": "assertWarns",
"type": "function"
},
{
"name": "assertWarnsRegex",
"type": "function"
},
{
"name": "asset_dir",
"type": "statement"
},
{
"name": "countTestCases",
"type": "function"
},
{
"name": "data",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "defaultTestResult",
"type": "function"
},
{
"name": "doClassCleanups",
"type": "function"
},
{
"name": "doCleanups",
"type": "function"
},
{
"name": "fail",
"type": "function"
},
{
"name": "failIf",
"type": "function"
},
{
"name": "failIfAlmostEqual",
"type": "function"
},
{
"name": "failIfEqual",
"type": "function"
},
{
"name": "failUnless",
"type": "function"
},
{
"name": "failUnlessAlmostEqual",
"type": "function"
},
{
"name": "failUnlessEqual",
"type": "function"
},
{
"name": "failUnlessRaises",
"type": "function"
},
{
"name": "failureException",
"type": "statement"
},
{
"name": "id",
"type": "function"
},
{
"name": "in_pd_df_by_series",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_dimension",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_index",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_measure",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_index",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_nan",
"type": "statement"
},
{
"name": "longMessage",
"type": "statement"
},
{
"name": "maxDiff",
"type": "statement"
},
{
"name": "ref_pd_df_by_series",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_max_rows",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "ref_pd_series",
"type": "statement"
},
{
"name": "ref_pd_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_nan",
"type": "statement"
},
{
"name": "run",
"type": "function"
},
{
"name": "set_up_pd_df",
"type": "function"
},
{
"name": "set_up_pd_series",
"type": "function"
},
{
"name": "setUp",
"type": "function"
},
{
"name": "setUpClass",
"type": "function"
},
{
"name": "shortDescription",
"type": "function"
},
{
"name": "skipTest",
"type": "function"
},
{
"name": "subTest",
"type": "function"
},
{
"name": "tearDown",
"type": "function"
},
{
"name": "tearDownClass",
"type": "function"
},
{
"name": "test_add_data_frame_index_with_series",
"type": "function"
},
{
"name": "_addSkip",
"type": "function"
},
{
"name": "_formatMessage",
"type": "function"
},
{
"name": "_getAssertEqualityFunc",
"type": "function"
},
{
"name": "_testMethodDoc",
"type": "statement"
},
{
"name": "_testMethodName",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__call__",
"type": "function"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.data.add_data_frame(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_data_frame_with_series_contains_na(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension_with_nan)
self.data.add_data_frame(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
class TestDfIndex(DataWithAssets):
def test_add_df_index_with_none(self) -> None:
self.data.add_df_index(None, column_name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_index_with_df(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df_index(df, column_name="Index")
self.assertEqual(
self.ref_pd_df_by_series_only_index,
self.data.build(),
)
class TestDataFrameIndex(DataWithAssets):
def test_add_data_frame_index_with_none(self) -> None:
self.data.add_data_frame_index(None, name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_index_with_df(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_data_frame_index(df, name="Index")
self.assertEqual(
self.ref_pd_df_by_series_only_index,
self.data.build(),
)
class TestDfIndexWithSeries(DataWithAssets):
def test_add_df_index_with_series(self) -> None:
self.data.add_df_index(
self.in_pd_series_dimension_with_index,
column_name="DimensionIndex",
)
self.data.add_df_index(
self.in_pd_series_measure_with_index,
column_name="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_only_index,
self.data.build(),
)
class TestDataFrameIndexWithSeries(DataWithAssets):
def test_add_data_frame_index_with_series(self) -> None:
self.data.add_data_frame_index(
self.
|
7,722 | 216 | 6,665 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
Unknown
|
data
| true |
statement
| 95 | 99 | false | false |
[
"data",
"ref_pd_series",
"in_pd_df_by_series",
"assertEqual",
"in_pd_series_dimension",
"test_add_data_frame_index_with_series",
"asset_dir",
"in_pd_df_by_series_with_duplicated_popularity",
"in_pd_df_by_series_with_index",
"in_pd_df_by_series_with_nan",
"in_pd_series_dimension_with_index",
"in_pd_series_dimension_with_nan",
"in_pd_series_measure",
"in_pd_series_measure_with_index",
"in_pd_series_measure_with_nan",
"ref_pd_df_by_series",
"ref_pd_df_by_series_max_rows",
"ref_pd_df_by_series_only_index",
"ref_pd_df_by_series_with_duplicated_popularity",
"ref_pd_df_by_series_with_index",
"ref_pd_df_by_series_with_nan",
"ref_pd_series_only_index",
"ref_pd_series_with_index",
"ref_pd_series_with_nan",
"set_up_pd_df",
"set_up_pd_series",
"setUp",
"setUpClass",
"addClassCleanup",
"addCleanup",
"addTypeEqualityFunc",
"assert_",
"assertAlmostEqual",
"assertAlmostEquals",
"assertCountEqual",
"assertDictContainsSubset",
"assertDictEqual",
"assertEquals",
"assertFalse",
"assertGreater",
"assertGreaterEqual",
"assertIn",
"assertIs",
"assertIsInstance",
"assertIsNone",
"assertIsNot",
"assertIsNotNone",
"assertLess",
"assertLessEqual",
"assertListEqual",
"assertLogs",
"assertMultiLineEqual",
"assertNoLogs",
"assertNotAlmostEqual",
"assertNotAlmostEquals",
"assertNotEqual",
"assertNotEquals",
"assertNotIn",
"assertNotIsInstance",
"assertNotRegex",
"assertNotRegexpMatches",
"assertRaises",
"assertRaisesRegex",
"assertRaisesRegexp",
"assertRegex",
"assertRegexpMatches",
"assertSequenceEqual",
"assertSetEqual",
"assertTrue",
"assertTupleEqual",
"assertWarns",
"assertWarnsRegex",
"countTestCases",
"debug",
"defaultTestResult",
"doClassCleanups",
"doCleanups",
"fail",
"failIf",
"failIfAlmostEqual",
"failIfEqual",
"failUnless",
"failUnlessAlmostEqual",
"failUnlessEqual",
"failUnlessRaises",
"failureException",
"id",
"longMessage",
"maxDiff",
"run",
"shortDescription",
"skipTest",
"subTest",
"tearDown",
"tearDownClass",
"__annotations__",
"__call__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "addClassCleanup",
"type": "function"
},
{
"name": "addCleanup",
"type": "function"
},
{
"name": "addTypeEqualityFunc",
"type": "function"
},
{
"name": "assert_",
"type": "function"
},
{
"name": "assertAlmostEqual",
"type": "function"
},
{
"name": "assertAlmostEquals",
"type": "function"
},
{
"name": "assertCountEqual",
"type": "function"
},
{
"name": "assertDictContainsSubset",
"type": "function"
},
{
"name": "assertDictEqual",
"type": "function"
},
{
"name": "assertEqual",
"type": "function"
},
{
"name": "assertEquals",
"type": "function"
},
{
"name": "assertFalse",
"type": "function"
},
{
"name": "assertGreater",
"type": "function"
},
{
"name": "assertGreaterEqual",
"type": "function"
},
{
"name": "assertIn",
"type": "function"
},
{
"name": "assertIs",
"type": "function"
},
{
"name": "assertIsInstance",
"type": "function"
},
{
"name": "assertIsNone",
"type": "function"
},
{
"name": "assertIsNot",
"type": "function"
},
{
"name": "assertIsNotNone",
"type": "function"
},
{
"name": "assertLess",
"type": "function"
},
{
"name": "assertLessEqual",
"type": "function"
},
{
"name": "assertListEqual",
"type": "function"
},
{
"name": "assertLogs",
"type": "function"
},
{
"name": "assertMultiLineEqual",
"type": "function"
},
{
"name": "assertNotAlmostEqual",
"type": "function"
},
{
"name": "assertNotAlmostEquals",
"type": "function"
},
{
"name": "assertNotEqual",
"type": "function"
},
{
"name": "assertNotEquals",
"type": "function"
},
{
"name": "assertNotIn",
"type": "function"
},
{
"name": "assertNotIsInstance",
"type": "function"
},
{
"name": "assertNotRegex",
"type": "function"
},
{
"name": "assertNotRegexpMatches",
"type": "function"
},
{
"name": "assertRaises",
"type": "function"
},
{
"name": "assertRaisesRegex",
"type": "function"
},
{
"name": "assertRaisesRegexp",
"type": "function"
},
{
"name": "assertRegex",
"type": "function"
},
{
"name": "assertRegexpMatches",
"type": "function"
},
{
"name": "assertSequenceEqual",
"type": "function"
},
{
"name": "assertSetEqual",
"type": "function"
},
{
"name": "assertTrue",
"type": "function"
},
{
"name": "assertTupleEqual",
"type": "function"
},
{
"name": "assertWarns",
"type": "function"
},
{
"name": "assertWarnsRegex",
"type": "function"
},
{
"name": "asset_dir",
"type": "statement"
},
{
"name": "countTestCases",
"type": "function"
},
{
"name": "data",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "defaultTestResult",
"type": "function"
},
{
"name": "doClassCleanups",
"type": "function"
},
{
"name": "doCleanups",
"type": "function"
},
{
"name": "fail",
"type": "function"
},
{
"name": "failIf",
"type": "function"
},
{
"name": "failIfAlmostEqual",
"type": "function"
},
{
"name": "failIfEqual",
"type": "function"
},
{
"name": "failUnless",
"type": "function"
},
{
"name": "failUnlessAlmostEqual",
"type": "function"
},
{
"name": "failUnlessEqual",
"type": "function"
},
{
"name": "failUnlessRaises",
"type": "function"
},
{
"name": "failureException",
"type": "statement"
},
{
"name": "id",
"type": "function"
},
{
"name": "in_pd_df_by_series",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_dimension",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_index",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_measure",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_index",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_nan",
"type": "statement"
},
{
"name": "longMessage",
"type": "statement"
},
{
"name": "maxDiff",
"type": "statement"
},
{
"name": "ref_pd_df_by_series",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_max_rows",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "ref_pd_series",
"type": "statement"
},
{
"name": "ref_pd_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_nan",
"type": "statement"
},
{
"name": "run",
"type": "function"
},
{
"name": "set_up_pd_df",
"type": "function"
},
{
"name": "set_up_pd_series",
"type": "function"
},
{
"name": "setUp",
"type": "function"
},
{
"name": "setUpClass",
"type": "function"
},
{
"name": "shortDescription",
"type": "function"
},
{
"name": "skipTest",
"type": "function"
},
{
"name": "subTest",
"type": "function"
},
{
"name": "tearDown",
"type": "function"
},
{
"name": "tearDownClass",
"type": "function"
},
{
"name": "test_add_data_frame_index_with_series",
"type": "function"
},
{
"name": "_addSkip",
"type": "function"
},
{
"name": "_formatMessage",
"type": "function"
},
{
"name": "_getAssertEqualityFunc",
"type": "function"
},
{
"name": "_testMethodDoc",
"type": "statement"
},
{
"name": "_testMethodName",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__call__",
"type": "function"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.data.add_data_frame(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_data_frame_with_series_contains_na(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension_with_nan)
self.data.add_data_frame(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
class TestDfIndex(DataWithAssets):
def test_add_df_index_with_none(self) -> None:
self.data.add_df_index(None, column_name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_index_with_df(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df_index(df, column_name="Index")
self.assertEqual(
self.ref_pd_df_by_series_only_index,
self.data.build(),
)
class TestDataFrameIndex(DataWithAssets):
def test_add_data_frame_index_with_none(self) -> None:
self.data.add_data_frame_index(None, name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_index_with_df(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_data_frame_index(df, name="Index")
self.assertEqual(
self.ref_pd_df_by_series_only_index,
self.data.build(),
)
class TestDfIndexWithSeries(DataWithAssets):
def test_add_df_index_with_series(self) -> None:
self.data.add_df_index(
self.in_pd_series_dimension_with_index,
column_name="DimensionIndex",
)
self.data.add_df_index(
self.in_pd_series_measure_with_index,
column_name="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_only_index,
self.data.build(),
)
class TestDataFrameIndexWithSeries(DataWithAssets):
def test_add_data_frame_index_with_series(self) -> None:
self.data.add_data_frame_index(
self.in_pd_series_dimension_with_index,
name="DimensionIndex",
)
self.
|
7,724 | 216 | 6,709 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
Unknown
|
in_pd_series_measure_with_index
| true |
statement
| 95 | 99 | false | false |
[
"data",
"ref_pd_series",
"in_pd_series_dimension",
"in_pd_series_measure_with_index",
"in_pd_series_dimension_with_index",
"test_add_data_frame_index_with_series",
"asset_dir",
"in_pd_df_by_series",
"in_pd_df_by_series_with_duplicated_popularity",
"in_pd_df_by_series_with_index",
"in_pd_df_by_series_with_nan",
"in_pd_series_dimension_with_nan",
"in_pd_series_measure",
"in_pd_series_measure_with_nan",
"ref_pd_df_by_series",
"ref_pd_df_by_series_max_rows",
"ref_pd_df_by_series_only_index",
"ref_pd_df_by_series_with_duplicated_popularity",
"ref_pd_df_by_series_with_index",
"ref_pd_df_by_series_with_nan",
"ref_pd_series_only_index",
"ref_pd_series_with_index",
"ref_pd_series_with_nan",
"set_up_pd_df",
"set_up_pd_series",
"setUp",
"setUpClass",
"addClassCleanup",
"addCleanup",
"addTypeEqualityFunc",
"assert_",
"assertAlmostEqual",
"assertAlmostEquals",
"assertCountEqual",
"assertDictContainsSubset",
"assertDictEqual",
"assertEqual",
"assertEquals",
"assertFalse",
"assertGreater",
"assertGreaterEqual",
"assertIn",
"assertIs",
"assertIsInstance",
"assertIsNone",
"assertIsNot",
"assertIsNotNone",
"assertLess",
"assertLessEqual",
"assertListEqual",
"assertLogs",
"assertMultiLineEqual",
"assertNoLogs",
"assertNotAlmostEqual",
"assertNotAlmostEquals",
"assertNotEqual",
"assertNotEquals",
"assertNotIn",
"assertNotIsInstance",
"assertNotRegex",
"assertNotRegexpMatches",
"assertRaises",
"assertRaisesRegex",
"assertRaisesRegexp",
"assertRegex",
"assertRegexpMatches",
"assertSequenceEqual",
"assertSetEqual",
"assertTrue",
"assertTupleEqual",
"assertWarns",
"assertWarnsRegex",
"countTestCases",
"debug",
"defaultTestResult",
"doClassCleanups",
"doCleanups",
"fail",
"failIf",
"failIfAlmostEqual",
"failIfEqual",
"failUnless",
"failUnlessAlmostEqual",
"failUnlessEqual",
"failUnlessRaises",
"failureException",
"id",
"longMessage",
"maxDiff",
"run",
"shortDescription",
"skipTest",
"subTest",
"tearDown",
"tearDownClass",
"__annotations__",
"__call__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "addClassCleanup",
"type": "function"
},
{
"name": "addCleanup",
"type": "function"
},
{
"name": "addTypeEqualityFunc",
"type": "function"
},
{
"name": "assert_",
"type": "function"
},
{
"name": "assertAlmostEqual",
"type": "function"
},
{
"name": "assertAlmostEquals",
"type": "function"
},
{
"name": "assertCountEqual",
"type": "function"
},
{
"name": "assertDictContainsSubset",
"type": "function"
},
{
"name": "assertDictEqual",
"type": "function"
},
{
"name": "assertEqual",
"type": "function"
},
{
"name": "assertEquals",
"type": "function"
},
{
"name": "assertFalse",
"type": "function"
},
{
"name": "assertGreater",
"type": "function"
},
{
"name": "assertGreaterEqual",
"type": "function"
},
{
"name": "assertIn",
"type": "function"
},
{
"name": "assertIs",
"type": "function"
},
{
"name": "assertIsInstance",
"type": "function"
},
{
"name": "assertIsNone",
"type": "function"
},
{
"name": "assertIsNot",
"type": "function"
},
{
"name": "assertIsNotNone",
"type": "function"
},
{
"name": "assertLess",
"type": "function"
},
{
"name": "assertLessEqual",
"type": "function"
},
{
"name": "assertListEqual",
"type": "function"
},
{
"name": "assertLogs",
"type": "function"
},
{
"name": "assertMultiLineEqual",
"type": "function"
},
{
"name": "assertNotAlmostEqual",
"type": "function"
},
{
"name": "assertNotAlmostEquals",
"type": "function"
},
{
"name": "assertNotEqual",
"type": "function"
},
{
"name": "assertNotEquals",
"type": "function"
},
{
"name": "assertNotIn",
"type": "function"
},
{
"name": "assertNotIsInstance",
"type": "function"
},
{
"name": "assertNotRegex",
"type": "function"
},
{
"name": "assertNotRegexpMatches",
"type": "function"
},
{
"name": "assertRaises",
"type": "function"
},
{
"name": "assertRaisesRegex",
"type": "function"
},
{
"name": "assertRaisesRegexp",
"type": "function"
},
{
"name": "assertRegex",
"type": "function"
},
{
"name": "assertRegexpMatches",
"type": "function"
},
{
"name": "assertSequenceEqual",
"type": "function"
},
{
"name": "assertSetEqual",
"type": "function"
},
{
"name": "assertTrue",
"type": "function"
},
{
"name": "assertTupleEqual",
"type": "function"
},
{
"name": "assertWarns",
"type": "function"
},
{
"name": "assertWarnsRegex",
"type": "function"
},
{
"name": "asset_dir",
"type": "statement"
},
{
"name": "countTestCases",
"type": "function"
},
{
"name": "data",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "defaultTestResult",
"type": "function"
},
{
"name": "doClassCleanups",
"type": "function"
},
{
"name": "doCleanups",
"type": "function"
},
{
"name": "fail",
"type": "function"
},
{
"name": "failIf",
"type": "function"
},
{
"name": "failIfAlmostEqual",
"type": "function"
},
{
"name": "failIfEqual",
"type": "function"
},
{
"name": "failUnless",
"type": "function"
},
{
"name": "failUnlessAlmostEqual",
"type": "function"
},
{
"name": "failUnlessEqual",
"type": "function"
},
{
"name": "failUnlessRaises",
"type": "function"
},
{
"name": "failureException",
"type": "statement"
},
{
"name": "id",
"type": "function"
},
{
"name": "in_pd_df_by_series",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_dimension",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_index",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_measure",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_index",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_nan",
"type": "statement"
},
{
"name": "longMessage",
"type": "statement"
},
{
"name": "maxDiff",
"type": "statement"
},
{
"name": "ref_pd_df_by_series",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_max_rows",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "ref_pd_series",
"type": "statement"
},
{
"name": "ref_pd_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_nan",
"type": "statement"
},
{
"name": "run",
"type": "function"
},
{
"name": "set_up_pd_df",
"type": "function"
},
{
"name": "set_up_pd_series",
"type": "function"
},
{
"name": "setUp",
"type": "function"
},
{
"name": "setUpClass",
"type": "function"
},
{
"name": "shortDescription",
"type": "function"
},
{
"name": "skipTest",
"type": "function"
},
{
"name": "subTest",
"type": "function"
},
{
"name": "tearDown",
"type": "function"
},
{
"name": "tearDownClass",
"type": "function"
},
{
"name": "test_add_data_frame_index_with_series",
"type": "function"
},
{
"name": "_addSkip",
"type": "function"
},
{
"name": "_formatMessage",
"type": "function"
},
{
"name": "_getAssertEqualityFunc",
"type": "function"
},
{
"name": "_testMethodDoc",
"type": "statement"
},
{
"name": "_testMethodName",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__call__",
"type": "function"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.data.add_data_frame(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_data_frame_with_series_contains_na(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension_with_nan)
self.data.add_data_frame(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
class TestDfIndex(DataWithAssets):
def test_add_df_index_with_none(self) -> None:
self.data.add_df_index(None, column_name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_index_with_df(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df_index(df, column_name="Index")
self.assertEqual(
self.ref_pd_df_by_series_only_index,
self.data.build(),
)
class TestDataFrameIndex(DataWithAssets):
def test_add_data_frame_index_with_none(self) -> None:
self.data.add_data_frame_index(None, name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_index_with_df(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_data_frame_index(df, name="Index")
self.assertEqual(
self.ref_pd_df_by_series_only_index,
self.data.build(),
)
class TestDfIndexWithSeries(DataWithAssets):
def test_add_df_index_with_series(self) -> None:
self.data.add_df_index(
self.in_pd_series_dimension_with_index,
column_name="DimensionIndex",
)
self.data.add_df_index(
self.in_pd_series_measure_with_index,
column_name="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_only_index,
self.data.build(),
)
class TestDataFrameIndexWithSeries(DataWithAssets):
def test_add_data_frame_index_with_series(self) -> None:
self.data.add_data_frame_index(
self.in_pd_series_dimension_with_index,
name="DimensionIndex",
)
self.data.add_data_frame_index(
self.
|
7,725 | 216 | 6,828 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
Unknown
|
ref_pd_series_only_index
| true |
statement
| 95 | 99 | false | false |
[
"data",
"ref_pd_series",
"in_pd_df_by_series",
"in_pd_series_dimension",
"in_pd_series_measure_with_index",
"test_add_data_frame_index_with_series",
"asset_dir",
"in_pd_df_by_series_with_duplicated_popularity",
"in_pd_df_by_series_with_index",
"in_pd_df_by_series_with_nan",
"in_pd_series_dimension_with_index",
"in_pd_series_dimension_with_nan",
"in_pd_series_measure",
"in_pd_series_measure_with_nan",
"ref_pd_df_by_series",
"ref_pd_df_by_series_max_rows",
"ref_pd_df_by_series_only_index",
"ref_pd_df_by_series_with_duplicated_popularity",
"ref_pd_df_by_series_with_index",
"ref_pd_df_by_series_with_nan",
"ref_pd_series_only_index",
"ref_pd_series_with_index",
"ref_pd_series_with_nan",
"set_up_pd_df",
"set_up_pd_series",
"setUp",
"setUpClass",
"addClassCleanup",
"addCleanup",
"addTypeEqualityFunc",
"assert_",
"assertAlmostEqual",
"assertAlmostEquals",
"assertCountEqual",
"assertDictContainsSubset",
"assertDictEqual",
"assertEqual",
"assertEquals",
"assertFalse",
"assertGreater",
"assertGreaterEqual",
"assertIn",
"assertIs",
"assertIsInstance",
"assertIsNone",
"assertIsNot",
"assertIsNotNone",
"assertLess",
"assertLessEqual",
"assertListEqual",
"assertLogs",
"assertMultiLineEqual",
"assertNoLogs",
"assertNotAlmostEqual",
"assertNotAlmostEquals",
"assertNotEqual",
"assertNotEquals",
"assertNotIn",
"assertNotIsInstance",
"assertNotRegex",
"assertNotRegexpMatches",
"assertRaises",
"assertRaisesRegex",
"assertRaisesRegexp",
"assertRegex",
"assertRegexpMatches",
"assertSequenceEqual",
"assertSetEqual",
"assertTrue",
"assertTupleEqual",
"assertWarns",
"assertWarnsRegex",
"countTestCases",
"debug",
"defaultTestResult",
"doClassCleanups",
"doCleanups",
"fail",
"failIf",
"failIfAlmostEqual",
"failIfEqual",
"failUnless",
"failUnlessAlmostEqual",
"failUnlessEqual",
"failUnlessRaises",
"failureException",
"id",
"longMessage",
"maxDiff",
"run",
"shortDescription",
"skipTest",
"subTest",
"tearDown",
"tearDownClass",
"__annotations__",
"__call__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "addClassCleanup",
"type": "function"
},
{
"name": "addCleanup",
"type": "function"
},
{
"name": "addTypeEqualityFunc",
"type": "function"
},
{
"name": "assert_",
"type": "function"
},
{
"name": "assertAlmostEqual",
"type": "function"
},
{
"name": "assertAlmostEquals",
"type": "function"
},
{
"name": "assertCountEqual",
"type": "function"
},
{
"name": "assertDictContainsSubset",
"type": "function"
},
{
"name": "assertDictEqual",
"type": "function"
},
{
"name": "assertEqual",
"type": "function"
},
{
"name": "assertEquals",
"type": "function"
},
{
"name": "assertFalse",
"type": "function"
},
{
"name": "assertGreater",
"type": "function"
},
{
"name": "assertGreaterEqual",
"type": "function"
},
{
"name": "assertIn",
"type": "function"
},
{
"name": "assertIs",
"type": "function"
},
{
"name": "assertIsInstance",
"type": "function"
},
{
"name": "assertIsNone",
"type": "function"
},
{
"name": "assertIsNot",
"type": "function"
},
{
"name": "assertIsNotNone",
"type": "function"
},
{
"name": "assertLess",
"type": "function"
},
{
"name": "assertLessEqual",
"type": "function"
},
{
"name": "assertListEqual",
"type": "function"
},
{
"name": "assertLogs",
"type": "function"
},
{
"name": "assertMultiLineEqual",
"type": "function"
},
{
"name": "assertNotAlmostEqual",
"type": "function"
},
{
"name": "assertNotAlmostEquals",
"type": "function"
},
{
"name": "assertNotEqual",
"type": "function"
},
{
"name": "assertNotEquals",
"type": "function"
},
{
"name": "assertNotIn",
"type": "function"
},
{
"name": "assertNotIsInstance",
"type": "function"
},
{
"name": "assertNotRegex",
"type": "function"
},
{
"name": "assertNotRegexpMatches",
"type": "function"
},
{
"name": "assertRaises",
"type": "function"
},
{
"name": "assertRaisesRegex",
"type": "function"
},
{
"name": "assertRaisesRegexp",
"type": "function"
},
{
"name": "assertRegex",
"type": "function"
},
{
"name": "assertRegexpMatches",
"type": "function"
},
{
"name": "assertSequenceEqual",
"type": "function"
},
{
"name": "assertSetEqual",
"type": "function"
},
{
"name": "assertTrue",
"type": "function"
},
{
"name": "assertTupleEqual",
"type": "function"
},
{
"name": "assertWarns",
"type": "function"
},
{
"name": "assertWarnsRegex",
"type": "function"
},
{
"name": "asset_dir",
"type": "statement"
},
{
"name": "countTestCases",
"type": "function"
},
{
"name": "data",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "defaultTestResult",
"type": "function"
},
{
"name": "doClassCleanups",
"type": "function"
},
{
"name": "doCleanups",
"type": "function"
},
{
"name": "fail",
"type": "function"
},
{
"name": "failIf",
"type": "function"
},
{
"name": "failIfAlmostEqual",
"type": "function"
},
{
"name": "failIfEqual",
"type": "function"
},
{
"name": "failUnless",
"type": "function"
},
{
"name": "failUnlessAlmostEqual",
"type": "function"
},
{
"name": "failUnlessEqual",
"type": "function"
},
{
"name": "failUnlessRaises",
"type": "function"
},
{
"name": "failureException",
"type": "statement"
},
{
"name": "id",
"type": "function"
},
{
"name": "in_pd_df_by_series",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_dimension",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_index",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_measure",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_index",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_nan",
"type": "statement"
},
{
"name": "longMessage",
"type": "statement"
},
{
"name": "maxDiff",
"type": "statement"
},
{
"name": "ref_pd_df_by_series",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_max_rows",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "ref_pd_series",
"type": "statement"
},
{
"name": "ref_pd_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_nan",
"type": "statement"
},
{
"name": "run",
"type": "function"
},
{
"name": "set_up_pd_df",
"type": "function"
},
{
"name": "set_up_pd_series",
"type": "function"
},
{
"name": "setUp",
"type": "function"
},
{
"name": "setUpClass",
"type": "function"
},
{
"name": "shortDescription",
"type": "function"
},
{
"name": "skipTest",
"type": "function"
},
{
"name": "subTest",
"type": "function"
},
{
"name": "tearDown",
"type": "function"
},
{
"name": "tearDownClass",
"type": "function"
},
{
"name": "test_add_data_frame_index_with_series",
"type": "function"
},
{
"name": "_addSkip",
"type": "function"
},
{
"name": "_formatMessage",
"type": "function"
},
{
"name": "_getAssertEqualityFunc",
"type": "function"
},
{
"name": "_testMethodDoc",
"type": "statement"
},
{
"name": "_testMethodName",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__call__",
"type": "function"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.data.add_data_frame(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_data_frame_with_series_contains_na(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension_with_nan)
self.data.add_data_frame(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
class TestDfIndex(DataWithAssets):
def test_add_df_index_with_none(self) -> None:
self.data.add_df_index(None, column_name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_index_with_df(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df_index(df, column_name="Index")
self.assertEqual(
self.ref_pd_df_by_series_only_index,
self.data.build(),
)
class TestDataFrameIndex(DataWithAssets):
def test_add_data_frame_index_with_none(self) -> None:
self.data.add_data_frame_index(None, name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_index_with_df(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_data_frame_index(df, name="Index")
self.assertEqual(
self.ref_pd_df_by_series_only_index,
self.data.build(),
)
class TestDfIndexWithSeries(DataWithAssets):
def test_add_df_index_with_series(self) -> None:
self.data.add_df_index(
self.in_pd_series_dimension_with_index,
column_name="DimensionIndex",
)
self.data.add_df_index(
self.in_pd_series_measure_with_index,
column_name="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_only_index,
self.data.build(),
)
class TestDataFrameIndexWithSeries(DataWithAssets):
def test_add_data_frame_index_with_series(self) -> None:
self.data.add_data_frame_index(
self.in_pd_series_dimension_with_index,
name="DimensionIndex",
)
self.data.add_data_frame_index(
self.in_pd_series_measure_with_index,
name="MeasureIndex",
)
self.assertEqual(
self.
|
7,726 | 216 | 6,871 |
vizzuhq__ipyvizzu
|
54b0334aa736a77f9430739c9375b16be145b3fd
|
tests/test_data/test_pandas.py
|
Unknown
|
data
| true |
statement
| 95 | 99 | false | false |
[
"data",
"ref_pd_series",
"in_pd_df_by_series",
"assertEqual",
"in_pd_series_dimension",
"test_add_data_frame_index_with_series",
"asset_dir",
"in_pd_df_by_series_with_duplicated_popularity",
"in_pd_df_by_series_with_index",
"in_pd_df_by_series_with_nan",
"in_pd_series_dimension_with_index",
"in_pd_series_dimension_with_nan",
"in_pd_series_measure",
"in_pd_series_measure_with_index",
"in_pd_series_measure_with_nan",
"ref_pd_df_by_series",
"ref_pd_df_by_series_max_rows",
"ref_pd_df_by_series_only_index",
"ref_pd_df_by_series_with_duplicated_popularity",
"ref_pd_df_by_series_with_index",
"ref_pd_df_by_series_with_nan",
"ref_pd_series_only_index",
"ref_pd_series_with_index",
"ref_pd_series_with_nan",
"set_up_pd_df",
"set_up_pd_series",
"setUp",
"setUpClass",
"addClassCleanup",
"addCleanup",
"addTypeEqualityFunc",
"assert_",
"assertAlmostEqual",
"assertAlmostEquals",
"assertCountEqual",
"assertDictContainsSubset",
"assertDictEqual",
"assertEquals",
"assertFalse",
"assertGreater",
"assertGreaterEqual",
"assertIn",
"assertIs",
"assertIsInstance",
"assertIsNone",
"assertIsNot",
"assertIsNotNone",
"assertLess",
"assertLessEqual",
"assertListEqual",
"assertLogs",
"assertMultiLineEqual",
"assertNoLogs",
"assertNotAlmostEqual",
"assertNotAlmostEquals",
"assertNotEqual",
"assertNotEquals",
"assertNotIn",
"assertNotIsInstance",
"assertNotRegex",
"assertNotRegexpMatches",
"assertRaises",
"assertRaisesRegex",
"assertRaisesRegexp",
"assertRegex",
"assertRegexpMatches",
"assertSequenceEqual",
"assertSetEqual",
"assertTrue",
"assertTupleEqual",
"assertWarns",
"assertWarnsRegex",
"countTestCases",
"debug",
"defaultTestResult",
"doClassCleanups",
"doCleanups",
"fail",
"failIf",
"failIfAlmostEqual",
"failIfEqual",
"failUnless",
"failUnlessAlmostEqual",
"failUnlessEqual",
"failUnlessRaises",
"failureException",
"id",
"longMessage",
"maxDiff",
"run",
"shortDescription",
"skipTest",
"subTest",
"tearDown",
"tearDownClass",
"__annotations__",
"__call__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "addClassCleanup",
"type": "function"
},
{
"name": "addCleanup",
"type": "function"
},
{
"name": "addTypeEqualityFunc",
"type": "function"
},
{
"name": "assert_",
"type": "function"
},
{
"name": "assertAlmostEqual",
"type": "function"
},
{
"name": "assertAlmostEquals",
"type": "function"
},
{
"name": "assertCountEqual",
"type": "function"
},
{
"name": "assertDictContainsSubset",
"type": "function"
},
{
"name": "assertDictEqual",
"type": "function"
},
{
"name": "assertEqual",
"type": "function"
},
{
"name": "assertEquals",
"type": "function"
},
{
"name": "assertFalse",
"type": "function"
},
{
"name": "assertGreater",
"type": "function"
},
{
"name": "assertGreaterEqual",
"type": "function"
},
{
"name": "assertIn",
"type": "function"
},
{
"name": "assertIs",
"type": "function"
},
{
"name": "assertIsInstance",
"type": "function"
},
{
"name": "assertIsNone",
"type": "function"
},
{
"name": "assertIsNot",
"type": "function"
},
{
"name": "assertIsNotNone",
"type": "function"
},
{
"name": "assertLess",
"type": "function"
},
{
"name": "assertLessEqual",
"type": "function"
},
{
"name": "assertListEqual",
"type": "function"
},
{
"name": "assertLogs",
"type": "function"
},
{
"name": "assertMultiLineEqual",
"type": "function"
},
{
"name": "assertNotAlmostEqual",
"type": "function"
},
{
"name": "assertNotAlmostEquals",
"type": "function"
},
{
"name": "assertNotEqual",
"type": "function"
},
{
"name": "assertNotEquals",
"type": "function"
},
{
"name": "assertNotIn",
"type": "function"
},
{
"name": "assertNotIsInstance",
"type": "function"
},
{
"name": "assertNotRegex",
"type": "function"
},
{
"name": "assertNotRegexpMatches",
"type": "function"
},
{
"name": "assertRaises",
"type": "function"
},
{
"name": "assertRaisesRegex",
"type": "function"
},
{
"name": "assertRaisesRegexp",
"type": "function"
},
{
"name": "assertRegex",
"type": "function"
},
{
"name": "assertRegexpMatches",
"type": "function"
},
{
"name": "assertSequenceEqual",
"type": "function"
},
{
"name": "assertSetEqual",
"type": "function"
},
{
"name": "assertTrue",
"type": "function"
},
{
"name": "assertTupleEqual",
"type": "function"
},
{
"name": "assertWarns",
"type": "function"
},
{
"name": "assertWarnsRegex",
"type": "function"
},
{
"name": "asset_dir",
"type": "statement"
},
{
"name": "countTestCases",
"type": "function"
},
{
"name": "data",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "defaultTestResult",
"type": "function"
},
{
"name": "doClassCleanups",
"type": "function"
},
{
"name": "doCleanups",
"type": "function"
},
{
"name": "fail",
"type": "function"
},
{
"name": "failIf",
"type": "function"
},
{
"name": "failIfAlmostEqual",
"type": "function"
},
{
"name": "failIfEqual",
"type": "function"
},
{
"name": "failUnless",
"type": "function"
},
{
"name": "failUnlessAlmostEqual",
"type": "function"
},
{
"name": "failUnlessEqual",
"type": "function"
},
{
"name": "failUnlessRaises",
"type": "function"
},
{
"name": "failureException",
"type": "statement"
},
{
"name": "id",
"type": "function"
},
{
"name": "in_pd_df_by_series",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "in_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_dimension",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_index",
"type": "statement"
},
{
"name": "in_pd_series_dimension_with_nan",
"type": "statement"
},
{
"name": "in_pd_series_measure",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_index",
"type": "statement"
},
{
"name": "in_pd_series_measure_with_nan",
"type": "statement"
},
{
"name": "longMessage",
"type": "statement"
},
{
"name": "maxDiff",
"type": "statement"
},
{
"name": "ref_pd_df_by_series",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_max_rows",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_duplicated_popularity",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_df_by_series_with_nan",
"type": "statement"
},
{
"name": "ref_pd_series",
"type": "statement"
},
{
"name": "ref_pd_series_only_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_index",
"type": "statement"
},
{
"name": "ref_pd_series_with_nan",
"type": "statement"
},
{
"name": "run",
"type": "function"
},
{
"name": "set_up_pd_df",
"type": "function"
},
{
"name": "set_up_pd_series",
"type": "function"
},
{
"name": "setUp",
"type": "function"
},
{
"name": "setUpClass",
"type": "function"
},
{
"name": "shortDescription",
"type": "function"
},
{
"name": "skipTest",
"type": "function"
},
{
"name": "subTest",
"type": "function"
},
{
"name": "tearDown",
"type": "function"
},
{
"name": "tearDownClass",
"type": "function"
},
{
"name": "test_add_data_frame_index_with_series",
"type": "function"
},
{
"name": "_addSkip",
"type": "function"
},
{
"name": "_formatMessage",
"type": "function"
},
{
"name": "_getAssertEqualityFunc",
"type": "function"
},
{
"name": "_testMethodDoc",
"type": "statement"
},
{
"name": "_testMethodName",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__call__",
"type": "function"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
import pandas as pd
from tests.test_data import DataWithAssets
from tests.utils.import_error import RaiseImportError
class TestDf(DataWithAssets):
def test_add_df_if_pandas_not_installed(self) -> None:
with RaiseImportError.module_name("pandas"):
with self.assertRaises(ImportError):
self.data.add_df(pd.DataFrame())
def test_add_df_with_none(self) -> None:
self.data.add_df(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_empty_df(self) -> None:
self.data.add_df(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_df_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_df(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
def test_add_df_with_df_and_with_include_index(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df(df, include_index="Index")
self.assertEqual(
self.ref_pd_df_by_series_with_index,
self.data.build(),
)
def test_add_df_with_df_and_max_rows(self) -> None:
df = self.in_pd_df_by_series
self.data.add_df(df, max_rows=2)
self.assertEqual(
self.ref_pd_df_by_series_max_rows,
self.data.build(),
)
class TestDataFrame(DataWithAssets):
def test_add_data_frame_with_none(self) -> None:
self.data.add_data_frame(None)
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_empty_df(self) -> None:
self.data.add_data_frame(pd.DataFrame())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_df(self) -> None:
df = self.in_pd_df_by_series_with_duplicated_popularity
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_duplicated_popularity,
self.data.build(),
)
def test_add_data_frame_with_df_contains_na(self) -> None:
df = self.in_pd_df_by_series_with_nan
self.data.add_data_frame(df)
self.assertEqual(
self.ref_pd_df_by_series_with_nan,
self.data.build(),
)
class TestDfWithSeries(DataWithAssets):
def test_add_df_with_empty_series(self) -> None:
self.data.add_df(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_with_series(self) -> None:
self.data.add_df(self.in_pd_series_dimension)
self.data.add_df(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_df_with_series_contains_na(self) -> None:
self.data.add_df(self.in_pd_series_dimension_with_nan)
self.data.add_df(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
def test_add_df_with_series_and_with_include_index(self) -> None:
self.data.add_df(
self.in_pd_series_dimension_with_index,
include_index="DimensionIndex",
)
self.data.add_df(
self.in_pd_series_measure_with_index,
include_index="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_with_index,
self.data.build(),
)
class TestDataFrameWithSeries(DataWithAssets):
def test_add_data_frame_with_empty_series(self) -> None:
self.data.add_data_frame(pd.Series())
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_with_series(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension)
self.data.add_data_frame(self.in_pd_series_measure)
self.assertEqual(
self.ref_pd_series,
self.data.build(),
)
def test_add_data_frame_with_series_contains_na(self) -> None:
self.data.add_data_frame(self.in_pd_series_dimension_with_nan)
self.data.add_data_frame(self.in_pd_series_measure_with_nan)
self.assertEqual(
self.ref_pd_series_with_nan,
self.data.build(),
)
class TestDfIndex(DataWithAssets):
def test_add_df_index_with_none(self) -> None:
self.data.add_df_index(None, column_name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_df_index_with_df(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_df_index(df, column_name="Index")
self.assertEqual(
self.ref_pd_df_by_series_only_index,
self.data.build(),
)
class TestDataFrameIndex(DataWithAssets):
def test_add_data_frame_index_with_none(self) -> None:
self.data.add_data_frame_index(None, name="Index")
self.assertEqual(
{"data": {}},
self.data.build(),
)
def test_add_data_frame_index_with_df(self) -> None:
df = self.in_pd_df_by_series_with_index
self.data.add_data_frame_index(df, name="Index")
self.assertEqual(
self.ref_pd_df_by_series_only_index,
self.data.build(),
)
class TestDfIndexWithSeries(DataWithAssets):
def test_add_df_index_with_series(self) -> None:
self.data.add_df_index(
self.in_pd_series_dimension_with_index,
column_name="DimensionIndex",
)
self.data.add_df_index(
self.in_pd_series_measure_with_index,
column_name="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_only_index,
self.data.build(),
)
class TestDataFrameIndexWithSeries(DataWithAssets):
def test_add_data_frame_index_with_series(self) -> None:
self.data.add_data_frame_index(
self.in_pd_series_dimension_with_index,
name="DimensionIndex",
)
self.data.add_data_frame_index(
self.in_pd_series_measure_with_index,
name="MeasureIndex",
)
self.assertEqual(
self.ref_pd_series_only_index,
self.
|
7,728 | 218 | 23,296 |
mage-ai__mage-ai
|
9ed779ccaf3538efbe1f2f54dd68f61fb1c3af55
|
src/data_cleaner/tests/transformer_actions/test_column.py
|
infile
|
__groupby_agg_action
| true |
function
| 104 | 108 | true | true |
[
"__groupby_agg_action",
"assertEqual",
"assertRaises",
"test_count",
"test_shift_down",
"test_add_column_addition",
"test_add_column_addition_days",
"test_add_column_constant",
"test_add_column_date_trunc",
"test_add_column_difference",
"test_add_column_difference_days",
"test_add_column_distance_between",
"test_add_column_divide",
"test_add_column_formatted_date",
"test_add_column_if_else",
"test_add_column_if_else_with_column",
"test_add_column_multiply",
"test_add_column_string_replace",
"test_add_column_string_split",
"test_add_column_substring",
"test_average",
"test_count_distinct",
"test_count_with_filter",
"test_count_with_time_window",
"test_diff",
"test_first_column",
"test_impute",
"test_last_column",
"test_max",
"test_median",
"test_min",
"test_remove_column",
"test_select",
"test_shift_down_with_groupby",
"test_shift_up",
"test_sum",
"setUp",
"tearDown",
"addClassCleanup",
"addCleanup",
"addTypeEqualityFunc",
"assert_",
"assertAlmostEqual",
"assertAlmostEquals",
"assertCountEqual",
"assertDictContainsSubset",
"assertDictEqual",
"assertEquals",
"assertFalse",
"assertGreater",
"assertGreaterEqual",
"assertIn",
"assertIs",
"assertIsInstance",
"assertIsNone",
"assertIsNot",
"assertIsNotNone",
"assertLess",
"assertLessEqual",
"assertListEqual",
"assertLogs",
"assertMultiLineEqual",
"assertNoLogs",
"assertNotAlmostEqual",
"assertNotAlmostEquals",
"assertNotEqual",
"assertNotEquals",
"assertNotIn",
"assertNotIsInstance",
"assertNotRegex",
"assertNotRegexpMatches",
"assertRaisesRegex",
"assertRaisesRegexp",
"assertRegex",
"assertRegexpMatches",
"assertSequenceEqual",
"assertSetEqual",
"assertTrue",
"assertTupleEqual",
"assertWarns",
"assertWarnsRegex",
"countTestCases",
"debug",
"defaultTestResult",
"doClassCleanups",
"doCleanups",
"fail",
"failIf",
"failIfAlmostEqual",
"failIfEqual",
"failUnless",
"failUnlessAlmostEqual",
"failUnlessEqual",
"failUnlessRaises",
"failureException",
"id",
"longMessage",
"maxDiff",
"run",
"setUpClass",
"shortDescription",
"skipTest",
"subTest",
"tearDownClass",
"__annotations__",
"__call__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "addClassCleanup",
"type": "function"
},
{
"name": "addCleanup",
"type": "function"
},
{
"name": "addTypeEqualityFunc",
"type": "function"
},
{
"name": "assert_",
"type": "function"
},
{
"name": "assertAlmostEqual",
"type": "function"
},
{
"name": "assertAlmostEquals",
"type": "function"
},
{
"name": "assertCountEqual",
"type": "function"
},
{
"name": "assertDictContainsSubset",
"type": "function"
},
{
"name": "assertDictEqual",
"type": "function"
},
{
"name": "assertEqual",
"type": "function"
},
{
"name": "assertEquals",
"type": "function"
},
{
"name": "assertFalse",
"type": "function"
},
{
"name": "assertGreater",
"type": "function"
},
{
"name": "assertGreaterEqual",
"type": "function"
},
{
"name": "assertIn",
"type": "function"
},
{
"name": "assertIs",
"type": "function"
},
{
"name": "assertIsInstance",
"type": "function"
},
{
"name": "assertIsNone",
"type": "function"
},
{
"name": "assertIsNot",
"type": "function"
},
{
"name": "assertIsNotNone",
"type": "function"
},
{
"name": "assertLess",
"type": "function"
},
{
"name": "assertLessEqual",
"type": "function"
},
{
"name": "assertListEqual",
"type": "function"
},
{
"name": "assertLogs",
"type": "function"
},
{
"name": "assertMultiLineEqual",
"type": "function"
},
{
"name": "assertNotAlmostEqual",
"type": "function"
},
{
"name": "assertNotAlmostEquals",
"type": "function"
},
{
"name": "assertNotEqual",
"type": "function"
},
{
"name": "assertNotEquals",
"type": "function"
},
{
"name": "assertNotIn",
"type": "function"
},
{
"name": "assertNotIsInstance",
"type": "function"
},
{
"name": "assertNotRegex",
"type": "function"
},
{
"name": "assertNotRegexpMatches",
"type": "function"
},
{
"name": "assertRaises",
"type": "function"
},
{
"name": "assertRaisesRegex",
"type": "function"
},
{
"name": "assertRaisesRegexp",
"type": "function"
},
{
"name": "assertRegex",
"type": "function"
},
{
"name": "assertRegexpMatches",
"type": "function"
},
{
"name": "assertSequenceEqual",
"type": "function"
},
{
"name": "assertSetEqual",
"type": "function"
},
{
"name": "assertTrue",
"type": "function"
},
{
"name": "assertTupleEqual",
"type": "function"
},
{
"name": "assertWarns",
"type": "function"
},
{
"name": "assertWarnsRegex",
"type": "function"
},
{
"name": "countTestCases",
"type": "function"
},
{
"name": "debug",
"type": "function"
},
{
"name": "defaultTestResult",
"type": "function"
},
{
"name": "doClassCleanups",
"type": "function"
},
{
"name": "doCleanups",
"type": "function"
},
{
"name": "fail",
"type": "function"
},
{
"name": "failIf",
"type": "function"
},
{
"name": "failIfAlmostEqual",
"type": "function"
},
{
"name": "failIfEqual",
"type": "function"
},
{
"name": "failUnless",
"type": "function"
},
{
"name": "failUnlessAlmostEqual",
"type": "function"
},
{
"name": "failUnlessEqual",
"type": "function"
},
{
"name": "failUnlessRaises",
"type": "function"
},
{
"name": "failureException",
"type": "statement"
},
{
"name": "id",
"type": "function"
},
{
"name": "longMessage",
"type": "statement"
},
{
"name": "maxDiff",
"type": "statement"
},
{
"name": "run",
"type": "function"
},
{
"name": "setUp",
"type": "function"
},
{
"name": "setUpClass",
"type": "function"
},
{
"name": "shortDescription",
"type": "function"
},
{
"name": "skipTest",
"type": "function"
},
{
"name": "subTest",
"type": "function"
},
{
"name": "tearDown",
"type": "function"
},
{
"name": "tearDownClass",
"type": "function"
},
{
"name": "test_add_column_addition",
"type": "function"
},
{
"name": "test_add_column_addition_days",
"type": "function"
},
{
"name": "test_add_column_constant",
"type": "function"
},
{
"name": "test_add_column_date_trunc",
"type": "function"
},
{
"name": "test_add_column_difference",
"type": "function"
},
{
"name": "test_add_column_difference_days",
"type": "function"
},
{
"name": "test_add_column_distance_between",
"type": "function"
},
{
"name": "test_add_column_divide",
"type": "function"
},
{
"name": "test_add_column_formatted_date",
"type": "function"
},
{
"name": "test_add_column_if_else",
"type": "function"
},
{
"name": "test_add_column_if_else_with_column",
"type": "function"
},
{
"name": "test_add_column_multiply",
"type": "function"
},
{
"name": "test_add_column_string_replace",
"type": "function"
},
{
"name": "test_add_column_string_split",
"type": "function"
},
{
"name": "test_add_column_substring",
"type": "function"
},
{
"name": "test_average",
"type": "function"
},
{
"name": "test_count",
"type": "function"
},
{
"name": "test_count_distinct",
"type": "function"
},
{
"name": "test_count_with_filter",
"type": "function"
},
{
"name": "test_count_with_time_window",
"type": "function"
},
{
"name": "test_diff",
"type": "function"
},
{
"name": "test_first_column",
"type": "function"
},
{
"name": "test_impute",
"type": "function"
},
{
"name": "test_last_column",
"type": "function"
},
{
"name": "test_max",
"type": "function"
},
{
"name": "test_median",
"type": "function"
},
{
"name": "test_min",
"type": "function"
},
{
"name": "test_remove_column",
"type": "function"
},
{
"name": "test_select",
"type": "function"
},
{
"name": "test_shift_down",
"type": "function"
},
{
"name": "test_shift_down_with_groupby",
"type": "function"
},
{
"name": "test_shift_up",
"type": "function"
},
{
"name": "test_sum",
"type": "function"
},
{
"name": "_addSkip",
"type": "function"
},
{
"name": "_formatMessage",
"type": "function"
},
{
"name": "_getAssertEqualityFunc",
"type": "function"
},
{
"name": "_testMethodDoc",
"type": "statement"
},
{
"name": "_testMethodName",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__call__",
"type": "function"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__groupby_agg_action",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
from data_cleaner.tests.base_test import TestCase
from data_cleaner.transformer_actions.column import (
add_column,
count,
count_distinct,
diff,
# expand_column,
first,
last,
remove_column,
select,
shift_down,
shift_up,
)
from pandas.util.testing import assert_frame_equal
import numpy as np
import pandas as pd
TEST_DATAFRAME = pd.DataFrame([
[1, 1000],
[2, 1050],
[1, 1100],
[2, 1150],
], columns=[
'group_id',
'amount',
])
class ColumnTests(TestCase):
def test_remove_column(self):
df = pd.DataFrame([
[0, False, 'a'],
[1, True, 'b'],
], columns=[
'integer',
'boolean',
'string',
])
action = dict(action_arguments=['string'])
df_new = remove_column(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
integer=0,
boolean=False,
),
dict(
integer=1,
boolean=True,
),
])
action = dict(action_arguments=['integer', 'boolean'])
df_new = remove_column(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
string='a',
),
dict(
string='b',
),
])
def test_add_column_addition(self):
df = pd.DataFrame([
[1, 3, 7, 9],
[4, 2, 9, 3],
], columns=[
'integer1',
'integer2',
'integer3',
'integer4',
])
action1 = dict(
action_arguments=[
'integer1',
'integer2',
'integer3',
],
action_options={
'udf': 'addition',
'value': None,
},
outputs=[
dict(
uuid='integer_addition',
column_type='number',
),
],
)
action2 = dict(
action_arguments=['integer1'],
action_options={
'udf': 'addition',
'value': 10,
},
outputs=[
dict(
uuid='integer_addition2',
column_type='number',
),
],
)
action3 = dict(
action_arguments=['integer1', 'integer4'],
action_options={
'udf': 'addition',
'value': 10,
},
outputs=[
dict(
uuid='integer_addition3',
column_type='number',
),
],
)
df_new = add_column(
add_column(
add_column(df, action1),
action2,
),
action3,
)
df_expected = pd.DataFrame([
[1, 3, 7, 9, 11, 11, 20],
[4, 2, 9, 3, 15, 14, 17],
], columns=[
'integer1',
'integer2',
'integer3',
'integer4',
'integer_addition',
'integer_addition2',
'integer_addition3',
])
assert_frame_equal(df_new, df_expected)
def test_add_column_addition_days(self):
df = pd.DataFrame([
['2021-08-31'],
['2021-08-28'],
], columns=[
'created_at',
])
action = dict(
action_arguments=['created_at'],
action_options=dict(
column_type='datetime',
time_unit='d',
udf='addition',
value=3,
),
outputs=[
dict(
uuid='3d_after_creation',
column_type='text',
),
],
)
df_new = add_column(df, action)
df_expected = pd.DataFrame([
['2021-08-31', '2021-09-03 00:00:00'],
['2021-08-28', '2021-08-31 00:00:00'],
], columns=[
'created_at',
'3d_after_creation'
])
assert_frame_equal(df_new, df_expected)
def test_add_column_constant(self):
df = pd.DataFrame([
[False],
[True],
], columns=[
'boolean',
])
action = dict(
action_arguments=[10],
action_options=dict(
udf='constant',
),
outputs=[
dict(
uuid='integer',
column_type='number',
),
],
)
df_new = add_column(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
boolean=False,
integer=10,
),
dict(
boolean=True,
integer=10,
),
])
def test_add_column_date_trunc(self):
df = pd.DataFrame([
['2021-08-31', False],
['2021-08-28', True],
], columns=[
'created_at',
'boolean',
])
action = dict(
action_arguments=['created_at'],
action_options=dict(
udf='date_trunc',
date_part='week',
),
outputs=[
dict(
uuid='week_date',
column_type='text',
),
],
)
df_new = add_column(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
created_at='2021-08-31',
boolean=False,
week_date='2021-08-30',
),
dict(
created_at='2021-08-28',
boolean=True,
week_date='2021-08-23',
),
])
def test_add_column_difference(self):
df = pd.DataFrame([
[1, 3],
[4, 2],
], columns=[
'integer1',
'integer2',
])
action1 = dict(
action_arguments=['integer1', 'integer2'],
action_options={
'udf': 'difference',
},
outputs=[
dict(
uuid='integer_difference',
column_type='number',
),
],
)
action2 = dict(
action_arguments=['integer1'],
action_options={
'udf': 'difference',
'value': 10,
},
outputs=[
dict(
uuid='integer_difference2',
column_type='number',
),
],
)
df_new = add_column(add_column(df, action1), action2)
df_expected = pd.DataFrame([
[1, 3, -2, -9],
[4, 2, 2, -6],
], columns=[
'integer1',
'integer2',
'integer_difference',
'integer_difference2'
])
assert_frame_equal(df_new, df_expected)
def test_add_column_difference_days(self):
df = pd.DataFrame([
['2021-08-31', '2021-09-14'],
['2021-08-28', '2021-09-03'],
], columns=[
'created_at',
'converted_at',
])
action = dict(
action_arguments=['converted_at', 'created_at'],
action_options=dict(
column_type='datetime',
time_unit='d',
udf='difference',
),
outputs=[
dict(
uuid='days_diff',
column_type='number',
),
],
)
df_new = add_column(df, action)
df_expected = pd.DataFrame([
['2021-08-31', '2021-09-14', 14],
['2021-08-28', '2021-09-03', 6],
], columns=[
'created_at',
'converted_at',
'days_diff',
])
assert_frame_equal(df_new, df_expected)
def test_add_column_distance_between(self):
df = pd.DataFrame([
[26.05308, -97.31838, 33.41939, -112.32606],
[39.71954, -84.13056, 33.41939, -112.32606],
], columns=[
'lat1',
'lng1',
'lat2',
'lng2',
])
action = dict(
action_arguments=['lat1', 'lng1', 'lat2', 'lng2'],
action_options=dict(
udf='distance_between',
),
outputs=[
dict(
uuid='distance',
column_type='number_with_decimals',
),
],
)
df_new = add_column(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
lat1=26.05308,
lng1=-97.31838,
lat2=33.41939,
lng2=-112.32606,
distance=1661.8978520305657,
),
dict(
lat1=39.71954,
lng1=-84.13056,
lat2=33.41939,
lng2=-112.32606,
distance=2601.5452571116184,
),
])
def test_add_column_divide(self):
df = pd.DataFrame([
[12, 3, 70, 9],
[4, 2, 90, 3],
], columns=[
'integer1',
'integer2',
'integer3',
'integer4',
])
action1 = dict(
action_arguments=[
'integer1',
'integer2',
],
action_options={
'udf': 'divide',
},
outputs=[
dict(
uuid='integer_divide',
column_type='number',
),
],
)
action2 = dict(
action_arguments=['integer3'],
action_options={
'udf': 'divide',
'value': 10,
},
outputs=[
dict(
uuid='integer_divide2',
column_type='number',
),
],
)
df_new = add_column(add_column(df, action1), action2)
df_expected = pd.DataFrame([
[12, 3, 70, 9, 4, 7],
[4, 2, 90, 3, 2, 9],
], columns=[
'integer1',
'integer2',
'integer3',
'integer4',
'integer_divide',
'integer_divide2'
])
assert_frame_equal(df_new, df_expected)
# def test_add_column_extract_dict_string(self):
# df = pd.DataFrame([
# '{\'country\': \'US\', \'age\': \'20\'}',
# '{\'country\': \'CA\'}',
# '{\'country\': \'UK\', \'age\': \'24\'}',
# '',
# ], columns=[
# 'properties',
# ])
# action = dict(
# action_arguments=['properties', 'country'],
# action_options=dict(
# udf='extract_dict_value',
# ),
# outputs=[
# dict(
# uuid='property_country',
# column_type='text',
# ),
# ],
# )
# df_new = add_column(df, action)
# self.assertEqual(df_new.to_dict(orient='records'), [
# dict(
# properties='{\'country\': \'US\', \'age\': \'20\'}',
# property_country='US',
# ),
# dict(
# properties='{\'country\': \'CA\'}',
# property_country='CA',
# ),
# dict(
# properties='{\'country\': \'UK\', \'age\': \'24\'}',
# property_country='UK',
# ),
# dict(
# properties='',
# property_country=np.NaN,
# ),
# ])
# action2 = dict(
# action_arguments=['properties', 'age'],
# action_options=dict(
# udf='extract_dict_value',
# ),
# outputs=[
# dict(
# uuid='property_age',
# column_type='number',
# ),
# ],
# )
# df_new2 = add_column(df, action2)
# self.assertEqual(df_new2.to_dict(orient='records'), [
# dict(
# properties='{\'country\': \'US\', \'age\': \'20\'}',
# property_age=20,
# ),
# dict(
# properties='{\'country\': \'CA\'}',
# property_age=0,
# ),
# dict(
# properties='{\'country\': \'UK\', \'age\': \'24\'}',
# property_age=24,
# ),
# dict(
# properties='',
# property_age=0,
# ),
# ])
# def test_add_column_extract_dict_string_with_json(self):
# df = pd.DataFrame([
# '{\"country\": \"US\", \"is_adult\": true}',
# '{\"country\": \"CA\"}',
# '{\"country\": \"UK\", \"is_adult\": false}',
# '',
# ], columns=[
# 'properties',
# ])
# action = dict(
# action_arguments=['properties', 'country'],
# action_options=dict(
# udf='extract_dict_value',
# ),
# outputs=[
# dict(
# uuid='property_country',
# column_type='text',
# ),
# ],
# )
# df_new = add_column(df, action)
# self.assertEqual(df_new.to_dict(orient='records'), [
# dict(
# properties='{\"country\": \"US\", \"is_adult\": true}',
# property_country='US',
# ),
# dict(
# properties='{\"country\": \"CA\"}',
# property_country='CA',
# ),
# dict(
# properties='{\"country\": \"UK\", \"is_adult\": false}',
# property_country='UK',
# ),
# dict(
# properties='',
# property_country=np.NaN,
# ),
# ])
# action2 = dict(
# action_arguments=['properties', 'is_adult'],
# action_options=dict(
# udf='extract_dict_value',
# ),
# outputs=[
# dict(
# uuid='property_is_adult',
# column_type='true_or_false',
# ),
# ],
# )
# df_new2 = add_column(df, action2)
# self.assertEqual(df_new2.to_dict(orient='records'), [
# dict(
# properties='{\"country\": \"US\", \"is_adult\": true}',
# property_is_adult=True,
# ),
# dict(
# properties='{\"country\": \"CA\"}',
# property_is_adult=None,
# ),
# dict(
# properties='{\"country\": \"UK\", \"is_adult\": false}',
# property_is_adult=False,
# ),
# dict(
# properties='',
# property_is_adult=None,
# ),
# ])
def test_add_column_formatted_date(self):
df = pd.DataFrame([
['2019-04-10 08:20:58', False],
['2019-03-05 03:30:30', True],
], columns=[
'created_at',
'boolean',
])
action = dict(
action_arguments=['created_at'],
action_options=dict(
udf='formatted_date',
format='%Y-%m-%d',
),
outputs=[
dict(
uuid='created_date',
column_type='text',
),
],
)
df_new = add_column(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
created_at='2019-04-10 08:20:58',
boolean=False,
created_date='2019-04-10',
),
dict(
created_at='2019-03-05 03:30:30',
boolean=True,
created_date='2019-03-05',
),
])
def test_add_column_if_else(self):
df = pd.DataFrame([
['2019-04-10 08:20:58'],
[None],
], columns=[
'converted_at'
])
action = dict(
action_arguments=[False, True],
action_code='converted_at == null',
action_options=dict(
udf='if_else',
),
outputs=[
dict(
uuid='converted',
column_type='true_or_false',
),
],
)
df_new = add_column(df, action, original_df=df)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
converted_at='2019-04-10 08:20:58',
converted=True,
),
dict(
converted_at=None,
converted=False,
),
])
def test_add_column_if_else_with_column(self):
df = pd.DataFrame([
['2019-04-10 08:20:58', 'test_user_id'],
[None, None],
], columns=[
'converted_at',
'user_id',
])
action = dict(
action_arguments=['unknown', 'user_id'],
action_code='converted_at == null',
action_options=dict(
udf='if_else',
arg1_type='value',
arg2_type='column',
),
outputs=[
dict(
uuid='user_id_clean',
column_type='text',
),
],
)
df_new = add_column(df, action, original_df=df)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
converted_at='2019-04-10 08:20:58',
user_id='test_user_id',
user_id_clean='test_user_id',
),
dict(
converted_at=None,
user_id=None,
user_id_clean='unknown',
),
])
def test_add_column_multiply(self):
df = pd.DataFrame([
[1, 3, 7, 9],
[4, 2, 9, 3],
], columns=[
'integer1',
'integer2',
'integer3',
'integer4',
])
action1 = dict(
action_arguments=[
'integer1',
'integer2',
],
action_options={
'udf': 'multiply',
},
outputs=[
dict(
uuid='integer_multiply',
column_type='number',
),
],
)
action2 = dict(
action_arguments=['integer3'],
action_options={
'udf': 'multiply',
'value': 10,
},
outputs=[
dict(
uuid='integer_multiply2',
column_type='number',
),
],
)
df_new = add_column(add_column(df, action1), action2)
df_expected = pd.DataFrame([
[1, 3, 7, 9, 3, 70],
[4, 2, 9, 3, 8, 90],
], columns=[
'integer1',
'integer2',
'integer3',
'integer4',
'integer_multiply',
'integer_multiply2'
])
assert_frame_equal(df_new, df_expected)
def test_add_column_string_replace(self):
df = pd.DataFrame([
['$1000'],
['$321. '],
['$4,321'],
], columns=[
'amount',
])
action = dict(
action_arguments=['amount'],
action_options={
'udf': 'string_replace',
'pattern': '\\$|\\.|\\,|\\s*',
'replacement': '',
},
outputs=[
dict(
uuid='amount_clean',
column_type='true_or_false',
),
],
)
df_new = add_column(df, action)
df_expected = pd.DataFrame([
['$1000', '1000'],
['$321. ', '321'],
['$4,321', '4321'],
], columns=[
'amount',
'amount_clean',
])
assert_frame_equal(df_new, df_expected)
def test_add_column_string_split(self):
df = pd.DataFrame([
['Street1, Long Beach, CA, '],
['Street2,Vernon, CA, 123'],
['Pacific Coast Highway, Los Angeles, CA, 111'],
], columns=[
'location',
])
action = dict(
action_arguments=['location'],
action_options={
'udf': 'string_split',
'separator': ',',
'part_index': 1,
},
outputs=[
dict(
uuid='location_city',
column_type='text',
),
],
)
action2 = dict(
action_arguments=['location'],
action_options={
'udf': 'string_split',
'separator': ',',
'part_index': 3,
},
outputs=[
dict(
uuid='num',
column_type='number',
),
],
)
df_new = add_column(add_column(df, action), action2)
df_expected = pd.DataFrame([
['Street1, Long Beach, CA, ', 'Long Beach', 0],
['Street2,Vernon, CA, 123', 'Vernon', 123],
['Pacific Coast Highway, Los Angeles, CA, 111', 'Los Angeles', 111],
], columns=[
'location',
'location_city',
'num',
])
assert_frame_equal(df_new, df_expected)
def test_add_column_substring(self):
df = pd.DataFrame([
['$1000.0'],
['$321.9'],
], columns=[
'amount',
])
action = dict(
action_arguments=['amount'],
action_options={
'udf': 'substring',
'start': 1,
'stop': -2,
},
outputs=[
dict(
uuid='amount_int',
column_type='text',
),
],
)
df_new = add_column(df, action)
df_expected = pd.DataFrame([
['$1000.0', '1000'],
['$321.9', '321'],
], columns=[
'amount',
'amount_int',
])
assert_frame_equal(df_new, df_expected)
def test_average(self):
from data_cleaner.transformer_actions.column import average
action = self.
|
7,729 | 218 | 41,482 |
mage-ai__mage-ai
|
9ed779ccaf3538efbe1f2f54dd68f61fb1c3af55
|
src/data_cleaner/tests/transformer_actions/test_column.py
|
infile
|
__groupby_agg_action
| true |
function
| 104 | 108 | true | false |
[
"__groupby_agg_action",
"assertEqual",
"assertRaises",
"test_count",
"test_shift_down",
"test_add_column_addition",
"test_add_column_addition_days",
"test_add_column_constant",
"test_add_column_date_trunc",
"test_add_column_difference",
"test_add_column_difference_days",
"test_add_column_distance_between",
"test_add_column_divide",
"test_add_column_formatted_date",
"test_add_column_if_else",
"test_add_column_if_else_with_column",
"test_add_column_multiply",
"test_add_column_string_replace",
"test_add_column_string_split",
"test_add_column_substring",
"test_average",
"test_count_distinct",
"test_count_with_filter",
"test_count_with_time_window",
"test_diff",
"test_first_column",
"test_impute",
"test_last_column",
"test_max",
"test_median",
"test_min",
"test_remove_column",
"test_select",
"test_shift_down_with_groupby",
"test_shift_up",
"test_sum",
"setUp",
"tearDown",
"addClassCleanup",
"addCleanup",
"addTypeEqualityFunc",
"assert_",
"assertAlmostEqual",
"assertAlmostEquals",
"assertCountEqual",
"assertDictContainsSubset",
"assertDictEqual",
"assertEquals",
"assertFalse",
"assertGreater",
"assertGreaterEqual",
"assertIn",
"assertIs",
"assertIsInstance",
"assertIsNone",
"assertIsNot",
"assertIsNotNone",
"assertLess",
"assertLessEqual",
"assertListEqual",
"assertLogs",
"assertMultiLineEqual",
"assertNoLogs",
"assertNotAlmostEqual",
"assertNotAlmostEquals",
"assertNotEqual",
"assertNotEquals",
"assertNotIn",
"assertNotIsInstance",
"assertNotRegex",
"assertNotRegexpMatches",
"assertRaisesRegex",
"assertRaisesRegexp",
"assertRegex",
"assertRegexpMatches",
"assertSequenceEqual",
"assertSetEqual",
"assertTrue",
"assertTupleEqual",
"assertWarns",
"assertWarnsRegex",
"countTestCases",
"debug",
"defaultTestResult",
"doClassCleanups",
"doCleanups",
"fail",
"failIf",
"failIfAlmostEqual",
"failIfEqual",
"failUnless",
"failUnlessAlmostEqual",
"failUnlessEqual",
"failUnlessRaises",
"failureException",
"id",
"longMessage",
"maxDiff",
"run",
"setUpClass",
"shortDescription",
"skipTest",
"subTest",
"tearDownClass",
"__annotations__",
"__call__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "addClassCleanup",
"type": "function"
},
{
"name": "addCleanup",
"type": "function"
},
{
"name": "addTypeEqualityFunc",
"type": "function"
},
{
"name": "assert_",
"type": "function"
},
{
"name": "assertAlmostEqual",
"type": "function"
},
{
"name": "assertAlmostEquals",
"type": "function"
},
{
"name": "assertCountEqual",
"type": "function"
},
{
"name": "assertDictContainsSubset",
"type": "function"
},
{
"name": "assertDictEqual",
"type": "function"
},
{
"name": "assertEqual",
"type": "function"
},
{
"name": "assertEquals",
"type": "function"
},
{
"name": "assertFalse",
"type": "function"
},
{
"name": "assertGreater",
"type": "function"
},
{
"name": "assertGreaterEqual",
"type": "function"
},
{
"name": "assertIn",
"type": "function"
},
{
"name": "assertIs",
"type": "function"
},
{
"name": "assertIsInstance",
"type": "function"
},
{
"name": "assertIsNone",
"type": "function"
},
{
"name": "assertIsNot",
"type": "function"
},
{
"name": "assertIsNotNone",
"type": "function"
},
{
"name": "assertLess",
"type": "function"
},
{
"name": "assertLessEqual",
"type": "function"
},
{
"name": "assertListEqual",
"type": "function"
},
{
"name": "assertLogs",
"type": "function"
},
{
"name": "assertMultiLineEqual",
"type": "function"
},
{
"name": "assertNotAlmostEqual",
"type": "function"
},
{
"name": "assertNotAlmostEquals",
"type": "function"
},
{
"name": "assertNotEqual",
"type": "function"
},
{
"name": "assertNotEquals",
"type": "function"
},
{
"name": "assertNotIn",
"type": "function"
},
{
"name": "assertNotIsInstance",
"type": "function"
},
{
"name": "assertNotRegex",
"type": "function"
},
{
"name": "assertNotRegexpMatches",
"type": "function"
},
{
"name": "assertRaises",
"type": "function"
},
{
"name": "assertRaisesRegex",
"type": "function"
},
{
"name": "assertRaisesRegexp",
"type": "function"
},
{
"name": "assertRegex",
"type": "function"
},
{
"name": "assertRegexpMatches",
"type": "function"
},
{
"name": "assertSequenceEqual",
"type": "function"
},
{
"name": "assertSetEqual",
"type": "function"
},
{
"name": "assertTrue",
"type": "function"
},
{
"name": "assertTupleEqual",
"type": "function"
},
{
"name": "assertWarns",
"type": "function"
},
{
"name": "assertWarnsRegex",
"type": "function"
},
{
"name": "countTestCases",
"type": "function"
},
{
"name": "debug",
"type": "function"
},
{
"name": "defaultTestResult",
"type": "function"
},
{
"name": "doClassCleanups",
"type": "function"
},
{
"name": "doCleanups",
"type": "function"
},
{
"name": "fail",
"type": "function"
},
{
"name": "failIf",
"type": "function"
},
{
"name": "failIfAlmostEqual",
"type": "function"
},
{
"name": "failIfEqual",
"type": "function"
},
{
"name": "failUnless",
"type": "function"
},
{
"name": "failUnlessAlmostEqual",
"type": "function"
},
{
"name": "failUnlessEqual",
"type": "function"
},
{
"name": "failUnlessRaises",
"type": "function"
},
{
"name": "failureException",
"type": "statement"
},
{
"name": "id",
"type": "function"
},
{
"name": "longMessage",
"type": "statement"
},
{
"name": "maxDiff",
"type": "statement"
},
{
"name": "run",
"type": "function"
},
{
"name": "setUp",
"type": "function"
},
{
"name": "setUpClass",
"type": "function"
},
{
"name": "shortDescription",
"type": "function"
},
{
"name": "skipTest",
"type": "function"
},
{
"name": "subTest",
"type": "function"
},
{
"name": "tearDown",
"type": "function"
},
{
"name": "tearDownClass",
"type": "function"
},
{
"name": "test_add_column_addition",
"type": "function"
},
{
"name": "test_add_column_addition_days",
"type": "function"
},
{
"name": "test_add_column_constant",
"type": "function"
},
{
"name": "test_add_column_date_trunc",
"type": "function"
},
{
"name": "test_add_column_difference",
"type": "function"
},
{
"name": "test_add_column_difference_days",
"type": "function"
},
{
"name": "test_add_column_distance_between",
"type": "function"
},
{
"name": "test_add_column_divide",
"type": "function"
},
{
"name": "test_add_column_formatted_date",
"type": "function"
},
{
"name": "test_add_column_if_else",
"type": "function"
},
{
"name": "test_add_column_if_else_with_column",
"type": "function"
},
{
"name": "test_add_column_multiply",
"type": "function"
},
{
"name": "test_add_column_string_replace",
"type": "function"
},
{
"name": "test_add_column_string_split",
"type": "function"
},
{
"name": "test_add_column_substring",
"type": "function"
},
{
"name": "test_average",
"type": "function"
},
{
"name": "test_count",
"type": "function"
},
{
"name": "test_count_distinct",
"type": "function"
},
{
"name": "test_count_with_filter",
"type": "function"
},
{
"name": "test_count_with_time_window",
"type": "function"
},
{
"name": "test_diff",
"type": "function"
},
{
"name": "test_first_column",
"type": "function"
},
{
"name": "test_impute",
"type": "function"
},
{
"name": "test_last_column",
"type": "function"
},
{
"name": "test_max",
"type": "function"
},
{
"name": "test_median",
"type": "function"
},
{
"name": "test_min",
"type": "function"
},
{
"name": "test_remove_column",
"type": "function"
},
{
"name": "test_select",
"type": "function"
},
{
"name": "test_shift_down",
"type": "function"
},
{
"name": "test_shift_down_with_groupby",
"type": "function"
},
{
"name": "test_shift_up",
"type": "function"
},
{
"name": "test_sum",
"type": "function"
},
{
"name": "_addSkip",
"type": "function"
},
{
"name": "_formatMessage",
"type": "function"
},
{
"name": "_getAssertEqualityFunc",
"type": "function"
},
{
"name": "_testMethodDoc",
"type": "statement"
},
{
"name": "_testMethodName",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__call__",
"type": "function"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__groupby_agg_action",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
from data_cleaner.tests.base_test import TestCase
from data_cleaner.transformer_actions.column import (
add_column,
count,
count_distinct,
diff,
# expand_column,
first,
last,
remove_column,
select,
shift_down,
shift_up,
)
from pandas.util.testing import assert_frame_equal
import numpy as np
import pandas as pd
TEST_DATAFRAME = pd.DataFrame([
[1, 1000],
[2, 1050],
[1, 1100],
[2, 1150],
], columns=[
'group_id',
'amount',
])
class ColumnTests(TestCase):
def test_remove_column(self):
df = pd.DataFrame([
[0, False, 'a'],
[1, True, 'b'],
], columns=[
'integer',
'boolean',
'string',
])
action = dict(action_arguments=['string'])
df_new = remove_column(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
integer=0,
boolean=False,
),
dict(
integer=1,
boolean=True,
),
])
action = dict(action_arguments=['integer', 'boolean'])
df_new = remove_column(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
string='a',
),
dict(
string='b',
),
])
def test_add_column_addition(self):
df = pd.DataFrame([
[1, 3, 7, 9],
[4, 2, 9, 3],
], columns=[
'integer1',
'integer2',
'integer3',
'integer4',
])
action1 = dict(
action_arguments=[
'integer1',
'integer2',
'integer3',
],
action_options={
'udf': 'addition',
'value': None,
},
outputs=[
dict(
uuid='integer_addition',
column_type='number',
),
],
)
action2 = dict(
action_arguments=['integer1'],
action_options={
'udf': 'addition',
'value': 10,
},
outputs=[
dict(
uuid='integer_addition2',
column_type='number',
),
],
)
action3 = dict(
action_arguments=['integer1', 'integer4'],
action_options={
'udf': 'addition',
'value': 10,
},
outputs=[
dict(
uuid='integer_addition3',
column_type='number',
),
],
)
df_new = add_column(
add_column(
add_column(df, action1),
action2,
),
action3,
)
df_expected = pd.DataFrame([
[1, 3, 7, 9, 11, 11, 20],
[4, 2, 9, 3, 15, 14, 17],
], columns=[
'integer1',
'integer2',
'integer3',
'integer4',
'integer_addition',
'integer_addition2',
'integer_addition3',
])
assert_frame_equal(df_new, df_expected)
def test_add_column_addition_days(self):
df = pd.DataFrame([
['2021-08-31'],
['2021-08-28'],
], columns=[
'created_at',
])
action = dict(
action_arguments=['created_at'],
action_options=dict(
column_type='datetime',
time_unit='d',
udf='addition',
value=3,
),
outputs=[
dict(
uuid='3d_after_creation',
column_type='text',
),
],
)
df_new = add_column(df, action)
df_expected = pd.DataFrame([
['2021-08-31', '2021-09-03 00:00:00'],
['2021-08-28', '2021-08-31 00:00:00'],
], columns=[
'created_at',
'3d_after_creation'
])
assert_frame_equal(df_new, df_expected)
def test_add_column_constant(self):
df = pd.DataFrame([
[False],
[True],
], columns=[
'boolean',
])
action = dict(
action_arguments=[10],
action_options=dict(
udf='constant',
),
outputs=[
dict(
uuid='integer',
column_type='number',
),
],
)
df_new = add_column(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
boolean=False,
integer=10,
),
dict(
boolean=True,
integer=10,
),
])
def test_add_column_date_trunc(self):
df = pd.DataFrame([
['2021-08-31', False],
['2021-08-28', True],
], columns=[
'created_at',
'boolean',
])
action = dict(
action_arguments=['created_at'],
action_options=dict(
udf='date_trunc',
date_part='week',
),
outputs=[
dict(
uuid='week_date',
column_type='text',
),
],
)
df_new = add_column(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
created_at='2021-08-31',
boolean=False,
week_date='2021-08-30',
),
dict(
created_at='2021-08-28',
boolean=True,
week_date='2021-08-23',
),
])
def test_add_column_difference(self):
df = pd.DataFrame([
[1, 3],
[4, 2],
], columns=[
'integer1',
'integer2',
])
action1 = dict(
action_arguments=['integer1', 'integer2'],
action_options={
'udf': 'difference',
},
outputs=[
dict(
uuid='integer_difference',
column_type='number',
),
],
)
action2 = dict(
action_arguments=['integer1'],
action_options={
'udf': 'difference',
'value': 10,
},
outputs=[
dict(
uuid='integer_difference2',
column_type='number',
),
],
)
df_new = add_column(add_column(df, action1), action2)
df_expected = pd.DataFrame([
[1, 3, -2, -9],
[4, 2, 2, -6],
], columns=[
'integer1',
'integer2',
'integer_difference',
'integer_difference2'
])
assert_frame_equal(df_new, df_expected)
def test_add_column_difference_days(self):
df = pd.DataFrame([
['2021-08-31', '2021-09-14'],
['2021-08-28', '2021-09-03'],
], columns=[
'created_at',
'converted_at',
])
action = dict(
action_arguments=['converted_at', 'created_at'],
action_options=dict(
column_type='datetime',
time_unit='d',
udf='difference',
),
outputs=[
dict(
uuid='days_diff',
column_type='number',
),
],
)
df_new = add_column(df, action)
df_expected = pd.DataFrame([
['2021-08-31', '2021-09-14', 14],
['2021-08-28', '2021-09-03', 6],
], columns=[
'created_at',
'converted_at',
'days_diff',
])
assert_frame_equal(df_new, df_expected)
def test_add_column_distance_between(self):
df = pd.DataFrame([
[26.05308, -97.31838, 33.41939, -112.32606],
[39.71954, -84.13056, 33.41939, -112.32606],
], columns=[
'lat1',
'lng1',
'lat2',
'lng2',
])
action = dict(
action_arguments=['lat1', 'lng1', 'lat2', 'lng2'],
action_options=dict(
udf='distance_between',
),
outputs=[
dict(
uuid='distance',
column_type='number_with_decimals',
),
],
)
df_new = add_column(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
lat1=26.05308,
lng1=-97.31838,
lat2=33.41939,
lng2=-112.32606,
distance=1661.8978520305657,
),
dict(
lat1=39.71954,
lng1=-84.13056,
lat2=33.41939,
lng2=-112.32606,
distance=2601.5452571116184,
),
])
def test_add_column_divide(self):
df = pd.DataFrame([
[12, 3, 70, 9],
[4, 2, 90, 3],
], columns=[
'integer1',
'integer2',
'integer3',
'integer4',
])
action1 = dict(
action_arguments=[
'integer1',
'integer2',
],
action_options={
'udf': 'divide',
},
outputs=[
dict(
uuid='integer_divide',
column_type='number',
),
],
)
action2 = dict(
action_arguments=['integer3'],
action_options={
'udf': 'divide',
'value': 10,
},
outputs=[
dict(
uuid='integer_divide2',
column_type='number',
),
],
)
df_new = add_column(add_column(df, action1), action2)
df_expected = pd.DataFrame([
[12, 3, 70, 9, 4, 7],
[4, 2, 90, 3, 2, 9],
], columns=[
'integer1',
'integer2',
'integer3',
'integer4',
'integer_divide',
'integer_divide2'
])
assert_frame_equal(df_new, df_expected)
# def test_add_column_extract_dict_string(self):
# df = pd.DataFrame([
# '{\'country\': \'US\', \'age\': \'20\'}',
# '{\'country\': \'CA\'}',
# '{\'country\': \'UK\', \'age\': \'24\'}',
# '',
# ], columns=[
# 'properties',
# ])
# action = dict(
# action_arguments=['properties', 'country'],
# action_options=dict(
# udf='extract_dict_value',
# ),
# outputs=[
# dict(
# uuid='property_country',
# column_type='text',
# ),
# ],
# )
# df_new = add_column(df, action)
# self.assertEqual(df_new.to_dict(orient='records'), [
# dict(
# properties='{\'country\': \'US\', \'age\': \'20\'}',
# property_country='US',
# ),
# dict(
# properties='{\'country\': \'CA\'}',
# property_country='CA',
# ),
# dict(
# properties='{\'country\': \'UK\', \'age\': \'24\'}',
# property_country='UK',
# ),
# dict(
# properties='',
# property_country=np.NaN,
# ),
# ])
# action2 = dict(
# action_arguments=['properties', 'age'],
# action_options=dict(
# udf='extract_dict_value',
# ),
# outputs=[
# dict(
# uuid='property_age',
# column_type='number',
# ),
# ],
# )
# df_new2 = add_column(df, action2)
# self.assertEqual(df_new2.to_dict(orient='records'), [
# dict(
# properties='{\'country\': \'US\', \'age\': \'20\'}',
# property_age=20,
# ),
# dict(
# properties='{\'country\': \'CA\'}',
# property_age=0,
# ),
# dict(
# properties='{\'country\': \'UK\', \'age\': \'24\'}',
# property_age=24,
# ),
# dict(
# properties='',
# property_age=0,
# ),
# ])
# def test_add_column_extract_dict_string_with_json(self):
# df = pd.DataFrame([
# '{\"country\": \"US\", \"is_adult\": true}',
# '{\"country\": \"CA\"}',
# '{\"country\": \"UK\", \"is_adult\": false}',
# '',
# ], columns=[
# 'properties',
# ])
# action = dict(
# action_arguments=['properties', 'country'],
# action_options=dict(
# udf='extract_dict_value',
# ),
# outputs=[
# dict(
# uuid='property_country',
# column_type='text',
# ),
# ],
# )
# df_new = add_column(df, action)
# self.assertEqual(df_new.to_dict(orient='records'), [
# dict(
# properties='{\"country\": \"US\", \"is_adult\": true}',
# property_country='US',
# ),
# dict(
# properties='{\"country\": \"CA\"}',
# property_country='CA',
# ),
# dict(
# properties='{\"country\": \"UK\", \"is_adult\": false}',
# property_country='UK',
# ),
# dict(
# properties='',
# property_country=np.NaN,
# ),
# ])
# action2 = dict(
# action_arguments=['properties', 'is_adult'],
# action_options=dict(
# udf='extract_dict_value',
# ),
# outputs=[
# dict(
# uuid='property_is_adult',
# column_type='true_or_false',
# ),
# ],
# )
# df_new2 = add_column(df, action2)
# self.assertEqual(df_new2.to_dict(orient='records'), [
# dict(
# properties='{\"country\": \"US\", \"is_adult\": true}',
# property_is_adult=True,
# ),
# dict(
# properties='{\"country\": \"CA\"}',
# property_is_adult=None,
# ),
# dict(
# properties='{\"country\": \"UK\", \"is_adult\": false}',
# property_is_adult=False,
# ),
# dict(
# properties='',
# property_is_adult=None,
# ),
# ])
def test_add_column_formatted_date(self):
df = pd.DataFrame([
['2019-04-10 08:20:58', False],
['2019-03-05 03:30:30', True],
], columns=[
'created_at',
'boolean',
])
action = dict(
action_arguments=['created_at'],
action_options=dict(
udf='formatted_date',
format='%Y-%m-%d',
),
outputs=[
dict(
uuid='created_date',
column_type='text',
),
],
)
df_new = add_column(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
created_at='2019-04-10 08:20:58',
boolean=False,
created_date='2019-04-10',
),
dict(
created_at='2019-03-05 03:30:30',
boolean=True,
created_date='2019-03-05',
),
])
def test_add_column_if_else(self):
df = pd.DataFrame([
['2019-04-10 08:20:58'],
[None],
], columns=[
'converted_at'
])
action = dict(
action_arguments=[False, True],
action_code='converted_at == null',
action_options=dict(
udf='if_else',
),
outputs=[
dict(
uuid='converted',
column_type='true_or_false',
),
],
)
df_new = add_column(df, action, original_df=df)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
converted_at='2019-04-10 08:20:58',
converted=True,
),
dict(
converted_at=None,
converted=False,
),
])
def test_add_column_if_else_with_column(self):
df = pd.DataFrame([
['2019-04-10 08:20:58', 'test_user_id'],
[None, None],
], columns=[
'converted_at',
'user_id',
])
action = dict(
action_arguments=['unknown', 'user_id'],
action_code='converted_at == null',
action_options=dict(
udf='if_else',
arg1_type='value',
arg2_type='column',
),
outputs=[
dict(
uuid='user_id_clean',
column_type='text',
),
],
)
df_new = add_column(df, action, original_df=df)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
converted_at='2019-04-10 08:20:58',
user_id='test_user_id',
user_id_clean='test_user_id',
),
dict(
converted_at=None,
user_id=None,
user_id_clean='unknown',
),
])
def test_add_column_multiply(self):
df = pd.DataFrame([
[1, 3, 7, 9],
[4, 2, 9, 3],
], columns=[
'integer1',
'integer2',
'integer3',
'integer4',
])
action1 = dict(
action_arguments=[
'integer1',
'integer2',
],
action_options={
'udf': 'multiply',
},
outputs=[
dict(
uuid='integer_multiply',
column_type='number',
),
],
)
action2 = dict(
action_arguments=['integer3'],
action_options={
'udf': 'multiply',
'value': 10,
},
outputs=[
dict(
uuid='integer_multiply2',
column_type='number',
),
],
)
df_new = add_column(add_column(df, action1), action2)
df_expected = pd.DataFrame([
[1, 3, 7, 9, 3, 70],
[4, 2, 9, 3, 8, 90],
], columns=[
'integer1',
'integer2',
'integer3',
'integer4',
'integer_multiply',
'integer_multiply2'
])
assert_frame_equal(df_new, df_expected)
def test_add_column_string_replace(self):
df = pd.DataFrame([
['$1000'],
['$321. '],
['$4,321'],
], columns=[
'amount',
])
action = dict(
action_arguments=['amount'],
action_options={
'udf': 'string_replace',
'pattern': '\\$|\\.|\\,|\\s*',
'replacement': '',
},
outputs=[
dict(
uuid='amount_clean',
column_type='true_or_false',
),
],
)
df_new = add_column(df, action)
df_expected = pd.DataFrame([
['$1000', '1000'],
['$321. ', '321'],
['$4,321', '4321'],
], columns=[
'amount',
'amount_clean',
])
assert_frame_equal(df_new, df_expected)
def test_add_column_string_split(self):
df = pd.DataFrame([
['Street1, Long Beach, CA, '],
['Street2,Vernon, CA, 123'],
['Pacific Coast Highway, Los Angeles, CA, 111'],
], columns=[
'location',
])
action = dict(
action_arguments=['location'],
action_options={
'udf': 'string_split',
'separator': ',',
'part_index': 1,
},
outputs=[
dict(
uuid='location_city',
column_type='text',
),
],
)
action2 = dict(
action_arguments=['location'],
action_options={
'udf': 'string_split',
'separator': ',',
'part_index': 3,
},
outputs=[
dict(
uuid='num',
column_type='number',
),
],
)
df_new = add_column(add_column(df, action), action2)
df_expected = pd.DataFrame([
['Street1, Long Beach, CA, ', 'Long Beach', 0],
['Street2,Vernon, CA, 123', 'Vernon', 123],
['Pacific Coast Highway, Los Angeles, CA, 111', 'Los Angeles', 111],
], columns=[
'location',
'location_city',
'num',
])
assert_frame_equal(df_new, df_expected)
def test_add_column_substring(self):
df = pd.DataFrame([
['$1000.0'],
['$321.9'],
], columns=[
'amount',
])
action = dict(
action_arguments=['amount'],
action_options={
'udf': 'substring',
'start': 1,
'stop': -2,
},
outputs=[
dict(
uuid='amount_int',
column_type='text',
),
],
)
df_new = add_column(df, action)
df_expected = pd.DataFrame([
['$1000.0', '1000'],
['$321.9', '321'],
], columns=[
'amount',
'amount_int',
])
assert_frame_equal(df_new, df_expected)
def test_average(self):
from data_cleaner.transformer_actions.column import average
action = self.__groupby_agg_action('average_amount')
df_new = average(TEST_DATAFRAME.copy(), action)
df_expected = pd.DataFrame([
[1, 1000, 1050],
[2, 1050, 1100],
[1, 1100, 1050],
[2, 1150, 1100],
], columns=[
'group_id',
'amount',
'average_amount'
])
assert_frame_equal(df_new, df_expected)
def test_count(self):
df = pd.DataFrame([
[1, 1000],
[1, 1050],
[1, 1100],
[2, 1150],
], columns=[
'group_id',
'order_id',
])
action = dict(
action_arguments=['order_id'],
action_options=dict(
groupby_columns=['group_id']
),
outputs=[
dict(uuid='order_count'),
],
)
df_new = count(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
group_id=1,
order_id=1000,
order_count=3,
),
dict(
group_id=1,
order_id=1050,
order_count=3,
),
dict(
group_id=1,
order_id=1100,
order_count=3,
),
dict(
group_id=2,
order_id=1150,
order_count=1,
),
])
def test_count_distinct(self):
df = pd.DataFrame([
[1, 1000],
[1, 1000],
[1, 1100],
[2, 1150],
], columns=[
'group_id',
'order_id',
])
action = dict(
action_arguments=['order_id'],
action_options=dict(
groupby_columns=['group_id']
),
outputs=[
dict(uuid='order_count'),
],
)
df_new = count_distinct(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
group_id=1,
order_id=1000,
order_count=2,
),
dict(
group_id=1,
order_id=1000,
order_count=2,
),
dict(
group_id=1,
order_id=1100,
order_count=2,
),
dict(
group_id=2,
order_id=1150,
order_count=1,
),
])
def test_count_with_time_window(self):
df = pd.DataFrame([
[1, 1000, '2021-10-01', '2021-09-01'],
[1, 1050, '2021-10-01', '2021-08-01'],
[1, 1100, '2021-10-01', '2021-01-01'],
[2, 1150, '2021-09-01', '2021-08-01'],
], columns=[
'group_id',
'order_id',
'group_churned_at',
'order_created_at',
])
action = dict(
action_arguments=['order_id'],
action_code='',
action_options=dict(
groupby_columns=['group_id'],
timestamp_feature_a='group_churned_at',
timestamp_feature_b='order_created_at',
window=90*24*3600,
),
outputs=[
dict(uuid='order_count'),
],
)
df_new = count(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
group_id=1,
order_id=1000,
group_churned_at='2021-10-01',
order_created_at='2021-09-01',
order_count=2,
),
dict(
group_id=1,
order_id=1050,
group_churned_at='2021-10-01',
order_created_at='2021-08-01',
order_count=2,
),
dict(
group_id=1,
order_id=1100,
group_churned_at='2021-10-01',
order_created_at='2021-01-01',
order_count=2,
),
dict(
group_id=2,
order_id=1150,
group_churned_at='2021-09-01',
order_created_at='2021-08-01',
order_count=1,
),
])
def test_count_with_filter(self):
df = pd.DataFrame([
[1, 1000, '2021-10-01', '2021-09-01'],
[1, 1050, '2021-10-01', '2021-08-01'],
[1, 1100, '2021-10-01', '2021-01-01'],
[2, 1150, '2021-09-01', '2021-08-01'],
[2, 1200, '2021-09-01', '2021-08-16'],
[2, 1250, '2021-09-01', '2021-08-14'],
], columns=[
'group_id',
'order_id',
'group_churned_at',
'order_created_at',
])
action = dict(
action_arguments=['order_id'],
action_code='order_created_at < \'2021-08-15\'',
action_options=dict(
groupby_columns=['group_id'],
),
outputs=[
dict(uuid='order_count'),
],
)
df_new = count(df, action)
df_expected = pd.DataFrame([
[1, 1000, '2021-10-01', '2021-09-01', 2],
[1, 1050, '2021-10-01', '2021-08-01', 2],
[1, 1100, '2021-10-01', '2021-01-01', 2],
[2, 1150, '2021-09-01', '2021-08-01', 2],
[2, 1200, '2021-09-01', '2021-08-16', 2],
[2, 1250, '2021-09-01', '2021-08-14', 2],
], columns=[
'group_id',
'order_id',
'group_churned_at',
'order_created_at',
'order_count',
])
assert_frame_equal(df_new, df_expected)
def test_diff(self):
df = pd.DataFrame([
['2020-01-01', 1000],
['2020-01-02', 1050],
['2020-01-03', 1200],
['2020-01-04', 990],
], columns=[
'date',
'sold',
])
action = dict(
action_arguments=['sold'],
outputs=[
dict(uuid='sold_diff'),
],
)
df_new = diff(df, action)
self.assertEqual(df_new.to_dict(orient='records')[1:], [
dict(
date='2020-01-02',
sold=1050,
sold_diff=50,
),
dict(
date='2020-01-03',
sold=1200,
sold_diff=150,
),
dict(
date='2020-01-04',
sold=990,
sold_diff=-210,
),
])
# def test_expand_column(self):
# df = pd.DataFrame([
# [1, 'game'],
# [1, 'book'],
# [1, 'game'],
# [2, 'Video Game'],
# [1, 'Video Game'],
# [2, 'book'],
# [1, 'Video Game'],
# [2, 'Video Game'],
# ], columns=[
# 'group_id',
# 'category',
# ])
# action = dict(
# action_arguments=['category'],
# action_options=dict(
# groupby_columns=['group_id']
# ),
# outputs=[
# dict(uuid='category_expanded_count_game'),
# dict(uuid='category_expanded_count_book'),
# dict(uuid='category_expanded_count_video_game'),
# dict(uuid='category_expanded_count_clothing'),
# ],
# )
# df_new = expand_column(df, action)
# df_expected = pd.DataFrame([
# [1, 'game', 2, 1, 2],
# [1, 'book', 2, 1, 2],
# [1, 'game', 2, 1, 2],
# [2, 'Video Game', 0, 1, 2],
# [1, 'Video Game', 2, 1, 2],
# [2, 'book', 0, 1, 2],
# [1, 'Video Game', 2, 1, 2],
# [2, 'Video Game', 0, 1, 2],
# ], columns=[
# 'group_id',
# 'category',
# 'category_expanded_count_game',
# 'category_expanded_count_book',
# 'category_expanded_count_video_game',
# ])
# assert_frame_equal(df_new, df_expected)
# def test_expand_column_with_time_window(self):
# df = pd.DataFrame([
# [1, 'game', '2021-01-02', '2021-01-04'],
# [1, 'book', '2021-01-02', '2021-01-04'],
# [1, 'game', '2021-01-03', '2021-01-04'],
# [2, 'Video Game', '2021-01-01', '2021-01-03'],
# [1, 'Video Game', '2021-01-01', '2021-01-04'],
# [2, 'book', '2021-01-02', '2021-01-03'],
# [1, 'Video Game', '2021-01-03', '2021-01-04'],
# [2, 'Video Game', '2020-12-30', '2021-01-03'],
# ], columns=[
# 'group_id',
# 'category',
# 'timestamp1',
# 'timestamp2',
# ])
# action = dict(
# action_arguments=['category'],
# action_options=dict(
# groupby_columns=['group_id'],
# timestamp_feature_a='timestamp2',
# timestamp_feature_b='timestamp1',
# window=172800,
# ),
# outputs=[
# dict(uuid='category_expanded_count_game_2d'),
# dict(uuid='category_expanded_count_book_2d'),
# dict(uuid='category_expanded_count_video_game_2d'),
# dict(uuid='category_expanded_count_clothing_2d'),
# ],
# )
# df_new = expand_column(df, action)
# df_expected = pd.DataFrame([
# [1, 'game', '2021-01-02', '2021-01-04', 2, 1, 1],
# [1, 'book', '2021-01-02', '2021-01-04', 2, 1, 1],
# [1, 'game', '2021-01-03', '2021-01-04', 2, 1, 1],
# [2, 'Video Game', '2021-01-01', '2021-01-03', 0, 1, 1],
# [1, 'Video Game', '2021-01-01', '2021-01-04', 2, 1, 1],
# [2, 'book', '2021-01-02', '2021-01-03', 0, 1, 1],
# [1, 'Video Game', '2021-01-03', '2021-01-04', 2, 1, 1],
# [2, 'Video Game', '2020-12-30', '2021-01-03', 0, 1, 1],
# ], columns=[
# 'group_id',
# 'category',
# 'timestamp1',
# 'timestamp2',
# 'category_expanded_count_game_2d',
# 'category_expanded_count_book_2d',
# 'category_expanded_count_video_game_2d',
# ])
# assert_frame_equal(df_new, df_expected)
def test_first_column(self):
df = pd.DataFrame([
[1, 1000],
[2, 1050],
[1, 1100],
[2, 1150],
], columns=[
'group_id',
'order_id',
])
action = dict(
action_arguments=['order_id'],
action_options=dict(
groupby_columns=['group_id']
),
outputs=[
dict(uuid='first_order'),
],
)
df_new = first(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
group_id=1,
order_id=1000,
first_order=1000,
),
dict(
group_id=2,
order_id=1050,
first_order=1050,
),
dict(
group_id=1,
order_id=1100,
first_order=1000,
),
dict(
group_id=2,
order_id=1150,
first_order=1050,
),
])
def test_impute(self):
from data_cleaner.transformer_actions.column import impute
df = pd.DataFrame([
['2020-01-01', 1000, ' ', 800],
['2020-01-02', '', 1200, 700],
['2020-01-03', 1200, np.NaN, 900],
['2020-01-04', np.NaN, ' ', 700],
['2020-01-05', 1700, 1300, 800],
], columns=[
'date',
'sold',
'curr_profit',
'prev_sold',
])
action1 = dict(
action_arguments=['sold', 'curr_profit'],
action_options={
'value': '0',
},
action_variables={
'0': {
'feature': {
'column_type': 'number',
'uuid': 'sold',
},
'type': 'feature',
},
'1': {
'feature': {
'column_type': 'number',
'uuid': 'curr_profit',
},
'type': 'feature',
},
},
)
action2 = dict(
action_arguments=['sold'],
action_options={
'value': '0',
},
action_variables={
'0': {
'feature': {
'column_type': 'number',
'uuid': 'sold',
},
'type': 'feature',
},
},
)
action3 = dict(
action_arguments=['sold', 'curr_profit'],
action_options={
'strategy': 'average',
},
)
action4 = dict(
action_arguments=['sold', 'curr_profit'],
action_options={
'strategy': 'median',
},
)
action5 = dict(
action_arguments=['sold', 'curr_profit'],
action_options={
'strategy': 'column',
'value': 'prev_sold',
},
)
action_invalid = dict(
action_arguments=['sold', 'curr_profit'],
action_options={
'strategy': 'mode',
},
)
df_new1 = impute(df.copy(), action1)
df_new2 = impute(df.copy(), action2)
df_new3 = impute(df.copy(), action3)
df_new4 = impute(df.copy(), action4)
df_new5 = impute(df.copy(), action5)
df_expected1 = pd.DataFrame([
['2020-01-01', 1000, 0, 800],
['2020-01-02', 0, 1200, 700],
['2020-01-03', 1200, 0, 900],
['2020-01-04', 0, 0, 700],
['2020-01-05', 1700, 1300, 800],
], columns=[
'date',
'sold',
'curr_profit',
'prev_sold',
])
df_expected2 = pd.DataFrame([
['2020-01-01', 1000, ' ', 800],
['2020-01-02', 0, 1200, 700],
['2020-01-03', 1200, np.nan, 900],
['2020-01-04', 0, ' ', 700],
['2020-01-05', 1700, 1300, 800],
], columns=[
'date',
'sold',
'curr_profit',
'prev_sold',
])
df_expected3 = pd.DataFrame([
['2020-01-01', 1000, 1250, 800],
['2020-01-02', 1300, 1200, 700],
['2020-01-03', 1200, 1250, 900],
['2020-01-04', 1300, 1250, 700],
['2020-01-05', 1700, 1300, 800],
], columns=[
'date',
'sold',
'curr_profit',
'prev_sold',
])
df_expected4 = pd.DataFrame([
['2020-01-01', 1000, 1250, 800],
['2020-01-02', 1200, 1200, 700],
['2020-01-03', 1200, 1250, 900],
['2020-01-04', 1200, 1250, 700],
['2020-01-05', 1700, 1300, 800],
], columns=[
'date',
'sold',
'curr_profit',
'prev_sold',
])
df_expected5 = pd.DataFrame([
['2020-01-01', 1000, 800, 800],
['2020-01-02', 700, 1200, 700],
['2020-01-03', 1200, 900, 900],
['2020-01-04', 700, 700, 700],
['2020-01-05', 1700, 1300, 800],
], columns=[
'date',
'sold',
'curr_profit',
'prev_sold',
])
df_new1['sold'] = df_new1['sold'].astype(int)
df_new1['curr_profit'] = df_new1['curr_profit'].astype(int)
df_new2['sold'] = df_new2['sold'].astype(int)
df_new3['sold'] = df_new3['sold'].astype(int)
df_new3['curr_profit'] = df_new3['curr_profit'].astype(int)
df_new4['sold'] = df_new4['sold'].astype(int)
df_new4['curr_profit'] = df_new4['curr_profit'].astype(int)
df_new5['sold'] = df_new5['sold'].astype(int)
df_new5['curr_profit'] = df_new5['curr_profit'].astype(int)
assert_frame_equal(df_new1, df_expected1)
assert_frame_equal(df_new2, df_expected2)
assert_frame_equal(df_new3, df_expected3)
assert_frame_equal(df_new4, df_expected4)
assert_frame_equal(df_new5, df_expected5)
with self.assertRaises(Exception):
_ = impute(df.copy(), action_invalid)
def test_last_column(self):
df = pd.DataFrame([
[1, 1000],
[2, 1050],
[1, 1100],
[2, 1150],
], columns=[
'group_id',
'order_id',
])
action = dict(
action_arguments=['order_id'],
action_options=dict(
groupby_columns=['group_id']
),
outputs=[
dict(uuid='last_order'),
],
)
df_new = last(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
group_id=1,
order_id=1000,
last_order=1100,
),
dict(
group_id=2,
order_id=1050,
last_order=1150,
),
dict(
group_id=1,
order_id=1100,
last_order=1100,
),
dict(
group_id=2,
order_id=1150,
last_order=1150,
),
])
def test_max(self):
from data_cleaner.transformer_actions.column import max
action = self.
|
7,730 | 218 | 42,543 |
mage-ai__mage-ai
|
9ed779ccaf3538efbe1f2f54dd68f61fb1c3af55
|
src/data_cleaner/tests/transformer_actions/test_column.py
|
infile
|
__groupby_agg_action
| true |
function
| 104 | 108 | true | false |
[
"__groupby_agg_action",
"assertEqual",
"assertRaises",
"test_count",
"test_shift_down",
"test_add_column_addition",
"test_add_column_addition_days",
"test_add_column_constant",
"test_add_column_date_trunc",
"test_add_column_difference",
"test_add_column_difference_days",
"test_add_column_distance_between",
"test_add_column_divide",
"test_add_column_formatted_date",
"test_add_column_if_else",
"test_add_column_if_else_with_column",
"test_add_column_multiply",
"test_add_column_string_replace",
"test_add_column_string_split",
"test_add_column_substring",
"test_average",
"test_count_distinct",
"test_count_with_filter",
"test_count_with_time_window",
"test_diff",
"test_first_column",
"test_impute",
"test_last_column",
"test_max",
"test_median",
"test_min",
"test_remove_column",
"test_select",
"test_shift_down_with_groupby",
"test_shift_up",
"test_sum",
"setUp",
"tearDown",
"addClassCleanup",
"addCleanup",
"addTypeEqualityFunc",
"assert_",
"assertAlmostEqual",
"assertAlmostEquals",
"assertCountEqual",
"assertDictContainsSubset",
"assertDictEqual",
"assertEquals",
"assertFalse",
"assertGreater",
"assertGreaterEqual",
"assertIn",
"assertIs",
"assertIsInstance",
"assertIsNone",
"assertIsNot",
"assertIsNotNone",
"assertLess",
"assertLessEqual",
"assertListEqual",
"assertLogs",
"assertMultiLineEqual",
"assertNoLogs",
"assertNotAlmostEqual",
"assertNotAlmostEquals",
"assertNotEqual",
"assertNotEquals",
"assertNotIn",
"assertNotIsInstance",
"assertNotRegex",
"assertNotRegexpMatches",
"assertRaisesRegex",
"assertRaisesRegexp",
"assertRegex",
"assertRegexpMatches",
"assertSequenceEqual",
"assertSetEqual",
"assertTrue",
"assertTupleEqual",
"assertWarns",
"assertWarnsRegex",
"countTestCases",
"debug",
"defaultTestResult",
"doClassCleanups",
"doCleanups",
"fail",
"failIf",
"failIfAlmostEqual",
"failIfEqual",
"failUnless",
"failUnlessAlmostEqual",
"failUnlessEqual",
"failUnlessRaises",
"failureException",
"id",
"longMessage",
"maxDiff",
"run",
"setUpClass",
"shortDescription",
"skipTest",
"subTest",
"tearDownClass",
"__annotations__",
"__call__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "addClassCleanup",
"type": "function"
},
{
"name": "addCleanup",
"type": "function"
},
{
"name": "addTypeEqualityFunc",
"type": "function"
},
{
"name": "assert_",
"type": "function"
},
{
"name": "assertAlmostEqual",
"type": "function"
},
{
"name": "assertAlmostEquals",
"type": "function"
},
{
"name": "assertCountEqual",
"type": "function"
},
{
"name": "assertDictContainsSubset",
"type": "function"
},
{
"name": "assertDictEqual",
"type": "function"
},
{
"name": "assertEqual",
"type": "function"
},
{
"name": "assertEquals",
"type": "function"
},
{
"name": "assertFalse",
"type": "function"
},
{
"name": "assertGreater",
"type": "function"
},
{
"name": "assertGreaterEqual",
"type": "function"
},
{
"name": "assertIn",
"type": "function"
},
{
"name": "assertIs",
"type": "function"
},
{
"name": "assertIsInstance",
"type": "function"
},
{
"name": "assertIsNone",
"type": "function"
},
{
"name": "assertIsNot",
"type": "function"
},
{
"name": "assertIsNotNone",
"type": "function"
},
{
"name": "assertLess",
"type": "function"
},
{
"name": "assertLessEqual",
"type": "function"
},
{
"name": "assertListEqual",
"type": "function"
},
{
"name": "assertLogs",
"type": "function"
},
{
"name": "assertMultiLineEqual",
"type": "function"
},
{
"name": "assertNotAlmostEqual",
"type": "function"
},
{
"name": "assertNotAlmostEquals",
"type": "function"
},
{
"name": "assertNotEqual",
"type": "function"
},
{
"name": "assertNotEquals",
"type": "function"
},
{
"name": "assertNotIn",
"type": "function"
},
{
"name": "assertNotIsInstance",
"type": "function"
},
{
"name": "assertNotRegex",
"type": "function"
},
{
"name": "assertNotRegexpMatches",
"type": "function"
},
{
"name": "assertRaises",
"type": "function"
},
{
"name": "assertRaisesRegex",
"type": "function"
},
{
"name": "assertRaisesRegexp",
"type": "function"
},
{
"name": "assertRegex",
"type": "function"
},
{
"name": "assertRegexpMatches",
"type": "function"
},
{
"name": "assertSequenceEqual",
"type": "function"
},
{
"name": "assertSetEqual",
"type": "function"
},
{
"name": "assertTrue",
"type": "function"
},
{
"name": "assertTupleEqual",
"type": "function"
},
{
"name": "assertWarns",
"type": "function"
},
{
"name": "assertWarnsRegex",
"type": "function"
},
{
"name": "countTestCases",
"type": "function"
},
{
"name": "debug",
"type": "function"
},
{
"name": "defaultTestResult",
"type": "function"
},
{
"name": "doClassCleanups",
"type": "function"
},
{
"name": "doCleanups",
"type": "function"
},
{
"name": "fail",
"type": "function"
},
{
"name": "failIf",
"type": "function"
},
{
"name": "failIfAlmostEqual",
"type": "function"
},
{
"name": "failIfEqual",
"type": "function"
},
{
"name": "failUnless",
"type": "function"
},
{
"name": "failUnlessAlmostEqual",
"type": "function"
},
{
"name": "failUnlessEqual",
"type": "function"
},
{
"name": "failUnlessRaises",
"type": "function"
},
{
"name": "failureException",
"type": "statement"
},
{
"name": "id",
"type": "function"
},
{
"name": "longMessage",
"type": "statement"
},
{
"name": "maxDiff",
"type": "statement"
},
{
"name": "run",
"type": "function"
},
{
"name": "setUp",
"type": "function"
},
{
"name": "setUpClass",
"type": "function"
},
{
"name": "shortDescription",
"type": "function"
},
{
"name": "skipTest",
"type": "function"
},
{
"name": "subTest",
"type": "function"
},
{
"name": "tearDown",
"type": "function"
},
{
"name": "tearDownClass",
"type": "function"
},
{
"name": "test_add_column_addition",
"type": "function"
},
{
"name": "test_add_column_addition_days",
"type": "function"
},
{
"name": "test_add_column_constant",
"type": "function"
},
{
"name": "test_add_column_date_trunc",
"type": "function"
},
{
"name": "test_add_column_difference",
"type": "function"
},
{
"name": "test_add_column_difference_days",
"type": "function"
},
{
"name": "test_add_column_distance_between",
"type": "function"
},
{
"name": "test_add_column_divide",
"type": "function"
},
{
"name": "test_add_column_formatted_date",
"type": "function"
},
{
"name": "test_add_column_if_else",
"type": "function"
},
{
"name": "test_add_column_if_else_with_column",
"type": "function"
},
{
"name": "test_add_column_multiply",
"type": "function"
},
{
"name": "test_add_column_string_replace",
"type": "function"
},
{
"name": "test_add_column_string_split",
"type": "function"
},
{
"name": "test_add_column_substring",
"type": "function"
},
{
"name": "test_average",
"type": "function"
},
{
"name": "test_count",
"type": "function"
},
{
"name": "test_count_distinct",
"type": "function"
},
{
"name": "test_count_with_filter",
"type": "function"
},
{
"name": "test_count_with_time_window",
"type": "function"
},
{
"name": "test_diff",
"type": "function"
},
{
"name": "test_first_column",
"type": "function"
},
{
"name": "test_impute",
"type": "function"
},
{
"name": "test_last_column",
"type": "function"
},
{
"name": "test_max",
"type": "function"
},
{
"name": "test_median",
"type": "function"
},
{
"name": "test_min",
"type": "function"
},
{
"name": "test_remove_column",
"type": "function"
},
{
"name": "test_select",
"type": "function"
},
{
"name": "test_shift_down",
"type": "function"
},
{
"name": "test_shift_down_with_groupby",
"type": "function"
},
{
"name": "test_shift_up",
"type": "function"
},
{
"name": "test_sum",
"type": "function"
},
{
"name": "_addSkip",
"type": "function"
},
{
"name": "_formatMessage",
"type": "function"
},
{
"name": "_getAssertEqualityFunc",
"type": "function"
},
{
"name": "_testMethodDoc",
"type": "statement"
},
{
"name": "_testMethodName",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__call__",
"type": "function"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__groupby_agg_action",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
from data_cleaner.tests.base_test import TestCase
from data_cleaner.transformer_actions.column import (
add_column,
count,
count_distinct,
diff,
# expand_column,
first,
last,
remove_column,
select,
shift_down,
shift_up,
)
from pandas.util.testing import assert_frame_equal
import numpy as np
import pandas as pd
TEST_DATAFRAME = pd.DataFrame([
[1, 1000],
[2, 1050],
[1, 1100],
[2, 1150],
], columns=[
'group_id',
'amount',
])
class ColumnTests(TestCase):
def test_remove_column(self):
df = pd.DataFrame([
[0, False, 'a'],
[1, True, 'b'],
], columns=[
'integer',
'boolean',
'string',
])
action = dict(action_arguments=['string'])
df_new = remove_column(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
integer=0,
boolean=False,
),
dict(
integer=1,
boolean=True,
),
])
action = dict(action_arguments=['integer', 'boolean'])
df_new = remove_column(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
string='a',
),
dict(
string='b',
),
])
def test_add_column_addition(self):
df = pd.DataFrame([
[1, 3, 7, 9],
[4, 2, 9, 3],
], columns=[
'integer1',
'integer2',
'integer3',
'integer4',
])
action1 = dict(
action_arguments=[
'integer1',
'integer2',
'integer3',
],
action_options={
'udf': 'addition',
'value': None,
},
outputs=[
dict(
uuid='integer_addition',
column_type='number',
),
],
)
action2 = dict(
action_arguments=['integer1'],
action_options={
'udf': 'addition',
'value': 10,
},
outputs=[
dict(
uuid='integer_addition2',
column_type='number',
),
],
)
action3 = dict(
action_arguments=['integer1', 'integer4'],
action_options={
'udf': 'addition',
'value': 10,
},
outputs=[
dict(
uuid='integer_addition3',
column_type='number',
),
],
)
df_new = add_column(
add_column(
add_column(df, action1),
action2,
),
action3,
)
df_expected = pd.DataFrame([
[1, 3, 7, 9, 11, 11, 20],
[4, 2, 9, 3, 15, 14, 17],
], columns=[
'integer1',
'integer2',
'integer3',
'integer4',
'integer_addition',
'integer_addition2',
'integer_addition3',
])
assert_frame_equal(df_new, df_expected)
def test_add_column_addition_days(self):
df = pd.DataFrame([
['2021-08-31'],
['2021-08-28'],
], columns=[
'created_at',
])
action = dict(
action_arguments=['created_at'],
action_options=dict(
column_type='datetime',
time_unit='d',
udf='addition',
value=3,
),
outputs=[
dict(
uuid='3d_after_creation',
column_type='text',
),
],
)
df_new = add_column(df, action)
df_expected = pd.DataFrame([
['2021-08-31', '2021-09-03 00:00:00'],
['2021-08-28', '2021-08-31 00:00:00'],
], columns=[
'created_at',
'3d_after_creation'
])
assert_frame_equal(df_new, df_expected)
def test_add_column_constant(self):
df = pd.DataFrame([
[False],
[True],
], columns=[
'boolean',
])
action = dict(
action_arguments=[10],
action_options=dict(
udf='constant',
),
outputs=[
dict(
uuid='integer',
column_type='number',
),
],
)
df_new = add_column(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
boolean=False,
integer=10,
),
dict(
boolean=True,
integer=10,
),
])
def test_add_column_date_trunc(self):
df = pd.DataFrame([
['2021-08-31', False],
['2021-08-28', True],
], columns=[
'created_at',
'boolean',
])
action = dict(
action_arguments=['created_at'],
action_options=dict(
udf='date_trunc',
date_part='week',
),
outputs=[
dict(
uuid='week_date',
column_type='text',
),
],
)
df_new = add_column(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
created_at='2021-08-31',
boolean=False,
week_date='2021-08-30',
),
dict(
created_at='2021-08-28',
boolean=True,
week_date='2021-08-23',
),
])
def test_add_column_difference(self):
df = pd.DataFrame([
[1, 3],
[4, 2],
], columns=[
'integer1',
'integer2',
])
action1 = dict(
action_arguments=['integer1', 'integer2'],
action_options={
'udf': 'difference',
},
outputs=[
dict(
uuid='integer_difference',
column_type='number',
),
],
)
action2 = dict(
action_arguments=['integer1'],
action_options={
'udf': 'difference',
'value': 10,
},
outputs=[
dict(
uuid='integer_difference2',
column_type='number',
),
],
)
df_new = add_column(add_column(df, action1), action2)
df_expected = pd.DataFrame([
[1, 3, -2, -9],
[4, 2, 2, -6],
], columns=[
'integer1',
'integer2',
'integer_difference',
'integer_difference2'
])
assert_frame_equal(df_new, df_expected)
def test_add_column_difference_days(self):
df = pd.DataFrame([
['2021-08-31', '2021-09-14'],
['2021-08-28', '2021-09-03'],
], columns=[
'created_at',
'converted_at',
])
action = dict(
action_arguments=['converted_at', 'created_at'],
action_options=dict(
column_type='datetime',
time_unit='d',
udf='difference',
),
outputs=[
dict(
uuid='days_diff',
column_type='number',
),
],
)
df_new = add_column(df, action)
df_expected = pd.DataFrame([
['2021-08-31', '2021-09-14', 14],
['2021-08-28', '2021-09-03', 6],
], columns=[
'created_at',
'converted_at',
'days_diff',
])
assert_frame_equal(df_new, df_expected)
def test_add_column_distance_between(self):
df = pd.DataFrame([
[26.05308, -97.31838, 33.41939, -112.32606],
[39.71954, -84.13056, 33.41939, -112.32606],
], columns=[
'lat1',
'lng1',
'lat2',
'lng2',
])
action = dict(
action_arguments=['lat1', 'lng1', 'lat2', 'lng2'],
action_options=dict(
udf='distance_between',
),
outputs=[
dict(
uuid='distance',
column_type='number_with_decimals',
),
],
)
df_new = add_column(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
lat1=26.05308,
lng1=-97.31838,
lat2=33.41939,
lng2=-112.32606,
distance=1661.8978520305657,
),
dict(
lat1=39.71954,
lng1=-84.13056,
lat2=33.41939,
lng2=-112.32606,
distance=2601.5452571116184,
),
])
def test_add_column_divide(self):
df = pd.DataFrame([
[12, 3, 70, 9],
[4, 2, 90, 3],
], columns=[
'integer1',
'integer2',
'integer3',
'integer4',
])
action1 = dict(
action_arguments=[
'integer1',
'integer2',
],
action_options={
'udf': 'divide',
},
outputs=[
dict(
uuid='integer_divide',
column_type='number',
),
],
)
action2 = dict(
action_arguments=['integer3'],
action_options={
'udf': 'divide',
'value': 10,
},
outputs=[
dict(
uuid='integer_divide2',
column_type='number',
),
],
)
df_new = add_column(add_column(df, action1), action2)
df_expected = pd.DataFrame([
[12, 3, 70, 9, 4, 7],
[4, 2, 90, 3, 2, 9],
], columns=[
'integer1',
'integer2',
'integer3',
'integer4',
'integer_divide',
'integer_divide2'
])
assert_frame_equal(df_new, df_expected)
# def test_add_column_extract_dict_string(self):
# df = pd.DataFrame([
# '{\'country\': \'US\', \'age\': \'20\'}',
# '{\'country\': \'CA\'}',
# '{\'country\': \'UK\', \'age\': \'24\'}',
# '',
# ], columns=[
# 'properties',
# ])
# action = dict(
# action_arguments=['properties', 'country'],
# action_options=dict(
# udf='extract_dict_value',
# ),
# outputs=[
# dict(
# uuid='property_country',
# column_type='text',
# ),
# ],
# )
# df_new = add_column(df, action)
# self.assertEqual(df_new.to_dict(orient='records'), [
# dict(
# properties='{\'country\': \'US\', \'age\': \'20\'}',
# property_country='US',
# ),
# dict(
# properties='{\'country\': \'CA\'}',
# property_country='CA',
# ),
# dict(
# properties='{\'country\': \'UK\', \'age\': \'24\'}',
# property_country='UK',
# ),
# dict(
# properties='',
# property_country=np.NaN,
# ),
# ])
# action2 = dict(
# action_arguments=['properties', 'age'],
# action_options=dict(
# udf='extract_dict_value',
# ),
# outputs=[
# dict(
# uuid='property_age',
# column_type='number',
# ),
# ],
# )
# df_new2 = add_column(df, action2)
# self.assertEqual(df_new2.to_dict(orient='records'), [
# dict(
# properties='{\'country\': \'US\', \'age\': \'20\'}',
# property_age=20,
# ),
# dict(
# properties='{\'country\': \'CA\'}',
# property_age=0,
# ),
# dict(
# properties='{\'country\': \'UK\', \'age\': \'24\'}',
# property_age=24,
# ),
# dict(
# properties='',
# property_age=0,
# ),
# ])
# def test_add_column_extract_dict_string_with_json(self):
# df = pd.DataFrame([
# '{\"country\": \"US\", \"is_adult\": true}',
# '{\"country\": \"CA\"}',
# '{\"country\": \"UK\", \"is_adult\": false}',
# '',
# ], columns=[
# 'properties',
# ])
# action = dict(
# action_arguments=['properties', 'country'],
# action_options=dict(
# udf='extract_dict_value',
# ),
# outputs=[
# dict(
# uuid='property_country',
# column_type='text',
# ),
# ],
# )
# df_new = add_column(df, action)
# self.assertEqual(df_new.to_dict(orient='records'), [
# dict(
# properties='{\"country\": \"US\", \"is_adult\": true}',
# property_country='US',
# ),
# dict(
# properties='{\"country\": \"CA\"}',
# property_country='CA',
# ),
# dict(
# properties='{\"country\": \"UK\", \"is_adult\": false}',
# property_country='UK',
# ),
# dict(
# properties='',
# property_country=np.NaN,
# ),
# ])
# action2 = dict(
# action_arguments=['properties', 'is_adult'],
# action_options=dict(
# udf='extract_dict_value',
# ),
# outputs=[
# dict(
# uuid='property_is_adult',
# column_type='true_or_false',
# ),
# ],
# )
# df_new2 = add_column(df, action2)
# self.assertEqual(df_new2.to_dict(orient='records'), [
# dict(
# properties='{\"country\": \"US\", \"is_adult\": true}',
# property_is_adult=True,
# ),
# dict(
# properties='{\"country\": \"CA\"}',
# property_is_adult=None,
# ),
# dict(
# properties='{\"country\": \"UK\", \"is_adult\": false}',
# property_is_adult=False,
# ),
# dict(
# properties='',
# property_is_adult=None,
# ),
# ])
def test_add_column_formatted_date(self):
df = pd.DataFrame([
['2019-04-10 08:20:58', False],
['2019-03-05 03:30:30', True],
], columns=[
'created_at',
'boolean',
])
action = dict(
action_arguments=['created_at'],
action_options=dict(
udf='formatted_date',
format='%Y-%m-%d',
),
outputs=[
dict(
uuid='created_date',
column_type='text',
),
],
)
df_new = add_column(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
created_at='2019-04-10 08:20:58',
boolean=False,
created_date='2019-04-10',
),
dict(
created_at='2019-03-05 03:30:30',
boolean=True,
created_date='2019-03-05',
),
])
def test_add_column_if_else(self):
df = pd.DataFrame([
['2019-04-10 08:20:58'],
[None],
], columns=[
'converted_at'
])
action = dict(
action_arguments=[False, True],
action_code='converted_at == null',
action_options=dict(
udf='if_else',
),
outputs=[
dict(
uuid='converted',
column_type='true_or_false',
),
],
)
df_new = add_column(df, action, original_df=df)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
converted_at='2019-04-10 08:20:58',
converted=True,
),
dict(
converted_at=None,
converted=False,
),
])
def test_add_column_if_else_with_column(self):
df = pd.DataFrame([
['2019-04-10 08:20:58', 'test_user_id'],
[None, None],
], columns=[
'converted_at',
'user_id',
])
action = dict(
action_arguments=['unknown', 'user_id'],
action_code='converted_at == null',
action_options=dict(
udf='if_else',
arg1_type='value',
arg2_type='column',
),
outputs=[
dict(
uuid='user_id_clean',
column_type='text',
),
],
)
df_new = add_column(df, action, original_df=df)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
converted_at='2019-04-10 08:20:58',
user_id='test_user_id',
user_id_clean='test_user_id',
),
dict(
converted_at=None,
user_id=None,
user_id_clean='unknown',
),
])
def test_add_column_multiply(self):
df = pd.DataFrame([
[1, 3, 7, 9],
[4, 2, 9, 3],
], columns=[
'integer1',
'integer2',
'integer3',
'integer4',
])
action1 = dict(
action_arguments=[
'integer1',
'integer2',
],
action_options={
'udf': 'multiply',
},
outputs=[
dict(
uuid='integer_multiply',
column_type='number',
),
],
)
action2 = dict(
action_arguments=['integer3'],
action_options={
'udf': 'multiply',
'value': 10,
},
outputs=[
dict(
uuid='integer_multiply2',
column_type='number',
),
],
)
df_new = add_column(add_column(df, action1), action2)
df_expected = pd.DataFrame([
[1, 3, 7, 9, 3, 70],
[4, 2, 9, 3, 8, 90],
], columns=[
'integer1',
'integer2',
'integer3',
'integer4',
'integer_multiply',
'integer_multiply2'
])
assert_frame_equal(df_new, df_expected)
def test_add_column_string_replace(self):
df = pd.DataFrame([
['$1000'],
['$321. '],
['$4,321'],
], columns=[
'amount',
])
action = dict(
action_arguments=['amount'],
action_options={
'udf': 'string_replace',
'pattern': '\\$|\\.|\\,|\\s*',
'replacement': '',
},
outputs=[
dict(
uuid='amount_clean',
column_type='true_or_false',
),
],
)
df_new = add_column(df, action)
df_expected = pd.DataFrame([
['$1000', '1000'],
['$321. ', '321'],
['$4,321', '4321'],
], columns=[
'amount',
'amount_clean',
])
assert_frame_equal(df_new, df_expected)
def test_add_column_string_split(self):
df = pd.DataFrame([
['Street1, Long Beach, CA, '],
['Street2,Vernon, CA, 123'],
['Pacific Coast Highway, Los Angeles, CA, 111'],
], columns=[
'location',
])
action = dict(
action_arguments=['location'],
action_options={
'udf': 'string_split',
'separator': ',',
'part_index': 1,
},
outputs=[
dict(
uuid='location_city',
column_type='text',
),
],
)
action2 = dict(
action_arguments=['location'],
action_options={
'udf': 'string_split',
'separator': ',',
'part_index': 3,
},
outputs=[
dict(
uuid='num',
column_type='number',
),
],
)
df_new = add_column(add_column(df, action), action2)
df_expected = pd.DataFrame([
['Street1, Long Beach, CA, ', 'Long Beach', 0],
['Street2,Vernon, CA, 123', 'Vernon', 123],
['Pacific Coast Highway, Los Angeles, CA, 111', 'Los Angeles', 111],
], columns=[
'location',
'location_city',
'num',
])
assert_frame_equal(df_new, df_expected)
def test_add_column_substring(self):
df = pd.DataFrame([
['$1000.0'],
['$321.9'],
], columns=[
'amount',
])
action = dict(
action_arguments=['amount'],
action_options={
'udf': 'substring',
'start': 1,
'stop': -2,
},
outputs=[
dict(
uuid='amount_int',
column_type='text',
),
],
)
df_new = add_column(df, action)
df_expected = pd.DataFrame([
['$1000.0', '1000'],
['$321.9', '321'],
], columns=[
'amount',
'amount_int',
])
assert_frame_equal(df_new, df_expected)
def test_average(self):
from data_cleaner.transformer_actions.column import average
action = self.__groupby_agg_action('average_amount')
df_new = average(TEST_DATAFRAME.copy(), action)
df_expected = pd.DataFrame([
[1, 1000, 1050],
[2, 1050, 1100],
[1, 1100, 1050],
[2, 1150, 1100],
], columns=[
'group_id',
'amount',
'average_amount'
])
assert_frame_equal(df_new, df_expected)
def test_count(self):
df = pd.DataFrame([
[1, 1000],
[1, 1050],
[1, 1100],
[2, 1150],
], columns=[
'group_id',
'order_id',
])
action = dict(
action_arguments=['order_id'],
action_options=dict(
groupby_columns=['group_id']
),
outputs=[
dict(uuid='order_count'),
],
)
df_new = count(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
group_id=1,
order_id=1000,
order_count=3,
),
dict(
group_id=1,
order_id=1050,
order_count=3,
),
dict(
group_id=1,
order_id=1100,
order_count=3,
),
dict(
group_id=2,
order_id=1150,
order_count=1,
),
])
def test_count_distinct(self):
df = pd.DataFrame([
[1, 1000],
[1, 1000],
[1, 1100],
[2, 1150],
], columns=[
'group_id',
'order_id',
])
action = dict(
action_arguments=['order_id'],
action_options=dict(
groupby_columns=['group_id']
),
outputs=[
dict(uuid='order_count'),
],
)
df_new = count_distinct(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
group_id=1,
order_id=1000,
order_count=2,
),
dict(
group_id=1,
order_id=1000,
order_count=2,
),
dict(
group_id=1,
order_id=1100,
order_count=2,
),
dict(
group_id=2,
order_id=1150,
order_count=1,
),
])
def test_count_with_time_window(self):
df = pd.DataFrame([
[1, 1000, '2021-10-01', '2021-09-01'],
[1, 1050, '2021-10-01', '2021-08-01'],
[1, 1100, '2021-10-01', '2021-01-01'],
[2, 1150, '2021-09-01', '2021-08-01'],
], columns=[
'group_id',
'order_id',
'group_churned_at',
'order_created_at',
])
action = dict(
action_arguments=['order_id'],
action_code='',
action_options=dict(
groupby_columns=['group_id'],
timestamp_feature_a='group_churned_at',
timestamp_feature_b='order_created_at',
window=90*24*3600,
),
outputs=[
dict(uuid='order_count'),
],
)
df_new = count(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
group_id=1,
order_id=1000,
group_churned_at='2021-10-01',
order_created_at='2021-09-01',
order_count=2,
),
dict(
group_id=1,
order_id=1050,
group_churned_at='2021-10-01',
order_created_at='2021-08-01',
order_count=2,
),
dict(
group_id=1,
order_id=1100,
group_churned_at='2021-10-01',
order_created_at='2021-01-01',
order_count=2,
),
dict(
group_id=2,
order_id=1150,
group_churned_at='2021-09-01',
order_created_at='2021-08-01',
order_count=1,
),
])
def test_count_with_filter(self):
df = pd.DataFrame([
[1, 1000, '2021-10-01', '2021-09-01'],
[1, 1050, '2021-10-01', '2021-08-01'],
[1, 1100, '2021-10-01', '2021-01-01'],
[2, 1150, '2021-09-01', '2021-08-01'],
[2, 1200, '2021-09-01', '2021-08-16'],
[2, 1250, '2021-09-01', '2021-08-14'],
], columns=[
'group_id',
'order_id',
'group_churned_at',
'order_created_at',
])
action = dict(
action_arguments=['order_id'],
action_code='order_created_at < \'2021-08-15\'',
action_options=dict(
groupby_columns=['group_id'],
),
outputs=[
dict(uuid='order_count'),
],
)
df_new = count(df, action)
df_expected = pd.DataFrame([
[1, 1000, '2021-10-01', '2021-09-01', 2],
[1, 1050, '2021-10-01', '2021-08-01', 2],
[1, 1100, '2021-10-01', '2021-01-01', 2],
[2, 1150, '2021-09-01', '2021-08-01', 2],
[2, 1200, '2021-09-01', '2021-08-16', 2],
[2, 1250, '2021-09-01', '2021-08-14', 2],
], columns=[
'group_id',
'order_id',
'group_churned_at',
'order_created_at',
'order_count',
])
assert_frame_equal(df_new, df_expected)
def test_diff(self):
df = pd.DataFrame([
['2020-01-01', 1000],
['2020-01-02', 1050],
['2020-01-03', 1200],
['2020-01-04', 990],
], columns=[
'date',
'sold',
])
action = dict(
action_arguments=['sold'],
outputs=[
dict(uuid='sold_diff'),
],
)
df_new = diff(df, action)
self.assertEqual(df_new.to_dict(orient='records')[1:], [
dict(
date='2020-01-02',
sold=1050,
sold_diff=50,
),
dict(
date='2020-01-03',
sold=1200,
sold_diff=150,
),
dict(
date='2020-01-04',
sold=990,
sold_diff=-210,
),
])
# def test_expand_column(self):
# df = pd.DataFrame([
# [1, 'game'],
# [1, 'book'],
# [1, 'game'],
# [2, 'Video Game'],
# [1, 'Video Game'],
# [2, 'book'],
# [1, 'Video Game'],
# [2, 'Video Game'],
# ], columns=[
# 'group_id',
# 'category',
# ])
# action = dict(
# action_arguments=['category'],
# action_options=dict(
# groupby_columns=['group_id']
# ),
# outputs=[
# dict(uuid='category_expanded_count_game'),
# dict(uuid='category_expanded_count_book'),
# dict(uuid='category_expanded_count_video_game'),
# dict(uuid='category_expanded_count_clothing'),
# ],
# )
# df_new = expand_column(df, action)
# df_expected = pd.DataFrame([
# [1, 'game', 2, 1, 2],
# [1, 'book', 2, 1, 2],
# [1, 'game', 2, 1, 2],
# [2, 'Video Game', 0, 1, 2],
# [1, 'Video Game', 2, 1, 2],
# [2, 'book', 0, 1, 2],
# [1, 'Video Game', 2, 1, 2],
# [2, 'Video Game', 0, 1, 2],
# ], columns=[
# 'group_id',
# 'category',
# 'category_expanded_count_game',
# 'category_expanded_count_book',
# 'category_expanded_count_video_game',
# ])
# assert_frame_equal(df_new, df_expected)
# def test_expand_column_with_time_window(self):
# df = pd.DataFrame([
# [1, 'game', '2021-01-02', '2021-01-04'],
# [1, 'book', '2021-01-02', '2021-01-04'],
# [1, 'game', '2021-01-03', '2021-01-04'],
# [2, 'Video Game', '2021-01-01', '2021-01-03'],
# [1, 'Video Game', '2021-01-01', '2021-01-04'],
# [2, 'book', '2021-01-02', '2021-01-03'],
# [1, 'Video Game', '2021-01-03', '2021-01-04'],
# [2, 'Video Game', '2020-12-30', '2021-01-03'],
# ], columns=[
# 'group_id',
# 'category',
# 'timestamp1',
# 'timestamp2',
# ])
# action = dict(
# action_arguments=['category'],
# action_options=dict(
# groupby_columns=['group_id'],
# timestamp_feature_a='timestamp2',
# timestamp_feature_b='timestamp1',
# window=172800,
# ),
# outputs=[
# dict(uuid='category_expanded_count_game_2d'),
# dict(uuid='category_expanded_count_book_2d'),
# dict(uuid='category_expanded_count_video_game_2d'),
# dict(uuid='category_expanded_count_clothing_2d'),
# ],
# )
# df_new = expand_column(df, action)
# df_expected = pd.DataFrame([
# [1, 'game', '2021-01-02', '2021-01-04', 2, 1, 1],
# [1, 'book', '2021-01-02', '2021-01-04', 2, 1, 1],
# [1, 'game', '2021-01-03', '2021-01-04', 2, 1, 1],
# [2, 'Video Game', '2021-01-01', '2021-01-03', 0, 1, 1],
# [1, 'Video Game', '2021-01-01', '2021-01-04', 2, 1, 1],
# [2, 'book', '2021-01-02', '2021-01-03', 0, 1, 1],
# [1, 'Video Game', '2021-01-03', '2021-01-04', 2, 1, 1],
# [2, 'Video Game', '2020-12-30', '2021-01-03', 0, 1, 1],
# ], columns=[
# 'group_id',
# 'category',
# 'timestamp1',
# 'timestamp2',
# 'category_expanded_count_game_2d',
# 'category_expanded_count_book_2d',
# 'category_expanded_count_video_game_2d',
# ])
# assert_frame_equal(df_new, df_expected)
def test_first_column(self):
df = pd.DataFrame([
[1, 1000],
[2, 1050],
[1, 1100],
[2, 1150],
], columns=[
'group_id',
'order_id',
])
action = dict(
action_arguments=['order_id'],
action_options=dict(
groupby_columns=['group_id']
),
outputs=[
dict(uuid='first_order'),
],
)
df_new = first(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
group_id=1,
order_id=1000,
first_order=1000,
),
dict(
group_id=2,
order_id=1050,
first_order=1050,
),
dict(
group_id=1,
order_id=1100,
first_order=1000,
),
dict(
group_id=2,
order_id=1150,
first_order=1050,
),
])
def test_impute(self):
from data_cleaner.transformer_actions.column import impute
df = pd.DataFrame([
['2020-01-01', 1000, ' ', 800],
['2020-01-02', '', 1200, 700],
['2020-01-03', 1200, np.NaN, 900],
['2020-01-04', np.NaN, ' ', 700],
['2020-01-05', 1700, 1300, 800],
], columns=[
'date',
'sold',
'curr_profit',
'prev_sold',
])
action1 = dict(
action_arguments=['sold', 'curr_profit'],
action_options={
'value': '0',
},
action_variables={
'0': {
'feature': {
'column_type': 'number',
'uuid': 'sold',
},
'type': 'feature',
},
'1': {
'feature': {
'column_type': 'number',
'uuid': 'curr_profit',
},
'type': 'feature',
},
},
)
action2 = dict(
action_arguments=['sold'],
action_options={
'value': '0',
},
action_variables={
'0': {
'feature': {
'column_type': 'number',
'uuid': 'sold',
},
'type': 'feature',
},
},
)
action3 = dict(
action_arguments=['sold', 'curr_profit'],
action_options={
'strategy': 'average',
},
)
action4 = dict(
action_arguments=['sold', 'curr_profit'],
action_options={
'strategy': 'median',
},
)
action5 = dict(
action_arguments=['sold', 'curr_profit'],
action_options={
'strategy': 'column',
'value': 'prev_sold',
},
)
action_invalid = dict(
action_arguments=['sold', 'curr_profit'],
action_options={
'strategy': 'mode',
},
)
df_new1 = impute(df.copy(), action1)
df_new2 = impute(df.copy(), action2)
df_new3 = impute(df.copy(), action3)
df_new4 = impute(df.copy(), action4)
df_new5 = impute(df.copy(), action5)
df_expected1 = pd.DataFrame([
['2020-01-01', 1000, 0, 800],
['2020-01-02', 0, 1200, 700],
['2020-01-03', 1200, 0, 900],
['2020-01-04', 0, 0, 700],
['2020-01-05', 1700, 1300, 800],
], columns=[
'date',
'sold',
'curr_profit',
'prev_sold',
])
df_expected2 = pd.DataFrame([
['2020-01-01', 1000, ' ', 800],
['2020-01-02', 0, 1200, 700],
['2020-01-03', 1200, np.nan, 900],
['2020-01-04', 0, ' ', 700],
['2020-01-05', 1700, 1300, 800],
], columns=[
'date',
'sold',
'curr_profit',
'prev_sold',
])
df_expected3 = pd.DataFrame([
['2020-01-01', 1000, 1250, 800],
['2020-01-02', 1300, 1200, 700],
['2020-01-03', 1200, 1250, 900],
['2020-01-04', 1300, 1250, 700],
['2020-01-05', 1700, 1300, 800],
], columns=[
'date',
'sold',
'curr_profit',
'prev_sold',
])
df_expected4 = pd.DataFrame([
['2020-01-01', 1000, 1250, 800],
['2020-01-02', 1200, 1200, 700],
['2020-01-03', 1200, 1250, 900],
['2020-01-04', 1200, 1250, 700],
['2020-01-05', 1700, 1300, 800],
], columns=[
'date',
'sold',
'curr_profit',
'prev_sold',
])
df_expected5 = pd.DataFrame([
['2020-01-01', 1000, 800, 800],
['2020-01-02', 700, 1200, 700],
['2020-01-03', 1200, 900, 900],
['2020-01-04', 700, 700, 700],
['2020-01-05', 1700, 1300, 800],
], columns=[
'date',
'sold',
'curr_profit',
'prev_sold',
])
df_new1['sold'] = df_new1['sold'].astype(int)
df_new1['curr_profit'] = df_new1['curr_profit'].astype(int)
df_new2['sold'] = df_new2['sold'].astype(int)
df_new3['sold'] = df_new3['sold'].astype(int)
df_new3['curr_profit'] = df_new3['curr_profit'].astype(int)
df_new4['sold'] = df_new4['sold'].astype(int)
df_new4['curr_profit'] = df_new4['curr_profit'].astype(int)
df_new5['sold'] = df_new5['sold'].astype(int)
df_new5['curr_profit'] = df_new5['curr_profit'].astype(int)
assert_frame_equal(df_new1, df_expected1)
assert_frame_equal(df_new2, df_expected2)
assert_frame_equal(df_new3, df_expected3)
assert_frame_equal(df_new4, df_expected4)
assert_frame_equal(df_new5, df_expected5)
with self.assertRaises(Exception):
_ = impute(df.copy(), action_invalid)
def test_last_column(self):
df = pd.DataFrame([
[1, 1000],
[2, 1050],
[1, 1100],
[2, 1150],
], columns=[
'group_id',
'order_id',
])
action = dict(
action_arguments=['order_id'],
action_options=dict(
groupby_columns=['group_id']
),
outputs=[
dict(uuid='last_order'),
],
)
df_new = last(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
group_id=1,
order_id=1000,
last_order=1100,
),
dict(
group_id=2,
order_id=1050,
last_order=1150,
),
dict(
group_id=1,
order_id=1100,
last_order=1100,
),
dict(
group_id=2,
order_id=1150,
last_order=1150,
),
])
def test_max(self):
from data_cleaner.transformer_actions.column import max
action = self.__groupby_agg_action('max_amount')
df_new = max(TEST_DATAFRAME.copy(), action)
df_expected = pd.DataFrame([
[1, 1000, 1100],
[2, 1050, 1150],
[1, 1100, 1100],
[2, 1150, 1150],
], columns=[
'group_id',
'amount',
'max_amount',
])
assert_frame_equal(df_new, df_expected)
action2 = dict(
action_arguments=['amount'],
action_options=dict(),
outputs=[
dict(uuid='max_amount'),
],
)
df_new2 = max(TEST_DATAFRAME.copy(), action2)
df_expected2 = pd.DataFrame([
[1, 1000, 1150],
[2, 1050, 1150],
[1, 1100, 1150],
[2, 1150, 1150],
], columns=[
'group_id',
'amount',
'max_amount',
])
assert_frame_equal(df_new2, df_expected2)
def test_median(self):
from data_cleaner.transformer_actions.column import median
action = self.
|
7,731 | 218 | 43,286 |
mage-ai__mage-ai
|
9ed779ccaf3538efbe1f2f54dd68f61fb1c3af55
|
src/data_cleaner/tests/transformer_actions/test_column.py
|
infile
|
__groupby_agg_action
| true |
function
| 104 | 108 | true | false |
[
"__groupby_agg_action",
"assertEqual",
"assertRaises",
"test_count",
"test_shift_down",
"test_add_column_addition",
"test_add_column_addition_days",
"test_add_column_constant",
"test_add_column_date_trunc",
"test_add_column_difference",
"test_add_column_difference_days",
"test_add_column_distance_between",
"test_add_column_divide",
"test_add_column_formatted_date",
"test_add_column_if_else",
"test_add_column_if_else_with_column",
"test_add_column_multiply",
"test_add_column_string_replace",
"test_add_column_string_split",
"test_add_column_substring",
"test_average",
"test_count_distinct",
"test_count_with_filter",
"test_count_with_time_window",
"test_diff",
"test_first_column",
"test_impute",
"test_last_column",
"test_max",
"test_median",
"test_min",
"test_remove_column",
"test_select",
"test_shift_down_with_groupby",
"test_shift_up",
"test_sum",
"setUp",
"tearDown",
"addClassCleanup",
"addCleanup",
"addTypeEqualityFunc",
"assert_",
"assertAlmostEqual",
"assertAlmostEquals",
"assertCountEqual",
"assertDictContainsSubset",
"assertDictEqual",
"assertEquals",
"assertFalse",
"assertGreater",
"assertGreaterEqual",
"assertIn",
"assertIs",
"assertIsInstance",
"assertIsNone",
"assertIsNot",
"assertIsNotNone",
"assertLess",
"assertLessEqual",
"assertListEqual",
"assertLogs",
"assertMultiLineEqual",
"assertNoLogs",
"assertNotAlmostEqual",
"assertNotAlmostEquals",
"assertNotEqual",
"assertNotEquals",
"assertNotIn",
"assertNotIsInstance",
"assertNotRegex",
"assertNotRegexpMatches",
"assertRaisesRegex",
"assertRaisesRegexp",
"assertRegex",
"assertRegexpMatches",
"assertSequenceEqual",
"assertSetEqual",
"assertTrue",
"assertTupleEqual",
"assertWarns",
"assertWarnsRegex",
"countTestCases",
"debug",
"defaultTestResult",
"doClassCleanups",
"doCleanups",
"fail",
"failIf",
"failIfAlmostEqual",
"failIfEqual",
"failUnless",
"failUnlessAlmostEqual",
"failUnlessEqual",
"failUnlessRaises",
"failureException",
"id",
"longMessage",
"maxDiff",
"run",
"setUpClass",
"shortDescription",
"skipTest",
"subTest",
"tearDownClass",
"__annotations__",
"__call__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "addClassCleanup",
"type": "function"
},
{
"name": "addCleanup",
"type": "function"
},
{
"name": "addTypeEqualityFunc",
"type": "function"
},
{
"name": "assert_",
"type": "function"
},
{
"name": "assertAlmostEqual",
"type": "function"
},
{
"name": "assertAlmostEquals",
"type": "function"
},
{
"name": "assertCountEqual",
"type": "function"
},
{
"name": "assertDictContainsSubset",
"type": "function"
},
{
"name": "assertDictEqual",
"type": "function"
},
{
"name": "assertEqual",
"type": "function"
},
{
"name": "assertEquals",
"type": "function"
},
{
"name": "assertFalse",
"type": "function"
},
{
"name": "assertGreater",
"type": "function"
},
{
"name": "assertGreaterEqual",
"type": "function"
},
{
"name": "assertIn",
"type": "function"
},
{
"name": "assertIs",
"type": "function"
},
{
"name": "assertIsInstance",
"type": "function"
},
{
"name": "assertIsNone",
"type": "function"
},
{
"name": "assertIsNot",
"type": "function"
},
{
"name": "assertIsNotNone",
"type": "function"
},
{
"name": "assertLess",
"type": "function"
},
{
"name": "assertLessEqual",
"type": "function"
},
{
"name": "assertListEqual",
"type": "function"
},
{
"name": "assertLogs",
"type": "function"
},
{
"name": "assertMultiLineEqual",
"type": "function"
},
{
"name": "assertNotAlmostEqual",
"type": "function"
},
{
"name": "assertNotAlmostEquals",
"type": "function"
},
{
"name": "assertNotEqual",
"type": "function"
},
{
"name": "assertNotEquals",
"type": "function"
},
{
"name": "assertNotIn",
"type": "function"
},
{
"name": "assertNotIsInstance",
"type": "function"
},
{
"name": "assertNotRegex",
"type": "function"
},
{
"name": "assertNotRegexpMatches",
"type": "function"
},
{
"name": "assertRaises",
"type": "function"
},
{
"name": "assertRaisesRegex",
"type": "function"
},
{
"name": "assertRaisesRegexp",
"type": "function"
},
{
"name": "assertRegex",
"type": "function"
},
{
"name": "assertRegexpMatches",
"type": "function"
},
{
"name": "assertSequenceEqual",
"type": "function"
},
{
"name": "assertSetEqual",
"type": "function"
},
{
"name": "assertTrue",
"type": "function"
},
{
"name": "assertTupleEqual",
"type": "function"
},
{
"name": "assertWarns",
"type": "function"
},
{
"name": "assertWarnsRegex",
"type": "function"
},
{
"name": "countTestCases",
"type": "function"
},
{
"name": "debug",
"type": "function"
},
{
"name": "defaultTestResult",
"type": "function"
},
{
"name": "doClassCleanups",
"type": "function"
},
{
"name": "doCleanups",
"type": "function"
},
{
"name": "fail",
"type": "function"
},
{
"name": "failIf",
"type": "function"
},
{
"name": "failIfAlmostEqual",
"type": "function"
},
{
"name": "failIfEqual",
"type": "function"
},
{
"name": "failUnless",
"type": "function"
},
{
"name": "failUnlessAlmostEqual",
"type": "function"
},
{
"name": "failUnlessEqual",
"type": "function"
},
{
"name": "failUnlessRaises",
"type": "function"
},
{
"name": "failureException",
"type": "statement"
},
{
"name": "id",
"type": "function"
},
{
"name": "longMessage",
"type": "statement"
},
{
"name": "maxDiff",
"type": "statement"
},
{
"name": "run",
"type": "function"
},
{
"name": "setUp",
"type": "function"
},
{
"name": "setUpClass",
"type": "function"
},
{
"name": "shortDescription",
"type": "function"
},
{
"name": "skipTest",
"type": "function"
},
{
"name": "subTest",
"type": "function"
},
{
"name": "tearDown",
"type": "function"
},
{
"name": "tearDownClass",
"type": "function"
},
{
"name": "test_add_column_addition",
"type": "function"
},
{
"name": "test_add_column_addition_days",
"type": "function"
},
{
"name": "test_add_column_constant",
"type": "function"
},
{
"name": "test_add_column_date_trunc",
"type": "function"
},
{
"name": "test_add_column_difference",
"type": "function"
},
{
"name": "test_add_column_difference_days",
"type": "function"
},
{
"name": "test_add_column_distance_between",
"type": "function"
},
{
"name": "test_add_column_divide",
"type": "function"
},
{
"name": "test_add_column_formatted_date",
"type": "function"
},
{
"name": "test_add_column_if_else",
"type": "function"
},
{
"name": "test_add_column_if_else_with_column",
"type": "function"
},
{
"name": "test_add_column_multiply",
"type": "function"
},
{
"name": "test_add_column_string_replace",
"type": "function"
},
{
"name": "test_add_column_string_split",
"type": "function"
},
{
"name": "test_add_column_substring",
"type": "function"
},
{
"name": "test_average",
"type": "function"
},
{
"name": "test_count",
"type": "function"
},
{
"name": "test_count_distinct",
"type": "function"
},
{
"name": "test_count_with_filter",
"type": "function"
},
{
"name": "test_count_with_time_window",
"type": "function"
},
{
"name": "test_diff",
"type": "function"
},
{
"name": "test_first_column",
"type": "function"
},
{
"name": "test_impute",
"type": "function"
},
{
"name": "test_last_column",
"type": "function"
},
{
"name": "test_max",
"type": "function"
},
{
"name": "test_median",
"type": "function"
},
{
"name": "test_min",
"type": "function"
},
{
"name": "test_remove_column",
"type": "function"
},
{
"name": "test_select",
"type": "function"
},
{
"name": "test_shift_down",
"type": "function"
},
{
"name": "test_shift_down_with_groupby",
"type": "function"
},
{
"name": "test_shift_up",
"type": "function"
},
{
"name": "test_sum",
"type": "function"
},
{
"name": "_addSkip",
"type": "function"
},
{
"name": "_formatMessage",
"type": "function"
},
{
"name": "_getAssertEqualityFunc",
"type": "function"
},
{
"name": "_testMethodDoc",
"type": "statement"
},
{
"name": "_testMethodName",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__call__",
"type": "function"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__groupby_agg_action",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
from data_cleaner.tests.base_test import TestCase
from data_cleaner.transformer_actions.column import (
add_column,
count,
count_distinct,
diff,
# expand_column,
first,
last,
remove_column,
select,
shift_down,
shift_up,
)
from pandas.util.testing import assert_frame_equal
import numpy as np
import pandas as pd
TEST_DATAFRAME = pd.DataFrame([
[1, 1000],
[2, 1050],
[1, 1100],
[2, 1150],
], columns=[
'group_id',
'amount',
])
class ColumnTests(TestCase):
def test_remove_column(self):
df = pd.DataFrame([
[0, False, 'a'],
[1, True, 'b'],
], columns=[
'integer',
'boolean',
'string',
])
action = dict(action_arguments=['string'])
df_new = remove_column(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
integer=0,
boolean=False,
),
dict(
integer=1,
boolean=True,
),
])
action = dict(action_arguments=['integer', 'boolean'])
df_new = remove_column(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
string='a',
),
dict(
string='b',
),
])
def test_add_column_addition(self):
df = pd.DataFrame([
[1, 3, 7, 9],
[4, 2, 9, 3],
], columns=[
'integer1',
'integer2',
'integer3',
'integer4',
])
action1 = dict(
action_arguments=[
'integer1',
'integer2',
'integer3',
],
action_options={
'udf': 'addition',
'value': None,
},
outputs=[
dict(
uuid='integer_addition',
column_type='number',
),
],
)
action2 = dict(
action_arguments=['integer1'],
action_options={
'udf': 'addition',
'value': 10,
},
outputs=[
dict(
uuid='integer_addition2',
column_type='number',
),
],
)
action3 = dict(
action_arguments=['integer1', 'integer4'],
action_options={
'udf': 'addition',
'value': 10,
},
outputs=[
dict(
uuid='integer_addition3',
column_type='number',
),
],
)
df_new = add_column(
add_column(
add_column(df, action1),
action2,
),
action3,
)
df_expected = pd.DataFrame([
[1, 3, 7, 9, 11, 11, 20],
[4, 2, 9, 3, 15, 14, 17],
], columns=[
'integer1',
'integer2',
'integer3',
'integer4',
'integer_addition',
'integer_addition2',
'integer_addition3',
])
assert_frame_equal(df_new, df_expected)
def test_add_column_addition_days(self):
df = pd.DataFrame([
['2021-08-31'],
['2021-08-28'],
], columns=[
'created_at',
])
action = dict(
action_arguments=['created_at'],
action_options=dict(
column_type='datetime',
time_unit='d',
udf='addition',
value=3,
),
outputs=[
dict(
uuid='3d_after_creation',
column_type='text',
),
],
)
df_new = add_column(df, action)
df_expected = pd.DataFrame([
['2021-08-31', '2021-09-03 00:00:00'],
['2021-08-28', '2021-08-31 00:00:00'],
], columns=[
'created_at',
'3d_after_creation'
])
assert_frame_equal(df_new, df_expected)
def test_add_column_constant(self):
df = pd.DataFrame([
[False],
[True],
], columns=[
'boolean',
])
action = dict(
action_arguments=[10],
action_options=dict(
udf='constant',
),
outputs=[
dict(
uuid='integer',
column_type='number',
),
],
)
df_new = add_column(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
boolean=False,
integer=10,
),
dict(
boolean=True,
integer=10,
),
])
def test_add_column_date_trunc(self):
df = pd.DataFrame([
['2021-08-31', False],
['2021-08-28', True],
], columns=[
'created_at',
'boolean',
])
action = dict(
action_arguments=['created_at'],
action_options=dict(
udf='date_trunc',
date_part='week',
),
outputs=[
dict(
uuid='week_date',
column_type='text',
),
],
)
df_new = add_column(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
created_at='2021-08-31',
boolean=False,
week_date='2021-08-30',
),
dict(
created_at='2021-08-28',
boolean=True,
week_date='2021-08-23',
),
])
def test_add_column_difference(self):
df = pd.DataFrame([
[1, 3],
[4, 2],
], columns=[
'integer1',
'integer2',
])
action1 = dict(
action_arguments=['integer1', 'integer2'],
action_options={
'udf': 'difference',
},
outputs=[
dict(
uuid='integer_difference',
column_type='number',
),
],
)
action2 = dict(
action_arguments=['integer1'],
action_options={
'udf': 'difference',
'value': 10,
},
outputs=[
dict(
uuid='integer_difference2',
column_type='number',
),
],
)
df_new = add_column(add_column(df, action1), action2)
df_expected = pd.DataFrame([
[1, 3, -2, -9],
[4, 2, 2, -6],
], columns=[
'integer1',
'integer2',
'integer_difference',
'integer_difference2'
])
assert_frame_equal(df_new, df_expected)
def test_add_column_difference_days(self):
df = pd.DataFrame([
['2021-08-31', '2021-09-14'],
['2021-08-28', '2021-09-03'],
], columns=[
'created_at',
'converted_at',
])
action = dict(
action_arguments=['converted_at', 'created_at'],
action_options=dict(
column_type='datetime',
time_unit='d',
udf='difference',
),
outputs=[
dict(
uuid='days_diff',
column_type='number',
),
],
)
df_new = add_column(df, action)
df_expected = pd.DataFrame([
['2021-08-31', '2021-09-14', 14],
['2021-08-28', '2021-09-03', 6],
], columns=[
'created_at',
'converted_at',
'days_diff',
])
assert_frame_equal(df_new, df_expected)
def test_add_column_distance_between(self):
df = pd.DataFrame([
[26.05308, -97.31838, 33.41939, -112.32606],
[39.71954, -84.13056, 33.41939, -112.32606],
], columns=[
'lat1',
'lng1',
'lat2',
'lng2',
])
action = dict(
action_arguments=['lat1', 'lng1', 'lat2', 'lng2'],
action_options=dict(
udf='distance_between',
),
outputs=[
dict(
uuid='distance',
column_type='number_with_decimals',
),
],
)
df_new = add_column(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
lat1=26.05308,
lng1=-97.31838,
lat2=33.41939,
lng2=-112.32606,
distance=1661.8978520305657,
),
dict(
lat1=39.71954,
lng1=-84.13056,
lat2=33.41939,
lng2=-112.32606,
distance=2601.5452571116184,
),
])
def test_add_column_divide(self):
df = pd.DataFrame([
[12, 3, 70, 9],
[4, 2, 90, 3],
], columns=[
'integer1',
'integer2',
'integer3',
'integer4',
])
action1 = dict(
action_arguments=[
'integer1',
'integer2',
],
action_options={
'udf': 'divide',
},
outputs=[
dict(
uuid='integer_divide',
column_type='number',
),
],
)
action2 = dict(
action_arguments=['integer3'],
action_options={
'udf': 'divide',
'value': 10,
},
outputs=[
dict(
uuid='integer_divide2',
column_type='number',
),
],
)
df_new = add_column(add_column(df, action1), action2)
df_expected = pd.DataFrame([
[12, 3, 70, 9, 4, 7],
[4, 2, 90, 3, 2, 9],
], columns=[
'integer1',
'integer2',
'integer3',
'integer4',
'integer_divide',
'integer_divide2'
])
assert_frame_equal(df_new, df_expected)
# def test_add_column_extract_dict_string(self):
# df = pd.DataFrame([
# '{\'country\': \'US\', \'age\': \'20\'}',
# '{\'country\': \'CA\'}',
# '{\'country\': \'UK\', \'age\': \'24\'}',
# '',
# ], columns=[
# 'properties',
# ])
# action = dict(
# action_arguments=['properties', 'country'],
# action_options=dict(
# udf='extract_dict_value',
# ),
# outputs=[
# dict(
# uuid='property_country',
# column_type='text',
# ),
# ],
# )
# df_new = add_column(df, action)
# self.assertEqual(df_new.to_dict(orient='records'), [
# dict(
# properties='{\'country\': \'US\', \'age\': \'20\'}',
# property_country='US',
# ),
# dict(
# properties='{\'country\': \'CA\'}',
# property_country='CA',
# ),
# dict(
# properties='{\'country\': \'UK\', \'age\': \'24\'}',
# property_country='UK',
# ),
# dict(
# properties='',
# property_country=np.NaN,
# ),
# ])
# action2 = dict(
# action_arguments=['properties', 'age'],
# action_options=dict(
# udf='extract_dict_value',
# ),
# outputs=[
# dict(
# uuid='property_age',
# column_type='number',
# ),
# ],
# )
# df_new2 = add_column(df, action2)
# self.assertEqual(df_new2.to_dict(orient='records'), [
# dict(
# properties='{\'country\': \'US\', \'age\': \'20\'}',
# property_age=20,
# ),
# dict(
# properties='{\'country\': \'CA\'}',
# property_age=0,
# ),
# dict(
# properties='{\'country\': \'UK\', \'age\': \'24\'}',
# property_age=24,
# ),
# dict(
# properties='',
# property_age=0,
# ),
# ])
# def test_add_column_extract_dict_string_with_json(self):
# df = pd.DataFrame([
# '{\"country\": \"US\", \"is_adult\": true}',
# '{\"country\": \"CA\"}',
# '{\"country\": \"UK\", \"is_adult\": false}',
# '',
# ], columns=[
# 'properties',
# ])
# action = dict(
# action_arguments=['properties', 'country'],
# action_options=dict(
# udf='extract_dict_value',
# ),
# outputs=[
# dict(
# uuid='property_country',
# column_type='text',
# ),
# ],
# )
# df_new = add_column(df, action)
# self.assertEqual(df_new.to_dict(orient='records'), [
# dict(
# properties='{\"country\": \"US\", \"is_adult\": true}',
# property_country='US',
# ),
# dict(
# properties='{\"country\": \"CA\"}',
# property_country='CA',
# ),
# dict(
# properties='{\"country\": \"UK\", \"is_adult\": false}',
# property_country='UK',
# ),
# dict(
# properties='',
# property_country=np.NaN,
# ),
# ])
# action2 = dict(
# action_arguments=['properties', 'is_adult'],
# action_options=dict(
# udf='extract_dict_value',
# ),
# outputs=[
# dict(
# uuid='property_is_adult',
# column_type='true_or_false',
# ),
# ],
# )
# df_new2 = add_column(df, action2)
# self.assertEqual(df_new2.to_dict(orient='records'), [
# dict(
# properties='{\"country\": \"US\", \"is_adult\": true}',
# property_is_adult=True,
# ),
# dict(
# properties='{\"country\": \"CA\"}',
# property_is_adult=None,
# ),
# dict(
# properties='{\"country\": \"UK\", \"is_adult\": false}',
# property_is_adult=False,
# ),
# dict(
# properties='',
# property_is_adult=None,
# ),
# ])
def test_add_column_formatted_date(self):
df = pd.DataFrame([
['2019-04-10 08:20:58', False],
['2019-03-05 03:30:30', True],
], columns=[
'created_at',
'boolean',
])
action = dict(
action_arguments=['created_at'],
action_options=dict(
udf='formatted_date',
format='%Y-%m-%d',
),
outputs=[
dict(
uuid='created_date',
column_type='text',
),
],
)
df_new = add_column(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
created_at='2019-04-10 08:20:58',
boolean=False,
created_date='2019-04-10',
),
dict(
created_at='2019-03-05 03:30:30',
boolean=True,
created_date='2019-03-05',
),
])
def test_add_column_if_else(self):
df = pd.DataFrame([
['2019-04-10 08:20:58'],
[None],
], columns=[
'converted_at'
])
action = dict(
action_arguments=[False, True],
action_code='converted_at == null',
action_options=dict(
udf='if_else',
),
outputs=[
dict(
uuid='converted',
column_type='true_or_false',
),
],
)
df_new = add_column(df, action, original_df=df)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
converted_at='2019-04-10 08:20:58',
converted=True,
),
dict(
converted_at=None,
converted=False,
),
])
def test_add_column_if_else_with_column(self):
df = pd.DataFrame([
['2019-04-10 08:20:58', 'test_user_id'],
[None, None],
], columns=[
'converted_at',
'user_id',
])
action = dict(
action_arguments=['unknown', 'user_id'],
action_code='converted_at == null',
action_options=dict(
udf='if_else',
arg1_type='value',
arg2_type='column',
),
outputs=[
dict(
uuid='user_id_clean',
column_type='text',
),
],
)
df_new = add_column(df, action, original_df=df)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
converted_at='2019-04-10 08:20:58',
user_id='test_user_id',
user_id_clean='test_user_id',
),
dict(
converted_at=None,
user_id=None,
user_id_clean='unknown',
),
])
def test_add_column_multiply(self):
df = pd.DataFrame([
[1, 3, 7, 9],
[4, 2, 9, 3],
], columns=[
'integer1',
'integer2',
'integer3',
'integer4',
])
action1 = dict(
action_arguments=[
'integer1',
'integer2',
],
action_options={
'udf': 'multiply',
},
outputs=[
dict(
uuid='integer_multiply',
column_type='number',
),
],
)
action2 = dict(
action_arguments=['integer3'],
action_options={
'udf': 'multiply',
'value': 10,
},
outputs=[
dict(
uuid='integer_multiply2',
column_type='number',
),
],
)
df_new = add_column(add_column(df, action1), action2)
df_expected = pd.DataFrame([
[1, 3, 7, 9, 3, 70],
[4, 2, 9, 3, 8, 90],
], columns=[
'integer1',
'integer2',
'integer3',
'integer4',
'integer_multiply',
'integer_multiply2'
])
assert_frame_equal(df_new, df_expected)
def test_add_column_string_replace(self):
df = pd.DataFrame([
['$1000'],
['$321. '],
['$4,321'],
], columns=[
'amount',
])
action = dict(
action_arguments=['amount'],
action_options={
'udf': 'string_replace',
'pattern': '\\$|\\.|\\,|\\s*',
'replacement': '',
},
outputs=[
dict(
uuid='amount_clean',
column_type='true_or_false',
),
],
)
df_new = add_column(df, action)
df_expected = pd.DataFrame([
['$1000', '1000'],
['$321. ', '321'],
['$4,321', '4321'],
], columns=[
'amount',
'amount_clean',
])
assert_frame_equal(df_new, df_expected)
def test_add_column_string_split(self):
df = pd.DataFrame([
['Street1, Long Beach, CA, '],
['Street2,Vernon, CA, 123'],
['Pacific Coast Highway, Los Angeles, CA, 111'],
], columns=[
'location',
])
action = dict(
action_arguments=['location'],
action_options={
'udf': 'string_split',
'separator': ',',
'part_index': 1,
},
outputs=[
dict(
uuid='location_city',
column_type='text',
),
],
)
action2 = dict(
action_arguments=['location'],
action_options={
'udf': 'string_split',
'separator': ',',
'part_index': 3,
},
outputs=[
dict(
uuid='num',
column_type='number',
),
],
)
df_new = add_column(add_column(df, action), action2)
df_expected = pd.DataFrame([
['Street1, Long Beach, CA, ', 'Long Beach', 0],
['Street2,Vernon, CA, 123', 'Vernon', 123],
['Pacific Coast Highway, Los Angeles, CA, 111', 'Los Angeles', 111],
], columns=[
'location',
'location_city',
'num',
])
assert_frame_equal(df_new, df_expected)
def test_add_column_substring(self):
df = pd.DataFrame([
['$1000.0'],
['$321.9'],
], columns=[
'amount',
])
action = dict(
action_arguments=['amount'],
action_options={
'udf': 'substring',
'start': 1,
'stop': -2,
},
outputs=[
dict(
uuid='amount_int',
column_type='text',
),
],
)
df_new = add_column(df, action)
df_expected = pd.DataFrame([
['$1000.0', '1000'],
['$321.9', '321'],
], columns=[
'amount',
'amount_int',
])
assert_frame_equal(df_new, df_expected)
def test_average(self):
from data_cleaner.transformer_actions.column import average
action = self.__groupby_agg_action('average_amount')
df_new = average(TEST_DATAFRAME.copy(), action)
df_expected = pd.DataFrame([
[1, 1000, 1050],
[2, 1050, 1100],
[1, 1100, 1050],
[2, 1150, 1100],
], columns=[
'group_id',
'amount',
'average_amount'
])
assert_frame_equal(df_new, df_expected)
def test_count(self):
df = pd.DataFrame([
[1, 1000],
[1, 1050],
[1, 1100],
[2, 1150],
], columns=[
'group_id',
'order_id',
])
action = dict(
action_arguments=['order_id'],
action_options=dict(
groupby_columns=['group_id']
),
outputs=[
dict(uuid='order_count'),
],
)
df_new = count(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
group_id=1,
order_id=1000,
order_count=3,
),
dict(
group_id=1,
order_id=1050,
order_count=3,
),
dict(
group_id=1,
order_id=1100,
order_count=3,
),
dict(
group_id=2,
order_id=1150,
order_count=1,
),
])
def test_count_distinct(self):
df = pd.DataFrame([
[1, 1000],
[1, 1000],
[1, 1100],
[2, 1150],
], columns=[
'group_id',
'order_id',
])
action = dict(
action_arguments=['order_id'],
action_options=dict(
groupby_columns=['group_id']
),
outputs=[
dict(uuid='order_count'),
],
)
df_new = count_distinct(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
group_id=1,
order_id=1000,
order_count=2,
),
dict(
group_id=1,
order_id=1000,
order_count=2,
),
dict(
group_id=1,
order_id=1100,
order_count=2,
),
dict(
group_id=2,
order_id=1150,
order_count=1,
),
])
def test_count_with_time_window(self):
df = pd.DataFrame([
[1, 1000, '2021-10-01', '2021-09-01'],
[1, 1050, '2021-10-01', '2021-08-01'],
[1, 1100, '2021-10-01', '2021-01-01'],
[2, 1150, '2021-09-01', '2021-08-01'],
], columns=[
'group_id',
'order_id',
'group_churned_at',
'order_created_at',
])
action = dict(
action_arguments=['order_id'],
action_code='',
action_options=dict(
groupby_columns=['group_id'],
timestamp_feature_a='group_churned_at',
timestamp_feature_b='order_created_at',
window=90*24*3600,
),
outputs=[
dict(uuid='order_count'),
],
)
df_new = count(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
group_id=1,
order_id=1000,
group_churned_at='2021-10-01',
order_created_at='2021-09-01',
order_count=2,
),
dict(
group_id=1,
order_id=1050,
group_churned_at='2021-10-01',
order_created_at='2021-08-01',
order_count=2,
),
dict(
group_id=1,
order_id=1100,
group_churned_at='2021-10-01',
order_created_at='2021-01-01',
order_count=2,
),
dict(
group_id=2,
order_id=1150,
group_churned_at='2021-09-01',
order_created_at='2021-08-01',
order_count=1,
),
])
def test_count_with_filter(self):
df = pd.DataFrame([
[1, 1000, '2021-10-01', '2021-09-01'],
[1, 1050, '2021-10-01', '2021-08-01'],
[1, 1100, '2021-10-01', '2021-01-01'],
[2, 1150, '2021-09-01', '2021-08-01'],
[2, 1200, '2021-09-01', '2021-08-16'],
[2, 1250, '2021-09-01', '2021-08-14'],
], columns=[
'group_id',
'order_id',
'group_churned_at',
'order_created_at',
])
action = dict(
action_arguments=['order_id'],
action_code='order_created_at < \'2021-08-15\'',
action_options=dict(
groupby_columns=['group_id'],
),
outputs=[
dict(uuid='order_count'),
],
)
df_new = count(df, action)
df_expected = pd.DataFrame([
[1, 1000, '2021-10-01', '2021-09-01', 2],
[1, 1050, '2021-10-01', '2021-08-01', 2],
[1, 1100, '2021-10-01', '2021-01-01', 2],
[2, 1150, '2021-09-01', '2021-08-01', 2],
[2, 1200, '2021-09-01', '2021-08-16', 2],
[2, 1250, '2021-09-01', '2021-08-14', 2],
], columns=[
'group_id',
'order_id',
'group_churned_at',
'order_created_at',
'order_count',
])
assert_frame_equal(df_new, df_expected)
def test_diff(self):
df = pd.DataFrame([
['2020-01-01', 1000],
['2020-01-02', 1050],
['2020-01-03', 1200],
['2020-01-04', 990],
], columns=[
'date',
'sold',
])
action = dict(
action_arguments=['sold'],
outputs=[
dict(uuid='sold_diff'),
],
)
df_new = diff(df, action)
self.assertEqual(df_new.to_dict(orient='records')[1:], [
dict(
date='2020-01-02',
sold=1050,
sold_diff=50,
),
dict(
date='2020-01-03',
sold=1200,
sold_diff=150,
),
dict(
date='2020-01-04',
sold=990,
sold_diff=-210,
),
])
# def test_expand_column(self):
# df = pd.DataFrame([
# [1, 'game'],
# [1, 'book'],
# [1, 'game'],
# [2, 'Video Game'],
# [1, 'Video Game'],
# [2, 'book'],
# [1, 'Video Game'],
# [2, 'Video Game'],
# ], columns=[
# 'group_id',
# 'category',
# ])
# action = dict(
# action_arguments=['category'],
# action_options=dict(
# groupby_columns=['group_id']
# ),
# outputs=[
# dict(uuid='category_expanded_count_game'),
# dict(uuid='category_expanded_count_book'),
# dict(uuid='category_expanded_count_video_game'),
# dict(uuid='category_expanded_count_clothing'),
# ],
# )
# df_new = expand_column(df, action)
# df_expected = pd.DataFrame([
# [1, 'game', 2, 1, 2],
# [1, 'book', 2, 1, 2],
# [1, 'game', 2, 1, 2],
# [2, 'Video Game', 0, 1, 2],
# [1, 'Video Game', 2, 1, 2],
# [2, 'book', 0, 1, 2],
# [1, 'Video Game', 2, 1, 2],
# [2, 'Video Game', 0, 1, 2],
# ], columns=[
# 'group_id',
# 'category',
# 'category_expanded_count_game',
# 'category_expanded_count_book',
# 'category_expanded_count_video_game',
# ])
# assert_frame_equal(df_new, df_expected)
# def test_expand_column_with_time_window(self):
# df = pd.DataFrame([
# [1, 'game', '2021-01-02', '2021-01-04'],
# [1, 'book', '2021-01-02', '2021-01-04'],
# [1, 'game', '2021-01-03', '2021-01-04'],
# [2, 'Video Game', '2021-01-01', '2021-01-03'],
# [1, 'Video Game', '2021-01-01', '2021-01-04'],
# [2, 'book', '2021-01-02', '2021-01-03'],
# [1, 'Video Game', '2021-01-03', '2021-01-04'],
# [2, 'Video Game', '2020-12-30', '2021-01-03'],
# ], columns=[
# 'group_id',
# 'category',
# 'timestamp1',
# 'timestamp2',
# ])
# action = dict(
# action_arguments=['category'],
# action_options=dict(
# groupby_columns=['group_id'],
# timestamp_feature_a='timestamp2',
# timestamp_feature_b='timestamp1',
# window=172800,
# ),
# outputs=[
# dict(uuid='category_expanded_count_game_2d'),
# dict(uuid='category_expanded_count_book_2d'),
# dict(uuid='category_expanded_count_video_game_2d'),
# dict(uuid='category_expanded_count_clothing_2d'),
# ],
# )
# df_new = expand_column(df, action)
# df_expected = pd.DataFrame([
# [1, 'game', '2021-01-02', '2021-01-04', 2, 1, 1],
# [1, 'book', '2021-01-02', '2021-01-04', 2, 1, 1],
# [1, 'game', '2021-01-03', '2021-01-04', 2, 1, 1],
# [2, 'Video Game', '2021-01-01', '2021-01-03', 0, 1, 1],
# [1, 'Video Game', '2021-01-01', '2021-01-04', 2, 1, 1],
# [2, 'book', '2021-01-02', '2021-01-03', 0, 1, 1],
# [1, 'Video Game', '2021-01-03', '2021-01-04', 2, 1, 1],
# [2, 'Video Game', '2020-12-30', '2021-01-03', 0, 1, 1],
# ], columns=[
# 'group_id',
# 'category',
# 'timestamp1',
# 'timestamp2',
# 'category_expanded_count_game_2d',
# 'category_expanded_count_book_2d',
# 'category_expanded_count_video_game_2d',
# ])
# assert_frame_equal(df_new, df_expected)
def test_first_column(self):
df = pd.DataFrame([
[1, 1000],
[2, 1050],
[1, 1100],
[2, 1150],
], columns=[
'group_id',
'order_id',
])
action = dict(
action_arguments=['order_id'],
action_options=dict(
groupby_columns=['group_id']
),
outputs=[
dict(uuid='first_order'),
],
)
df_new = first(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
group_id=1,
order_id=1000,
first_order=1000,
),
dict(
group_id=2,
order_id=1050,
first_order=1050,
),
dict(
group_id=1,
order_id=1100,
first_order=1000,
),
dict(
group_id=2,
order_id=1150,
first_order=1050,
),
])
def test_impute(self):
from data_cleaner.transformer_actions.column import impute
df = pd.DataFrame([
['2020-01-01', 1000, ' ', 800],
['2020-01-02', '', 1200, 700],
['2020-01-03', 1200, np.NaN, 900],
['2020-01-04', np.NaN, ' ', 700],
['2020-01-05', 1700, 1300, 800],
], columns=[
'date',
'sold',
'curr_profit',
'prev_sold',
])
action1 = dict(
action_arguments=['sold', 'curr_profit'],
action_options={
'value': '0',
},
action_variables={
'0': {
'feature': {
'column_type': 'number',
'uuid': 'sold',
},
'type': 'feature',
},
'1': {
'feature': {
'column_type': 'number',
'uuid': 'curr_profit',
},
'type': 'feature',
},
},
)
action2 = dict(
action_arguments=['sold'],
action_options={
'value': '0',
},
action_variables={
'0': {
'feature': {
'column_type': 'number',
'uuid': 'sold',
},
'type': 'feature',
},
},
)
action3 = dict(
action_arguments=['sold', 'curr_profit'],
action_options={
'strategy': 'average',
},
)
action4 = dict(
action_arguments=['sold', 'curr_profit'],
action_options={
'strategy': 'median',
},
)
action5 = dict(
action_arguments=['sold', 'curr_profit'],
action_options={
'strategy': 'column',
'value': 'prev_sold',
},
)
action_invalid = dict(
action_arguments=['sold', 'curr_profit'],
action_options={
'strategy': 'mode',
},
)
df_new1 = impute(df.copy(), action1)
df_new2 = impute(df.copy(), action2)
df_new3 = impute(df.copy(), action3)
df_new4 = impute(df.copy(), action4)
df_new5 = impute(df.copy(), action5)
df_expected1 = pd.DataFrame([
['2020-01-01', 1000, 0, 800],
['2020-01-02', 0, 1200, 700],
['2020-01-03', 1200, 0, 900],
['2020-01-04', 0, 0, 700],
['2020-01-05', 1700, 1300, 800],
], columns=[
'date',
'sold',
'curr_profit',
'prev_sold',
])
df_expected2 = pd.DataFrame([
['2020-01-01', 1000, ' ', 800],
['2020-01-02', 0, 1200, 700],
['2020-01-03', 1200, np.nan, 900],
['2020-01-04', 0, ' ', 700],
['2020-01-05', 1700, 1300, 800],
], columns=[
'date',
'sold',
'curr_profit',
'prev_sold',
])
df_expected3 = pd.DataFrame([
['2020-01-01', 1000, 1250, 800],
['2020-01-02', 1300, 1200, 700],
['2020-01-03', 1200, 1250, 900],
['2020-01-04', 1300, 1250, 700],
['2020-01-05', 1700, 1300, 800],
], columns=[
'date',
'sold',
'curr_profit',
'prev_sold',
])
df_expected4 = pd.DataFrame([
['2020-01-01', 1000, 1250, 800],
['2020-01-02', 1200, 1200, 700],
['2020-01-03', 1200, 1250, 900],
['2020-01-04', 1200, 1250, 700],
['2020-01-05', 1700, 1300, 800],
], columns=[
'date',
'sold',
'curr_profit',
'prev_sold',
])
df_expected5 = pd.DataFrame([
['2020-01-01', 1000, 800, 800],
['2020-01-02', 700, 1200, 700],
['2020-01-03', 1200, 900, 900],
['2020-01-04', 700, 700, 700],
['2020-01-05', 1700, 1300, 800],
], columns=[
'date',
'sold',
'curr_profit',
'prev_sold',
])
df_new1['sold'] = df_new1['sold'].astype(int)
df_new1['curr_profit'] = df_new1['curr_profit'].astype(int)
df_new2['sold'] = df_new2['sold'].astype(int)
df_new3['sold'] = df_new3['sold'].astype(int)
df_new3['curr_profit'] = df_new3['curr_profit'].astype(int)
df_new4['sold'] = df_new4['sold'].astype(int)
df_new4['curr_profit'] = df_new4['curr_profit'].astype(int)
df_new5['sold'] = df_new5['sold'].astype(int)
df_new5['curr_profit'] = df_new5['curr_profit'].astype(int)
assert_frame_equal(df_new1, df_expected1)
assert_frame_equal(df_new2, df_expected2)
assert_frame_equal(df_new3, df_expected3)
assert_frame_equal(df_new4, df_expected4)
assert_frame_equal(df_new5, df_expected5)
with self.assertRaises(Exception):
_ = impute(df.copy(), action_invalid)
def test_last_column(self):
df = pd.DataFrame([
[1, 1000],
[2, 1050],
[1, 1100],
[2, 1150],
], columns=[
'group_id',
'order_id',
])
action = dict(
action_arguments=['order_id'],
action_options=dict(
groupby_columns=['group_id']
),
outputs=[
dict(uuid='last_order'),
],
)
df_new = last(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
group_id=1,
order_id=1000,
last_order=1100,
),
dict(
group_id=2,
order_id=1050,
last_order=1150,
),
dict(
group_id=1,
order_id=1100,
last_order=1100,
),
dict(
group_id=2,
order_id=1150,
last_order=1150,
),
])
def test_max(self):
from data_cleaner.transformer_actions.column import max
action = self.__groupby_agg_action('max_amount')
df_new = max(TEST_DATAFRAME.copy(), action)
df_expected = pd.DataFrame([
[1, 1000, 1100],
[2, 1050, 1150],
[1, 1100, 1100],
[2, 1150, 1150],
], columns=[
'group_id',
'amount',
'max_amount',
])
assert_frame_equal(df_new, df_expected)
action2 = dict(
action_arguments=['amount'],
action_options=dict(),
outputs=[
dict(uuid='max_amount'),
],
)
df_new2 = max(TEST_DATAFRAME.copy(), action2)
df_expected2 = pd.DataFrame([
[1, 1000, 1150],
[2, 1050, 1150],
[1, 1100, 1150],
[2, 1150, 1150],
], columns=[
'group_id',
'amount',
'max_amount',
])
assert_frame_equal(df_new2, df_expected2)
def test_median(self):
from data_cleaner.transformer_actions.column import median
action = self.__groupby_agg_action('median_amount')
df = pd.DataFrame([
[1, 1000],
[2, 1050],
[1, 1100],
[2, 1550],
[2, 1150],
], columns=[
'group_id',
'amount',
])
df_new = median(df, action)
df_expected = pd.DataFrame([
[1, 1000, 1050],
[2, 1050, 1150],
[1, 1100, 1050],
[2, 1550, 1150],
[2, 1150, 1150],
], columns=[
'group_id',
'amount',
'median_amount',
])
assert_frame_equal(df_new, df_expected)
def test_min(self):
from data_cleaner.transformer_actions.column import min
action = self.
|
7,732 | 218 | 47,333 |
mage-ai__mage-ai
|
9ed779ccaf3538efbe1f2f54dd68f61fb1c3af55
|
src/data_cleaner/tests/transformer_actions/test_column.py
|
infile
|
__groupby_agg_action
| true |
function
| 104 | 108 | true | false |
[
"__groupby_agg_action",
"assertEqual",
"test_count",
"assertRaises",
"test_shift_down",
"test_add_column_addition",
"test_add_column_addition_days",
"test_add_column_constant",
"test_add_column_date_trunc",
"test_add_column_difference",
"test_add_column_difference_days",
"test_add_column_distance_between",
"test_add_column_divide",
"test_add_column_formatted_date",
"test_add_column_if_else",
"test_add_column_if_else_with_column",
"test_add_column_multiply",
"test_add_column_string_replace",
"test_add_column_string_split",
"test_add_column_substring",
"test_average",
"test_count_distinct",
"test_count_with_filter",
"test_count_with_time_window",
"test_diff",
"test_first_column",
"test_impute",
"test_last_column",
"test_max",
"test_median",
"test_min",
"test_remove_column",
"test_select",
"test_shift_down_with_groupby",
"test_shift_up",
"test_sum",
"setUp",
"tearDown",
"addClassCleanup",
"addCleanup",
"addTypeEqualityFunc",
"assert_",
"assertAlmostEqual",
"assertAlmostEquals",
"assertCountEqual",
"assertDictContainsSubset",
"assertDictEqual",
"assertEquals",
"assertFalse",
"assertGreater",
"assertGreaterEqual",
"assertIn",
"assertIs",
"assertIsInstance",
"assertIsNone",
"assertIsNot",
"assertIsNotNone",
"assertLess",
"assertLessEqual",
"assertListEqual",
"assertLogs",
"assertMultiLineEqual",
"assertNoLogs",
"assertNotAlmostEqual",
"assertNotAlmostEquals",
"assertNotEqual",
"assertNotEquals",
"assertNotIn",
"assertNotIsInstance",
"assertNotRegex",
"assertNotRegexpMatches",
"assertRaisesRegex",
"assertRaisesRegexp",
"assertRegex",
"assertRegexpMatches",
"assertSequenceEqual",
"assertSetEqual",
"assertTrue",
"assertTupleEqual",
"assertWarns",
"assertWarnsRegex",
"countTestCases",
"debug",
"defaultTestResult",
"doClassCleanups",
"doCleanups",
"fail",
"failIf",
"failIfAlmostEqual",
"failIfEqual",
"failUnless",
"failUnlessAlmostEqual",
"failUnlessEqual",
"failUnlessRaises",
"failureException",
"id",
"longMessage",
"maxDiff",
"run",
"setUpClass",
"shortDescription",
"skipTest",
"subTest",
"tearDownClass",
"__annotations__",
"__call__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "addClassCleanup",
"type": "function"
},
{
"name": "addCleanup",
"type": "function"
},
{
"name": "addTypeEqualityFunc",
"type": "function"
},
{
"name": "assert_",
"type": "function"
},
{
"name": "assertAlmostEqual",
"type": "function"
},
{
"name": "assertAlmostEquals",
"type": "function"
},
{
"name": "assertCountEqual",
"type": "function"
},
{
"name": "assertDictContainsSubset",
"type": "function"
},
{
"name": "assertDictEqual",
"type": "function"
},
{
"name": "assertEqual",
"type": "function"
},
{
"name": "assertEquals",
"type": "function"
},
{
"name": "assertFalse",
"type": "function"
},
{
"name": "assertGreater",
"type": "function"
},
{
"name": "assertGreaterEqual",
"type": "function"
},
{
"name": "assertIn",
"type": "function"
},
{
"name": "assertIs",
"type": "function"
},
{
"name": "assertIsInstance",
"type": "function"
},
{
"name": "assertIsNone",
"type": "function"
},
{
"name": "assertIsNot",
"type": "function"
},
{
"name": "assertIsNotNone",
"type": "function"
},
{
"name": "assertLess",
"type": "function"
},
{
"name": "assertLessEqual",
"type": "function"
},
{
"name": "assertListEqual",
"type": "function"
},
{
"name": "assertLogs",
"type": "function"
},
{
"name": "assertMultiLineEqual",
"type": "function"
},
{
"name": "assertNotAlmostEqual",
"type": "function"
},
{
"name": "assertNotAlmostEquals",
"type": "function"
},
{
"name": "assertNotEqual",
"type": "function"
},
{
"name": "assertNotEquals",
"type": "function"
},
{
"name": "assertNotIn",
"type": "function"
},
{
"name": "assertNotIsInstance",
"type": "function"
},
{
"name": "assertNotRegex",
"type": "function"
},
{
"name": "assertNotRegexpMatches",
"type": "function"
},
{
"name": "assertRaises",
"type": "function"
},
{
"name": "assertRaisesRegex",
"type": "function"
},
{
"name": "assertRaisesRegexp",
"type": "function"
},
{
"name": "assertRegex",
"type": "function"
},
{
"name": "assertRegexpMatches",
"type": "function"
},
{
"name": "assertSequenceEqual",
"type": "function"
},
{
"name": "assertSetEqual",
"type": "function"
},
{
"name": "assertTrue",
"type": "function"
},
{
"name": "assertTupleEqual",
"type": "function"
},
{
"name": "assertWarns",
"type": "function"
},
{
"name": "assertWarnsRegex",
"type": "function"
},
{
"name": "countTestCases",
"type": "function"
},
{
"name": "debug",
"type": "function"
},
{
"name": "defaultTestResult",
"type": "function"
},
{
"name": "doClassCleanups",
"type": "function"
},
{
"name": "doCleanups",
"type": "function"
},
{
"name": "fail",
"type": "function"
},
{
"name": "failIf",
"type": "function"
},
{
"name": "failIfAlmostEqual",
"type": "function"
},
{
"name": "failIfEqual",
"type": "function"
},
{
"name": "failUnless",
"type": "function"
},
{
"name": "failUnlessAlmostEqual",
"type": "function"
},
{
"name": "failUnlessEqual",
"type": "function"
},
{
"name": "failUnlessRaises",
"type": "function"
},
{
"name": "failureException",
"type": "statement"
},
{
"name": "id",
"type": "function"
},
{
"name": "longMessage",
"type": "statement"
},
{
"name": "maxDiff",
"type": "statement"
},
{
"name": "run",
"type": "function"
},
{
"name": "setUp",
"type": "function"
},
{
"name": "setUpClass",
"type": "function"
},
{
"name": "shortDescription",
"type": "function"
},
{
"name": "skipTest",
"type": "function"
},
{
"name": "subTest",
"type": "function"
},
{
"name": "tearDown",
"type": "function"
},
{
"name": "tearDownClass",
"type": "function"
},
{
"name": "test_add_column_addition",
"type": "function"
},
{
"name": "test_add_column_addition_days",
"type": "function"
},
{
"name": "test_add_column_constant",
"type": "function"
},
{
"name": "test_add_column_date_trunc",
"type": "function"
},
{
"name": "test_add_column_difference",
"type": "function"
},
{
"name": "test_add_column_difference_days",
"type": "function"
},
{
"name": "test_add_column_distance_between",
"type": "function"
},
{
"name": "test_add_column_divide",
"type": "function"
},
{
"name": "test_add_column_formatted_date",
"type": "function"
},
{
"name": "test_add_column_if_else",
"type": "function"
},
{
"name": "test_add_column_if_else_with_column",
"type": "function"
},
{
"name": "test_add_column_multiply",
"type": "function"
},
{
"name": "test_add_column_string_replace",
"type": "function"
},
{
"name": "test_add_column_string_split",
"type": "function"
},
{
"name": "test_add_column_substring",
"type": "function"
},
{
"name": "test_average",
"type": "function"
},
{
"name": "test_count",
"type": "function"
},
{
"name": "test_count_distinct",
"type": "function"
},
{
"name": "test_count_with_filter",
"type": "function"
},
{
"name": "test_count_with_time_window",
"type": "function"
},
{
"name": "test_diff",
"type": "function"
},
{
"name": "test_first_column",
"type": "function"
},
{
"name": "test_impute",
"type": "function"
},
{
"name": "test_last_column",
"type": "function"
},
{
"name": "test_max",
"type": "function"
},
{
"name": "test_median",
"type": "function"
},
{
"name": "test_min",
"type": "function"
},
{
"name": "test_remove_column",
"type": "function"
},
{
"name": "test_select",
"type": "function"
},
{
"name": "test_shift_down",
"type": "function"
},
{
"name": "test_shift_down_with_groupby",
"type": "function"
},
{
"name": "test_shift_up",
"type": "function"
},
{
"name": "test_sum",
"type": "function"
},
{
"name": "_addSkip",
"type": "function"
},
{
"name": "_formatMessage",
"type": "function"
},
{
"name": "_getAssertEqualityFunc",
"type": "function"
},
{
"name": "_testMethodDoc",
"type": "statement"
},
{
"name": "_testMethodName",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__call__",
"type": "function"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__groupby_agg_action",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
from data_cleaner.tests.base_test import TestCase
from data_cleaner.transformer_actions.column import (
add_column,
count,
count_distinct,
diff,
# expand_column,
first,
last,
remove_column,
select,
shift_down,
shift_up,
)
from pandas.util.testing import assert_frame_equal
import numpy as np
import pandas as pd
TEST_DATAFRAME = pd.DataFrame([
[1, 1000],
[2, 1050],
[1, 1100],
[2, 1150],
], columns=[
'group_id',
'amount',
])
class ColumnTests(TestCase):
def test_remove_column(self):
df = pd.DataFrame([
[0, False, 'a'],
[1, True, 'b'],
], columns=[
'integer',
'boolean',
'string',
])
action = dict(action_arguments=['string'])
df_new = remove_column(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
integer=0,
boolean=False,
),
dict(
integer=1,
boolean=True,
),
])
action = dict(action_arguments=['integer', 'boolean'])
df_new = remove_column(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
string='a',
),
dict(
string='b',
),
])
def test_add_column_addition(self):
df = pd.DataFrame([
[1, 3, 7, 9],
[4, 2, 9, 3],
], columns=[
'integer1',
'integer2',
'integer3',
'integer4',
])
action1 = dict(
action_arguments=[
'integer1',
'integer2',
'integer3',
],
action_options={
'udf': 'addition',
'value': None,
},
outputs=[
dict(
uuid='integer_addition',
column_type='number',
),
],
)
action2 = dict(
action_arguments=['integer1'],
action_options={
'udf': 'addition',
'value': 10,
},
outputs=[
dict(
uuid='integer_addition2',
column_type='number',
),
],
)
action3 = dict(
action_arguments=['integer1', 'integer4'],
action_options={
'udf': 'addition',
'value': 10,
},
outputs=[
dict(
uuid='integer_addition3',
column_type='number',
),
],
)
df_new = add_column(
add_column(
add_column(df, action1),
action2,
),
action3,
)
df_expected = pd.DataFrame([
[1, 3, 7, 9, 11, 11, 20],
[4, 2, 9, 3, 15, 14, 17],
], columns=[
'integer1',
'integer2',
'integer3',
'integer4',
'integer_addition',
'integer_addition2',
'integer_addition3',
])
assert_frame_equal(df_new, df_expected)
def test_add_column_addition_days(self):
df = pd.DataFrame([
['2021-08-31'],
['2021-08-28'],
], columns=[
'created_at',
])
action = dict(
action_arguments=['created_at'],
action_options=dict(
column_type='datetime',
time_unit='d',
udf='addition',
value=3,
),
outputs=[
dict(
uuid='3d_after_creation',
column_type='text',
),
],
)
df_new = add_column(df, action)
df_expected = pd.DataFrame([
['2021-08-31', '2021-09-03 00:00:00'],
['2021-08-28', '2021-08-31 00:00:00'],
], columns=[
'created_at',
'3d_after_creation'
])
assert_frame_equal(df_new, df_expected)
def test_add_column_constant(self):
df = pd.DataFrame([
[False],
[True],
], columns=[
'boolean',
])
action = dict(
action_arguments=[10],
action_options=dict(
udf='constant',
),
outputs=[
dict(
uuid='integer',
column_type='number',
),
],
)
df_new = add_column(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
boolean=False,
integer=10,
),
dict(
boolean=True,
integer=10,
),
])
def test_add_column_date_trunc(self):
df = pd.DataFrame([
['2021-08-31', False],
['2021-08-28', True],
], columns=[
'created_at',
'boolean',
])
action = dict(
action_arguments=['created_at'],
action_options=dict(
udf='date_trunc',
date_part='week',
),
outputs=[
dict(
uuid='week_date',
column_type='text',
),
],
)
df_new = add_column(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
created_at='2021-08-31',
boolean=False,
week_date='2021-08-30',
),
dict(
created_at='2021-08-28',
boolean=True,
week_date='2021-08-23',
),
])
def test_add_column_difference(self):
df = pd.DataFrame([
[1, 3],
[4, 2],
], columns=[
'integer1',
'integer2',
])
action1 = dict(
action_arguments=['integer1', 'integer2'],
action_options={
'udf': 'difference',
},
outputs=[
dict(
uuid='integer_difference',
column_type='number',
),
],
)
action2 = dict(
action_arguments=['integer1'],
action_options={
'udf': 'difference',
'value': 10,
},
outputs=[
dict(
uuid='integer_difference2',
column_type='number',
),
],
)
df_new = add_column(add_column(df, action1), action2)
df_expected = pd.DataFrame([
[1, 3, -2, -9],
[4, 2, 2, -6],
], columns=[
'integer1',
'integer2',
'integer_difference',
'integer_difference2'
])
assert_frame_equal(df_new, df_expected)
def test_add_column_difference_days(self):
df = pd.DataFrame([
['2021-08-31', '2021-09-14'],
['2021-08-28', '2021-09-03'],
], columns=[
'created_at',
'converted_at',
])
action = dict(
action_arguments=['converted_at', 'created_at'],
action_options=dict(
column_type='datetime',
time_unit='d',
udf='difference',
),
outputs=[
dict(
uuid='days_diff',
column_type='number',
),
],
)
df_new = add_column(df, action)
df_expected = pd.DataFrame([
['2021-08-31', '2021-09-14', 14],
['2021-08-28', '2021-09-03', 6],
], columns=[
'created_at',
'converted_at',
'days_diff',
])
assert_frame_equal(df_new, df_expected)
def test_add_column_distance_between(self):
df = pd.DataFrame([
[26.05308, -97.31838, 33.41939, -112.32606],
[39.71954, -84.13056, 33.41939, -112.32606],
], columns=[
'lat1',
'lng1',
'lat2',
'lng2',
])
action = dict(
action_arguments=['lat1', 'lng1', 'lat2', 'lng2'],
action_options=dict(
udf='distance_between',
),
outputs=[
dict(
uuid='distance',
column_type='number_with_decimals',
),
],
)
df_new = add_column(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
lat1=26.05308,
lng1=-97.31838,
lat2=33.41939,
lng2=-112.32606,
distance=1661.8978520305657,
),
dict(
lat1=39.71954,
lng1=-84.13056,
lat2=33.41939,
lng2=-112.32606,
distance=2601.5452571116184,
),
])
def test_add_column_divide(self):
df = pd.DataFrame([
[12, 3, 70, 9],
[4, 2, 90, 3],
], columns=[
'integer1',
'integer2',
'integer3',
'integer4',
])
action1 = dict(
action_arguments=[
'integer1',
'integer2',
],
action_options={
'udf': 'divide',
},
outputs=[
dict(
uuid='integer_divide',
column_type='number',
),
],
)
action2 = dict(
action_arguments=['integer3'],
action_options={
'udf': 'divide',
'value': 10,
},
outputs=[
dict(
uuid='integer_divide2',
column_type='number',
),
],
)
df_new = add_column(add_column(df, action1), action2)
df_expected = pd.DataFrame([
[12, 3, 70, 9, 4, 7],
[4, 2, 90, 3, 2, 9],
], columns=[
'integer1',
'integer2',
'integer3',
'integer4',
'integer_divide',
'integer_divide2'
])
assert_frame_equal(df_new, df_expected)
# def test_add_column_extract_dict_string(self):
# df = pd.DataFrame([
# '{\'country\': \'US\', \'age\': \'20\'}',
# '{\'country\': \'CA\'}',
# '{\'country\': \'UK\', \'age\': \'24\'}',
# '',
# ], columns=[
# 'properties',
# ])
# action = dict(
# action_arguments=['properties', 'country'],
# action_options=dict(
# udf='extract_dict_value',
# ),
# outputs=[
# dict(
# uuid='property_country',
# column_type='text',
# ),
# ],
# )
# df_new = add_column(df, action)
# self.assertEqual(df_new.to_dict(orient='records'), [
# dict(
# properties='{\'country\': \'US\', \'age\': \'20\'}',
# property_country='US',
# ),
# dict(
# properties='{\'country\': \'CA\'}',
# property_country='CA',
# ),
# dict(
# properties='{\'country\': \'UK\', \'age\': \'24\'}',
# property_country='UK',
# ),
# dict(
# properties='',
# property_country=np.NaN,
# ),
# ])
# action2 = dict(
# action_arguments=['properties', 'age'],
# action_options=dict(
# udf='extract_dict_value',
# ),
# outputs=[
# dict(
# uuid='property_age',
# column_type='number',
# ),
# ],
# )
# df_new2 = add_column(df, action2)
# self.assertEqual(df_new2.to_dict(orient='records'), [
# dict(
# properties='{\'country\': \'US\', \'age\': \'20\'}',
# property_age=20,
# ),
# dict(
# properties='{\'country\': \'CA\'}',
# property_age=0,
# ),
# dict(
# properties='{\'country\': \'UK\', \'age\': \'24\'}',
# property_age=24,
# ),
# dict(
# properties='',
# property_age=0,
# ),
# ])
# def test_add_column_extract_dict_string_with_json(self):
# df = pd.DataFrame([
# '{\"country\": \"US\", \"is_adult\": true}',
# '{\"country\": \"CA\"}',
# '{\"country\": \"UK\", \"is_adult\": false}',
# '',
# ], columns=[
# 'properties',
# ])
# action = dict(
# action_arguments=['properties', 'country'],
# action_options=dict(
# udf='extract_dict_value',
# ),
# outputs=[
# dict(
# uuid='property_country',
# column_type='text',
# ),
# ],
# )
# df_new = add_column(df, action)
# self.assertEqual(df_new.to_dict(orient='records'), [
# dict(
# properties='{\"country\": \"US\", \"is_adult\": true}',
# property_country='US',
# ),
# dict(
# properties='{\"country\": \"CA\"}',
# property_country='CA',
# ),
# dict(
# properties='{\"country\": \"UK\", \"is_adult\": false}',
# property_country='UK',
# ),
# dict(
# properties='',
# property_country=np.NaN,
# ),
# ])
# action2 = dict(
# action_arguments=['properties', 'is_adult'],
# action_options=dict(
# udf='extract_dict_value',
# ),
# outputs=[
# dict(
# uuid='property_is_adult',
# column_type='true_or_false',
# ),
# ],
# )
# df_new2 = add_column(df, action2)
# self.assertEqual(df_new2.to_dict(orient='records'), [
# dict(
# properties='{\"country\": \"US\", \"is_adult\": true}',
# property_is_adult=True,
# ),
# dict(
# properties='{\"country\": \"CA\"}',
# property_is_adult=None,
# ),
# dict(
# properties='{\"country\": \"UK\", \"is_adult\": false}',
# property_is_adult=False,
# ),
# dict(
# properties='',
# property_is_adult=None,
# ),
# ])
def test_add_column_formatted_date(self):
df = pd.DataFrame([
['2019-04-10 08:20:58', False],
['2019-03-05 03:30:30', True],
], columns=[
'created_at',
'boolean',
])
action = dict(
action_arguments=['created_at'],
action_options=dict(
udf='formatted_date',
format='%Y-%m-%d',
),
outputs=[
dict(
uuid='created_date',
column_type='text',
),
],
)
df_new = add_column(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
created_at='2019-04-10 08:20:58',
boolean=False,
created_date='2019-04-10',
),
dict(
created_at='2019-03-05 03:30:30',
boolean=True,
created_date='2019-03-05',
),
])
def test_add_column_if_else(self):
df = pd.DataFrame([
['2019-04-10 08:20:58'],
[None],
], columns=[
'converted_at'
])
action = dict(
action_arguments=[False, True],
action_code='converted_at == null',
action_options=dict(
udf='if_else',
),
outputs=[
dict(
uuid='converted',
column_type='true_or_false',
),
],
)
df_new = add_column(df, action, original_df=df)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
converted_at='2019-04-10 08:20:58',
converted=True,
),
dict(
converted_at=None,
converted=False,
),
])
def test_add_column_if_else_with_column(self):
df = pd.DataFrame([
['2019-04-10 08:20:58', 'test_user_id'],
[None, None],
], columns=[
'converted_at',
'user_id',
])
action = dict(
action_arguments=['unknown', 'user_id'],
action_code='converted_at == null',
action_options=dict(
udf='if_else',
arg1_type='value',
arg2_type='column',
),
outputs=[
dict(
uuid='user_id_clean',
column_type='text',
),
],
)
df_new = add_column(df, action, original_df=df)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
converted_at='2019-04-10 08:20:58',
user_id='test_user_id',
user_id_clean='test_user_id',
),
dict(
converted_at=None,
user_id=None,
user_id_clean='unknown',
),
])
def test_add_column_multiply(self):
df = pd.DataFrame([
[1, 3, 7, 9],
[4, 2, 9, 3],
], columns=[
'integer1',
'integer2',
'integer3',
'integer4',
])
action1 = dict(
action_arguments=[
'integer1',
'integer2',
],
action_options={
'udf': 'multiply',
},
outputs=[
dict(
uuid='integer_multiply',
column_type='number',
),
],
)
action2 = dict(
action_arguments=['integer3'],
action_options={
'udf': 'multiply',
'value': 10,
},
outputs=[
dict(
uuid='integer_multiply2',
column_type='number',
),
],
)
df_new = add_column(add_column(df, action1), action2)
df_expected = pd.DataFrame([
[1, 3, 7, 9, 3, 70],
[4, 2, 9, 3, 8, 90],
], columns=[
'integer1',
'integer2',
'integer3',
'integer4',
'integer_multiply',
'integer_multiply2'
])
assert_frame_equal(df_new, df_expected)
def test_add_column_string_replace(self):
df = pd.DataFrame([
['$1000'],
['$321. '],
['$4,321'],
], columns=[
'amount',
])
action = dict(
action_arguments=['amount'],
action_options={
'udf': 'string_replace',
'pattern': '\\$|\\.|\\,|\\s*',
'replacement': '',
},
outputs=[
dict(
uuid='amount_clean',
column_type='true_or_false',
),
],
)
df_new = add_column(df, action)
df_expected = pd.DataFrame([
['$1000', '1000'],
['$321. ', '321'],
['$4,321', '4321'],
], columns=[
'amount',
'amount_clean',
])
assert_frame_equal(df_new, df_expected)
def test_add_column_string_split(self):
df = pd.DataFrame([
['Street1, Long Beach, CA, '],
['Street2,Vernon, CA, 123'],
['Pacific Coast Highway, Los Angeles, CA, 111'],
], columns=[
'location',
])
action = dict(
action_arguments=['location'],
action_options={
'udf': 'string_split',
'separator': ',',
'part_index': 1,
},
outputs=[
dict(
uuid='location_city',
column_type='text',
),
],
)
action2 = dict(
action_arguments=['location'],
action_options={
'udf': 'string_split',
'separator': ',',
'part_index': 3,
},
outputs=[
dict(
uuid='num',
column_type='number',
),
],
)
df_new = add_column(add_column(df, action), action2)
df_expected = pd.DataFrame([
['Street1, Long Beach, CA, ', 'Long Beach', 0],
['Street2,Vernon, CA, 123', 'Vernon', 123],
['Pacific Coast Highway, Los Angeles, CA, 111', 'Los Angeles', 111],
], columns=[
'location',
'location_city',
'num',
])
assert_frame_equal(df_new, df_expected)
def test_add_column_substring(self):
df = pd.DataFrame([
['$1000.0'],
['$321.9'],
], columns=[
'amount',
])
action = dict(
action_arguments=['amount'],
action_options={
'udf': 'substring',
'start': 1,
'stop': -2,
},
outputs=[
dict(
uuid='amount_int',
column_type='text',
),
],
)
df_new = add_column(df, action)
df_expected = pd.DataFrame([
['$1000.0', '1000'],
['$321.9', '321'],
], columns=[
'amount',
'amount_int',
])
assert_frame_equal(df_new, df_expected)
def test_average(self):
from data_cleaner.transformer_actions.column import average
action = self.__groupby_agg_action('average_amount')
df_new = average(TEST_DATAFRAME.copy(), action)
df_expected = pd.DataFrame([
[1, 1000, 1050],
[2, 1050, 1100],
[1, 1100, 1050],
[2, 1150, 1100],
], columns=[
'group_id',
'amount',
'average_amount'
])
assert_frame_equal(df_new, df_expected)
def test_count(self):
df = pd.DataFrame([
[1, 1000],
[1, 1050],
[1, 1100],
[2, 1150],
], columns=[
'group_id',
'order_id',
])
action = dict(
action_arguments=['order_id'],
action_options=dict(
groupby_columns=['group_id']
),
outputs=[
dict(uuid='order_count'),
],
)
df_new = count(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
group_id=1,
order_id=1000,
order_count=3,
),
dict(
group_id=1,
order_id=1050,
order_count=3,
),
dict(
group_id=1,
order_id=1100,
order_count=3,
),
dict(
group_id=2,
order_id=1150,
order_count=1,
),
])
def test_count_distinct(self):
df = pd.DataFrame([
[1, 1000],
[1, 1000],
[1, 1100],
[2, 1150],
], columns=[
'group_id',
'order_id',
])
action = dict(
action_arguments=['order_id'],
action_options=dict(
groupby_columns=['group_id']
),
outputs=[
dict(uuid='order_count'),
],
)
df_new = count_distinct(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
group_id=1,
order_id=1000,
order_count=2,
),
dict(
group_id=1,
order_id=1000,
order_count=2,
),
dict(
group_id=1,
order_id=1100,
order_count=2,
),
dict(
group_id=2,
order_id=1150,
order_count=1,
),
])
def test_count_with_time_window(self):
df = pd.DataFrame([
[1, 1000, '2021-10-01', '2021-09-01'],
[1, 1050, '2021-10-01', '2021-08-01'],
[1, 1100, '2021-10-01', '2021-01-01'],
[2, 1150, '2021-09-01', '2021-08-01'],
], columns=[
'group_id',
'order_id',
'group_churned_at',
'order_created_at',
])
action = dict(
action_arguments=['order_id'],
action_code='',
action_options=dict(
groupby_columns=['group_id'],
timestamp_feature_a='group_churned_at',
timestamp_feature_b='order_created_at',
window=90*24*3600,
),
outputs=[
dict(uuid='order_count'),
],
)
df_new = count(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
group_id=1,
order_id=1000,
group_churned_at='2021-10-01',
order_created_at='2021-09-01',
order_count=2,
),
dict(
group_id=1,
order_id=1050,
group_churned_at='2021-10-01',
order_created_at='2021-08-01',
order_count=2,
),
dict(
group_id=1,
order_id=1100,
group_churned_at='2021-10-01',
order_created_at='2021-01-01',
order_count=2,
),
dict(
group_id=2,
order_id=1150,
group_churned_at='2021-09-01',
order_created_at='2021-08-01',
order_count=1,
),
])
def test_count_with_filter(self):
df = pd.DataFrame([
[1, 1000, '2021-10-01', '2021-09-01'],
[1, 1050, '2021-10-01', '2021-08-01'],
[1, 1100, '2021-10-01', '2021-01-01'],
[2, 1150, '2021-09-01', '2021-08-01'],
[2, 1200, '2021-09-01', '2021-08-16'],
[2, 1250, '2021-09-01', '2021-08-14'],
], columns=[
'group_id',
'order_id',
'group_churned_at',
'order_created_at',
])
action = dict(
action_arguments=['order_id'],
action_code='order_created_at < \'2021-08-15\'',
action_options=dict(
groupby_columns=['group_id'],
),
outputs=[
dict(uuid='order_count'),
],
)
df_new = count(df, action)
df_expected = pd.DataFrame([
[1, 1000, '2021-10-01', '2021-09-01', 2],
[1, 1050, '2021-10-01', '2021-08-01', 2],
[1, 1100, '2021-10-01', '2021-01-01', 2],
[2, 1150, '2021-09-01', '2021-08-01', 2],
[2, 1200, '2021-09-01', '2021-08-16', 2],
[2, 1250, '2021-09-01', '2021-08-14', 2],
], columns=[
'group_id',
'order_id',
'group_churned_at',
'order_created_at',
'order_count',
])
assert_frame_equal(df_new, df_expected)
def test_diff(self):
df = pd.DataFrame([
['2020-01-01', 1000],
['2020-01-02', 1050],
['2020-01-03', 1200],
['2020-01-04', 990],
], columns=[
'date',
'sold',
])
action = dict(
action_arguments=['sold'],
outputs=[
dict(uuid='sold_diff'),
],
)
df_new = diff(df, action)
self.assertEqual(df_new.to_dict(orient='records')[1:], [
dict(
date='2020-01-02',
sold=1050,
sold_diff=50,
),
dict(
date='2020-01-03',
sold=1200,
sold_diff=150,
),
dict(
date='2020-01-04',
sold=990,
sold_diff=-210,
),
])
# def test_expand_column(self):
# df = pd.DataFrame([
# [1, 'game'],
# [1, 'book'],
# [1, 'game'],
# [2, 'Video Game'],
# [1, 'Video Game'],
# [2, 'book'],
# [1, 'Video Game'],
# [2, 'Video Game'],
# ], columns=[
# 'group_id',
# 'category',
# ])
# action = dict(
# action_arguments=['category'],
# action_options=dict(
# groupby_columns=['group_id']
# ),
# outputs=[
# dict(uuid='category_expanded_count_game'),
# dict(uuid='category_expanded_count_book'),
# dict(uuid='category_expanded_count_video_game'),
# dict(uuid='category_expanded_count_clothing'),
# ],
# )
# df_new = expand_column(df, action)
# df_expected = pd.DataFrame([
# [1, 'game', 2, 1, 2],
# [1, 'book', 2, 1, 2],
# [1, 'game', 2, 1, 2],
# [2, 'Video Game', 0, 1, 2],
# [1, 'Video Game', 2, 1, 2],
# [2, 'book', 0, 1, 2],
# [1, 'Video Game', 2, 1, 2],
# [2, 'Video Game', 0, 1, 2],
# ], columns=[
# 'group_id',
# 'category',
# 'category_expanded_count_game',
# 'category_expanded_count_book',
# 'category_expanded_count_video_game',
# ])
# assert_frame_equal(df_new, df_expected)
# def test_expand_column_with_time_window(self):
# df = pd.DataFrame([
# [1, 'game', '2021-01-02', '2021-01-04'],
# [1, 'book', '2021-01-02', '2021-01-04'],
# [1, 'game', '2021-01-03', '2021-01-04'],
# [2, 'Video Game', '2021-01-01', '2021-01-03'],
# [1, 'Video Game', '2021-01-01', '2021-01-04'],
# [2, 'book', '2021-01-02', '2021-01-03'],
# [1, 'Video Game', '2021-01-03', '2021-01-04'],
# [2, 'Video Game', '2020-12-30', '2021-01-03'],
# ], columns=[
# 'group_id',
# 'category',
# 'timestamp1',
# 'timestamp2',
# ])
# action = dict(
# action_arguments=['category'],
# action_options=dict(
# groupby_columns=['group_id'],
# timestamp_feature_a='timestamp2',
# timestamp_feature_b='timestamp1',
# window=172800,
# ),
# outputs=[
# dict(uuid='category_expanded_count_game_2d'),
# dict(uuid='category_expanded_count_book_2d'),
# dict(uuid='category_expanded_count_video_game_2d'),
# dict(uuid='category_expanded_count_clothing_2d'),
# ],
# )
# df_new = expand_column(df, action)
# df_expected = pd.DataFrame([
# [1, 'game', '2021-01-02', '2021-01-04', 2, 1, 1],
# [1, 'book', '2021-01-02', '2021-01-04', 2, 1, 1],
# [1, 'game', '2021-01-03', '2021-01-04', 2, 1, 1],
# [2, 'Video Game', '2021-01-01', '2021-01-03', 0, 1, 1],
# [1, 'Video Game', '2021-01-01', '2021-01-04', 2, 1, 1],
# [2, 'book', '2021-01-02', '2021-01-03', 0, 1, 1],
# [1, 'Video Game', '2021-01-03', '2021-01-04', 2, 1, 1],
# [2, 'Video Game', '2020-12-30', '2021-01-03', 0, 1, 1],
# ], columns=[
# 'group_id',
# 'category',
# 'timestamp1',
# 'timestamp2',
# 'category_expanded_count_game_2d',
# 'category_expanded_count_book_2d',
# 'category_expanded_count_video_game_2d',
# ])
# assert_frame_equal(df_new, df_expected)
def test_first_column(self):
df = pd.DataFrame([
[1, 1000],
[2, 1050],
[1, 1100],
[2, 1150],
], columns=[
'group_id',
'order_id',
])
action = dict(
action_arguments=['order_id'],
action_options=dict(
groupby_columns=['group_id']
),
outputs=[
dict(uuid='first_order'),
],
)
df_new = first(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
group_id=1,
order_id=1000,
first_order=1000,
),
dict(
group_id=2,
order_id=1050,
first_order=1050,
),
dict(
group_id=1,
order_id=1100,
first_order=1000,
),
dict(
group_id=2,
order_id=1150,
first_order=1050,
),
])
def test_impute(self):
from data_cleaner.transformer_actions.column import impute
df = pd.DataFrame([
['2020-01-01', 1000, ' ', 800],
['2020-01-02', '', 1200, 700],
['2020-01-03', 1200, np.NaN, 900],
['2020-01-04', np.NaN, ' ', 700],
['2020-01-05', 1700, 1300, 800],
], columns=[
'date',
'sold',
'curr_profit',
'prev_sold',
])
action1 = dict(
action_arguments=['sold', 'curr_profit'],
action_options={
'value': '0',
},
action_variables={
'0': {
'feature': {
'column_type': 'number',
'uuid': 'sold',
},
'type': 'feature',
},
'1': {
'feature': {
'column_type': 'number',
'uuid': 'curr_profit',
},
'type': 'feature',
},
},
)
action2 = dict(
action_arguments=['sold'],
action_options={
'value': '0',
},
action_variables={
'0': {
'feature': {
'column_type': 'number',
'uuid': 'sold',
},
'type': 'feature',
},
},
)
action3 = dict(
action_arguments=['sold', 'curr_profit'],
action_options={
'strategy': 'average',
},
)
action4 = dict(
action_arguments=['sold', 'curr_profit'],
action_options={
'strategy': 'median',
},
)
action5 = dict(
action_arguments=['sold', 'curr_profit'],
action_options={
'strategy': 'column',
'value': 'prev_sold',
},
)
action_invalid = dict(
action_arguments=['sold', 'curr_profit'],
action_options={
'strategy': 'mode',
},
)
df_new1 = impute(df.copy(), action1)
df_new2 = impute(df.copy(), action2)
df_new3 = impute(df.copy(), action3)
df_new4 = impute(df.copy(), action4)
df_new5 = impute(df.copy(), action5)
df_expected1 = pd.DataFrame([
['2020-01-01', 1000, 0, 800],
['2020-01-02', 0, 1200, 700],
['2020-01-03', 1200, 0, 900],
['2020-01-04', 0, 0, 700],
['2020-01-05', 1700, 1300, 800],
], columns=[
'date',
'sold',
'curr_profit',
'prev_sold',
])
df_expected2 = pd.DataFrame([
['2020-01-01', 1000, ' ', 800],
['2020-01-02', 0, 1200, 700],
['2020-01-03', 1200, np.nan, 900],
['2020-01-04', 0, ' ', 700],
['2020-01-05', 1700, 1300, 800],
], columns=[
'date',
'sold',
'curr_profit',
'prev_sold',
])
df_expected3 = pd.DataFrame([
['2020-01-01', 1000, 1250, 800],
['2020-01-02', 1300, 1200, 700],
['2020-01-03', 1200, 1250, 900],
['2020-01-04', 1300, 1250, 700],
['2020-01-05', 1700, 1300, 800],
], columns=[
'date',
'sold',
'curr_profit',
'prev_sold',
])
df_expected4 = pd.DataFrame([
['2020-01-01', 1000, 1250, 800],
['2020-01-02', 1200, 1200, 700],
['2020-01-03', 1200, 1250, 900],
['2020-01-04', 1200, 1250, 700],
['2020-01-05', 1700, 1300, 800],
], columns=[
'date',
'sold',
'curr_profit',
'prev_sold',
])
df_expected5 = pd.DataFrame([
['2020-01-01', 1000, 800, 800],
['2020-01-02', 700, 1200, 700],
['2020-01-03', 1200, 900, 900],
['2020-01-04', 700, 700, 700],
['2020-01-05', 1700, 1300, 800],
], columns=[
'date',
'sold',
'curr_profit',
'prev_sold',
])
df_new1['sold'] = df_new1['sold'].astype(int)
df_new1['curr_profit'] = df_new1['curr_profit'].astype(int)
df_new2['sold'] = df_new2['sold'].astype(int)
df_new3['sold'] = df_new3['sold'].astype(int)
df_new3['curr_profit'] = df_new3['curr_profit'].astype(int)
df_new4['sold'] = df_new4['sold'].astype(int)
df_new4['curr_profit'] = df_new4['curr_profit'].astype(int)
df_new5['sold'] = df_new5['sold'].astype(int)
df_new5['curr_profit'] = df_new5['curr_profit'].astype(int)
assert_frame_equal(df_new1, df_expected1)
assert_frame_equal(df_new2, df_expected2)
assert_frame_equal(df_new3, df_expected3)
assert_frame_equal(df_new4, df_expected4)
assert_frame_equal(df_new5, df_expected5)
with self.assertRaises(Exception):
_ = impute(df.copy(), action_invalid)
def test_last_column(self):
df = pd.DataFrame([
[1, 1000],
[2, 1050],
[1, 1100],
[2, 1150],
], columns=[
'group_id',
'order_id',
])
action = dict(
action_arguments=['order_id'],
action_options=dict(
groupby_columns=['group_id']
),
outputs=[
dict(uuid='last_order'),
],
)
df_new = last(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
group_id=1,
order_id=1000,
last_order=1100,
),
dict(
group_id=2,
order_id=1050,
last_order=1150,
),
dict(
group_id=1,
order_id=1100,
last_order=1100,
),
dict(
group_id=2,
order_id=1150,
last_order=1150,
),
])
def test_max(self):
from data_cleaner.transformer_actions.column import max
action = self.__groupby_agg_action('max_amount')
df_new = max(TEST_DATAFRAME.copy(), action)
df_expected = pd.DataFrame([
[1, 1000, 1100],
[2, 1050, 1150],
[1, 1100, 1100],
[2, 1150, 1150],
], columns=[
'group_id',
'amount',
'max_amount',
])
assert_frame_equal(df_new, df_expected)
action2 = dict(
action_arguments=['amount'],
action_options=dict(),
outputs=[
dict(uuid='max_amount'),
],
)
df_new2 = max(TEST_DATAFRAME.copy(), action2)
df_expected2 = pd.DataFrame([
[1, 1000, 1150],
[2, 1050, 1150],
[1, 1100, 1150],
[2, 1150, 1150],
], columns=[
'group_id',
'amount',
'max_amount',
])
assert_frame_equal(df_new2, df_expected2)
def test_median(self):
from data_cleaner.transformer_actions.column import median
action = self.__groupby_agg_action('median_amount')
df = pd.DataFrame([
[1, 1000],
[2, 1050],
[1, 1100],
[2, 1550],
[2, 1150],
], columns=[
'group_id',
'amount',
])
df_new = median(df, action)
df_expected = pd.DataFrame([
[1, 1000, 1050],
[2, 1050, 1150],
[1, 1100, 1050],
[2, 1550, 1150],
[2, 1150, 1150],
], columns=[
'group_id',
'amount',
'median_amount',
])
assert_frame_equal(df_new, df_expected)
def test_min(self):
from data_cleaner.transformer_actions.column import min
action = self.__groupby_agg_action('min_amount')
df_new = min(TEST_DATAFRAME.copy(), action)
df_expected = pd.DataFrame([
[1, 1000, 1000],
[2, 1050, 1050],
[1, 1100, 1000],
[2, 1150, 1050],
], columns=[
'group_id',
'amount',
'min_amount',
])
assert_frame_equal(df_new, df_expected)
def test_select(self):
df = pd.DataFrame([
[1, 1000],
[2, 1050],
], columns=[
'group_id',
'order_id',
])
action = dict(
action_arguments=['group_id']
)
df_new = select(df, action)
self.assertEqual(df_new.to_dict(orient='records'), [
dict(
group_id=1,
),
dict(
group_id=2,
),
])
def test_shift_down(self):
df = pd.DataFrame([
['2020-01-01', 1000],
['2020-01-02', 1050],
['2020-01-03', 1200],
['2020-01-04', 990],
], columns=[
'date',
'sold',
])
action = dict(
action_arguments=['sold'],
outputs=[
dict(uuid='prev_sold'),
],
)
df_new = shift_down(df, action)
self.assertEqual(df_new.to_dict(orient='records')[1:], [
dict(
date='2020-01-02',
sold=1050,
prev_sold=1000,
),
dict(
date='2020-01-03',
sold=1200,
prev_sold=1050,
),
dict(
date='2020-01-04',
sold=990,
prev_sold=1200,
),
])
def test_shift_down_with_groupby(self):
df = pd.DataFrame([
[1, '2020-01-01', 1000],
[1, '2020-01-02', 1050],
[2, '2020-01-03', 1200],
[1, '2020-01-04', 990],
[2, '2020-01-05', 980],
[2, '2020-01-06', 970],
[2, '2020-01-07', 960],
], columns=[
'group_id',
'date',
'sold',
])
action = dict(
action_arguments=['sold'],
action_options=dict(
groupby_columns=['group_id'],
periods=2,
),
outputs=[
dict(uuid='prev_sold'),
],
)
df_new = shift_down(df, action)
df_expected = pd.DataFrame([
[1, '2020-01-01', 1000, None],
[1, '2020-01-02', 1050, None],
[2, '2020-01-03', 1200, None],
[1, '2020-01-04', 990, 1000],
[2, '2020-01-05', 980, None],
[2, '2020-01-06', 970, 1200],
[2, '2020-01-07', 960, 980],
], columns=[
'group_id',
'date',
'sold',
'prev_sold',
])
assert_frame_equal(df_new, df_expected)
def test_shift_up(self):
df = pd.DataFrame([
['2020-01-01', 1000],
['2020-01-02', 1050],
['2020-01-03', 1200],
['2020-01-04', 990],
], columns=[
'date',
'sold',
])
action = dict(
action_arguments=['sold'],
outputs=[
dict(uuid='next_sold'),
],
)
df_new = shift_up(df, action)
self.assertEqual(df_new.to_dict(orient='records')[:-1], [
dict(
date='2020-01-01',
sold=1000,
next_sold=1050,
),
dict(
date='2020-01-02',
sold=1050,
next_sold=1200,
),
dict(
date='2020-01-03',
sold=1200,
next_sold=990,
),
])
def test_sum(self):
from data_cleaner.transformer_actions.column import sum
action = self.
|
7,733 | 219 | 13,763 |
mage-ai__mage-ai
|
9ed779ccaf3538efbe1f2f54dd68f61fb1c3af55
|
src/data_cleaner/tests/transformer_actions/test_row.py
|
common
|
execute
| true |
function
| 9 | 9 | false | true |
[
"action",
"action_type",
"hydrate_action",
"axis",
"join",
"columns_by_type",
"execute",
"groupby",
"transform",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "action",
"type": "statement"
},
{
"name": "action_type",
"type": "property"
},
{
"name": "axis",
"type": "property"
},
{
"name": "columns_by_type",
"type": "statement"
},
{
"name": "execute",
"type": "function"
},
{
"name": "groupby",
"type": "function"
},
{
"name": "hydrate_action",
"type": "function"
},
{
"name": "join",
"type": "function"
},
{
"name": "transform",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
from data_cleaner.tests.base_test import TestCase
from data_cleaner.transformer_actions.base import BaseAction
from data_cleaner.transformer_actions.row import (
drop_duplicates,
# explode,
filter_rows,
sort_rows,
)
from pandas.util.testing import assert_frame_equal
import numpy as np
import pandas as pd
class RowTests(TestCase):
def test_drop_duplicates(self):
df = pd.DataFrame([
[0, False, 'a'],
[1, True, 'b'],
[1, True, 'c'],
[0, True, 'd'],
], columns=[
'integer',
'boolean',
'string',
])
test_cases = [
(dict(action_arguments=['integer']), df.iloc[[2, 3]]),
(dict(action_arguments=['integer'], action_options=dict(keep='first')), df.iloc[[0, 1]]),
(dict(action_arguments=['boolean']), df.iloc[[0, 3]]),
(dict(action_arguments=['boolean'], action_options=dict(keep='first')), df.iloc[[0, 1]]),
(dict(action_arguments=['integer', 'boolean']), df.iloc[[0, 2, 3]]),
]
for action, val in test_cases:
self.assertTrue(drop_duplicates(df, action).equals(val))
# def test_explode(self):
# df = pd.DataFrame([
# ['(a, b, c)'],
# ['[b, c, d]'],
# [' e, f '],
# ], columns=['tags'])
# action = dict(
# action_arguments=['tags'],
# action_options={
# 'separator': ',',
# },
# outputs=[
# dict(
# uuid='tag',
# column_type='text',
# ),
# ],
# )
# df_new = explode(df, action)
# df_expected = pd.DataFrame([
# ['a', '(a, b, c)'],
# ['b', '(a, b, c)'],
# ['c', '(a, b, c)'],
# ['b', '[b, c, d]'],
# ['c', '[b, c, d]'],
# ['d', '[b, c, d]'],
# ['e', ' e, f '],
# ['f', ' e, f '],
# ], columns=['tag', 'tags'])
# assert_frame_equal(df_new.reset_index(drop=True), df_expected)
def test_filter_rows(self):
df = pd.DataFrame([
[0, False, 'a'],
[1, True, 'b'],
], columns=[
'integer',
'boolean',
'string',
])
test_cases = [
([0, False, 'a'], 'integer == 0'),
([0, False, 'a'], 'string == \'a\''),
([1, True, 'b'], 'boolean == True'),
([1, True, 'b'], 'integer >= 1'),
([1, True, 'b'], 'integer >= 1 and boolean == True'),
([1, True, 'b'], 'integer >= 1 and (boolean == False or string == \'b\')'),
]
for val, query in test_cases:
self.assertEqual(
val,
filter_rows(df, dict(action_code=query)).iloc[0].values.tolist(),
)
def test_filter_rows_is_null(self):
df = pd.DataFrame([
[None, False, 'a'],
[2, True, 'b'],
[3, False, 'c'],
[1, None, 'a'],
[2, True, 'b'],
[3, '', 'c'],
[1, False, None],
[2, True, 'b'],
[3, False, ''],
], columns=[
'integer',
'boolean',
'string',
])
integer_rows = filter_rows(
df,
dict(action_code='integer == null'),
original_df=df,
).values.tolist()
self.assertEqual(len(integer_rows), 1)
self.assertEqual(integer_rows[0][1], False)
self.assertEqual(integer_rows[0][2], 'a')
boolean_rows = filter_rows(
df,
dict(action_code='boolean == null'),
original_df=df,
).values.tolist()
self.assertEqual(len(boolean_rows), 2)
self.assertEqual(boolean_rows[0][0], 1.0)
self.assertEqual(boolean_rows[0][1], None)
self.assertEqual(boolean_rows[0][2], 'a')
self.assertEqual(boolean_rows[1][0], 3.0)
self.assertEqual(boolean_rows[1][1], '')
self.assertEqual(boolean_rows[1][2], 'c')
string_rows = filter_rows(
df,
dict(action_code='string == null'),
original_df=df,
).values.tolist()
self.assertEqual(len(string_rows), 2)
self.assertEqual(string_rows[0][0], 1.0)
self.assertEqual(string_rows[0][1], False)
self.assertEqual(string_rows[0][2], None)
self.assertEqual(string_rows[1][0], 3.0)
self.assertEqual(string_rows[1][1], False)
self.assertEqual(string_rows[1][2], '')
def test_filter_rows_is_not_null(self):
df = pd.DataFrame([
[None, False, 'a'],
[2, True, 'b'],
[3, False, 'c'],
[1, None, 'a'],
[2, True, 'b'],
[3, '', 'c'],
[1, False, None],
[2, True, 'b'],
[3, False, ''],
], columns=[
'integer',
'boolean',
'string',
])
integer_rows = filter_rows(
df,
dict(action_code='integer != null'),
original_df=df,
)['integer'].values.tolist()
self.assertEqual(integer_rows, [
2,
3,
1,
2,
3,
1,
2,
3,
])
boolean_rows = filter_rows(
df,
dict(action_code='boolean != null'),
original_df=df,
)['boolean'].values.tolist()
self.assertEqual(boolean_rows, [
False,
True,
False,
True,
False,
True,
False,
])
string_rows = filter_rows(
df,
dict(action_code='string != null'),
original_df=df,
)['string'].values.tolist()
self.assertEqual(string_rows, [
'a',
'b',
'c',
'a',
'b',
'c',
'b',
])
def test_filter_row_contains_string(self):
df = pd.DataFrame([
['fsdijfosidjfiosfj'],
['[email protected]'],
[np.NaN],
['fsdfsdfdsfdsf'],
['[email protected]'],
], columns=[
'id',
])
action = dict(
action_code='id contains @',
)
action2 = dict(
action_code='id contains \'@\'',
)
df_new = filter_rows(df, action, original_df=df).reset_index(drop=True)
df_new2 = filter_rows(df, action2, original_df=df).reset_index(drop=True)
df_expected = pd.DataFrame([
['[email protected]'],
['[email protected]'],
], columns=[
'id',
])
assert_frame_equal(df_new, df_expected)
assert_frame_equal(df_new2, df_expected)
def test_filter_row_not_contains_string(self):
df = pd.DataFrame([
[np.NaN, False],
['[email protected]', True],
['[email protected]', True],
['fsdfsdfdsfdsf', False],
['[email protected]', False],
['eeeeasdf', True]
], columns=[
'email',
'subscription'
])
action = dict(
action_code='email not contains mailnet',
)
action2 = dict(
action_code='email not contains \'mailnet\'',
)
action3 = dict(
action_code = 'email not contains @',
)
action4 = dict(
action_code = 'email not contains \'^e+\w\'',
)
action_invalid = dict(
action_code='subscription not contains False'
)
df_new = filter_rows(df, action, original_df=df).reset_index(drop=True)
df_new2 = filter_rows(df, action2, original_df=df).reset_index(drop=True)
df_new3 = filter_rows(df, action3, original_df=df).reset_index(drop=True)
df_new4 = filter_rows(df, action4, original_df=df).reset_index(drop=True)
df_expected1 = pd.DataFrame([
[np.NaN, False],
['[email protected]', True],
['fsdfsdfdsfdsf', False],
['eeeeasdf', True]
], columns=[
'email',
'subscription'
])
df_expected2 = pd.DataFrame([
[np.NaN, False],
['fsdfsdfdsfdsf', False],
['eeeeasdf', True]
], columns=[
'email',
'subscription'
])
df_expected3 = pd.DataFrame([
[np.NaN, False],
['[email protected]', True],
['[email protected]', True],
['fsdfsdfdsfdsf', False],
['[email protected]', False]
], columns=[
'email',
'subscription'
])
assert_frame_equal(df_new, df_expected1)
assert_frame_equal(df_new2, df_expected1)
assert_frame_equal(df_new3, df_expected2)
assert_frame_equal(df_new4, df_expected3)
with self.assertRaises(Exception):
_ = filter_rows(df, action_invalid, original_df=df).reset_index(drop=True)
def test_filter_rows_multi_condition(self):
df = pd.DataFrame(
[
[100, None, '', 10],
[250, 'brand1', False, np.NaN],
[np.NaN, 'brand2', None, 18],
[50, 'brand1', True, 13],
[75, '', '', 80],
[None, 'company3', False, 23],
],
columns=['value', 'brand', 'discounted', 'inventory']
)
action = dict(action_code='(value < 110 and value >= 50) and (value != null)')
action2 = dict(action_code='brand contains brand and inventory != null')
action3 = dict(action_code='(brand != null and value > 60) or (discounted == null)')
action4 = dict(
action_code='(discounted == True and inventory > 15)'
' or (discounted == False and value != null)'
)
action5 = dict(
action_code='(brand not contains company and value == 75 and inventory <= 80)'
' or (discounted != null)'
)
df_expected = pd.DataFrame(
[
[100, None, '', 10],
[50, 'brand1', True, 13],
[75, '', '', 80],
],
columns=['value', 'brand', 'discounted', 'inventory']
)
df_expected2 = pd.DataFrame(
[
[np.NaN, 'brand2', None, 18],
[50, 'brand1', True, 13],
],
columns=['value', 'brand', 'discounted', 'inventory']
)
df_expected3 = pd.DataFrame(
[
[100, None, '', 10],
[250, 'brand1', False, np.NaN],
[np.NaN, 'brand2', None, 18],
[75, '', '', 80],
],
columns=['value', 'brand', 'discounted', 'inventory']
)
df_expected4 = pd.DataFrame(
[
[250, 'brand1', False, np.NaN],
],
columns=['value', 'brand', 'discounted', 'inventory']
)
df_expected5 = pd.DataFrame(
[
[250, 'brand1', False, np.NaN],
[50, 'brand1', True, 13],
[75, '', '', 80],
[None, 'company3', False, 23],
],
columns=['value', 'brand', 'discounted', 'inventory']
)
df_new = filter_rows(df, action, original_df=df).reset_index(drop=True)
df_new2 = filter_rows(df, action2, original_df=df).reset_index(drop=True)
df_new3 = filter_rows(df, action3, original_df=df).reset_index(drop=True)
df_new4 = filter_rows(df, action4, original_df=df).reset_index(drop=True)
df_new5 = filter_rows(df, action5, original_df=df).reset_index(drop=True)
df_new['value'] = df_new['value'].astype(int)
df_new['inventory'] = df_new['inventory'].astype(int)
df_new2['brand'] = df_new2['brand'].astype(str)
df_new2['inventory'] = df_new2['inventory'].astype(int)
df_new4['value'] = df_new4['value'].astype(int)
df_new4['brand'] = df_new4['brand'].astype(str)
df_new4['discounted'] = df_new4['discounted'].astype(bool)
assert_frame_equal(df_expected, df_new)
assert_frame_equal(df_expected2, df_new2)
assert_frame_equal(df_expected3, df_new3)
assert_frame_equal(df_expected4, df_new4)
assert_frame_equal(df_expected5, df_new5)
def test_filter_row_implicit_null(self):
# tests that implicit null values in the transformed dataframe are still removed
df = pd.DataFrame(
[
[100, None, '', 10],
[250, 'brand1', False, np.NaN],
[np.NaN, 'brand2', None, 18],
[50, 'brand1', True, 13],
[75, '', '', 80],
[None, 'company3', False, 23],
],
columns=['value', 'brand', 'discounted', 'inventory']
)
action_payload = {
'action_type': 'filter',
'action_code': '%{1} != null',
'action_arguments': [],
'action_options': {},
'axis': 'row',
'action_variables': {
'1': {
'id': 'value',
'type': 'feature',
'feature': {
'column_type': 'number',
'uuid': 'value'
}
},
},
'outputs': []
}
action = BaseAction(action_payload)
df_new = action.
|
7,735 | 220 | 351 |
mage-ai__mage-ai
|
5d823db31cab8cb2e89873aec656b5a522769873
|
mage_ai/tests/data_cleaner/cleaning_rules/test_remove_collinear_columns.py
|
commited
|
setUp
| true |
function
| 75 | 74 | false | true |
[
"setUp",
"assertEqual",
"assertAlmostEqual",
"fail",
"failIf",
"tearDown",
"addClassCleanup",
"addCleanup",
"addTypeEqualityFunc",
"assert_",
"assertAlmostEquals",
"assertCountEqual",
"assertDictContainsSubset",
"assertDictEqual",
"assertEquals",
"assertFalse",
"assertGreater",
"assertGreaterEqual",
"assertIn",
"assertIs",
"assertIsInstance",
"assertIsNone",
"assertIsNot",
"assertIsNotNone",
"assertLess",
"assertLessEqual",
"assertListEqual",
"assertLogs",
"assertMultiLineEqual",
"assertNoLogs",
"assertNotAlmostEqual",
"assertNotAlmostEquals",
"assertNotEqual",
"assertNotEquals",
"assertNotIn",
"assertNotIsInstance",
"assertNotRegex",
"assertNotRegexpMatches",
"assertRaises",
"assertRaisesRegex",
"assertRaisesRegexp",
"assertRegex",
"assertRegexpMatches",
"assertSequenceEqual",
"assertSetEqual",
"assertTrue",
"assertTupleEqual",
"assertWarns",
"assertWarnsRegex",
"countTestCases",
"debug",
"defaultTestResult",
"doClassCleanups",
"doCleanups",
"failIfAlmostEqual",
"failIfEqual",
"failUnless",
"failUnlessAlmostEqual",
"failUnlessEqual",
"failUnlessRaises",
"failureException",
"id",
"longMessage",
"maxDiff",
"run",
"setUpClass",
"shortDescription",
"skipTest",
"subTest",
"tearDownClass",
"_addSkip",
"_formatMessage",
"_getAssertEqualityFunc",
"_testMethodDoc",
"_testMethodName",
"__annotations__",
"__call__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "addClassCleanup",
"type": "function"
},
{
"name": "addCleanup",
"type": "function"
},
{
"name": "addTypeEqualityFunc",
"type": "function"
},
{
"name": "assert_",
"type": "function"
},
{
"name": "assertAlmostEqual",
"type": "function"
},
{
"name": "assertAlmostEquals",
"type": "function"
},
{
"name": "assertCountEqual",
"type": "function"
},
{
"name": "assertDictContainsSubset",
"type": "function"
},
{
"name": "assertDictEqual",
"type": "function"
},
{
"name": "assertEqual",
"type": "function"
},
{
"name": "assertEquals",
"type": "function"
},
{
"name": "assertFalse",
"type": "function"
},
{
"name": "assertGreater",
"type": "function"
},
{
"name": "assertGreaterEqual",
"type": "function"
},
{
"name": "assertIn",
"type": "function"
},
{
"name": "assertIs",
"type": "function"
},
{
"name": "assertIsInstance",
"type": "function"
},
{
"name": "assertIsNone",
"type": "function"
},
{
"name": "assertIsNot",
"type": "function"
},
{
"name": "assertIsNotNone",
"type": "function"
},
{
"name": "assertLess",
"type": "function"
},
{
"name": "assertLessEqual",
"type": "function"
},
{
"name": "assertListEqual",
"type": "function"
},
{
"name": "assertLogs",
"type": "function"
},
{
"name": "assertMultiLineEqual",
"type": "function"
},
{
"name": "assertNotAlmostEqual",
"type": "function"
},
{
"name": "assertNotAlmostEquals",
"type": "function"
},
{
"name": "assertNotEqual",
"type": "function"
},
{
"name": "assertNotEquals",
"type": "function"
},
{
"name": "assertNotIn",
"type": "function"
},
{
"name": "assertNotIsInstance",
"type": "function"
},
{
"name": "assertNotRegex",
"type": "function"
},
{
"name": "assertNotRegexpMatches",
"type": "function"
},
{
"name": "assertRaises",
"type": "function"
},
{
"name": "assertRaisesRegex",
"type": "function"
},
{
"name": "assertRaisesRegexp",
"type": "function"
},
{
"name": "assertRegex",
"type": "function"
},
{
"name": "assertRegexpMatches",
"type": "function"
},
{
"name": "assertSequenceEqual",
"type": "function"
},
{
"name": "assertSetEqual",
"type": "function"
},
{
"name": "assertTrue",
"type": "function"
},
{
"name": "assertTupleEqual",
"type": "function"
},
{
"name": "assertWarns",
"type": "function"
},
{
"name": "assertWarnsRegex",
"type": "function"
},
{
"name": "countTestCases",
"type": "function"
},
{
"name": "debug",
"type": "function"
},
{
"name": "defaultTestResult",
"type": "function"
},
{
"name": "doClassCleanups",
"type": "function"
},
{
"name": "doCleanups",
"type": "function"
},
{
"name": "fail",
"type": "function"
},
{
"name": "failIf",
"type": "function"
},
{
"name": "failIfAlmostEqual",
"type": "function"
},
{
"name": "failIfEqual",
"type": "function"
},
{
"name": "failUnless",
"type": "function"
},
{
"name": "failUnlessAlmostEqual",
"type": "function"
},
{
"name": "failUnlessEqual",
"type": "function"
},
{
"name": "failUnlessRaises",
"type": "function"
},
{
"name": "failureException",
"type": "statement"
},
{
"name": "id",
"type": "function"
},
{
"name": "longMessage",
"type": "statement"
},
{
"name": "maxDiff",
"type": "statement"
},
{
"name": "run",
"type": "function"
},
{
"name": "setUp",
"type": "function"
},
{
"name": "setUpClass",
"type": "function"
},
{
"name": "shortDescription",
"type": "function"
},
{
"name": "skipTest",
"type": "function"
},
{
"name": "subTest",
"type": "function"
},
{
"name": "tearDown",
"type": "function"
},
{
"name": "tearDownClass",
"type": "function"
},
{
"name": "_addSkip",
"type": "function"
},
{
"name": "_formatMessage",
"type": "function"
},
{
"name": "_getAssertEqualityFunc",
"type": "function"
},
{
"name": "_testMethodDoc",
"type": "statement"
},
{
"name": "_testMethodName",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__call__",
"type": "function"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
from data_cleaner.cleaning_rules.remove_collinear_columns import RemoveCollinearColumns
from tests.base_test import TestCase
from pandas.util.testing import assert_frame_equal
import numpy as np
import pandas as pd
class RemoveCollinearColumnsTests(TestCase):
def setUp(self):
self.rng = np.random.default_rng(42)
return super().
|
7,736 | 220 | 1,107 |
mage-ai__mage-ai
|
5d823db31cab8cb2e89873aec656b5a522769873
|
mage_ai/tests/data_cleaner/cleaning_rules/test_remove_collinear_columns.py
|
Unknown
|
evaluate
| true |
function
| 14 | 15 | false | true |
[
"numeric_columns",
"evaluate",
"df_columns",
"column_types",
"numeric_df",
"df",
"EPSILON",
"filter_numeric_types",
"get_variance_inflation_factor",
"MIN_ENTRIES",
"numeric_indices",
"ROW_SAMPLE_SIZE",
"statistics",
"VIF_UB",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "column_types",
"type": "statement"
},
{
"name": "df",
"type": "statement"
},
{
"name": "df_columns",
"type": "statement"
},
{
"name": "EPSILON",
"type": "statement"
},
{
"name": "evaluate",
"type": "function"
},
{
"name": "filter_numeric_types",
"type": "function"
},
{
"name": "get_variance_inflation_factor",
"type": "function"
},
{
"name": "MIN_ENTRIES",
"type": "statement"
},
{
"name": "numeric_columns",
"type": "statement"
},
{
"name": "numeric_df",
"type": "statement"
},
{
"name": "numeric_indices",
"type": "statement"
},
{
"name": "ROW_SAMPLE_SIZE",
"type": "statement"
},
{
"name": "statistics",
"type": "statement"
},
{
"name": "VIF_UB",
"type": "statement"
},
{
"name": "_build_transformer_action_suggestion",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
from data_cleaner.cleaning_rules.remove_collinear_columns import RemoveCollinearColumns
from tests.base_test import TestCase
from pandas.util.testing import assert_frame_equal
import numpy as np
import pandas as pd
class RemoveCollinearColumnsTests(TestCase):
def setUp(self):
self.rng = np.random.default_rng(42)
return super().setUp()
def test_categorical_data_frame(self):
df = pd.DataFrame([
[1, 1000, '2021-10-01', '2021-09-01'],
[1, 1050, '2021-10-01', '2021-08-01'],
[1, 1100, '2021-10-01', '2021-01-01'],
[2, 1150, '2021-09-01', '2021-08-01'],
], columns=[
'group_id',
'order_id',
'group_churned_at',
'order_created_at',
])
column_types = {
'group_id': 'category',
'order_id': 'category',
'group_churned_at': 'datetime',
'order_created_at': 'datetime'
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).
|
7,737 | 220 | 1,916 |
mage-ai__mage-ai
|
5d823db31cab8cb2e89873aec656b5a522769873
|
mage_ai/tests/data_cleaner/cleaning_rules/test_remove_collinear_columns.py
|
Unknown
|
evaluate
| true |
function
| 14 | 15 | false | false |
[
"numeric_columns",
"evaluate",
"df_columns",
"column_types",
"numeric_df",
"df",
"EPSILON",
"filter_numeric_types",
"get_variance_inflation_factor",
"MIN_ENTRIES",
"numeric_indices",
"ROW_SAMPLE_SIZE",
"statistics",
"VIF_UB",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "column_types",
"type": "statement"
},
{
"name": "df",
"type": "statement"
},
{
"name": "df_columns",
"type": "statement"
},
{
"name": "EPSILON",
"type": "statement"
},
{
"name": "evaluate",
"type": "function"
},
{
"name": "filter_numeric_types",
"type": "function"
},
{
"name": "get_variance_inflation_factor",
"type": "function"
},
{
"name": "MIN_ENTRIES",
"type": "statement"
},
{
"name": "numeric_columns",
"type": "statement"
},
{
"name": "numeric_df",
"type": "statement"
},
{
"name": "numeric_indices",
"type": "statement"
},
{
"name": "ROW_SAMPLE_SIZE",
"type": "statement"
},
{
"name": "statistics",
"type": "statement"
},
{
"name": "VIF_UB",
"type": "statement"
},
{
"name": "_build_transformer_action_suggestion",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
from data_cleaner.cleaning_rules.remove_collinear_columns import RemoveCollinearColumns
from tests.base_test import TestCase
from pandas.util.testing import assert_frame_equal
import numpy as np
import pandas as pd
class RemoveCollinearColumnsTests(TestCase):
def setUp(self):
self.rng = np.random.default_rng(42)
return super().setUp()
def test_categorical_data_frame(self):
df = pd.DataFrame([
[1, 1000, '2021-10-01', '2021-09-01'],
[1, 1050, '2021-10-01', '2021-08-01'],
[1, 1100, '2021-10-01', '2021-01-01'],
[2, 1150, '2021-09-01', '2021-08-01'],
], columns=[
'group_id',
'order_id',
'group_churned_at',
'order_created_at',
])
column_types = {
'group_id': 'category',
'order_id': 'category',
'group_churned_at': 'datetime',
'order_created_at': 'datetime'
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
self.assertEqual(result, [])
def test_clean_removes_all_data_frame(self):
df = pd.DataFrame([
[None, 1000, '2021-10-01', '2021-09-01'],
[1, None, '2021-10-01', '2021-08-01'],
[np.nan, 1100, '2021-10-01', '2021-01-01'],
[None, 1150, '2021-09-01', '2021-08-01'],
], columns=[
'group_id',
'order_id',
'group_churned_at',
'order_created_at',
])
column_types = {
'group_id': 'number',
'order_id': 'number',
'group_churned_at': 'datetime',
'order_created_at': 'datetime'
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).
|
7,738 | 220 | 2,473 |
mage-ai__mage-ai
|
5d823db31cab8cb2e89873aec656b5a522769873
|
mage_ai/tests/data_cleaner/cleaning_rules/test_remove_collinear_columns.py
|
inproject
|
evaluate
| true |
function
| 14 | 15 | false | false |
[
"numeric_columns",
"evaluate",
"df_columns",
"column_types",
"numeric_df",
"df",
"EPSILON",
"filter_numeric_types",
"get_variance_inflation_factor",
"MIN_ENTRIES",
"numeric_indices",
"ROW_SAMPLE_SIZE",
"statistics",
"VIF_UB",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "column_types",
"type": "statement"
},
{
"name": "df",
"type": "statement"
},
{
"name": "df_columns",
"type": "statement"
},
{
"name": "EPSILON",
"type": "statement"
},
{
"name": "evaluate",
"type": "function"
},
{
"name": "filter_numeric_types",
"type": "function"
},
{
"name": "get_variance_inflation_factor",
"type": "function"
},
{
"name": "MIN_ENTRIES",
"type": "statement"
},
{
"name": "numeric_columns",
"type": "statement"
},
{
"name": "numeric_df",
"type": "statement"
},
{
"name": "numeric_indices",
"type": "statement"
},
{
"name": "ROW_SAMPLE_SIZE",
"type": "statement"
},
{
"name": "statistics",
"type": "statement"
},
{
"name": "VIF_UB",
"type": "statement"
},
{
"name": "_build_transformer_action_suggestion",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
from data_cleaner.cleaning_rules.remove_collinear_columns import RemoveCollinearColumns
from tests.base_test import TestCase
from pandas.util.testing import assert_frame_equal
import numpy as np
import pandas as pd
class RemoveCollinearColumnsTests(TestCase):
def setUp(self):
self.rng = np.random.default_rng(42)
return super().setUp()
def test_categorical_data_frame(self):
df = pd.DataFrame([
[1, 1000, '2021-10-01', '2021-09-01'],
[1, 1050, '2021-10-01', '2021-08-01'],
[1, 1100, '2021-10-01', '2021-01-01'],
[2, 1150, '2021-09-01', '2021-08-01'],
], columns=[
'group_id',
'order_id',
'group_churned_at',
'order_created_at',
])
column_types = {
'group_id': 'category',
'order_id': 'category',
'group_churned_at': 'datetime',
'order_created_at': 'datetime'
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
self.assertEqual(result, [])
def test_clean_removes_all_data_frame(self):
df = pd.DataFrame([
[None, 1000, '2021-10-01', '2021-09-01'],
[1, None, '2021-10-01', '2021-08-01'],
[np.nan, 1100, '2021-10-01', '2021-01-01'],
[None, 1150, '2021-09-01', '2021-08-01'],
], columns=[
'group_id',
'order_id',
'group_churned_at',
'order_created_at',
])
column_types = {
'group_id': 'number',
'order_id': 'number',
'group_churned_at': 'datetime',
'order_created_at': 'datetime'
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
self.assertEqual(result, [])
def test_collinear_no_results(self):
df = pd.DataFrame([
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1]
], columns=['number_of_users', 'views', 'revenue', 'losses'])
column_types = {
'number_of_users': 'number',
'views': 'number',
'revenue': 'number',
'losses': 'number',
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).
|
7,739 | 220 | 3,533 |
mage-ai__mage-ai
|
5d823db31cab8cb2e89873aec656b5a522769873
|
mage_ai/tests/data_cleaner/cleaning_rules/test_remove_collinear_columns.py
|
Unknown
|
evaluate
| true |
function
| 14 | 15 | false | false |
[
"evaluate",
"numeric_columns",
"numeric_df",
"get_variance_inflation_factor",
"column_types",
"df",
"df_columns",
"EPSILON",
"filter_numeric_types",
"MIN_ENTRIES",
"numeric_indices",
"ROW_SAMPLE_SIZE",
"statistics",
"VIF_UB",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "column_types",
"type": "statement"
},
{
"name": "df",
"type": "statement"
},
{
"name": "df_columns",
"type": "statement"
},
{
"name": "EPSILON",
"type": "statement"
},
{
"name": "evaluate",
"type": "function"
},
{
"name": "filter_numeric_types",
"type": "function"
},
{
"name": "get_variance_inflation_factor",
"type": "function"
},
{
"name": "MIN_ENTRIES",
"type": "statement"
},
{
"name": "numeric_columns",
"type": "statement"
},
{
"name": "numeric_df",
"type": "statement"
},
{
"name": "numeric_indices",
"type": "statement"
},
{
"name": "ROW_SAMPLE_SIZE",
"type": "statement"
},
{
"name": "statistics",
"type": "statement"
},
{
"name": "VIF_UB",
"type": "statement"
},
{
"name": "_build_transformer_action_suggestion",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
from data_cleaner.cleaning_rules.remove_collinear_columns import RemoveCollinearColumns
from tests.base_test import TestCase
from pandas.util.testing import assert_frame_equal
import numpy as np
import pandas as pd
class RemoveCollinearColumnsTests(TestCase):
def setUp(self):
self.rng = np.random.default_rng(42)
return super().setUp()
def test_categorical_data_frame(self):
df = pd.DataFrame([
[1, 1000, '2021-10-01', '2021-09-01'],
[1, 1050, '2021-10-01', '2021-08-01'],
[1, 1100, '2021-10-01', '2021-01-01'],
[2, 1150, '2021-09-01', '2021-08-01'],
], columns=[
'group_id',
'order_id',
'group_churned_at',
'order_created_at',
])
column_types = {
'group_id': 'category',
'order_id': 'category',
'group_churned_at': 'datetime',
'order_created_at': 'datetime'
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
self.assertEqual(result, [])
def test_clean_removes_all_data_frame(self):
df = pd.DataFrame([
[None, 1000, '2021-10-01', '2021-09-01'],
[1, None, '2021-10-01', '2021-08-01'],
[np.nan, 1100, '2021-10-01', '2021-01-01'],
[None, 1150, '2021-09-01', '2021-08-01'],
], columns=[
'group_id',
'order_id',
'group_churned_at',
'order_created_at',
])
column_types = {
'group_id': 'number',
'order_id': 'number',
'group_churned_at': 'datetime',
'order_created_at': 'datetime'
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
self.assertEqual(result, [])
def test_collinear_no_results(self):
df = pd.DataFrame([
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1]
], columns=['number_of_users', 'views', 'revenue', 'losses'])
column_types = {
'number_of_users': 'number',
'views': 'number',
'revenue': 'number',
'losses': 'number',
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
self.assertEqual(result, [])
def test_evaluate(self):
df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
])
column_types = {
'number_of_users': 'number',
'views': 'number',
'number_of_creators': 'number',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
results = rule.
|
7,740 | 220 | 6,861 |
mage-ai__mage-ai
|
5d823db31cab8cb2e89873aec656b5a522769873
|
mage_ai/tests/data_cleaner/cleaning_rules/test_remove_collinear_columns.py
|
Unknown
|
numeric_df
| true |
statement
| 14 | 15 | false | true |
[
"df",
"evaluate",
"column_types",
"numeric_df",
"numeric_columns",
"df_columns",
"EPSILON",
"filter_numeric_types",
"get_variance_inflation_factor",
"MIN_ENTRIES",
"numeric_indices",
"ROW_SAMPLE_SIZE",
"statistics",
"VIF_UB",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "column_types",
"type": "statement"
},
{
"name": "df",
"type": "statement"
},
{
"name": "df_columns",
"type": "statement"
},
{
"name": "EPSILON",
"type": "statement"
},
{
"name": "evaluate",
"type": "function"
},
{
"name": "filter_numeric_types",
"type": "function"
},
{
"name": "get_variance_inflation_factor",
"type": "function"
},
{
"name": "MIN_ENTRIES",
"type": "statement"
},
{
"name": "numeric_columns",
"type": "statement"
},
{
"name": "numeric_df",
"type": "statement"
},
{
"name": "numeric_indices",
"type": "statement"
},
{
"name": "ROW_SAMPLE_SIZE",
"type": "statement"
},
{
"name": "statistics",
"type": "statement"
},
{
"name": "VIF_UB",
"type": "statement"
},
{
"name": "_build_transformer_action_suggestion",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
from data_cleaner.cleaning_rules.remove_collinear_columns import RemoveCollinearColumns
from tests.base_test import TestCase
from pandas.util.testing import assert_frame_equal
import numpy as np
import pandas as pd
class RemoveCollinearColumnsTests(TestCase):
def setUp(self):
self.rng = np.random.default_rng(42)
return super().setUp()
def test_categorical_data_frame(self):
df = pd.DataFrame([
[1, 1000, '2021-10-01', '2021-09-01'],
[1, 1050, '2021-10-01', '2021-08-01'],
[1, 1100, '2021-10-01', '2021-01-01'],
[2, 1150, '2021-09-01', '2021-08-01'],
], columns=[
'group_id',
'order_id',
'group_churned_at',
'order_created_at',
])
column_types = {
'group_id': 'category',
'order_id': 'category',
'group_churned_at': 'datetime',
'order_created_at': 'datetime'
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
self.assertEqual(result, [])
def test_clean_removes_all_data_frame(self):
df = pd.DataFrame([
[None, 1000, '2021-10-01', '2021-09-01'],
[1, None, '2021-10-01', '2021-08-01'],
[np.nan, 1100, '2021-10-01', '2021-01-01'],
[None, 1150, '2021-09-01', '2021-08-01'],
], columns=[
'group_id',
'order_id',
'group_churned_at',
'order_created_at',
])
column_types = {
'group_id': 'number',
'order_id': 'number',
'group_churned_at': 'datetime',
'order_created_at': 'datetime'
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
self.assertEqual(result, [])
def test_collinear_no_results(self):
df = pd.DataFrame([
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1]
], columns=['number_of_users', 'views', 'revenue', 'losses'])
column_types = {
'number_of_users': 'number',
'views': 'number',
'revenue': 'number',
'losses': 'number',
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
self.assertEqual(result, [])
def test_evaluate(self):
df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
])
column_types = {
'number_of_users': 'number',
'views': 'number',
'number_of_creators': 'number',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
results = rule.evaluate()
expected_results = [
dict(
title='Remove collinear columns',
message='The following columns are strongly correlated '
'with other columns in the dataset: [\'number_of_users\']. '
'Removing these columns may increase data quality '
'by removing redundant and closely related data.',
action_payload=dict(
action_type='remove',
action_arguments=['number_of_users'],
axis='column',
action_options = {},
action_variables = {},
action_code = '',
outputs = [],
)
)
]
self.assertEqual(results, expected_results)
def test_evaluate_bad_dtypes(self):
df = pd.DataFrame([
[1000, 'US', 30000, '10', 'cute animal #1', 100, '30'],
['500', 'CA', 10000, '20', 'intro to regression', 3000, '20'],
[200, '', np.nan, 50, 'daily news #1', None, '75'],
[250, 'CA', 7500, 25, 'machine learning seminar', 8000, '20'],
['1000', 'MX', 45003, '20', 'cute animal #4', 90, '40'],
[1500, 'MX', 75000, '30', '', 70, 25],
[1500, 'US', 75000, np.nan, 'daily news #3', 70, '25'],
[None, 'US', 75000, '30', 'tutorial: how to start a startup', 70, np.nan],
[1250, 'US', 60000, '50', 'cute animal #3', 80, '20'],
['200', 'CA', 5000, '30', '', 10000, '30'],
[800, 'US', 12050, '40', 'meme compilation', 2000, '45'],
['600', 'CA', 11000, '50', 'daily news #2', 3000, '50'],
[600, 'CA', '', 50, '', 3000, None],
['700', 'MX', 11750, '20', 'cute animal #2', 2750, '55'],
[700, '', None, 20, '', None, '55'],
[700, 'MX', 11750, '', '', 2750, '55'],
[1200, 'MX', 52000, '10', 'vc funding strats', 75, '60']
], columns=[
'number_of_users',
'location',
'views',
'number_of_creators',
'name',
'losses',
'number_of_advertisers'
])
cleaned_df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
]).astype(float)
column_types = {
'number_of_users': 'number',
'location': 'category',
'views': 'number',
'number_of_creators': 'number',
'name': 'text',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
assert_frame_equal(cleaned_df, rule.
|
7,741 | 220 | 6,919 |
mage-ai__mage-ai
|
5d823db31cab8cb2e89873aec656b5a522769873
|
mage_ai/tests/data_cleaner/cleaning_rules/test_remove_collinear_columns.py
|
Unknown
|
evaluate
| true |
function
| 14 | 15 | false | false |
[
"evaluate",
"numeric_df",
"numeric_columns",
"get_variance_inflation_factor",
"df",
"column_types",
"df_columns",
"EPSILON",
"filter_numeric_types",
"MIN_ENTRIES",
"numeric_indices",
"ROW_SAMPLE_SIZE",
"statistics",
"VIF_UB",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "column_types",
"type": "statement"
},
{
"name": "df",
"type": "statement"
},
{
"name": "df_columns",
"type": "statement"
},
{
"name": "EPSILON",
"type": "statement"
},
{
"name": "evaluate",
"type": "function"
},
{
"name": "filter_numeric_types",
"type": "function"
},
{
"name": "get_variance_inflation_factor",
"type": "function"
},
{
"name": "MIN_ENTRIES",
"type": "statement"
},
{
"name": "numeric_columns",
"type": "statement"
},
{
"name": "numeric_df",
"type": "statement"
},
{
"name": "numeric_indices",
"type": "statement"
},
{
"name": "ROW_SAMPLE_SIZE",
"type": "statement"
},
{
"name": "statistics",
"type": "statement"
},
{
"name": "VIF_UB",
"type": "statement"
},
{
"name": "_build_transformer_action_suggestion",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
from data_cleaner.cleaning_rules.remove_collinear_columns import RemoveCollinearColumns
from tests.base_test import TestCase
from pandas.util.testing import assert_frame_equal
import numpy as np
import pandas as pd
class RemoveCollinearColumnsTests(TestCase):
def setUp(self):
self.rng = np.random.default_rng(42)
return super().setUp()
def test_categorical_data_frame(self):
df = pd.DataFrame([
[1, 1000, '2021-10-01', '2021-09-01'],
[1, 1050, '2021-10-01', '2021-08-01'],
[1, 1100, '2021-10-01', '2021-01-01'],
[2, 1150, '2021-09-01', '2021-08-01'],
], columns=[
'group_id',
'order_id',
'group_churned_at',
'order_created_at',
])
column_types = {
'group_id': 'category',
'order_id': 'category',
'group_churned_at': 'datetime',
'order_created_at': 'datetime'
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
self.assertEqual(result, [])
def test_clean_removes_all_data_frame(self):
df = pd.DataFrame([
[None, 1000, '2021-10-01', '2021-09-01'],
[1, None, '2021-10-01', '2021-08-01'],
[np.nan, 1100, '2021-10-01', '2021-01-01'],
[None, 1150, '2021-09-01', '2021-08-01'],
], columns=[
'group_id',
'order_id',
'group_churned_at',
'order_created_at',
])
column_types = {
'group_id': 'number',
'order_id': 'number',
'group_churned_at': 'datetime',
'order_created_at': 'datetime'
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
self.assertEqual(result, [])
def test_collinear_no_results(self):
df = pd.DataFrame([
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1]
], columns=['number_of_users', 'views', 'revenue', 'losses'])
column_types = {
'number_of_users': 'number',
'views': 'number',
'revenue': 'number',
'losses': 'number',
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
self.assertEqual(result, [])
def test_evaluate(self):
df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
])
column_types = {
'number_of_users': 'number',
'views': 'number',
'number_of_creators': 'number',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
results = rule.evaluate()
expected_results = [
dict(
title='Remove collinear columns',
message='The following columns are strongly correlated '
'with other columns in the dataset: [\'number_of_users\']. '
'Removing these columns may increase data quality '
'by removing redundant and closely related data.',
action_payload=dict(
action_type='remove',
action_arguments=['number_of_users'],
axis='column',
action_options = {},
action_variables = {},
action_code = '',
outputs = [],
)
)
]
self.assertEqual(results, expected_results)
def test_evaluate_bad_dtypes(self):
df = pd.DataFrame([
[1000, 'US', 30000, '10', 'cute animal #1', 100, '30'],
['500', 'CA', 10000, '20', 'intro to regression', 3000, '20'],
[200, '', np.nan, 50, 'daily news #1', None, '75'],
[250, 'CA', 7500, 25, 'machine learning seminar', 8000, '20'],
['1000', 'MX', 45003, '20', 'cute animal #4', 90, '40'],
[1500, 'MX', 75000, '30', '', 70, 25],
[1500, 'US', 75000, np.nan, 'daily news #3', 70, '25'],
[None, 'US', 75000, '30', 'tutorial: how to start a startup', 70, np.nan],
[1250, 'US', 60000, '50', 'cute animal #3', 80, '20'],
['200', 'CA', 5000, '30', '', 10000, '30'],
[800, 'US', 12050, '40', 'meme compilation', 2000, '45'],
['600', 'CA', 11000, '50', 'daily news #2', 3000, '50'],
[600, 'CA', '', 50, '', 3000, None],
['700', 'MX', 11750, '20', 'cute animal #2', 2750, '55'],
[700, '', None, 20, '', None, '55'],
[700, 'MX', 11750, '', '', 2750, '55'],
[1200, 'MX', 52000, '10', 'vc funding strats', 75, '60']
], columns=[
'number_of_users',
'location',
'views',
'number_of_creators',
'name',
'losses',
'number_of_advertisers'
])
cleaned_df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
]).astype(float)
column_types = {
'number_of_users': 'number',
'location': 'category',
'views': 'number',
'number_of_creators': 'number',
'name': 'text',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
assert_frame_equal(cleaned_df, rule.numeric_df.reset_index(drop=True))
results = rule.
|
7,742 | 220 | 9,708 |
mage-ai__mage-ai
|
5d823db31cab8cb2e89873aec656b5a522769873
|
mage_ai/tests/data_cleaner/cleaning_rules/test_remove_collinear_columns.py
|
Unknown
|
numeric_df
| true |
statement
| 14 | 15 | false | false |
[
"df",
"evaluate",
"column_types",
"numeric_df",
"numeric_columns",
"df_columns",
"EPSILON",
"filter_numeric_types",
"get_variance_inflation_factor",
"MIN_ENTRIES",
"numeric_indices",
"ROW_SAMPLE_SIZE",
"statistics",
"VIF_UB",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "column_types",
"type": "statement"
},
{
"name": "df",
"type": "statement"
},
{
"name": "df_columns",
"type": "statement"
},
{
"name": "EPSILON",
"type": "statement"
},
{
"name": "evaluate",
"type": "function"
},
{
"name": "filter_numeric_types",
"type": "function"
},
{
"name": "get_variance_inflation_factor",
"type": "function"
},
{
"name": "MIN_ENTRIES",
"type": "statement"
},
{
"name": "numeric_columns",
"type": "statement"
},
{
"name": "numeric_df",
"type": "statement"
},
{
"name": "numeric_indices",
"type": "statement"
},
{
"name": "ROW_SAMPLE_SIZE",
"type": "statement"
},
{
"name": "statistics",
"type": "statement"
},
{
"name": "VIF_UB",
"type": "statement"
},
{
"name": "_build_transformer_action_suggestion",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
from data_cleaner.cleaning_rules.remove_collinear_columns import RemoveCollinearColumns
from tests.base_test import TestCase
from pandas.util.testing import assert_frame_equal
import numpy as np
import pandas as pd
class RemoveCollinearColumnsTests(TestCase):
def setUp(self):
self.rng = np.random.default_rng(42)
return super().setUp()
def test_categorical_data_frame(self):
df = pd.DataFrame([
[1, 1000, '2021-10-01', '2021-09-01'],
[1, 1050, '2021-10-01', '2021-08-01'],
[1, 1100, '2021-10-01', '2021-01-01'],
[2, 1150, '2021-09-01', '2021-08-01'],
], columns=[
'group_id',
'order_id',
'group_churned_at',
'order_created_at',
])
column_types = {
'group_id': 'category',
'order_id': 'category',
'group_churned_at': 'datetime',
'order_created_at': 'datetime'
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
self.assertEqual(result, [])
def test_clean_removes_all_data_frame(self):
df = pd.DataFrame([
[None, 1000, '2021-10-01', '2021-09-01'],
[1, None, '2021-10-01', '2021-08-01'],
[np.nan, 1100, '2021-10-01', '2021-01-01'],
[None, 1150, '2021-09-01', '2021-08-01'],
], columns=[
'group_id',
'order_id',
'group_churned_at',
'order_created_at',
])
column_types = {
'group_id': 'number',
'order_id': 'number',
'group_churned_at': 'datetime',
'order_created_at': 'datetime'
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
self.assertEqual(result, [])
def test_collinear_no_results(self):
df = pd.DataFrame([
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1]
], columns=['number_of_users', 'views', 'revenue', 'losses'])
column_types = {
'number_of_users': 'number',
'views': 'number',
'revenue': 'number',
'losses': 'number',
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
self.assertEqual(result, [])
def test_evaluate(self):
df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
])
column_types = {
'number_of_users': 'number',
'views': 'number',
'number_of_creators': 'number',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
results = rule.evaluate()
expected_results = [
dict(
title='Remove collinear columns',
message='The following columns are strongly correlated '
'with other columns in the dataset: [\'number_of_users\']. '
'Removing these columns may increase data quality '
'by removing redundant and closely related data.',
action_payload=dict(
action_type='remove',
action_arguments=['number_of_users'],
axis='column',
action_options = {},
action_variables = {},
action_code = '',
outputs = [],
)
)
]
self.assertEqual(results, expected_results)
def test_evaluate_bad_dtypes(self):
df = pd.DataFrame([
[1000, 'US', 30000, '10', 'cute animal #1', 100, '30'],
['500', 'CA', 10000, '20', 'intro to regression', 3000, '20'],
[200, '', np.nan, 50, 'daily news #1', None, '75'],
[250, 'CA', 7500, 25, 'machine learning seminar', 8000, '20'],
['1000', 'MX', 45003, '20', 'cute animal #4', 90, '40'],
[1500, 'MX', 75000, '30', '', 70, 25],
[1500, 'US', 75000, np.nan, 'daily news #3', 70, '25'],
[None, 'US', 75000, '30', 'tutorial: how to start a startup', 70, np.nan],
[1250, 'US', 60000, '50', 'cute animal #3', 80, '20'],
['200', 'CA', 5000, '30', '', 10000, '30'],
[800, 'US', 12050, '40', 'meme compilation', 2000, '45'],
['600', 'CA', 11000, '50', 'daily news #2', 3000, '50'],
[600, 'CA', '', 50, '', 3000, None],
['700', 'MX', 11750, '20', 'cute animal #2', 2750, '55'],
[700, '', None, 20, '', None, '55'],
[700, 'MX', 11750, '', '', 2750, '55'],
[1200, 'MX', 52000, '10', 'vc funding strats', 75, '60']
], columns=[
'number_of_users',
'location',
'views',
'number_of_creators',
'name',
'losses',
'number_of_advertisers'
])
cleaned_df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
]).astype(float)
column_types = {
'number_of_users': 'number',
'location': 'category',
'views': 'number',
'number_of_creators': 'number',
'name': 'text',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
assert_frame_equal(cleaned_df, rule.numeric_df.reset_index(drop=True))
results = rule.evaluate()
expected_results = [
dict(
title='Remove collinear columns',
message='The following columns are strongly correlated '
'with other columns in the dataset: [\'number_of_users\']. '
'Removing these columns may increase data quality '
'by removing redundant and closely related data.',
action_payload=dict(
action_type='remove',
action_arguments=['number_of_users'],
axis='column',
action_options = {},
action_variables = {},
action_code = '',
outputs = [],
)
)
]
self.assertEqual(results, expected_results)
def test_evaluate_dirty(self):
df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[200, np.nan, 50, None, 75],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1500, 75000, np.nan, 70, 25],
[None, 75000, 30, 70, np.nan],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[600, '', 50, 3000, None],
[700, 11750, 20, 2750, 55],
[700, None, 20, None, 55],
[700, 11750, '', 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
])
cleaned_df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
]).astype(float)
column_types = {
'number_of_users': 'number',
'views': 'number',
'number_of_creators': 'number',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
assert_frame_equal(cleaned_df, rule.
|
7,743 | 220 | 9,766 |
mage-ai__mage-ai
|
5d823db31cab8cb2e89873aec656b5a522769873
|
mage_ai/tests/data_cleaner/cleaning_rules/test_remove_collinear_columns.py
|
inproject
|
evaluate
| true |
function
| 14 | 15 | false | false |
[
"evaluate",
"numeric_df",
"numeric_columns",
"get_variance_inflation_factor",
"df",
"column_types",
"df_columns",
"EPSILON",
"filter_numeric_types",
"MIN_ENTRIES",
"numeric_indices",
"ROW_SAMPLE_SIZE",
"statistics",
"VIF_UB",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "column_types",
"type": "statement"
},
{
"name": "df",
"type": "statement"
},
{
"name": "df_columns",
"type": "statement"
},
{
"name": "EPSILON",
"type": "statement"
},
{
"name": "evaluate",
"type": "function"
},
{
"name": "filter_numeric_types",
"type": "function"
},
{
"name": "get_variance_inflation_factor",
"type": "function"
},
{
"name": "MIN_ENTRIES",
"type": "statement"
},
{
"name": "numeric_columns",
"type": "statement"
},
{
"name": "numeric_df",
"type": "statement"
},
{
"name": "numeric_indices",
"type": "statement"
},
{
"name": "ROW_SAMPLE_SIZE",
"type": "statement"
},
{
"name": "statistics",
"type": "statement"
},
{
"name": "VIF_UB",
"type": "statement"
},
{
"name": "_build_transformer_action_suggestion",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
from data_cleaner.cleaning_rules.remove_collinear_columns import RemoveCollinearColumns
from tests.base_test import TestCase
from pandas.util.testing import assert_frame_equal
import numpy as np
import pandas as pd
class RemoveCollinearColumnsTests(TestCase):
def setUp(self):
self.rng = np.random.default_rng(42)
return super().setUp()
def test_categorical_data_frame(self):
df = pd.DataFrame([
[1, 1000, '2021-10-01', '2021-09-01'],
[1, 1050, '2021-10-01', '2021-08-01'],
[1, 1100, '2021-10-01', '2021-01-01'],
[2, 1150, '2021-09-01', '2021-08-01'],
], columns=[
'group_id',
'order_id',
'group_churned_at',
'order_created_at',
])
column_types = {
'group_id': 'category',
'order_id': 'category',
'group_churned_at': 'datetime',
'order_created_at': 'datetime'
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
self.assertEqual(result, [])
def test_clean_removes_all_data_frame(self):
df = pd.DataFrame([
[None, 1000, '2021-10-01', '2021-09-01'],
[1, None, '2021-10-01', '2021-08-01'],
[np.nan, 1100, '2021-10-01', '2021-01-01'],
[None, 1150, '2021-09-01', '2021-08-01'],
], columns=[
'group_id',
'order_id',
'group_churned_at',
'order_created_at',
])
column_types = {
'group_id': 'number',
'order_id': 'number',
'group_churned_at': 'datetime',
'order_created_at': 'datetime'
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
self.assertEqual(result, [])
def test_collinear_no_results(self):
df = pd.DataFrame([
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1]
], columns=['number_of_users', 'views', 'revenue', 'losses'])
column_types = {
'number_of_users': 'number',
'views': 'number',
'revenue': 'number',
'losses': 'number',
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
self.assertEqual(result, [])
def test_evaluate(self):
df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
])
column_types = {
'number_of_users': 'number',
'views': 'number',
'number_of_creators': 'number',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
results = rule.evaluate()
expected_results = [
dict(
title='Remove collinear columns',
message='The following columns are strongly correlated '
'with other columns in the dataset: [\'number_of_users\']. '
'Removing these columns may increase data quality '
'by removing redundant and closely related data.',
action_payload=dict(
action_type='remove',
action_arguments=['number_of_users'],
axis='column',
action_options = {},
action_variables = {},
action_code = '',
outputs = [],
)
)
]
self.assertEqual(results, expected_results)
def test_evaluate_bad_dtypes(self):
df = pd.DataFrame([
[1000, 'US', 30000, '10', 'cute animal #1', 100, '30'],
['500', 'CA', 10000, '20', 'intro to regression', 3000, '20'],
[200, '', np.nan, 50, 'daily news #1', None, '75'],
[250, 'CA', 7500, 25, 'machine learning seminar', 8000, '20'],
['1000', 'MX', 45003, '20', 'cute animal #4', 90, '40'],
[1500, 'MX', 75000, '30', '', 70, 25],
[1500, 'US', 75000, np.nan, 'daily news #3', 70, '25'],
[None, 'US', 75000, '30', 'tutorial: how to start a startup', 70, np.nan],
[1250, 'US', 60000, '50', 'cute animal #3', 80, '20'],
['200', 'CA', 5000, '30', '', 10000, '30'],
[800, 'US', 12050, '40', 'meme compilation', 2000, '45'],
['600', 'CA', 11000, '50', 'daily news #2', 3000, '50'],
[600, 'CA', '', 50, '', 3000, None],
['700', 'MX', 11750, '20', 'cute animal #2', 2750, '55'],
[700, '', None, 20, '', None, '55'],
[700, 'MX', 11750, '', '', 2750, '55'],
[1200, 'MX', 52000, '10', 'vc funding strats', 75, '60']
], columns=[
'number_of_users',
'location',
'views',
'number_of_creators',
'name',
'losses',
'number_of_advertisers'
])
cleaned_df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
]).astype(float)
column_types = {
'number_of_users': 'number',
'location': 'category',
'views': 'number',
'number_of_creators': 'number',
'name': 'text',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
assert_frame_equal(cleaned_df, rule.numeric_df.reset_index(drop=True))
results = rule.evaluate()
expected_results = [
dict(
title='Remove collinear columns',
message='The following columns are strongly correlated '
'with other columns in the dataset: [\'number_of_users\']. '
'Removing these columns may increase data quality '
'by removing redundant and closely related data.',
action_payload=dict(
action_type='remove',
action_arguments=['number_of_users'],
axis='column',
action_options = {},
action_variables = {},
action_code = '',
outputs = [],
)
)
]
self.assertEqual(results, expected_results)
def test_evaluate_dirty(self):
df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[200, np.nan, 50, None, 75],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1500, 75000, np.nan, 70, 25],
[None, 75000, 30, 70, np.nan],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[600, '', 50, 3000, None],
[700, 11750, 20, 2750, 55],
[700, None, 20, None, 55],
[700, 11750, '', 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
])
cleaned_df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
]).astype(float)
column_types = {
'number_of_users': 'number',
'views': 'number',
'number_of_creators': 'number',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
assert_frame_equal(cleaned_df, rule.numeric_df.reset_index(drop=True))
results = rule.
|
7,744 | 220 | 13,035 |
mage-ai__mage-ai
|
5d823db31cab8cb2e89873aec656b5a522769873
|
mage_ai/tests/data_cleaner/cleaning_rules/test_remove_collinear_columns.py
|
Unknown
|
numeric_df
| true |
statement
| 14 | 15 | false | false |
[
"numeric_df",
"df",
"evaluate",
"column_types",
"numeric_columns",
"df_columns",
"EPSILON",
"filter_numeric_types",
"get_variance_inflation_factor",
"MIN_ENTRIES",
"numeric_indices",
"ROW_SAMPLE_SIZE",
"statistics",
"VIF_UB",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "column_types",
"type": "statement"
},
{
"name": "df",
"type": "statement"
},
{
"name": "df_columns",
"type": "statement"
},
{
"name": "EPSILON",
"type": "statement"
},
{
"name": "evaluate",
"type": "function"
},
{
"name": "filter_numeric_types",
"type": "function"
},
{
"name": "get_variance_inflation_factor",
"type": "function"
},
{
"name": "MIN_ENTRIES",
"type": "statement"
},
{
"name": "numeric_columns",
"type": "statement"
},
{
"name": "numeric_df",
"type": "statement"
},
{
"name": "numeric_indices",
"type": "statement"
},
{
"name": "ROW_SAMPLE_SIZE",
"type": "statement"
},
{
"name": "statistics",
"type": "statement"
},
{
"name": "VIF_UB",
"type": "statement"
},
{
"name": "_build_transformer_action_suggestion",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
from data_cleaner.cleaning_rules.remove_collinear_columns import RemoveCollinearColumns
from tests.base_test import TestCase
from pandas.util.testing import assert_frame_equal
import numpy as np
import pandas as pd
class RemoveCollinearColumnsTests(TestCase):
def setUp(self):
self.rng = np.random.default_rng(42)
return super().setUp()
def test_categorical_data_frame(self):
df = pd.DataFrame([
[1, 1000, '2021-10-01', '2021-09-01'],
[1, 1050, '2021-10-01', '2021-08-01'],
[1, 1100, '2021-10-01', '2021-01-01'],
[2, 1150, '2021-09-01', '2021-08-01'],
], columns=[
'group_id',
'order_id',
'group_churned_at',
'order_created_at',
])
column_types = {
'group_id': 'category',
'order_id': 'category',
'group_churned_at': 'datetime',
'order_created_at': 'datetime'
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
self.assertEqual(result, [])
def test_clean_removes_all_data_frame(self):
df = pd.DataFrame([
[None, 1000, '2021-10-01', '2021-09-01'],
[1, None, '2021-10-01', '2021-08-01'],
[np.nan, 1100, '2021-10-01', '2021-01-01'],
[None, 1150, '2021-09-01', '2021-08-01'],
], columns=[
'group_id',
'order_id',
'group_churned_at',
'order_created_at',
])
column_types = {
'group_id': 'number',
'order_id': 'number',
'group_churned_at': 'datetime',
'order_created_at': 'datetime'
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
self.assertEqual(result, [])
def test_collinear_no_results(self):
df = pd.DataFrame([
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1]
], columns=['number_of_users', 'views', 'revenue', 'losses'])
column_types = {
'number_of_users': 'number',
'views': 'number',
'revenue': 'number',
'losses': 'number',
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
self.assertEqual(result, [])
def test_evaluate(self):
df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
])
column_types = {
'number_of_users': 'number',
'views': 'number',
'number_of_creators': 'number',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
results = rule.evaluate()
expected_results = [
dict(
title='Remove collinear columns',
message='The following columns are strongly correlated '
'with other columns in the dataset: [\'number_of_users\']. '
'Removing these columns may increase data quality '
'by removing redundant and closely related data.',
action_payload=dict(
action_type='remove',
action_arguments=['number_of_users'],
axis='column',
action_options = {},
action_variables = {},
action_code = '',
outputs = [],
)
)
]
self.assertEqual(results, expected_results)
def test_evaluate_bad_dtypes(self):
df = pd.DataFrame([
[1000, 'US', 30000, '10', 'cute animal #1', 100, '30'],
['500', 'CA', 10000, '20', 'intro to regression', 3000, '20'],
[200, '', np.nan, 50, 'daily news #1', None, '75'],
[250, 'CA', 7500, 25, 'machine learning seminar', 8000, '20'],
['1000', 'MX', 45003, '20', 'cute animal #4', 90, '40'],
[1500, 'MX', 75000, '30', '', 70, 25],
[1500, 'US', 75000, np.nan, 'daily news #3', 70, '25'],
[None, 'US', 75000, '30', 'tutorial: how to start a startup', 70, np.nan],
[1250, 'US', 60000, '50', 'cute animal #3', 80, '20'],
['200', 'CA', 5000, '30', '', 10000, '30'],
[800, 'US', 12050, '40', 'meme compilation', 2000, '45'],
['600', 'CA', 11000, '50', 'daily news #2', 3000, '50'],
[600, 'CA', '', 50, '', 3000, None],
['700', 'MX', 11750, '20', 'cute animal #2', 2750, '55'],
[700, '', None, 20, '', None, '55'],
[700, 'MX', 11750, '', '', 2750, '55'],
[1200, 'MX', 52000, '10', 'vc funding strats', 75, '60']
], columns=[
'number_of_users',
'location',
'views',
'number_of_creators',
'name',
'losses',
'number_of_advertisers'
])
cleaned_df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
]).astype(float)
column_types = {
'number_of_users': 'number',
'location': 'category',
'views': 'number',
'number_of_creators': 'number',
'name': 'text',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
assert_frame_equal(cleaned_df, rule.numeric_df.reset_index(drop=True))
results = rule.evaluate()
expected_results = [
dict(
title='Remove collinear columns',
message='The following columns are strongly correlated '
'with other columns in the dataset: [\'number_of_users\']. '
'Removing these columns may increase data quality '
'by removing redundant and closely related data.',
action_payload=dict(
action_type='remove',
action_arguments=['number_of_users'],
axis='column',
action_options = {},
action_variables = {},
action_code = '',
outputs = [],
)
)
]
self.assertEqual(results, expected_results)
def test_evaluate_dirty(self):
df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[200, np.nan, 50, None, 75],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1500, 75000, np.nan, 70, 25],
[None, 75000, 30, 70, np.nan],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[600, '', 50, 3000, None],
[700, 11750, 20, 2750, 55],
[700, None, 20, None, 55],
[700, 11750, '', 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
])
cleaned_df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
]).astype(float)
column_types = {
'number_of_users': 'number',
'views': 'number',
'number_of_creators': 'number',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
assert_frame_equal(cleaned_df, rule.numeric_df.reset_index(drop=True))
results = rule.evaluate()
expected_results = [
dict(
title='Remove collinear columns',
message='The following columns are strongly correlated '
'with other columns in the dataset: [\'number_of_users\']. '
'Removing these columns may increase data quality '
'by removing redundant and closely related data.',
action_payload=dict(
action_type='remove',
action_arguments=['number_of_users'],
axis='column',
action_options = {},
action_variables = {},
action_code = '',
outputs = [],
)
)
]
self.assertEqual(results, expected_results)
def test_evaluate_non_numeric(self):
df = pd.DataFrame([
[1000, 'US', 30000, 10, 'cute animal #1', 100, 30],
[500, 'CA', 10000, 20, 'intro to regression', 3000, 20],
[200, '', np.nan, 50, 'daily news #1', None, 75],
[250, 'CA', 7500, 25, 'machine learning seminar', 8000, 20],
[1000, 'MX', 45003, 20, 'cute animal #4', 90, 40],
[1500, 'MX', 75000, 30, '', 70, 25],
[1500, 'US', 75000, np.nan, 'daily news #3', 70, 25],
[None, 'US', 75000, 30, 'tutorial: how to start a startup', 70, np.nan],
[1250, 'US', 60000, 50, 'cute animal #3', 80, 20],
[200, 'CA', 5000, 30, '', 10000, 30],
[800, 'US', 12050, 40, 'meme compilation', 2000, 45],
[600, 'CA', 11000, 50, 'daily news #2', 3000, 50],
[600, 'CA', '', 50, '', 3000, None],
[700, 'MX', 11750, 20, 'cute animal #2', 2750, 55],
[700, '', None, 20, '', None, 55],
[700, 'MX', 11750, '', '', 2750, 55],
[1200, 'MX', 52000, 10, 'vc funding strats', 75, 60]
], columns=[
'number_of_users',
'location',
'views',
'number_of_creators',
'name',
'losses',
'number_of_advertisers'
])
cleaned_df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
]).astype(float)
column_types = {
'number_of_users': 'number',
'location': 'category',
'views': 'number',
'number_of_creators': 'number',
'name': 'text',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
assert_frame_equal(cleaned_df, rule.
|
7,745 | 220 | 13,093 |
mage-ai__mage-ai
|
5d823db31cab8cb2e89873aec656b5a522769873
|
mage_ai/tests/data_cleaner/cleaning_rules/test_remove_collinear_columns.py
|
Unknown
|
evaluate
| true |
function
| 14 | 15 | false | false |
[
"evaluate",
"numeric_df",
"numeric_columns",
"get_variance_inflation_factor",
"df",
"column_types",
"df_columns",
"EPSILON",
"filter_numeric_types",
"MIN_ENTRIES",
"numeric_indices",
"ROW_SAMPLE_SIZE",
"statistics",
"VIF_UB",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "column_types",
"type": "statement"
},
{
"name": "df",
"type": "statement"
},
{
"name": "df_columns",
"type": "statement"
},
{
"name": "EPSILON",
"type": "statement"
},
{
"name": "evaluate",
"type": "function"
},
{
"name": "filter_numeric_types",
"type": "function"
},
{
"name": "get_variance_inflation_factor",
"type": "function"
},
{
"name": "MIN_ENTRIES",
"type": "statement"
},
{
"name": "numeric_columns",
"type": "statement"
},
{
"name": "numeric_df",
"type": "statement"
},
{
"name": "numeric_indices",
"type": "statement"
},
{
"name": "ROW_SAMPLE_SIZE",
"type": "statement"
},
{
"name": "statistics",
"type": "statement"
},
{
"name": "VIF_UB",
"type": "statement"
},
{
"name": "_build_transformer_action_suggestion",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
from data_cleaner.cleaning_rules.remove_collinear_columns import RemoveCollinearColumns
from tests.base_test import TestCase
from pandas.util.testing import assert_frame_equal
import numpy as np
import pandas as pd
class RemoveCollinearColumnsTests(TestCase):
def setUp(self):
self.rng = np.random.default_rng(42)
return super().setUp()
def test_categorical_data_frame(self):
df = pd.DataFrame([
[1, 1000, '2021-10-01', '2021-09-01'],
[1, 1050, '2021-10-01', '2021-08-01'],
[1, 1100, '2021-10-01', '2021-01-01'],
[2, 1150, '2021-09-01', '2021-08-01'],
], columns=[
'group_id',
'order_id',
'group_churned_at',
'order_created_at',
])
column_types = {
'group_id': 'category',
'order_id': 'category',
'group_churned_at': 'datetime',
'order_created_at': 'datetime'
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
self.assertEqual(result, [])
def test_clean_removes_all_data_frame(self):
df = pd.DataFrame([
[None, 1000, '2021-10-01', '2021-09-01'],
[1, None, '2021-10-01', '2021-08-01'],
[np.nan, 1100, '2021-10-01', '2021-01-01'],
[None, 1150, '2021-09-01', '2021-08-01'],
], columns=[
'group_id',
'order_id',
'group_churned_at',
'order_created_at',
])
column_types = {
'group_id': 'number',
'order_id': 'number',
'group_churned_at': 'datetime',
'order_created_at': 'datetime'
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
self.assertEqual(result, [])
def test_collinear_no_results(self):
df = pd.DataFrame([
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1]
], columns=['number_of_users', 'views', 'revenue', 'losses'])
column_types = {
'number_of_users': 'number',
'views': 'number',
'revenue': 'number',
'losses': 'number',
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
self.assertEqual(result, [])
def test_evaluate(self):
df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
])
column_types = {
'number_of_users': 'number',
'views': 'number',
'number_of_creators': 'number',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
results = rule.evaluate()
expected_results = [
dict(
title='Remove collinear columns',
message='The following columns are strongly correlated '
'with other columns in the dataset: [\'number_of_users\']. '
'Removing these columns may increase data quality '
'by removing redundant and closely related data.',
action_payload=dict(
action_type='remove',
action_arguments=['number_of_users'],
axis='column',
action_options = {},
action_variables = {},
action_code = '',
outputs = [],
)
)
]
self.assertEqual(results, expected_results)
def test_evaluate_bad_dtypes(self):
df = pd.DataFrame([
[1000, 'US', 30000, '10', 'cute animal #1', 100, '30'],
['500', 'CA', 10000, '20', 'intro to regression', 3000, '20'],
[200, '', np.nan, 50, 'daily news #1', None, '75'],
[250, 'CA', 7500, 25, 'machine learning seminar', 8000, '20'],
['1000', 'MX', 45003, '20', 'cute animal #4', 90, '40'],
[1500, 'MX', 75000, '30', '', 70, 25],
[1500, 'US', 75000, np.nan, 'daily news #3', 70, '25'],
[None, 'US', 75000, '30', 'tutorial: how to start a startup', 70, np.nan],
[1250, 'US', 60000, '50', 'cute animal #3', 80, '20'],
['200', 'CA', 5000, '30', '', 10000, '30'],
[800, 'US', 12050, '40', 'meme compilation', 2000, '45'],
['600', 'CA', 11000, '50', 'daily news #2', 3000, '50'],
[600, 'CA', '', 50, '', 3000, None],
['700', 'MX', 11750, '20', 'cute animal #2', 2750, '55'],
[700, '', None, 20, '', None, '55'],
[700, 'MX', 11750, '', '', 2750, '55'],
[1200, 'MX', 52000, '10', 'vc funding strats', 75, '60']
], columns=[
'number_of_users',
'location',
'views',
'number_of_creators',
'name',
'losses',
'number_of_advertisers'
])
cleaned_df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
]).astype(float)
column_types = {
'number_of_users': 'number',
'location': 'category',
'views': 'number',
'number_of_creators': 'number',
'name': 'text',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
assert_frame_equal(cleaned_df, rule.numeric_df.reset_index(drop=True))
results = rule.evaluate()
expected_results = [
dict(
title='Remove collinear columns',
message='The following columns are strongly correlated '
'with other columns in the dataset: [\'number_of_users\']. '
'Removing these columns may increase data quality '
'by removing redundant and closely related data.',
action_payload=dict(
action_type='remove',
action_arguments=['number_of_users'],
axis='column',
action_options = {},
action_variables = {},
action_code = '',
outputs = [],
)
)
]
self.assertEqual(results, expected_results)
def test_evaluate_dirty(self):
df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[200, np.nan, 50, None, 75],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1500, 75000, np.nan, 70, 25],
[None, 75000, 30, 70, np.nan],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[600, '', 50, 3000, None],
[700, 11750, 20, 2750, 55],
[700, None, 20, None, 55],
[700, 11750, '', 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
])
cleaned_df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
]).astype(float)
column_types = {
'number_of_users': 'number',
'views': 'number',
'number_of_creators': 'number',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
assert_frame_equal(cleaned_df, rule.numeric_df.reset_index(drop=True))
results = rule.evaluate()
expected_results = [
dict(
title='Remove collinear columns',
message='The following columns are strongly correlated '
'with other columns in the dataset: [\'number_of_users\']. '
'Removing these columns may increase data quality '
'by removing redundant and closely related data.',
action_payload=dict(
action_type='remove',
action_arguments=['number_of_users'],
axis='column',
action_options = {},
action_variables = {},
action_code = '',
outputs = [],
)
)
]
self.assertEqual(results, expected_results)
def test_evaluate_non_numeric(self):
df = pd.DataFrame([
[1000, 'US', 30000, 10, 'cute animal #1', 100, 30],
[500, 'CA', 10000, 20, 'intro to regression', 3000, 20],
[200, '', np.nan, 50, 'daily news #1', None, 75],
[250, 'CA', 7500, 25, 'machine learning seminar', 8000, 20],
[1000, 'MX', 45003, 20, 'cute animal #4', 90, 40],
[1500, 'MX', 75000, 30, '', 70, 25],
[1500, 'US', 75000, np.nan, 'daily news #3', 70, 25],
[None, 'US', 75000, 30, 'tutorial: how to start a startup', 70, np.nan],
[1250, 'US', 60000, 50, 'cute animal #3', 80, 20],
[200, 'CA', 5000, 30, '', 10000, 30],
[800, 'US', 12050, 40, 'meme compilation', 2000, 45],
[600, 'CA', 11000, 50, 'daily news #2', 3000, 50],
[600, 'CA', '', 50, '', 3000, None],
[700, 'MX', 11750, 20, 'cute animal #2', 2750, 55],
[700, '', None, 20, '', None, 55],
[700, 'MX', 11750, '', '', 2750, 55],
[1200, 'MX', 52000, 10, 'vc funding strats', 75, 60]
], columns=[
'number_of_users',
'location',
'views',
'number_of_creators',
'name',
'losses',
'number_of_advertisers'
])
cleaned_df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
]).astype(float)
column_types = {
'number_of_users': 'number',
'location': 'category',
'views': 'number',
'number_of_creators': 'number',
'name': 'text',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
assert_frame_equal(cleaned_df, rule.numeric_df.reset_index(drop=True))
results = rule.
|
7,746 | 220 | 14,004 |
mage-ai__mage-ai
|
5d823db31cab8cb2e89873aec656b5a522769873
|
mage_ai/tests/data_cleaner/cleaning_rules/test_remove_collinear_columns.py
|
Unknown
|
rng
| true |
statement
| 80 | 84 | false | true |
[
"test_evaluate",
"rng",
"setUp",
"test_collinear_no_results",
"test_evaluate_dirty",
"test_categorical_data_frame",
"test_clean_removes_all_data_frame",
"test_evaluate_bad_dtypes",
"test_evaluate_non_numeric",
"test_perfectly_collinear",
"test_vif_calcuation",
"tearDown",
"addClassCleanup",
"addCleanup",
"addTypeEqualityFunc",
"assert_",
"assertAlmostEqual",
"assertAlmostEquals",
"assertCountEqual",
"assertDictContainsSubset",
"assertDictEqual",
"assertEqual",
"assertEquals",
"assertFalse",
"assertGreater",
"assertGreaterEqual",
"assertIn",
"assertIs",
"assertIsInstance",
"assertIsNone",
"assertIsNot",
"assertIsNotNone",
"assertLess",
"assertLessEqual",
"assertListEqual",
"assertLogs",
"assertMultiLineEqual",
"assertNoLogs",
"assertNotAlmostEqual",
"assertNotAlmostEquals",
"assertNotEqual",
"assertNotEquals",
"assertNotIn",
"assertNotIsInstance",
"assertNotRegex",
"assertNotRegexpMatches",
"assertRaises",
"assertRaisesRegex",
"assertRaisesRegexp",
"assertRegex",
"assertRegexpMatches",
"assertSequenceEqual",
"assertSetEqual",
"assertTrue",
"assertTupleEqual",
"assertWarns",
"assertWarnsRegex",
"countTestCases",
"debug",
"defaultTestResult",
"doClassCleanups",
"doCleanups",
"fail",
"failIf",
"failIfAlmostEqual",
"failIfEqual",
"failUnless",
"failUnlessAlmostEqual",
"failUnlessEqual",
"failUnlessRaises",
"failureException",
"id",
"longMessage",
"maxDiff",
"run",
"setUpClass",
"shortDescription",
"skipTest",
"subTest",
"tearDownClass",
"__annotations__",
"__call__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "addClassCleanup",
"type": "function"
},
{
"name": "addCleanup",
"type": "function"
},
{
"name": "addTypeEqualityFunc",
"type": "function"
},
{
"name": "assert_",
"type": "function"
},
{
"name": "assertAlmostEqual",
"type": "function"
},
{
"name": "assertAlmostEquals",
"type": "function"
},
{
"name": "assertCountEqual",
"type": "function"
},
{
"name": "assertDictContainsSubset",
"type": "function"
},
{
"name": "assertDictEqual",
"type": "function"
},
{
"name": "assertEqual",
"type": "function"
},
{
"name": "assertEquals",
"type": "function"
},
{
"name": "assertFalse",
"type": "function"
},
{
"name": "assertGreater",
"type": "function"
},
{
"name": "assertGreaterEqual",
"type": "function"
},
{
"name": "assertIn",
"type": "function"
},
{
"name": "assertIs",
"type": "function"
},
{
"name": "assertIsInstance",
"type": "function"
},
{
"name": "assertIsNone",
"type": "function"
},
{
"name": "assertIsNot",
"type": "function"
},
{
"name": "assertIsNotNone",
"type": "function"
},
{
"name": "assertLess",
"type": "function"
},
{
"name": "assertLessEqual",
"type": "function"
},
{
"name": "assertListEqual",
"type": "function"
},
{
"name": "assertLogs",
"type": "function"
},
{
"name": "assertMultiLineEqual",
"type": "function"
},
{
"name": "assertNotAlmostEqual",
"type": "function"
},
{
"name": "assertNotAlmostEquals",
"type": "function"
},
{
"name": "assertNotEqual",
"type": "function"
},
{
"name": "assertNotEquals",
"type": "function"
},
{
"name": "assertNotIn",
"type": "function"
},
{
"name": "assertNotIsInstance",
"type": "function"
},
{
"name": "assertNotRegex",
"type": "function"
},
{
"name": "assertNotRegexpMatches",
"type": "function"
},
{
"name": "assertRaises",
"type": "function"
},
{
"name": "assertRaisesRegex",
"type": "function"
},
{
"name": "assertRaisesRegexp",
"type": "function"
},
{
"name": "assertRegex",
"type": "function"
},
{
"name": "assertRegexpMatches",
"type": "function"
},
{
"name": "assertSequenceEqual",
"type": "function"
},
{
"name": "assertSetEqual",
"type": "function"
},
{
"name": "assertTrue",
"type": "function"
},
{
"name": "assertTupleEqual",
"type": "function"
},
{
"name": "assertWarns",
"type": "function"
},
{
"name": "assertWarnsRegex",
"type": "function"
},
{
"name": "countTestCases",
"type": "function"
},
{
"name": "debug",
"type": "function"
},
{
"name": "defaultTestResult",
"type": "function"
},
{
"name": "doClassCleanups",
"type": "function"
},
{
"name": "doCleanups",
"type": "function"
},
{
"name": "fail",
"type": "function"
},
{
"name": "failIf",
"type": "function"
},
{
"name": "failIfAlmostEqual",
"type": "function"
},
{
"name": "failIfEqual",
"type": "function"
},
{
"name": "failUnless",
"type": "function"
},
{
"name": "failUnlessAlmostEqual",
"type": "function"
},
{
"name": "failUnlessEqual",
"type": "function"
},
{
"name": "failUnlessRaises",
"type": "function"
},
{
"name": "failureException",
"type": "statement"
},
{
"name": "id",
"type": "function"
},
{
"name": "longMessage",
"type": "statement"
},
{
"name": "maxDiff",
"type": "statement"
},
{
"name": "rng",
"type": "statement"
},
{
"name": "run",
"type": "function"
},
{
"name": "setUp",
"type": "function"
},
{
"name": "setUpClass",
"type": "function"
},
{
"name": "shortDescription",
"type": "function"
},
{
"name": "skipTest",
"type": "function"
},
{
"name": "subTest",
"type": "function"
},
{
"name": "tearDown",
"type": "function"
},
{
"name": "tearDownClass",
"type": "function"
},
{
"name": "test_categorical_data_frame",
"type": "function"
},
{
"name": "test_clean_removes_all_data_frame",
"type": "function"
},
{
"name": "test_collinear_no_results",
"type": "function"
},
{
"name": "test_evaluate",
"type": "function"
},
{
"name": "test_evaluate_bad_dtypes",
"type": "function"
},
{
"name": "test_evaluate_dirty",
"type": "function"
},
{
"name": "test_evaluate_non_numeric",
"type": "function"
},
{
"name": "test_perfectly_collinear",
"type": "function"
},
{
"name": "test_vif_calcuation",
"type": "function"
},
{
"name": "_addSkip",
"type": "function"
},
{
"name": "_formatMessage",
"type": "function"
},
{
"name": "_getAssertEqualityFunc",
"type": "function"
},
{
"name": "_testMethodDoc",
"type": "statement"
},
{
"name": "_testMethodName",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__call__",
"type": "function"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
from data_cleaner.cleaning_rules.remove_collinear_columns import RemoveCollinearColumns
from tests.base_test import TestCase
from pandas.util.testing import assert_frame_equal
import numpy as np
import pandas as pd
class RemoveCollinearColumnsTests(TestCase):
def setUp(self):
self.rng = np.random.default_rng(42)
return super().setUp()
def test_categorical_data_frame(self):
df = pd.DataFrame([
[1, 1000, '2021-10-01', '2021-09-01'],
[1, 1050, '2021-10-01', '2021-08-01'],
[1, 1100, '2021-10-01', '2021-01-01'],
[2, 1150, '2021-09-01', '2021-08-01'],
], columns=[
'group_id',
'order_id',
'group_churned_at',
'order_created_at',
])
column_types = {
'group_id': 'category',
'order_id': 'category',
'group_churned_at': 'datetime',
'order_created_at': 'datetime'
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
self.assertEqual(result, [])
def test_clean_removes_all_data_frame(self):
df = pd.DataFrame([
[None, 1000, '2021-10-01', '2021-09-01'],
[1, None, '2021-10-01', '2021-08-01'],
[np.nan, 1100, '2021-10-01', '2021-01-01'],
[None, 1150, '2021-09-01', '2021-08-01'],
], columns=[
'group_id',
'order_id',
'group_churned_at',
'order_created_at',
])
column_types = {
'group_id': 'number',
'order_id': 'number',
'group_churned_at': 'datetime',
'order_created_at': 'datetime'
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
self.assertEqual(result, [])
def test_collinear_no_results(self):
df = pd.DataFrame([
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1]
], columns=['number_of_users', 'views', 'revenue', 'losses'])
column_types = {
'number_of_users': 'number',
'views': 'number',
'revenue': 'number',
'losses': 'number',
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
self.assertEqual(result, [])
def test_evaluate(self):
df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
])
column_types = {
'number_of_users': 'number',
'views': 'number',
'number_of_creators': 'number',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
results = rule.evaluate()
expected_results = [
dict(
title='Remove collinear columns',
message='The following columns are strongly correlated '
'with other columns in the dataset: [\'number_of_users\']. '
'Removing these columns may increase data quality '
'by removing redundant and closely related data.',
action_payload=dict(
action_type='remove',
action_arguments=['number_of_users'],
axis='column',
action_options = {},
action_variables = {},
action_code = '',
outputs = [],
)
)
]
self.assertEqual(results, expected_results)
def test_evaluate_bad_dtypes(self):
df = pd.DataFrame([
[1000, 'US', 30000, '10', 'cute animal #1', 100, '30'],
['500', 'CA', 10000, '20', 'intro to regression', 3000, '20'],
[200, '', np.nan, 50, 'daily news #1', None, '75'],
[250, 'CA', 7500, 25, 'machine learning seminar', 8000, '20'],
['1000', 'MX', 45003, '20', 'cute animal #4', 90, '40'],
[1500, 'MX', 75000, '30', '', 70, 25],
[1500, 'US', 75000, np.nan, 'daily news #3', 70, '25'],
[None, 'US', 75000, '30', 'tutorial: how to start a startup', 70, np.nan],
[1250, 'US', 60000, '50', 'cute animal #3', 80, '20'],
['200', 'CA', 5000, '30', '', 10000, '30'],
[800, 'US', 12050, '40', 'meme compilation', 2000, '45'],
['600', 'CA', 11000, '50', 'daily news #2', 3000, '50'],
[600, 'CA', '', 50, '', 3000, None],
['700', 'MX', 11750, '20', 'cute animal #2', 2750, '55'],
[700, '', None, 20, '', None, '55'],
[700, 'MX', 11750, '', '', 2750, '55'],
[1200, 'MX', 52000, '10', 'vc funding strats', 75, '60']
], columns=[
'number_of_users',
'location',
'views',
'number_of_creators',
'name',
'losses',
'number_of_advertisers'
])
cleaned_df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
]).astype(float)
column_types = {
'number_of_users': 'number',
'location': 'category',
'views': 'number',
'number_of_creators': 'number',
'name': 'text',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
assert_frame_equal(cleaned_df, rule.numeric_df.reset_index(drop=True))
results = rule.evaluate()
expected_results = [
dict(
title='Remove collinear columns',
message='The following columns are strongly correlated '
'with other columns in the dataset: [\'number_of_users\']. '
'Removing these columns may increase data quality '
'by removing redundant and closely related data.',
action_payload=dict(
action_type='remove',
action_arguments=['number_of_users'],
axis='column',
action_options = {},
action_variables = {},
action_code = '',
outputs = [],
)
)
]
self.assertEqual(results, expected_results)
def test_evaluate_dirty(self):
df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[200, np.nan, 50, None, 75],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1500, 75000, np.nan, 70, 25],
[None, 75000, 30, 70, np.nan],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[600, '', 50, 3000, None],
[700, 11750, 20, 2750, 55],
[700, None, 20, None, 55],
[700, 11750, '', 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
])
cleaned_df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
]).astype(float)
column_types = {
'number_of_users': 'number',
'views': 'number',
'number_of_creators': 'number',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
assert_frame_equal(cleaned_df, rule.numeric_df.reset_index(drop=True))
results = rule.evaluate()
expected_results = [
dict(
title='Remove collinear columns',
message='The following columns are strongly correlated '
'with other columns in the dataset: [\'number_of_users\']. '
'Removing these columns may increase data quality '
'by removing redundant and closely related data.',
action_payload=dict(
action_type='remove',
action_arguments=['number_of_users'],
axis='column',
action_options = {},
action_variables = {},
action_code = '',
outputs = [],
)
)
]
self.assertEqual(results, expected_results)
def test_evaluate_non_numeric(self):
df = pd.DataFrame([
[1000, 'US', 30000, 10, 'cute animal #1', 100, 30],
[500, 'CA', 10000, 20, 'intro to regression', 3000, 20],
[200, '', np.nan, 50, 'daily news #1', None, 75],
[250, 'CA', 7500, 25, 'machine learning seminar', 8000, 20],
[1000, 'MX', 45003, 20, 'cute animal #4', 90, 40],
[1500, 'MX', 75000, 30, '', 70, 25],
[1500, 'US', 75000, np.nan, 'daily news #3', 70, 25],
[None, 'US', 75000, 30, 'tutorial: how to start a startup', 70, np.nan],
[1250, 'US', 60000, 50, 'cute animal #3', 80, 20],
[200, 'CA', 5000, 30, '', 10000, 30],
[800, 'US', 12050, 40, 'meme compilation', 2000, 45],
[600, 'CA', 11000, 50, 'daily news #2', 3000, 50],
[600, 'CA', '', 50, '', 3000, None],
[700, 'MX', 11750, 20, 'cute animal #2', 2750, 55],
[700, '', None, 20, '', None, 55],
[700, 'MX', 11750, '', '', 2750, 55],
[1200, 'MX', 52000, 10, 'vc funding strats', 75, 60]
], columns=[
'number_of_users',
'location',
'views',
'number_of_creators',
'name',
'losses',
'number_of_advertisers'
])
cleaned_df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
]).astype(float)
column_types = {
'number_of_users': 'number',
'location': 'category',
'views': 'number',
'number_of_creators': 'number',
'name': 'text',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
assert_frame_equal(cleaned_df, rule.numeric_df.reset_index(drop=True))
results = rule.evaluate()
expected_results = [
dict(
title='Remove collinear columns',
message='The following columns are strongly correlated '
'with other columns in the dataset: [\'number_of_users\']. '
'Removing these columns may increase data quality '
'by removing redundant and closely related data.',
action_payload=dict(
action_type='remove',
action_arguments=['number_of_users'],
axis='column',
action_options = {},
action_variables = {},
action_code = '',
outputs = [],
)
)
]
self.assertEqual(results, expected_results)
def test_perfectly_collinear(self):
number_of_users = self.
|
7,747 | 220 | 14,619 |
mage-ai__mage-ai
|
5d823db31cab8cb2e89873aec656b5a522769873
|
mage_ai/tests/data_cleaner/cleaning_rules/test_remove_collinear_columns.py
|
Unknown
|
evaluate
| true |
function
| 14 | 15 | false | false |
[
"numeric_columns",
"evaluate",
"df_columns",
"column_types",
"numeric_df",
"df",
"EPSILON",
"filter_numeric_types",
"get_variance_inflation_factor",
"MIN_ENTRIES",
"numeric_indices",
"ROW_SAMPLE_SIZE",
"statistics",
"VIF_UB",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "column_types",
"type": "statement"
},
{
"name": "df",
"type": "statement"
},
{
"name": "df_columns",
"type": "statement"
},
{
"name": "EPSILON",
"type": "statement"
},
{
"name": "evaluate",
"type": "function"
},
{
"name": "filter_numeric_types",
"type": "function"
},
{
"name": "get_variance_inflation_factor",
"type": "function"
},
{
"name": "MIN_ENTRIES",
"type": "statement"
},
{
"name": "numeric_columns",
"type": "statement"
},
{
"name": "numeric_df",
"type": "statement"
},
{
"name": "numeric_indices",
"type": "statement"
},
{
"name": "ROW_SAMPLE_SIZE",
"type": "statement"
},
{
"name": "statistics",
"type": "statement"
},
{
"name": "VIF_UB",
"type": "statement"
},
{
"name": "_build_transformer_action_suggestion",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
from data_cleaner.cleaning_rules.remove_collinear_columns import RemoveCollinearColumns
from tests.base_test import TestCase
from pandas.util.testing import assert_frame_equal
import numpy as np
import pandas as pd
class RemoveCollinearColumnsTests(TestCase):
def setUp(self):
self.rng = np.random.default_rng(42)
return super().setUp()
def test_categorical_data_frame(self):
df = pd.DataFrame([
[1, 1000, '2021-10-01', '2021-09-01'],
[1, 1050, '2021-10-01', '2021-08-01'],
[1, 1100, '2021-10-01', '2021-01-01'],
[2, 1150, '2021-09-01', '2021-08-01'],
], columns=[
'group_id',
'order_id',
'group_churned_at',
'order_created_at',
])
column_types = {
'group_id': 'category',
'order_id': 'category',
'group_churned_at': 'datetime',
'order_created_at': 'datetime'
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
self.assertEqual(result, [])
def test_clean_removes_all_data_frame(self):
df = pd.DataFrame([
[None, 1000, '2021-10-01', '2021-09-01'],
[1, None, '2021-10-01', '2021-08-01'],
[np.nan, 1100, '2021-10-01', '2021-01-01'],
[None, 1150, '2021-09-01', '2021-08-01'],
], columns=[
'group_id',
'order_id',
'group_churned_at',
'order_created_at',
])
column_types = {
'group_id': 'number',
'order_id': 'number',
'group_churned_at': 'datetime',
'order_created_at': 'datetime'
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
self.assertEqual(result, [])
def test_collinear_no_results(self):
df = pd.DataFrame([
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1]
], columns=['number_of_users', 'views', 'revenue', 'losses'])
column_types = {
'number_of_users': 'number',
'views': 'number',
'revenue': 'number',
'losses': 'number',
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
self.assertEqual(result, [])
def test_evaluate(self):
df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
])
column_types = {
'number_of_users': 'number',
'views': 'number',
'number_of_creators': 'number',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
results = rule.evaluate()
expected_results = [
dict(
title='Remove collinear columns',
message='The following columns are strongly correlated '
'with other columns in the dataset: [\'number_of_users\']. '
'Removing these columns may increase data quality '
'by removing redundant and closely related data.',
action_payload=dict(
action_type='remove',
action_arguments=['number_of_users'],
axis='column',
action_options = {},
action_variables = {},
action_code = '',
outputs = [],
)
)
]
self.assertEqual(results, expected_results)
def test_evaluate_bad_dtypes(self):
df = pd.DataFrame([
[1000, 'US', 30000, '10', 'cute animal #1', 100, '30'],
['500', 'CA', 10000, '20', 'intro to regression', 3000, '20'],
[200, '', np.nan, 50, 'daily news #1', None, '75'],
[250, 'CA', 7500, 25, 'machine learning seminar', 8000, '20'],
['1000', 'MX', 45003, '20', 'cute animal #4', 90, '40'],
[1500, 'MX', 75000, '30', '', 70, 25],
[1500, 'US', 75000, np.nan, 'daily news #3', 70, '25'],
[None, 'US', 75000, '30', 'tutorial: how to start a startup', 70, np.nan],
[1250, 'US', 60000, '50', 'cute animal #3', 80, '20'],
['200', 'CA', 5000, '30', '', 10000, '30'],
[800, 'US', 12050, '40', 'meme compilation', 2000, '45'],
['600', 'CA', 11000, '50', 'daily news #2', 3000, '50'],
[600, 'CA', '', 50, '', 3000, None],
['700', 'MX', 11750, '20', 'cute animal #2', 2750, '55'],
[700, '', None, 20, '', None, '55'],
[700, 'MX', 11750, '', '', 2750, '55'],
[1200, 'MX', 52000, '10', 'vc funding strats', 75, '60']
], columns=[
'number_of_users',
'location',
'views',
'number_of_creators',
'name',
'losses',
'number_of_advertisers'
])
cleaned_df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
]).astype(float)
column_types = {
'number_of_users': 'number',
'location': 'category',
'views': 'number',
'number_of_creators': 'number',
'name': 'text',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
assert_frame_equal(cleaned_df, rule.numeric_df.reset_index(drop=True))
results = rule.evaluate()
expected_results = [
dict(
title='Remove collinear columns',
message='The following columns are strongly correlated '
'with other columns in the dataset: [\'number_of_users\']. '
'Removing these columns may increase data quality '
'by removing redundant and closely related data.',
action_payload=dict(
action_type='remove',
action_arguments=['number_of_users'],
axis='column',
action_options = {},
action_variables = {},
action_code = '',
outputs = [],
)
)
]
self.assertEqual(results, expected_results)
def test_evaluate_dirty(self):
df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[200, np.nan, 50, None, 75],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1500, 75000, np.nan, 70, 25],
[None, 75000, 30, 70, np.nan],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[600, '', 50, 3000, None],
[700, 11750, 20, 2750, 55],
[700, None, 20, None, 55],
[700, 11750, '', 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
])
cleaned_df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
]).astype(float)
column_types = {
'number_of_users': 'number',
'views': 'number',
'number_of_creators': 'number',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
assert_frame_equal(cleaned_df, rule.numeric_df.reset_index(drop=True))
results = rule.evaluate()
expected_results = [
dict(
title='Remove collinear columns',
message='The following columns are strongly correlated '
'with other columns in the dataset: [\'number_of_users\']. '
'Removing these columns may increase data quality '
'by removing redundant and closely related data.',
action_payload=dict(
action_type='remove',
action_arguments=['number_of_users'],
axis='column',
action_options = {},
action_variables = {},
action_code = '',
outputs = [],
)
)
]
self.assertEqual(results, expected_results)
def test_evaluate_non_numeric(self):
df = pd.DataFrame([
[1000, 'US', 30000, 10, 'cute animal #1', 100, 30],
[500, 'CA', 10000, 20, 'intro to regression', 3000, 20],
[200, '', np.nan, 50, 'daily news #1', None, 75],
[250, 'CA', 7500, 25, 'machine learning seminar', 8000, 20],
[1000, 'MX', 45003, 20, 'cute animal #4', 90, 40],
[1500, 'MX', 75000, 30, '', 70, 25],
[1500, 'US', 75000, np.nan, 'daily news #3', 70, 25],
[None, 'US', 75000, 30, 'tutorial: how to start a startup', 70, np.nan],
[1250, 'US', 60000, 50, 'cute animal #3', 80, 20],
[200, 'CA', 5000, 30, '', 10000, 30],
[800, 'US', 12050, 40, 'meme compilation', 2000, 45],
[600, 'CA', 11000, 50, 'daily news #2', 3000, 50],
[600, 'CA', '', 50, '', 3000, None],
[700, 'MX', 11750, 20, 'cute animal #2', 2750, 55],
[700, '', None, 20, '', None, 55],
[700, 'MX', 11750, '', '', 2750, 55],
[1200, 'MX', 52000, 10, 'vc funding strats', 75, 60]
], columns=[
'number_of_users',
'location',
'views',
'number_of_creators',
'name',
'losses',
'number_of_advertisers'
])
cleaned_df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
]).astype(float)
column_types = {
'number_of_users': 'number',
'location': 'category',
'views': 'number',
'number_of_creators': 'number',
'name': 'text',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
assert_frame_equal(cleaned_df, rule.numeric_df.reset_index(drop=True))
results = rule.evaluate()
expected_results = [
dict(
title='Remove collinear columns',
message='The following columns are strongly correlated '
'with other columns in the dataset: [\'number_of_users\']. '
'Removing these columns may increase data quality '
'by removing redundant and closely related data.',
action_payload=dict(
action_type='remove',
action_arguments=['number_of_users'],
axis='column',
action_options = {},
action_variables = {},
action_code = '',
outputs = [],
)
)
]
self.assertEqual(results, expected_results)
def test_perfectly_collinear(self):
number_of_users = self.rng.integers(1000, 500000, (10000))
views = number_of_users * 300
revenue = 2 * views - number_of_users
losses = revenue / views + number_of_users
df = pd.DataFrame({
'number_of_users':number_of_users,
'views': views,
'revenue': revenue,
'losses': losses
})
column_types = {
'number_of_users': 'number',
'views': 'number',
'revenue': 'number',
'losses': 'number',
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).
|
7,748 | 220 | 16,938 |
mage-ai__mage-ai
|
5d823db31cab8cb2e89873aec656b5a522769873
|
mage_ai/tests/data_cleaner/cleaning_rules/test_remove_collinear_columns.py
|
Unknown
|
numeric_columns
| true |
statement
| 14 | 15 | false | true |
[
"numeric_columns",
"numeric_df",
"evaluate",
"VIF_UB",
"column_types",
"df",
"df_columns",
"EPSILON",
"filter_numeric_types",
"get_variance_inflation_factor",
"MIN_ENTRIES",
"numeric_indices",
"ROW_SAMPLE_SIZE",
"statistics",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "column_types",
"type": "statement"
},
{
"name": "df",
"type": "statement"
},
{
"name": "df_columns",
"type": "statement"
},
{
"name": "EPSILON",
"type": "statement"
},
{
"name": "evaluate",
"type": "function"
},
{
"name": "filter_numeric_types",
"type": "function"
},
{
"name": "get_variance_inflation_factor",
"type": "function"
},
{
"name": "MIN_ENTRIES",
"type": "statement"
},
{
"name": "numeric_columns",
"type": "statement"
},
{
"name": "numeric_df",
"type": "statement"
},
{
"name": "numeric_indices",
"type": "statement"
},
{
"name": "ROW_SAMPLE_SIZE",
"type": "statement"
},
{
"name": "statistics",
"type": "statement"
},
{
"name": "VIF_UB",
"type": "statement"
},
{
"name": "_build_transformer_action_suggestion",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
from data_cleaner.cleaning_rules.remove_collinear_columns import RemoveCollinearColumns
from tests.base_test import TestCase
from pandas.util.testing import assert_frame_equal
import numpy as np
import pandas as pd
class RemoveCollinearColumnsTests(TestCase):
def setUp(self):
self.rng = np.random.default_rng(42)
return super().setUp()
def test_categorical_data_frame(self):
df = pd.DataFrame([
[1, 1000, '2021-10-01', '2021-09-01'],
[1, 1050, '2021-10-01', '2021-08-01'],
[1, 1100, '2021-10-01', '2021-01-01'],
[2, 1150, '2021-09-01', '2021-08-01'],
], columns=[
'group_id',
'order_id',
'group_churned_at',
'order_created_at',
])
column_types = {
'group_id': 'category',
'order_id': 'category',
'group_churned_at': 'datetime',
'order_created_at': 'datetime'
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
self.assertEqual(result, [])
def test_clean_removes_all_data_frame(self):
df = pd.DataFrame([
[None, 1000, '2021-10-01', '2021-09-01'],
[1, None, '2021-10-01', '2021-08-01'],
[np.nan, 1100, '2021-10-01', '2021-01-01'],
[None, 1150, '2021-09-01', '2021-08-01'],
], columns=[
'group_id',
'order_id',
'group_churned_at',
'order_created_at',
])
column_types = {
'group_id': 'number',
'order_id': 'number',
'group_churned_at': 'datetime',
'order_created_at': 'datetime'
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
self.assertEqual(result, [])
def test_collinear_no_results(self):
df = pd.DataFrame([
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1]
], columns=['number_of_users', 'views', 'revenue', 'losses'])
column_types = {
'number_of_users': 'number',
'views': 'number',
'revenue': 'number',
'losses': 'number',
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
self.assertEqual(result, [])
def test_evaluate(self):
df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
])
column_types = {
'number_of_users': 'number',
'views': 'number',
'number_of_creators': 'number',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
results = rule.evaluate()
expected_results = [
dict(
title='Remove collinear columns',
message='The following columns are strongly correlated '
'with other columns in the dataset: [\'number_of_users\']. '
'Removing these columns may increase data quality '
'by removing redundant and closely related data.',
action_payload=dict(
action_type='remove',
action_arguments=['number_of_users'],
axis='column',
action_options = {},
action_variables = {},
action_code = '',
outputs = [],
)
)
]
self.assertEqual(results, expected_results)
def test_evaluate_bad_dtypes(self):
df = pd.DataFrame([
[1000, 'US', 30000, '10', 'cute animal #1', 100, '30'],
['500', 'CA', 10000, '20', 'intro to regression', 3000, '20'],
[200, '', np.nan, 50, 'daily news #1', None, '75'],
[250, 'CA', 7500, 25, 'machine learning seminar', 8000, '20'],
['1000', 'MX', 45003, '20', 'cute animal #4', 90, '40'],
[1500, 'MX', 75000, '30', '', 70, 25],
[1500, 'US', 75000, np.nan, 'daily news #3', 70, '25'],
[None, 'US', 75000, '30', 'tutorial: how to start a startup', 70, np.nan],
[1250, 'US', 60000, '50', 'cute animal #3', 80, '20'],
['200', 'CA', 5000, '30', '', 10000, '30'],
[800, 'US', 12050, '40', 'meme compilation', 2000, '45'],
['600', 'CA', 11000, '50', 'daily news #2', 3000, '50'],
[600, 'CA', '', 50, '', 3000, None],
['700', 'MX', 11750, '20', 'cute animal #2', 2750, '55'],
[700, '', None, 20, '', None, '55'],
[700, 'MX', 11750, '', '', 2750, '55'],
[1200, 'MX', 52000, '10', 'vc funding strats', 75, '60']
], columns=[
'number_of_users',
'location',
'views',
'number_of_creators',
'name',
'losses',
'number_of_advertisers'
])
cleaned_df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
]).astype(float)
column_types = {
'number_of_users': 'number',
'location': 'category',
'views': 'number',
'number_of_creators': 'number',
'name': 'text',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
assert_frame_equal(cleaned_df, rule.numeric_df.reset_index(drop=True))
results = rule.evaluate()
expected_results = [
dict(
title='Remove collinear columns',
message='The following columns are strongly correlated '
'with other columns in the dataset: [\'number_of_users\']. '
'Removing these columns may increase data quality '
'by removing redundant and closely related data.',
action_payload=dict(
action_type='remove',
action_arguments=['number_of_users'],
axis='column',
action_options = {},
action_variables = {},
action_code = '',
outputs = [],
)
)
]
self.assertEqual(results, expected_results)
def test_evaluate_dirty(self):
df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[200, np.nan, 50, None, 75],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1500, 75000, np.nan, 70, 25],
[None, 75000, 30, 70, np.nan],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[600, '', 50, 3000, None],
[700, 11750, 20, 2750, 55],
[700, None, 20, None, 55],
[700, 11750, '', 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
])
cleaned_df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
]).astype(float)
column_types = {
'number_of_users': 'number',
'views': 'number',
'number_of_creators': 'number',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
assert_frame_equal(cleaned_df, rule.numeric_df.reset_index(drop=True))
results = rule.evaluate()
expected_results = [
dict(
title='Remove collinear columns',
message='The following columns are strongly correlated '
'with other columns in the dataset: [\'number_of_users\']. '
'Removing these columns may increase data quality '
'by removing redundant and closely related data.',
action_payload=dict(
action_type='remove',
action_arguments=['number_of_users'],
axis='column',
action_options = {},
action_variables = {},
action_code = '',
outputs = [],
)
)
]
self.assertEqual(results, expected_results)
def test_evaluate_non_numeric(self):
df = pd.DataFrame([
[1000, 'US', 30000, 10, 'cute animal #1', 100, 30],
[500, 'CA', 10000, 20, 'intro to regression', 3000, 20],
[200, '', np.nan, 50, 'daily news #1', None, 75],
[250, 'CA', 7500, 25, 'machine learning seminar', 8000, 20],
[1000, 'MX', 45003, 20, 'cute animal #4', 90, 40],
[1500, 'MX', 75000, 30, '', 70, 25],
[1500, 'US', 75000, np.nan, 'daily news #3', 70, 25],
[None, 'US', 75000, 30, 'tutorial: how to start a startup', 70, np.nan],
[1250, 'US', 60000, 50, 'cute animal #3', 80, 20],
[200, 'CA', 5000, 30, '', 10000, 30],
[800, 'US', 12050, 40, 'meme compilation', 2000, 45],
[600, 'CA', 11000, 50, 'daily news #2', 3000, 50],
[600, 'CA', '', 50, '', 3000, None],
[700, 'MX', 11750, 20, 'cute animal #2', 2750, 55],
[700, '', None, 20, '', None, 55],
[700, 'MX', 11750, '', '', 2750, 55],
[1200, 'MX', 52000, 10, 'vc funding strats', 75, 60]
], columns=[
'number_of_users',
'location',
'views',
'number_of_creators',
'name',
'losses',
'number_of_advertisers'
])
cleaned_df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
]).astype(float)
column_types = {
'number_of_users': 'number',
'location': 'category',
'views': 'number',
'number_of_creators': 'number',
'name': 'text',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
assert_frame_equal(cleaned_df, rule.numeric_df.reset_index(drop=True))
results = rule.evaluate()
expected_results = [
dict(
title='Remove collinear columns',
message='The following columns are strongly correlated '
'with other columns in the dataset: [\'number_of_users\']. '
'Removing these columns may increase data quality '
'by removing redundant and closely related data.',
action_payload=dict(
action_type='remove',
action_arguments=['number_of_users'],
axis='column',
action_options = {},
action_variables = {},
action_code = '',
outputs = [],
)
)
]
self.assertEqual(results, expected_results)
def test_perfectly_collinear(self):
number_of_users = self.rng.integers(1000, 500000, (10000))
views = number_of_users * 300
revenue = 2 * views - number_of_users
losses = revenue / views + number_of_users
df = pd.DataFrame({
'number_of_users':number_of_users,
'views': views,
'revenue': revenue,
'losses': losses
})
column_types = {
'number_of_users': 'number',
'views': 'number',
'revenue': 'number',
'losses': 'number',
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
expected_results = [
dict(
title='Remove collinear columns',
message='The following columns are strongly correlated '
'with other columns in the dataset:'
' [\'number_of_users\', \'views\', \'revenue\']. '
'Removing these columns may increase data quality '
'by removing redundant and closely related data.',
action_payload=dict(
action_type='remove',
action_arguments=['number_of_users', 'views', 'revenue'],
axis='column',
action_options = {},
action_variables = {},
action_code = '',
outputs = [],
)
)
]
self.assertEqual(result, expected_results)
def test_vif_calcuation(self):
df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
])
column_types = {
'number_of_users': 'number',
'views': 'number',
'number_of_creators': 'number',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
expected_vifs_no_remove = (
59.32817701051733,
26.10502642724925,
5.6541251174451315,
2.6033835916281176,
10.735934980453335
)
expected_vifs_remove = (
59.32817701051733,
2.941751614824833,
3.4216357503903243,
1.370441833599666
)
for column, expected_vif in zip(rule.
|
7,749 | 220 | 17,004 |
mage-ai__mage-ai
|
5d823db31cab8cb2e89873aec656b5a522769873
|
mage_ai/tests/data_cleaner/cleaning_rules/test_remove_collinear_columns.py
|
Unknown
|
get_variance_inflation_factor
| true |
function
| 14 | 15 | false | true |
[
"evaluate",
"numeric_columns",
"numeric_df",
"VIF_UB",
"get_variance_inflation_factor",
"column_types",
"df",
"df_columns",
"EPSILON",
"filter_numeric_types",
"MIN_ENTRIES",
"numeric_indices",
"ROW_SAMPLE_SIZE",
"statistics",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "column_types",
"type": "statement"
},
{
"name": "df",
"type": "statement"
},
{
"name": "df_columns",
"type": "statement"
},
{
"name": "EPSILON",
"type": "statement"
},
{
"name": "evaluate",
"type": "function"
},
{
"name": "filter_numeric_types",
"type": "function"
},
{
"name": "get_variance_inflation_factor",
"type": "function"
},
{
"name": "MIN_ENTRIES",
"type": "statement"
},
{
"name": "numeric_columns",
"type": "statement"
},
{
"name": "numeric_df",
"type": "statement"
},
{
"name": "numeric_indices",
"type": "statement"
},
{
"name": "ROW_SAMPLE_SIZE",
"type": "statement"
},
{
"name": "statistics",
"type": "statement"
},
{
"name": "VIF_UB",
"type": "statement"
},
{
"name": "_build_transformer_action_suggestion",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
from data_cleaner.cleaning_rules.remove_collinear_columns import RemoveCollinearColumns
from tests.base_test import TestCase
from pandas.util.testing import assert_frame_equal
import numpy as np
import pandas as pd
class RemoveCollinearColumnsTests(TestCase):
def setUp(self):
self.rng = np.random.default_rng(42)
return super().setUp()
def test_categorical_data_frame(self):
df = pd.DataFrame([
[1, 1000, '2021-10-01', '2021-09-01'],
[1, 1050, '2021-10-01', '2021-08-01'],
[1, 1100, '2021-10-01', '2021-01-01'],
[2, 1150, '2021-09-01', '2021-08-01'],
], columns=[
'group_id',
'order_id',
'group_churned_at',
'order_created_at',
])
column_types = {
'group_id': 'category',
'order_id': 'category',
'group_churned_at': 'datetime',
'order_created_at': 'datetime'
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
self.assertEqual(result, [])
def test_clean_removes_all_data_frame(self):
df = pd.DataFrame([
[None, 1000, '2021-10-01', '2021-09-01'],
[1, None, '2021-10-01', '2021-08-01'],
[np.nan, 1100, '2021-10-01', '2021-01-01'],
[None, 1150, '2021-09-01', '2021-08-01'],
], columns=[
'group_id',
'order_id',
'group_churned_at',
'order_created_at',
])
column_types = {
'group_id': 'number',
'order_id': 'number',
'group_churned_at': 'datetime',
'order_created_at': 'datetime'
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
self.assertEqual(result, [])
def test_collinear_no_results(self):
df = pd.DataFrame([
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1]
], columns=['number_of_users', 'views', 'revenue', 'losses'])
column_types = {
'number_of_users': 'number',
'views': 'number',
'revenue': 'number',
'losses': 'number',
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
self.assertEqual(result, [])
def test_evaluate(self):
df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
])
column_types = {
'number_of_users': 'number',
'views': 'number',
'number_of_creators': 'number',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
results = rule.evaluate()
expected_results = [
dict(
title='Remove collinear columns',
message='The following columns are strongly correlated '
'with other columns in the dataset: [\'number_of_users\']. '
'Removing these columns may increase data quality '
'by removing redundant and closely related data.',
action_payload=dict(
action_type='remove',
action_arguments=['number_of_users'],
axis='column',
action_options = {},
action_variables = {},
action_code = '',
outputs = [],
)
)
]
self.assertEqual(results, expected_results)
def test_evaluate_bad_dtypes(self):
df = pd.DataFrame([
[1000, 'US', 30000, '10', 'cute animal #1', 100, '30'],
['500', 'CA', 10000, '20', 'intro to regression', 3000, '20'],
[200, '', np.nan, 50, 'daily news #1', None, '75'],
[250, 'CA', 7500, 25, 'machine learning seminar', 8000, '20'],
['1000', 'MX', 45003, '20', 'cute animal #4', 90, '40'],
[1500, 'MX', 75000, '30', '', 70, 25],
[1500, 'US', 75000, np.nan, 'daily news #3', 70, '25'],
[None, 'US', 75000, '30', 'tutorial: how to start a startup', 70, np.nan],
[1250, 'US', 60000, '50', 'cute animal #3', 80, '20'],
['200', 'CA', 5000, '30', '', 10000, '30'],
[800, 'US', 12050, '40', 'meme compilation', 2000, '45'],
['600', 'CA', 11000, '50', 'daily news #2', 3000, '50'],
[600, 'CA', '', 50, '', 3000, None],
['700', 'MX', 11750, '20', 'cute animal #2', 2750, '55'],
[700, '', None, 20, '', None, '55'],
[700, 'MX', 11750, '', '', 2750, '55'],
[1200, 'MX', 52000, '10', 'vc funding strats', 75, '60']
], columns=[
'number_of_users',
'location',
'views',
'number_of_creators',
'name',
'losses',
'number_of_advertisers'
])
cleaned_df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
]).astype(float)
column_types = {
'number_of_users': 'number',
'location': 'category',
'views': 'number',
'number_of_creators': 'number',
'name': 'text',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
assert_frame_equal(cleaned_df, rule.numeric_df.reset_index(drop=True))
results = rule.evaluate()
expected_results = [
dict(
title='Remove collinear columns',
message='The following columns are strongly correlated '
'with other columns in the dataset: [\'number_of_users\']. '
'Removing these columns may increase data quality '
'by removing redundant and closely related data.',
action_payload=dict(
action_type='remove',
action_arguments=['number_of_users'],
axis='column',
action_options = {},
action_variables = {},
action_code = '',
outputs = [],
)
)
]
self.assertEqual(results, expected_results)
def test_evaluate_dirty(self):
df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[200, np.nan, 50, None, 75],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1500, 75000, np.nan, 70, 25],
[None, 75000, 30, 70, np.nan],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[600, '', 50, 3000, None],
[700, 11750, 20, 2750, 55],
[700, None, 20, None, 55],
[700, 11750, '', 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
])
cleaned_df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
]).astype(float)
column_types = {
'number_of_users': 'number',
'views': 'number',
'number_of_creators': 'number',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
assert_frame_equal(cleaned_df, rule.numeric_df.reset_index(drop=True))
results = rule.evaluate()
expected_results = [
dict(
title='Remove collinear columns',
message='The following columns are strongly correlated '
'with other columns in the dataset: [\'number_of_users\']. '
'Removing these columns may increase data quality '
'by removing redundant and closely related data.',
action_payload=dict(
action_type='remove',
action_arguments=['number_of_users'],
axis='column',
action_options = {},
action_variables = {},
action_code = '',
outputs = [],
)
)
]
self.assertEqual(results, expected_results)
def test_evaluate_non_numeric(self):
df = pd.DataFrame([
[1000, 'US', 30000, 10, 'cute animal #1', 100, 30],
[500, 'CA', 10000, 20, 'intro to regression', 3000, 20],
[200, '', np.nan, 50, 'daily news #1', None, 75],
[250, 'CA', 7500, 25, 'machine learning seminar', 8000, 20],
[1000, 'MX', 45003, 20, 'cute animal #4', 90, 40],
[1500, 'MX', 75000, 30, '', 70, 25],
[1500, 'US', 75000, np.nan, 'daily news #3', 70, 25],
[None, 'US', 75000, 30, 'tutorial: how to start a startup', 70, np.nan],
[1250, 'US', 60000, 50, 'cute animal #3', 80, 20],
[200, 'CA', 5000, 30, '', 10000, 30],
[800, 'US', 12050, 40, 'meme compilation', 2000, 45],
[600, 'CA', 11000, 50, 'daily news #2', 3000, 50],
[600, 'CA', '', 50, '', 3000, None],
[700, 'MX', 11750, 20, 'cute animal #2', 2750, 55],
[700, '', None, 20, '', None, 55],
[700, 'MX', 11750, '', '', 2750, 55],
[1200, 'MX', 52000, 10, 'vc funding strats', 75, 60]
], columns=[
'number_of_users',
'location',
'views',
'number_of_creators',
'name',
'losses',
'number_of_advertisers'
])
cleaned_df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
]).astype(float)
column_types = {
'number_of_users': 'number',
'location': 'category',
'views': 'number',
'number_of_creators': 'number',
'name': 'text',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
assert_frame_equal(cleaned_df, rule.numeric_df.reset_index(drop=True))
results = rule.evaluate()
expected_results = [
dict(
title='Remove collinear columns',
message='The following columns are strongly correlated '
'with other columns in the dataset: [\'number_of_users\']. '
'Removing these columns may increase data quality '
'by removing redundant and closely related data.',
action_payload=dict(
action_type='remove',
action_arguments=['number_of_users'],
axis='column',
action_options = {},
action_variables = {},
action_code = '',
outputs = [],
)
)
]
self.assertEqual(results, expected_results)
def test_perfectly_collinear(self):
number_of_users = self.rng.integers(1000, 500000, (10000))
views = number_of_users * 300
revenue = 2 * views - number_of_users
losses = revenue / views + number_of_users
df = pd.DataFrame({
'number_of_users':number_of_users,
'views': views,
'revenue': revenue,
'losses': losses
})
column_types = {
'number_of_users': 'number',
'views': 'number',
'revenue': 'number',
'losses': 'number',
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
expected_results = [
dict(
title='Remove collinear columns',
message='The following columns are strongly correlated '
'with other columns in the dataset:'
' [\'number_of_users\', \'views\', \'revenue\']. '
'Removing these columns may increase data quality '
'by removing redundant and closely related data.',
action_payload=dict(
action_type='remove',
action_arguments=['number_of_users', 'views', 'revenue'],
axis='column',
action_options = {},
action_variables = {},
action_code = '',
outputs = [],
)
)
]
self.assertEqual(result, expected_results)
def test_vif_calcuation(self):
df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
])
column_types = {
'number_of_users': 'number',
'views': 'number',
'number_of_creators': 'number',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
expected_vifs_no_remove = (
59.32817701051733,
26.10502642724925,
5.6541251174451315,
2.6033835916281176,
10.735934980453335
)
expected_vifs_remove = (
59.32817701051733,
2.941751614824833,
3.4216357503903243,
1.370441833599666
)
for column, expected_vif in zip(rule.numeric_columns, expected_vifs_no_remove):
vif = rule.
|
7,750 | 220 | 17,141 |
mage-ai__mage-ai
|
5d823db31cab8cb2e89873aec656b5a522769873
|
mage_ai/tests/data_cleaner/cleaning_rules/test_remove_collinear_columns.py
|
Unknown
|
numeric_columns
| true |
statement
| 14 | 15 | false | false |
[
"numeric_columns",
"numeric_df",
"evaluate",
"VIF_UB",
"column_types",
"df",
"df_columns",
"EPSILON",
"filter_numeric_types",
"get_variance_inflation_factor",
"MIN_ENTRIES",
"numeric_indices",
"ROW_SAMPLE_SIZE",
"statistics",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "column_types",
"type": "statement"
},
{
"name": "df",
"type": "statement"
},
{
"name": "df_columns",
"type": "statement"
},
{
"name": "EPSILON",
"type": "statement"
},
{
"name": "evaluate",
"type": "function"
},
{
"name": "filter_numeric_types",
"type": "function"
},
{
"name": "get_variance_inflation_factor",
"type": "function"
},
{
"name": "MIN_ENTRIES",
"type": "statement"
},
{
"name": "numeric_columns",
"type": "statement"
},
{
"name": "numeric_df",
"type": "statement"
},
{
"name": "numeric_indices",
"type": "statement"
},
{
"name": "ROW_SAMPLE_SIZE",
"type": "statement"
},
{
"name": "statistics",
"type": "statement"
},
{
"name": "VIF_UB",
"type": "statement"
},
{
"name": "_build_transformer_action_suggestion",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
from data_cleaner.cleaning_rules.remove_collinear_columns import RemoveCollinearColumns
from tests.base_test import TestCase
from pandas.util.testing import assert_frame_equal
import numpy as np
import pandas as pd
class RemoveCollinearColumnsTests(TestCase):
def setUp(self):
self.rng = np.random.default_rng(42)
return super().setUp()
def test_categorical_data_frame(self):
df = pd.DataFrame([
[1, 1000, '2021-10-01', '2021-09-01'],
[1, 1050, '2021-10-01', '2021-08-01'],
[1, 1100, '2021-10-01', '2021-01-01'],
[2, 1150, '2021-09-01', '2021-08-01'],
], columns=[
'group_id',
'order_id',
'group_churned_at',
'order_created_at',
])
column_types = {
'group_id': 'category',
'order_id': 'category',
'group_churned_at': 'datetime',
'order_created_at': 'datetime'
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
self.assertEqual(result, [])
def test_clean_removes_all_data_frame(self):
df = pd.DataFrame([
[None, 1000, '2021-10-01', '2021-09-01'],
[1, None, '2021-10-01', '2021-08-01'],
[np.nan, 1100, '2021-10-01', '2021-01-01'],
[None, 1150, '2021-09-01', '2021-08-01'],
], columns=[
'group_id',
'order_id',
'group_churned_at',
'order_created_at',
])
column_types = {
'group_id': 'number',
'order_id': 'number',
'group_churned_at': 'datetime',
'order_created_at': 'datetime'
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
self.assertEqual(result, [])
def test_collinear_no_results(self):
df = pd.DataFrame([
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1]
], columns=['number_of_users', 'views', 'revenue', 'losses'])
column_types = {
'number_of_users': 'number',
'views': 'number',
'revenue': 'number',
'losses': 'number',
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
self.assertEqual(result, [])
def test_evaluate(self):
df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
])
column_types = {
'number_of_users': 'number',
'views': 'number',
'number_of_creators': 'number',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
results = rule.evaluate()
expected_results = [
dict(
title='Remove collinear columns',
message='The following columns are strongly correlated '
'with other columns in the dataset: [\'number_of_users\']. '
'Removing these columns may increase data quality '
'by removing redundant and closely related data.',
action_payload=dict(
action_type='remove',
action_arguments=['number_of_users'],
axis='column',
action_options = {},
action_variables = {},
action_code = '',
outputs = [],
)
)
]
self.assertEqual(results, expected_results)
def test_evaluate_bad_dtypes(self):
df = pd.DataFrame([
[1000, 'US', 30000, '10', 'cute animal #1', 100, '30'],
['500', 'CA', 10000, '20', 'intro to regression', 3000, '20'],
[200, '', np.nan, 50, 'daily news #1', None, '75'],
[250, 'CA', 7500, 25, 'machine learning seminar', 8000, '20'],
['1000', 'MX', 45003, '20', 'cute animal #4', 90, '40'],
[1500, 'MX', 75000, '30', '', 70, 25],
[1500, 'US', 75000, np.nan, 'daily news #3', 70, '25'],
[None, 'US', 75000, '30', 'tutorial: how to start a startup', 70, np.nan],
[1250, 'US', 60000, '50', 'cute animal #3', 80, '20'],
['200', 'CA', 5000, '30', '', 10000, '30'],
[800, 'US', 12050, '40', 'meme compilation', 2000, '45'],
['600', 'CA', 11000, '50', 'daily news #2', 3000, '50'],
[600, 'CA', '', 50, '', 3000, None],
['700', 'MX', 11750, '20', 'cute animal #2', 2750, '55'],
[700, '', None, 20, '', None, '55'],
[700, 'MX', 11750, '', '', 2750, '55'],
[1200, 'MX', 52000, '10', 'vc funding strats', 75, '60']
], columns=[
'number_of_users',
'location',
'views',
'number_of_creators',
'name',
'losses',
'number_of_advertisers'
])
cleaned_df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
]).astype(float)
column_types = {
'number_of_users': 'number',
'location': 'category',
'views': 'number',
'number_of_creators': 'number',
'name': 'text',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
assert_frame_equal(cleaned_df, rule.numeric_df.reset_index(drop=True))
results = rule.evaluate()
expected_results = [
dict(
title='Remove collinear columns',
message='The following columns are strongly correlated '
'with other columns in the dataset: [\'number_of_users\']. '
'Removing these columns may increase data quality '
'by removing redundant and closely related data.',
action_payload=dict(
action_type='remove',
action_arguments=['number_of_users'],
axis='column',
action_options = {},
action_variables = {},
action_code = '',
outputs = [],
)
)
]
self.assertEqual(results, expected_results)
def test_evaluate_dirty(self):
df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[200, np.nan, 50, None, 75],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1500, 75000, np.nan, 70, 25],
[None, 75000, 30, 70, np.nan],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[600, '', 50, 3000, None],
[700, 11750, 20, 2750, 55],
[700, None, 20, None, 55],
[700, 11750, '', 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
])
cleaned_df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
]).astype(float)
column_types = {
'number_of_users': 'number',
'views': 'number',
'number_of_creators': 'number',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
assert_frame_equal(cleaned_df, rule.numeric_df.reset_index(drop=True))
results = rule.evaluate()
expected_results = [
dict(
title='Remove collinear columns',
message='The following columns are strongly correlated '
'with other columns in the dataset: [\'number_of_users\']. '
'Removing these columns may increase data quality '
'by removing redundant and closely related data.',
action_payload=dict(
action_type='remove',
action_arguments=['number_of_users'],
axis='column',
action_options = {},
action_variables = {},
action_code = '',
outputs = [],
)
)
]
self.assertEqual(results, expected_results)
def test_evaluate_non_numeric(self):
df = pd.DataFrame([
[1000, 'US', 30000, 10, 'cute animal #1', 100, 30],
[500, 'CA', 10000, 20, 'intro to regression', 3000, 20],
[200, '', np.nan, 50, 'daily news #1', None, 75],
[250, 'CA', 7500, 25, 'machine learning seminar', 8000, 20],
[1000, 'MX', 45003, 20, 'cute animal #4', 90, 40],
[1500, 'MX', 75000, 30, '', 70, 25],
[1500, 'US', 75000, np.nan, 'daily news #3', 70, 25],
[None, 'US', 75000, 30, 'tutorial: how to start a startup', 70, np.nan],
[1250, 'US', 60000, 50, 'cute animal #3', 80, 20],
[200, 'CA', 5000, 30, '', 10000, 30],
[800, 'US', 12050, 40, 'meme compilation', 2000, 45],
[600, 'CA', 11000, 50, 'daily news #2', 3000, 50],
[600, 'CA', '', 50, '', 3000, None],
[700, 'MX', 11750, 20, 'cute animal #2', 2750, 55],
[700, '', None, 20, '', None, 55],
[700, 'MX', 11750, '', '', 2750, 55],
[1200, 'MX', 52000, 10, 'vc funding strats', 75, 60]
], columns=[
'number_of_users',
'location',
'views',
'number_of_creators',
'name',
'losses',
'number_of_advertisers'
])
cleaned_df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
]).astype(float)
column_types = {
'number_of_users': 'number',
'location': 'category',
'views': 'number',
'number_of_creators': 'number',
'name': 'text',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
assert_frame_equal(cleaned_df, rule.numeric_df.reset_index(drop=True))
results = rule.evaluate()
expected_results = [
dict(
title='Remove collinear columns',
message='The following columns are strongly correlated '
'with other columns in the dataset: [\'number_of_users\']. '
'Removing these columns may increase data quality '
'by removing redundant and closely related data.',
action_payload=dict(
action_type='remove',
action_arguments=['number_of_users'],
axis='column',
action_options = {},
action_variables = {},
action_code = '',
outputs = [],
)
)
]
self.assertEqual(results, expected_results)
def test_perfectly_collinear(self):
number_of_users = self.rng.integers(1000, 500000, (10000))
views = number_of_users * 300
revenue = 2 * views - number_of_users
losses = revenue / views + number_of_users
df = pd.DataFrame({
'number_of_users':number_of_users,
'views': views,
'revenue': revenue,
'losses': losses
})
column_types = {
'number_of_users': 'number',
'views': 'number',
'revenue': 'number',
'losses': 'number',
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
expected_results = [
dict(
title='Remove collinear columns',
message='The following columns are strongly correlated '
'with other columns in the dataset:'
' [\'number_of_users\', \'views\', \'revenue\']. '
'Removing these columns may increase data quality '
'by removing redundant and closely related data.',
action_payload=dict(
action_type='remove',
action_arguments=['number_of_users', 'views', 'revenue'],
axis='column',
action_options = {},
action_variables = {},
action_code = '',
outputs = [],
)
)
]
self.assertEqual(result, expected_results)
def test_vif_calcuation(self):
df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
])
column_types = {
'number_of_users': 'number',
'views': 'number',
'number_of_creators': 'number',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
expected_vifs_no_remove = (
59.32817701051733,
26.10502642724925,
5.6541251174451315,
2.6033835916281176,
10.735934980453335
)
expected_vifs_remove = (
59.32817701051733,
2.941751614824833,
3.4216357503903243,
1.370441833599666
)
for column, expected_vif in zip(rule.numeric_columns, expected_vifs_no_remove):
vif = rule.get_variance_inflation_factor(column)
self.assertAlmostEqual(vif, expected_vif)
for column, expected_vif in zip(rule.
|
7,751 | 220 | 17,209 |
mage-ai__mage-ai
|
5d823db31cab8cb2e89873aec656b5a522769873
|
mage_ai/tests/data_cleaner/cleaning_rules/test_remove_collinear_columns.py
|
Unknown
|
get_variance_inflation_factor
| true |
function
| 14 | 15 | false | false |
[
"evaluate",
"numeric_columns",
"numeric_df",
"VIF_UB",
"get_variance_inflation_factor",
"column_types",
"df",
"df_columns",
"EPSILON",
"filter_numeric_types",
"MIN_ENTRIES",
"numeric_indices",
"ROW_SAMPLE_SIZE",
"statistics",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "column_types",
"type": "statement"
},
{
"name": "df",
"type": "statement"
},
{
"name": "df_columns",
"type": "statement"
},
{
"name": "EPSILON",
"type": "statement"
},
{
"name": "evaluate",
"type": "function"
},
{
"name": "filter_numeric_types",
"type": "function"
},
{
"name": "get_variance_inflation_factor",
"type": "function"
},
{
"name": "MIN_ENTRIES",
"type": "statement"
},
{
"name": "numeric_columns",
"type": "statement"
},
{
"name": "numeric_df",
"type": "statement"
},
{
"name": "numeric_indices",
"type": "statement"
},
{
"name": "ROW_SAMPLE_SIZE",
"type": "statement"
},
{
"name": "statistics",
"type": "statement"
},
{
"name": "VIF_UB",
"type": "statement"
},
{
"name": "_build_transformer_action_suggestion",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
from data_cleaner.cleaning_rules.remove_collinear_columns import RemoveCollinearColumns
from tests.base_test import TestCase
from pandas.util.testing import assert_frame_equal
import numpy as np
import pandas as pd
class RemoveCollinearColumnsTests(TestCase):
def setUp(self):
self.rng = np.random.default_rng(42)
return super().setUp()
def test_categorical_data_frame(self):
df = pd.DataFrame([
[1, 1000, '2021-10-01', '2021-09-01'],
[1, 1050, '2021-10-01', '2021-08-01'],
[1, 1100, '2021-10-01', '2021-01-01'],
[2, 1150, '2021-09-01', '2021-08-01'],
], columns=[
'group_id',
'order_id',
'group_churned_at',
'order_created_at',
])
column_types = {
'group_id': 'category',
'order_id': 'category',
'group_churned_at': 'datetime',
'order_created_at': 'datetime'
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
self.assertEqual(result, [])
def test_clean_removes_all_data_frame(self):
df = pd.DataFrame([
[None, 1000, '2021-10-01', '2021-09-01'],
[1, None, '2021-10-01', '2021-08-01'],
[np.nan, 1100, '2021-10-01', '2021-01-01'],
[None, 1150, '2021-09-01', '2021-08-01'],
], columns=[
'group_id',
'order_id',
'group_churned_at',
'order_created_at',
])
column_types = {
'group_id': 'number',
'order_id': 'number',
'group_churned_at': 'datetime',
'order_created_at': 'datetime'
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
self.assertEqual(result, [])
def test_collinear_no_results(self):
df = pd.DataFrame([
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1]
], columns=['number_of_users', 'views', 'revenue', 'losses'])
column_types = {
'number_of_users': 'number',
'views': 'number',
'revenue': 'number',
'losses': 'number',
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
self.assertEqual(result, [])
def test_evaluate(self):
df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
])
column_types = {
'number_of_users': 'number',
'views': 'number',
'number_of_creators': 'number',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
results = rule.evaluate()
expected_results = [
dict(
title='Remove collinear columns',
message='The following columns are strongly correlated '
'with other columns in the dataset: [\'number_of_users\']. '
'Removing these columns may increase data quality '
'by removing redundant and closely related data.',
action_payload=dict(
action_type='remove',
action_arguments=['number_of_users'],
axis='column',
action_options = {},
action_variables = {},
action_code = '',
outputs = [],
)
)
]
self.assertEqual(results, expected_results)
def test_evaluate_bad_dtypes(self):
df = pd.DataFrame([
[1000, 'US', 30000, '10', 'cute animal #1', 100, '30'],
['500', 'CA', 10000, '20', 'intro to regression', 3000, '20'],
[200, '', np.nan, 50, 'daily news #1', None, '75'],
[250, 'CA', 7500, 25, 'machine learning seminar', 8000, '20'],
['1000', 'MX', 45003, '20', 'cute animal #4', 90, '40'],
[1500, 'MX', 75000, '30', '', 70, 25],
[1500, 'US', 75000, np.nan, 'daily news #3', 70, '25'],
[None, 'US', 75000, '30', 'tutorial: how to start a startup', 70, np.nan],
[1250, 'US', 60000, '50', 'cute animal #3', 80, '20'],
['200', 'CA', 5000, '30', '', 10000, '30'],
[800, 'US', 12050, '40', 'meme compilation', 2000, '45'],
['600', 'CA', 11000, '50', 'daily news #2', 3000, '50'],
[600, 'CA', '', 50, '', 3000, None],
['700', 'MX', 11750, '20', 'cute animal #2', 2750, '55'],
[700, '', None, 20, '', None, '55'],
[700, 'MX', 11750, '', '', 2750, '55'],
[1200, 'MX', 52000, '10', 'vc funding strats', 75, '60']
], columns=[
'number_of_users',
'location',
'views',
'number_of_creators',
'name',
'losses',
'number_of_advertisers'
])
cleaned_df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
]).astype(float)
column_types = {
'number_of_users': 'number',
'location': 'category',
'views': 'number',
'number_of_creators': 'number',
'name': 'text',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
assert_frame_equal(cleaned_df, rule.numeric_df.reset_index(drop=True))
results = rule.evaluate()
expected_results = [
dict(
title='Remove collinear columns',
message='The following columns are strongly correlated '
'with other columns in the dataset: [\'number_of_users\']. '
'Removing these columns may increase data quality '
'by removing redundant and closely related data.',
action_payload=dict(
action_type='remove',
action_arguments=['number_of_users'],
axis='column',
action_options = {},
action_variables = {},
action_code = '',
outputs = [],
)
)
]
self.assertEqual(results, expected_results)
def test_evaluate_dirty(self):
df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[200, np.nan, 50, None, 75],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1500, 75000, np.nan, 70, 25],
[None, 75000, 30, 70, np.nan],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[600, '', 50, 3000, None],
[700, 11750, 20, 2750, 55],
[700, None, 20, None, 55],
[700, 11750, '', 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
])
cleaned_df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
]).astype(float)
column_types = {
'number_of_users': 'number',
'views': 'number',
'number_of_creators': 'number',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
assert_frame_equal(cleaned_df, rule.numeric_df.reset_index(drop=True))
results = rule.evaluate()
expected_results = [
dict(
title='Remove collinear columns',
message='The following columns are strongly correlated '
'with other columns in the dataset: [\'number_of_users\']. '
'Removing these columns may increase data quality '
'by removing redundant and closely related data.',
action_payload=dict(
action_type='remove',
action_arguments=['number_of_users'],
axis='column',
action_options = {},
action_variables = {},
action_code = '',
outputs = [],
)
)
]
self.assertEqual(results, expected_results)
def test_evaluate_non_numeric(self):
df = pd.DataFrame([
[1000, 'US', 30000, 10, 'cute animal #1', 100, 30],
[500, 'CA', 10000, 20, 'intro to regression', 3000, 20],
[200, '', np.nan, 50, 'daily news #1', None, 75],
[250, 'CA', 7500, 25, 'machine learning seminar', 8000, 20],
[1000, 'MX', 45003, 20, 'cute animal #4', 90, 40],
[1500, 'MX', 75000, 30, '', 70, 25],
[1500, 'US', 75000, np.nan, 'daily news #3', 70, 25],
[None, 'US', 75000, 30, 'tutorial: how to start a startup', 70, np.nan],
[1250, 'US', 60000, 50, 'cute animal #3', 80, 20],
[200, 'CA', 5000, 30, '', 10000, 30],
[800, 'US', 12050, 40, 'meme compilation', 2000, 45],
[600, 'CA', 11000, 50, 'daily news #2', 3000, 50],
[600, 'CA', '', 50, '', 3000, None],
[700, 'MX', 11750, 20, 'cute animal #2', 2750, 55],
[700, '', None, 20, '', None, 55],
[700, 'MX', 11750, '', '', 2750, 55],
[1200, 'MX', 52000, 10, 'vc funding strats', 75, 60]
], columns=[
'number_of_users',
'location',
'views',
'number_of_creators',
'name',
'losses',
'number_of_advertisers'
])
cleaned_df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
]).astype(float)
column_types = {
'number_of_users': 'number',
'location': 'category',
'views': 'number',
'number_of_creators': 'number',
'name': 'text',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
assert_frame_equal(cleaned_df, rule.numeric_df.reset_index(drop=True))
results = rule.evaluate()
expected_results = [
dict(
title='Remove collinear columns',
message='The following columns are strongly correlated '
'with other columns in the dataset: [\'number_of_users\']. '
'Removing these columns may increase data quality '
'by removing redundant and closely related data.',
action_payload=dict(
action_type='remove',
action_arguments=['number_of_users'],
axis='column',
action_options = {},
action_variables = {},
action_code = '',
outputs = [],
)
)
]
self.assertEqual(results, expected_results)
def test_perfectly_collinear(self):
number_of_users = self.rng.integers(1000, 500000, (10000))
views = number_of_users * 300
revenue = 2 * views - number_of_users
losses = revenue / views + number_of_users
df = pd.DataFrame({
'number_of_users':number_of_users,
'views': views,
'revenue': revenue,
'losses': losses
})
column_types = {
'number_of_users': 'number',
'views': 'number',
'revenue': 'number',
'losses': 'number',
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
expected_results = [
dict(
title='Remove collinear columns',
message='The following columns are strongly correlated '
'with other columns in the dataset:'
' [\'number_of_users\', \'views\', \'revenue\']. '
'Removing these columns may increase data quality '
'by removing redundant and closely related data.',
action_payload=dict(
action_type='remove',
action_arguments=['number_of_users', 'views', 'revenue'],
axis='column',
action_options = {},
action_variables = {},
action_code = '',
outputs = [],
)
)
]
self.assertEqual(result, expected_results)
def test_vif_calcuation(self):
df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
])
column_types = {
'number_of_users': 'number',
'views': 'number',
'number_of_creators': 'number',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
expected_vifs_no_remove = (
59.32817701051733,
26.10502642724925,
5.6541251174451315,
2.6033835916281176,
10.735934980453335
)
expected_vifs_remove = (
59.32817701051733,
2.941751614824833,
3.4216357503903243,
1.370441833599666
)
for column, expected_vif in zip(rule.numeric_columns, expected_vifs_no_remove):
vif = rule.get_variance_inflation_factor(column)
self.assertAlmostEqual(vif, expected_vif)
for column, expected_vif in zip(rule.numeric_columns[:-1], expected_vifs_remove):
vif = rule.
|
7,752 | 220 | 17,318 |
mage-ai__mage-ai
|
5d823db31cab8cb2e89873aec656b5a522769873
|
mage_ai/tests/data_cleaner/cleaning_rules/test_remove_collinear_columns.py
|
inproject
|
numeric_df
| true |
statement
| 14 | 15 | false | false |
[
"column_types",
"numeric_columns",
"df",
"VIF_UB",
"statistics",
"df_columns",
"EPSILON",
"evaluate",
"filter_numeric_types",
"get_variance_inflation_factor",
"MIN_ENTRIES",
"numeric_df",
"numeric_indices",
"ROW_SAMPLE_SIZE",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "column_types",
"type": "statement"
},
{
"name": "df",
"type": "statement"
},
{
"name": "df_columns",
"type": "statement"
},
{
"name": "EPSILON",
"type": "statement"
},
{
"name": "evaluate",
"type": "function"
},
{
"name": "filter_numeric_types",
"type": "function"
},
{
"name": "get_variance_inflation_factor",
"type": "function"
},
{
"name": "MIN_ENTRIES",
"type": "statement"
},
{
"name": "numeric_columns",
"type": "statement"
},
{
"name": "numeric_df",
"type": "statement"
},
{
"name": "numeric_indices",
"type": "statement"
},
{
"name": "ROW_SAMPLE_SIZE",
"type": "statement"
},
{
"name": "statistics",
"type": "statement"
},
{
"name": "VIF_UB",
"type": "statement"
},
{
"name": "_build_transformer_action_suggestion",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
from data_cleaner.cleaning_rules.remove_collinear_columns import RemoveCollinearColumns
from tests.base_test import TestCase
from pandas.util.testing import assert_frame_equal
import numpy as np
import pandas as pd
class RemoveCollinearColumnsTests(TestCase):
def setUp(self):
self.rng = np.random.default_rng(42)
return super().setUp()
def test_categorical_data_frame(self):
df = pd.DataFrame([
[1, 1000, '2021-10-01', '2021-09-01'],
[1, 1050, '2021-10-01', '2021-08-01'],
[1, 1100, '2021-10-01', '2021-01-01'],
[2, 1150, '2021-09-01', '2021-08-01'],
], columns=[
'group_id',
'order_id',
'group_churned_at',
'order_created_at',
])
column_types = {
'group_id': 'category',
'order_id': 'category',
'group_churned_at': 'datetime',
'order_created_at': 'datetime'
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
self.assertEqual(result, [])
def test_clean_removes_all_data_frame(self):
df = pd.DataFrame([
[None, 1000, '2021-10-01', '2021-09-01'],
[1, None, '2021-10-01', '2021-08-01'],
[np.nan, 1100, '2021-10-01', '2021-01-01'],
[None, 1150, '2021-09-01', '2021-08-01'],
], columns=[
'group_id',
'order_id',
'group_churned_at',
'order_created_at',
])
column_types = {
'group_id': 'number',
'order_id': 'number',
'group_churned_at': 'datetime',
'order_created_at': 'datetime'
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
self.assertEqual(result, [])
def test_collinear_no_results(self):
df = pd.DataFrame([
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1]
], columns=['number_of_users', 'views', 'revenue', 'losses'])
column_types = {
'number_of_users': 'number',
'views': 'number',
'revenue': 'number',
'losses': 'number',
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
self.assertEqual(result, [])
def test_evaluate(self):
df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
])
column_types = {
'number_of_users': 'number',
'views': 'number',
'number_of_creators': 'number',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
results = rule.evaluate()
expected_results = [
dict(
title='Remove collinear columns',
message='The following columns are strongly correlated '
'with other columns in the dataset: [\'number_of_users\']. '
'Removing these columns may increase data quality '
'by removing redundant and closely related data.',
action_payload=dict(
action_type='remove',
action_arguments=['number_of_users'],
axis='column',
action_options = {},
action_variables = {},
action_code = '',
outputs = [],
)
)
]
self.assertEqual(results, expected_results)
def test_evaluate_bad_dtypes(self):
df = pd.DataFrame([
[1000, 'US', 30000, '10', 'cute animal #1', 100, '30'],
['500', 'CA', 10000, '20', 'intro to regression', 3000, '20'],
[200, '', np.nan, 50, 'daily news #1', None, '75'],
[250, 'CA', 7500, 25, 'machine learning seminar', 8000, '20'],
['1000', 'MX', 45003, '20', 'cute animal #4', 90, '40'],
[1500, 'MX', 75000, '30', '', 70, 25],
[1500, 'US', 75000, np.nan, 'daily news #3', 70, '25'],
[None, 'US', 75000, '30', 'tutorial: how to start a startup', 70, np.nan],
[1250, 'US', 60000, '50', 'cute animal #3', 80, '20'],
['200', 'CA', 5000, '30', '', 10000, '30'],
[800, 'US', 12050, '40', 'meme compilation', 2000, '45'],
['600', 'CA', 11000, '50', 'daily news #2', 3000, '50'],
[600, 'CA', '', 50, '', 3000, None],
['700', 'MX', 11750, '20', 'cute animal #2', 2750, '55'],
[700, '', None, 20, '', None, '55'],
[700, 'MX', 11750, '', '', 2750, '55'],
[1200, 'MX', 52000, '10', 'vc funding strats', 75, '60']
], columns=[
'number_of_users',
'location',
'views',
'number_of_creators',
'name',
'losses',
'number_of_advertisers'
])
cleaned_df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
]).astype(float)
column_types = {
'number_of_users': 'number',
'location': 'category',
'views': 'number',
'number_of_creators': 'number',
'name': 'text',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
assert_frame_equal(cleaned_df, rule.numeric_df.reset_index(drop=True))
results = rule.evaluate()
expected_results = [
dict(
title='Remove collinear columns',
message='The following columns are strongly correlated '
'with other columns in the dataset: [\'number_of_users\']. '
'Removing these columns may increase data quality '
'by removing redundant and closely related data.',
action_payload=dict(
action_type='remove',
action_arguments=['number_of_users'],
axis='column',
action_options = {},
action_variables = {},
action_code = '',
outputs = [],
)
)
]
self.assertEqual(results, expected_results)
def test_evaluate_dirty(self):
df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[200, np.nan, 50, None, 75],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1500, 75000, np.nan, 70, 25],
[None, 75000, 30, 70, np.nan],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[600, '', 50, 3000, None],
[700, 11750, 20, 2750, 55],
[700, None, 20, None, 55],
[700, 11750, '', 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
])
cleaned_df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
]).astype(float)
column_types = {
'number_of_users': 'number',
'views': 'number',
'number_of_creators': 'number',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
assert_frame_equal(cleaned_df, rule.numeric_df.reset_index(drop=True))
results = rule.evaluate()
expected_results = [
dict(
title='Remove collinear columns',
message='The following columns are strongly correlated '
'with other columns in the dataset: [\'number_of_users\']. '
'Removing these columns may increase data quality '
'by removing redundant and closely related data.',
action_payload=dict(
action_type='remove',
action_arguments=['number_of_users'],
axis='column',
action_options = {},
action_variables = {},
action_code = '',
outputs = [],
)
)
]
self.assertEqual(results, expected_results)
def test_evaluate_non_numeric(self):
df = pd.DataFrame([
[1000, 'US', 30000, 10, 'cute animal #1', 100, 30],
[500, 'CA', 10000, 20, 'intro to regression', 3000, 20],
[200, '', np.nan, 50, 'daily news #1', None, 75],
[250, 'CA', 7500, 25, 'machine learning seminar', 8000, 20],
[1000, 'MX', 45003, 20, 'cute animal #4', 90, 40],
[1500, 'MX', 75000, 30, '', 70, 25],
[1500, 'US', 75000, np.nan, 'daily news #3', 70, 25],
[None, 'US', 75000, 30, 'tutorial: how to start a startup', 70, np.nan],
[1250, 'US', 60000, 50, 'cute animal #3', 80, 20],
[200, 'CA', 5000, 30, '', 10000, 30],
[800, 'US', 12050, 40, 'meme compilation', 2000, 45],
[600, 'CA', 11000, 50, 'daily news #2', 3000, 50],
[600, 'CA', '', 50, '', 3000, None],
[700, 'MX', 11750, 20, 'cute animal #2', 2750, 55],
[700, '', None, 20, '', None, 55],
[700, 'MX', 11750, '', '', 2750, 55],
[1200, 'MX', 52000, 10, 'vc funding strats', 75, 60]
], columns=[
'number_of_users',
'location',
'views',
'number_of_creators',
'name',
'losses',
'number_of_advertisers'
])
cleaned_df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
]).astype(float)
column_types = {
'number_of_users': 'number',
'location': 'category',
'views': 'number',
'number_of_creators': 'number',
'name': 'text',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
assert_frame_equal(cleaned_df, rule.numeric_df.reset_index(drop=True))
results = rule.evaluate()
expected_results = [
dict(
title='Remove collinear columns',
message='The following columns are strongly correlated '
'with other columns in the dataset: [\'number_of_users\']. '
'Removing these columns may increase data quality '
'by removing redundant and closely related data.',
action_payload=dict(
action_type='remove',
action_arguments=['number_of_users'],
axis='column',
action_options = {},
action_variables = {},
action_code = '',
outputs = [],
)
)
]
self.assertEqual(results, expected_results)
def test_perfectly_collinear(self):
number_of_users = self.rng.integers(1000, 500000, (10000))
views = number_of_users * 300
revenue = 2 * views - number_of_users
losses = revenue / views + number_of_users
df = pd.DataFrame({
'number_of_users':number_of_users,
'views': views,
'revenue': revenue,
'losses': losses
})
column_types = {
'number_of_users': 'number',
'views': 'number',
'revenue': 'number',
'losses': 'number',
}
statistics = {}
result = RemoveCollinearColumns(df, column_types, statistics).evaluate()
expected_results = [
dict(
title='Remove collinear columns',
message='The following columns are strongly correlated '
'with other columns in the dataset:'
' [\'number_of_users\', \'views\', \'revenue\']. '
'Removing these columns may increase data quality '
'by removing redundant and closely related data.',
action_payload=dict(
action_type='remove',
action_arguments=['number_of_users', 'views', 'revenue'],
axis='column',
action_options = {},
action_variables = {},
action_code = '',
outputs = [],
)
)
]
self.assertEqual(result, expected_results)
def test_vif_calcuation(self):
df = pd.DataFrame([
[1000, 30000, 10, 100, 30],
[500, 10000, 20, 3000, 20],
[250, 7500, 25, 8000, 20],
[1000, 45003, 20, 90, 40],
[1500, 75000, 30, 70, 25],
[1250, 60000, 50, 80, 20],
[200, 5000, 30, 10000, 30],
[800, 12050, 40, 2000, 45],
[600, 11000, 50, 3000, 50],
[700, 11750, 20, 2750, 55],
[1200, 52000, 10, 75, 60]
], columns=[
'number_of_users',
'views',
'number_of_creators',
'losses',
'number_of_advertisers'
])
column_types = {
'number_of_users': 'number',
'views': 'number',
'number_of_creators': 'number',
'losses': 'number',
'number_of_advertisers': 'number'
}
statistics = {}
rule = RemoveCollinearColumns(df, column_types, statistics)
expected_vifs_no_remove = (
59.32817701051733,
26.10502642724925,
5.6541251174451315,
2.6033835916281176,
10.735934980453335
)
expected_vifs_remove = (
59.32817701051733,
2.941751614824833,
3.4216357503903243,
1.370441833599666
)
for column, expected_vif in zip(rule.numeric_columns, expected_vifs_no_remove):
vif = rule.get_variance_inflation_factor(column)
self.assertAlmostEqual(vif, expected_vif)
for column, expected_vif in zip(rule.numeric_columns[:-1], expected_vifs_remove):
vif = rule.get_variance_inflation_factor(column)
self.assertAlmostEqual(vif, expected_vif)
rule.
|
7,753 | 221 | 817 |
mage-ai__mage-ai
|
5d174cafe82dab0bc46d82b5ac6dff808a60f5ac
|
mage_ai/server/routes.py
|
infile
|
data
| true |
statement
| 17 | 18 | false | true |
[
"metadata",
"data",
"to_dict",
"write_files",
"id",
"dir",
"folder_name",
"insights",
"objects",
"path",
"path_name",
"pipeline",
"read_json_file",
"sample_data",
"statistics",
"suggestions",
"write_json_file",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "data",
"type": "statement"
},
{
"name": "dir",
"type": "statement"
},
{
"name": "folder_name",
"type": "function"
},
{
"name": "id",
"type": "statement"
},
{
"name": "insights",
"type": "statement"
},
{
"name": "metadata",
"type": "statement"
},
{
"name": "objects",
"type": "function"
},
{
"name": "path",
"type": "statement"
},
{
"name": "path_name",
"type": "function"
},
{
"name": "pipeline",
"type": "property"
},
{
"name": "read_json_file",
"type": "function"
},
{
"name": "sample_data",
"type": "property"
},
{
"name": "statistics",
"type": "statement"
},
{
"name": "suggestions",
"type": "statement"
},
{
"name": "to_dict",
"type": "function"
},
{
"name": "write_files",
"type": "function"
},
{
"name": "write_json_file",
"type": "function"
},
{
"name": "_data",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
from data_cleaner.data_cleaner import analyze, clean as clean_data
from data_cleaner.pipelines.base import BasePipeline
from flask import render_template, request
from numpyencoder import NumpyEncoder
from server.data.models import FeatureSet, Pipeline
from server import app
import json
import threading
@app.route("/")
def index():
return render_template('index.html')
"""
request: {
id: string (feature set id)
clean: boolean
}
response: {
id,
metadata,
sample_data,
statistics,
insights,
suggestions
}
"""
@app.route("/process", methods=["POST"])
def process():
request_data = request.json
if not request_data:
request_data = request.form
id = request_data['id']
if not id:
return
feature_set = FeatureSet(id=id)
df = feature_set.
|
7,754 | 221 | 849 |
mage-ai__mage-ai
|
5d174cafe82dab0bc46d82b5ac6dff808a60f5ac
|
mage_ai/server/routes.py
|
Unknown
|
metadata
| true |
statement
| 17 | 18 | false | true |
[
"metadata",
"data",
"to_dict",
"write_files",
"id",
"dir",
"folder_name",
"insights",
"objects",
"path",
"path_name",
"pipeline",
"read_json_file",
"sample_data",
"statistics",
"suggestions",
"write_json_file",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "data",
"type": "statement"
},
{
"name": "dir",
"type": "statement"
},
{
"name": "folder_name",
"type": "function"
},
{
"name": "id",
"type": "statement"
},
{
"name": "insights",
"type": "statement"
},
{
"name": "metadata",
"type": "statement"
},
{
"name": "objects",
"type": "function"
},
{
"name": "path",
"type": "statement"
},
{
"name": "path_name",
"type": "function"
},
{
"name": "pipeline",
"type": "property"
},
{
"name": "read_json_file",
"type": "function"
},
{
"name": "sample_data",
"type": "property"
},
{
"name": "statistics",
"type": "statement"
},
{
"name": "suggestions",
"type": "statement"
},
{
"name": "to_dict",
"type": "function"
},
{
"name": "write_files",
"type": "function"
},
{
"name": "write_json_file",
"type": "function"
},
{
"name": "_data",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
from data_cleaner.data_cleaner import analyze, clean as clean_data
from data_cleaner.pipelines.base import BasePipeline
from flask import render_template, request
from numpyencoder import NumpyEncoder
from server.data.models import FeatureSet, Pipeline
from server import app
import json
import threading
@app.route("/")
def index():
return render_template('index.html')
"""
request: {
id: string (feature set id)
clean: boolean
}
response: {
id,
metadata,
sample_data,
statistics,
insights,
suggestions
}
"""
@app.route("/process", methods=["POST"])
def process():
request_data = request.json
if not request_data:
request_data = request.form
id = request_data['id']
if not id:
return
feature_set = FeatureSet(id=id)
df = feature_set.data
metadata = feature_set.
|
7,755 | 221 | 987 |
mage-ai__mage-ai
|
5d174cafe82dab0bc46d82b5ac6dff808a60f5ac
|
mage_ai/server/routes.py
|
Unknown
|
write_files
| true |
function
| 17 | 18 | false | true |
[
"write_files",
"metadata",
"data",
"to_dict",
"id",
"dir",
"folder_name",
"insights",
"objects",
"path",
"path_name",
"pipeline",
"read_json_file",
"sample_data",
"statistics",
"suggestions",
"write_json_file",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "data",
"type": "statement"
},
{
"name": "dir",
"type": "statement"
},
{
"name": "folder_name",
"type": "function"
},
{
"name": "id",
"type": "statement"
},
{
"name": "insights",
"type": "statement"
},
{
"name": "metadata",
"type": "statement"
},
{
"name": "objects",
"type": "function"
},
{
"name": "path",
"type": "statement"
},
{
"name": "path_name",
"type": "function"
},
{
"name": "pipeline",
"type": "property"
},
{
"name": "read_json_file",
"type": "function"
},
{
"name": "sample_data",
"type": "property"
},
{
"name": "statistics",
"type": "statement"
},
{
"name": "suggestions",
"type": "statement"
},
{
"name": "to_dict",
"type": "function"
},
{
"name": "write_files",
"type": "function"
},
{
"name": "write_json_file",
"type": "function"
},
{
"name": "_data",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
from data_cleaner.data_cleaner import analyze, clean as clean_data
from data_cleaner.pipelines.base import BasePipeline
from flask import render_template, request
from numpyencoder import NumpyEncoder
from server.data.models import FeatureSet, Pipeline
from server import app
import json
import threading
@app.route("/")
def index():
return render_template('index.html')
"""
request: {
id: string (feature set id)
clean: boolean
}
response: {
id,
metadata,
sample_data,
statistics,
insights,
suggestions
}
"""
@app.route("/process", methods=["POST"])
def process():
request_data = request.json
if not request_data:
request_data = request.form
id = request_data['id']
if not id:
return
feature_set = FeatureSet(id=id)
df = feature_set.data
metadata = feature_set.metadata
if request_data.get('clean', True):
result = clean_data(df)
else:
result = analyze(df)
feature_set.
|
7,757 | 221 | 1,211 |
mage-ai__mage-ai
|
5d174cafe82dab0bc46d82b5ac6dff808a60f5ac
|
mage_ai/server/routes.py
|
Unknown
|
to_dict
| true |
function
| 17 | 18 | false | true |
[
"metadata",
"to_dict",
"data",
"write_files",
"id",
"dir",
"folder_name",
"insights",
"objects",
"path",
"path_name",
"pipeline",
"read_json_file",
"sample_data",
"statistics",
"suggestions",
"write_json_file",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "data",
"type": "statement"
},
{
"name": "dir",
"type": "statement"
},
{
"name": "folder_name",
"type": "function"
},
{
"name": "id",
"type": "statement"
},
{
"name": "insights",
"type": "statement"
},
{
"name": "metadata",
"type": "statement"
},
{
"name": "objects",
"type": "function"
},
{
"name": "path",
"type": "statement"
},
{
"name": "path_name",
"type": "function"
},
{
"name": "pipeline",
"type": "property"
},
{
"name": "read_json_file",
"type": "function"
},
{
"name": "sample_data",
"type": "property"
},
{
"name": "statistics",
"type": "statement"
},
{
"name": "suggestions",
"type": "statement"
},
{
"name": "to_dict",
"type": "function"
},
{
"name": "write_files",
"type": "function"
},
{
"name": "write_json_file",
"type": "function"
},
{
"name": "_data",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
from data_cleaner.data_cleaner import analyze, clean as clean_data
from data_cleaner.pipelines.base import BasePipeline
from flask import render_template, request
from numpyencoder import NumpyEncoder
from server.data.models import FeatureSet, Pipeline
from server import app
import json
import threading
@app.route("/")
def index():
return render_template('index.html')
"""
request: {
id: string (feature set id)
clean: boolean
}
response: {
id,
metadata,
sample_data,
statistics,
insights,
suggestions
}
"""
@app.route("/process", methods=["POST"])
def process():
request_data = request.json
if not request_data:
request_data = request.form
id = request_data['id']
if not id:
return
feature_set = FeatureSet(id=id)
df = feature_set.data
metadata = feature_set.metadata
if request_data.get('clean', True):
result = clean_data(df)
else:
result = analyze(df)
feature_set.write_files(result)
column_types = result['column_types']
metadata['column_types'] = column_types
feature_set.metadata = metadata
response = app.response_class(
response=json.dumps(feature_set.
|
7,758 | 221 | 1,505 |
mage-ai__mage-ai
|
5d174cafe82dab0bc46d82b5ac6dff808a60f5ac
|
mage_ai/server/routes.py
|
infile
|
objects
| true |
function
| 15 | 15 | false | true |
[
"to_dict",
"data",
"metadata",
"write_files",
"pipeline",
"folder_name",
"insights",
"objects",
"path_name",
"read_json_file",
"sample_data",
"statistics",
"suggestions",
"write_json_file",
"mro",
"__init__",
"__annotations__",
"__base__",
"__bases__",
"__basicsize__",
"__call__",
"__delattr__",
"__dict__",
"__dictoffset__",
"__dir__",
"__eq__",
"__flags__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__instancecheck__",
"__itemsize__",
"__mro__",
"__name__",
"__ne__",
"__new__",
"__or__",
"__prepare__",
"__qualname__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__ror__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasscheck__",
"__subclasses__",
"__subclasshook__",
"__text_signature__",
"__weakrefoffset__",
"__class__",
"__doc__",
"__module__"
] |
[
{
"name": "data",
"type": "property"
},
{
"name": "folder_name",
"type": "function"
},
{
"name": "insights",
"type": "property"
},
{
"name": "metadata",
"type": "property"
},
{
"name": "mro",
"type": "function"
},
{
"name": "objects",
"type": "function"
},
{
"name": "path_name",
"type": "function"
},
{
"name": "pipeline",
"type": "property"
},
{
"name": "read_json_file",
"type": "function"
},
{
"name": "sample_data",
"type": "property"
},
{
"name": "statistics",
"type": "property"
},
{
"name": "suggestions",
"type": "property"
},
{
"name": "to_dict",
"type": "function"
},
{
"name": "write_files",
"type": "function"
},
{
"name": "write_json_file",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__base__",
"type": "statement"
},
{
"name": "__bases__",
"type": "statement"
},
{
"name": "__basicsize__",
"type": "statement"
},
{
"name": "__call__",
"type": "function"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dictoffset__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__flags__",
"type": "statement"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__instancecheck__",
"type": "function"
},
{
"name": "__itemsize__",
"type": "statement"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__mro__",
"type": "statement"
},
{
"name": "__name__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__prepare__",
"type": "function"
},
{
"name": "__qualname__",
"type": "statement"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
},
{
"name": "__subclasscheck__",
"type": "function"
},
{
"name": "__subclasses__",
"type": "function"
},
{
"name": "__text_signature__",
"type": "statement"
},
{
"name": "__weakrefoffset__",
"type": "statement"
}
] |
from data_cleaner.data_cleaner import analyze, clean as clean_data
from data_cleaner.pipelines.base import BasePipeline
from flask import render_template, request
from numpyencoder import NumpyEncoder
from server.data.models import FeatureSet, Pipeline
from server import app
import json
import threading
@app.route("/")
def index():
return render_template('index.html')
"""
request: {
id: string (feature set id)
clean: boolean
}
response: {
id,
metadata,
sample_data,
statistics,
insights,
suggestions
}
"""
@app.route("/process", methods=["POST"])
def process():
request_data = request.json
if not request_data:
request_data = request.form
id = request_data['id']
if not id:
return
feature_set = FeatureSet(id=id)
df = feature_set.data
metadata = feature_set.metadata
if request_data.get('clean', True):
result = clean_data(df)
else:
result = analyze(df)
feature_set.write_files(result)
column_types = result['column_types']
metadata['column_types'] = column_types
feature_set.metadata = metadata
response = app.response_class(
response=json.dumps(feature_set.to_dict(), cls=NumpyEncoder),
status=200,
mimetype='application/json'
)
return response
"""
response: [
{
id,
metadata,
}
]
"""
@app.route("/feature_sets")
def feature_sets():
feature_sets = list(map(lambda fs: fs.to_dict(False), FeatureSet.
|
7,761 | 222 | 4,543 |
qiboteam__qibolab
|
097a3fc1a66f8aff1fce4978054707b1d7b2c596
|
examples/qili_single_qubit/diagnostics.py
|
common
|
platform
| true |
statement
| 6 | 6 | false | true |
[
"platform",
"sequence",
"name",
"label",
"unit",
"__init__",
"get",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "get",
"type": "function"
},
{
"name": "label",
"type": "statement"
},
{
"name": "name",
"type": "statement"
},
{
"name": "platform",
"type": "statement"
},
{
"name": "sequence",
"type": "statement"
},
{
"name": "unit",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import pathlib
import numpy as np
import matplotlib.pyplot as plt
import yaml
# TODO: Have a look in the documentation of ``MeasurementControl``
from quantify_core.measurement import MeasurementControl
from quantify_core.measurement.control import Gettable, Settable
from quantify_core.data.handling import set_datadir
from scipy.signal import savgol_filter
# TODO: Check why this set_datadir is needed
set_datadir(pathlib.Path(__file__).parent / "data" / "quantify")
def backup_config_file():
import os
import shutil
import errno
from datetime import datetime
original = os.path.realpath(os.path.join(os.path.dirname(__file__), '..', '..', 'src', 'qibolab', 'runcards', 'tiiq.yml'))
now = datetime.now()
now = now.strftime("%d%m%Y%H%M%S")
destination_file_name = "tiiq_" + now + ".yml"
target = os.path.realpath(os.path.join(os.path.dirname(__file__), 'data/settings_backups', destination_file_name))
try:
print("Copying file: " + original)
print("Destination file" + target)
shutil.copyfile(original, target)
print("Platform settings backup done")
except IOError as e:
# ENOENT(2): file does not exist, raised also on missing dest parent dir
if e.errno != errno.ENOENT:
raise
# try creating parent directories
os.makedirs(os.path.dirname(target))
shutil.copy(original, target)
def get_config_parameter(dictID, dictID1, key):
import os
calibration_path = os.path.realpath(os.path.join(os.path.dirname(__file__), '..', '..', 'src', 'qibolab', 'runcards', 'tiiq.yml'))
with open(calibration_path) as file:
settings = yaml.safe_load(file)
file.close()
if (not dictID1):
return settings[dictID][key]
else:
return settings[dictID][dictID1][key]
def save_config_parameter(dictID, dictID1, key, value):
import os
calibration_path = os.path.realpath(os.path.join(os.path.dirname(__file__), '..', '..', 'src', 'qibolab', 'runcards', 'tiiq.yml'))
with open(calibration_path, "r") as file:
settings = yaml.safe_load(file)
file.close()
if (not dictID1):
settings[dictID][key] = value
print("Saved value: " + str(settings[dictID][key]))
else:
settings[dictID][dictID1][key] = value
print("Saved value: " + str(settings[dictID][dictID1][key]))
with open(calibration_path, "w") as file:
settings = yaml.dump(settings, file, sort_keys=False, indent=4)
file.close()
def plot(smooth_dataset, dataset, label, type):
if (type == 0): #cavity plots
fig, ax = plt.subplots(1, 1, figsize=(15, 15/2/1.61))
ax.plot(dataset['x0'].values, dataset['y0'].values,'-',color='C0')
ax.plot(dataset['x0'].values, smooth_dataset,'-',color='C1')
ax.title.set_text(label)
ax.plot(dataset['x0'].values[smooth_dataset.argmax()], smooth_dataset[smooth_dataset.argmax()], 'o', color='C2')
plt.savefig(pathlib.Path("data") / f"{label}.pdf")
return
if (type == 1): #qubit spec, rabi, ramsey, t1 plots
fig, ax = plt.subplots(1, 1, figsize=(15, 15/2/1.61))
ax.plot(dataset['x0'].values, dataset['y0'].values,'-',color='C0')
ax.plot(dataset['x0'].values, smooth_dataset,'-',color='C1')
ax.title.set_text(label)
ax.plot(dataset['x0'].values[smooth_dataset.argmin()], smooth_dataset[smooth_dataset.argmin()], 'o', color='C2')
plt.savefig(pathlib.Path("data") / f"{label}.pdf")
return
def create_measurement_control(name):
import os
if os.environ.get("ENABLE_PLOTMON", True):
mc = MeasurementControl(f'MC {name}')
from quantify_core.visualization.pyqt_plotmon import PlotMonitor_pyqt
plotmon = PlotMonitor_pyqt(f'Plot Monitor {name}')
plotmon.tuids_max_num(3)
mc.instr_plotmon(plotmon.name)
from quantify_core.visualization.instrument_monitor import InstrumentMonitor
insmon = InstrumentMonitor(f"Instruments Monitor {name}")
mc.instrument_monitor(insmon.name)
return mc, plotmon, insmon
else:
mc = MeasurementControl(f'MC {name}')
return mc, None, None
class ROController():
# Quantify Gettable Interface Implementation
label = ['Amplitude', 'Phase','I','Q']
unit = ['V', 'Radians','V','V']
name = ['A', 'Phi','I','Q']
def __init__(self, platform, sequence):
self.platform = platform
self.sequence = sequence
def get(self):
return self.
|
7,762 | 222 | 4,565 |
qiboteam__qibolab
|
097a3fc1a66f8aff1fce4978054707b1d7b2c596
|
examples/qili_single_qubit/diagnostics.py
|
common
|
sequence
| true |
statement
| 6 | 6 | false | true |
[
"platform",
"name",
"sequence",
"label",
"unit",
"__init__",
"get",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "get",
"type": "function"
},
{
"name": "label",
"type": "statement"
},
{
"name": "name",
"type": "statement"
},
{
"name": "platform",
"type": "statement"
},
{
"name": "sequence",
"type": "statement"
},
{
"name": "unit",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import pathlib
import numpy as np
import matplotlib.pyplot as plt
import yaml
# TODO: Have a look in the documentation of ``MeasurementControl``
from quantify_core.measurement import MeasurementControl
from quantify_core.measurement.control import Gettable, Settable
from quantify_core.data.handling import set_datadir
from scipy.signal import savgol_filter
# TODO: Check why this set_datadir is needed
set_datadir(pathlib.Path(__file__).parent / "data" / "quantify")
def backup_config_file():
import os
import shutil
import errno
from datetime import datetime
original = os.path.realpath(os.path.join(os.path.dirname(__file__), '..', '..', 'src', 'qibolab', 'runcards', 'tiiq.yml'))
now = datetime.now()
now = now.strftime("%d%m%Y%H%M%S")
destination_file_name = "tiiq_" + now + ".yml"
target = os.path.realpath(os.path.join(os.path.dirname(__file__), 'data/settings_backups', destination_file_name))
try:
print("Copying file: " + original)
print("Destination file" + target)
shutil.copyfile(original, target)
print("Platform settings backup done")
except IOError as e:
# ENOENT(2): file does not exist, raised also on missing dest parent dir
if e.errno != errno.ENOENT:
raise
# try creating parent directories
os.makedirs(os.path.dirname(target))
shutil.copy(original, target)
def get_config_parameter(dictID, dictID1, key):
import os
calibration_path = os.path.realpath(os.path.join(os.path.dirname(__file__), '..', '..', 'src', 'qibolab', 'runcards', 'tiiq.yml'))
with open(calibration_path) as file:
settings = yaml.safe_load(file)
file.close()
if (not dictID1):
return settings[dictID][key]
else:
return settings[dictID][dictID1][key]
def save_config_parameter(dictID, dictID1, key, value):
import os
calibration_path = os.path.realpath(os.path.join(os.path.dirname(__file__), '..', '..', 'src', 'qibolab', 'runcards', 'tiiq.yml'))
with open(calibration_path, "r") as file:
settings = yaml.safe_load(file)
file.close()
if (not dictID1):
settings[dictID][key] = value
print("Saved value: " + str(settings[dictID][key]))
else:
settings[dictID][dictID1][key] = value
print("Saved value: " + str(settings[dictID][dictID1][key]))
with open(calibration_path, "w") as file:
settings = yaml.dump(settings, file, sort_keys=False, indent=4)
file.close()
def plot(smooth_dataset, dataset, label, type):
if (type == 0): #cavity plots
fig, ax = plt.subplots(1, 1, figsize=(15, 15/2/1.61))
ax.plot(dataset['x0'].values, dataset['y0'].values,'-',color='C0')
ax.plot(dataset['x0'].values, smooth_dataset,'-',color='C1')
ax.title.set_text(label)
ax.plot(dataset['x0'].values[smooth_dataset.argmax()], smooth_dataset[smooth_dataset.argmax()], 'o', color='C2')
plt.savefig(pathlib.Path("data") / f"{label}.pdf")
return
if (type == 1): #qubit spec, rabi, ramsey, t1 plots
fig, ax = plt.subplots(1, 1, figsize=(15, 15/2/1.61))
ax.plot(dataset['x0'].values, dataset['y0'].values,'-',color='C0')
ax.plot(dataset['x0'].values, smooth_dataset,'-',color='C1')
ax.title.set_text(label)
ax.plot(dataset['x0'].values[smooth_dataset.argmin()], smooth_dataset[smooth_dataset.argmin()], 'o', color='C2')
plt.savefig(pathlib.Path("data") / f"{label}.pdf")
return
def create_measurement_control(name):
import os
if os.environ.get("ENABLE_PLOTMON", True):
mc = MeasurementControl(f'MC {name}')
from quantify_core.visualization.pyqt_plotmon import PlotMonitor_pyqt
plotmon = PlotMonitor_pyqt(f'Plot Monitor {name}')
plotmon.tuids_max_num(3)
mc.instr_plotmon(plotmon.name)
from quantify_core.visualization.instrument_monitor import InstrumentMonitor
insmon = InstrumentMonitor(f"Instruments Monitor {name}")
mc.instrument_monitor(insmon.name)
return mc, plotmon, insmon
else:
mc = MeasurementControl(f'MC {name}')
return mc, None, None
class ROController():
# Quantify Gettable Interface Implementation
label = ['Amplitude', 'Phase','I','Q']
unit = ['V', 'Radians','V','V']
name = ['A', 'Phi','I','Q']
def __init__(self, platform, sequence):
self.platform = platform
self.sequence = sequence
def get(self):
return self.platform.execute(self.
|
7,805 | 223 | 1,131 |
qiboteam__qibolab
|
e4b0e8e6dd612e696a161da9972f4bb9b6bf8cd0
|
src/qibolab/calibration/calibration.py
|
Unknown
|
platform
| true |
statement
| 11 | 11 | false | true |
[
"platform",
"mc",
"pl",
"load_settings",
"ins",
"__init__",
"auto_calibrate_plaform",
"callibrate_qubit_states",
"run_qubit_spectroscopy",
"run_rabi_pulse_length",
"run_resonator_spectroscopy",
"run_t1",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "auto_calibrate_plaform",
"type": "function"
},
{
"name": "callibrate_qubit_states",
"type": "function"
},
{
"name": "ins",
"type": "statement"
},
{
"name": "load_settings",
"type": "function"
},
{
"name": "mc",
"type": "statement"
},
{
"name": "pl",
"type": "statement"
},
{
"name": "platform",
"type": "statement"
},
{
"name": "run_qubit_spectroscopy",
"type": "function"
},
{
"name": "run_rabi_pulse_length",
"type": "function"
},
{
"name": "run_resonator_spectroscopy",
"type": "function"
},
{
"name": "run_t1",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import pathlib
import numpy as np
#import matplotlib.pyplot as plt
import utils
import yaml
import fitting
from qibolab import Platform
# TODO: Have a look in the documentation of ``MeasurementControl``
#from quantify_core.measurement import MeasurementControl
from quantify_core.measurement.control import Gettable, Settable
from quantify_core.data.handling import set_datadir
from scipy.signal import savgol_filter
from qibolab.pulses import Pulse, ReadoutPulse
from qibolab.circuit import PulseSequence
from qibolab.pulse_shapes import Rectangular, Gaussian
# TODO: Check why this set_datadir is needed
#set_datadir(pathlib.Path("data") / "quantify")
set_datadir(pathlib.Path(__file__).parent / "data" / "quantify")
class Calibration():
def __init__(self, platform: Platform):
self.platform = platform
self.mc, self.pl, self.ins = utils.create_measurement_control('Calibration')
def load_settings(self):
# Load diagnostics settings
with open("calibration.yml", "r") as file:
return yaml.safe_load(file)
def run_resonator_spectroscopy(self):
platform = self.
|
7,806 | 223 | 1,193 |
qiboteam__qibolab
|
e4b0e8e6dd612e696a161da9972f4bb9b6bf8cd0
|
src/qibolab/calibration/calibration.py
|
Unknown
|
mc
| true |
statement
| 11 | 11 | false | true |
[
"mc",
"platform",
"pl",
"load_settings",
"ins",
"__init__",
"auto_calibrate_plaform",
"callibrate_qubit_states",
"run_qubit_spectroscopy",
"run_rabi_pulse_length",
"run_resonator_spectroscopy",
"run_t1",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "auto_calibrate_plaform",
"type": "function"
},
{
"name": "callibrate_qubit_states",
"type": "function"
},
{
"name": "ins",
"type": "statement"
},
{
"name": "load_settings",
"type": "function"
},
{
"name": "mc",
"type": "statement"
},
{
"name": "pl",
"type": "statement"
},
{
"name": "platform",
"type": "statement"
},
{
"name": "run_qubit_spectroscopy",
"type": "function"
},
{
"name": "run_rabi_pulse_length",
"type": "function"
},
{
"name": "run_resonator_spectroscopy",
"type": "function"
},
{
"name": "run_t1",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import pathlib
import numpy as np
#import matplotlib.pyplot as plt
import utils
import yaml
import fitting
from qibolab import Platform
# TODO: Have a look in the documentation of ``MeasurementControl``
#from quantify_core.measurement import MeasurementControl
from quantify_core.measurement.control import Gettable, Settable
from quantify_core.data.handling import set_datadir
from scipy.signal import savgol_filter
from qibolab.pulses import Pulse, ReadoutPulse
from qibolab.circuit import PulseSequence
from qibolab.pulse_shapes import Rectangular, Gaussian
# TODO: Check why this set_datadir is needed
#set_datadir(pathlib.Path("data") / "quantify")
set_datadir(pathlib.Path(__file__).parent / "data" / "quantify")
class Calibration():
def __init__(self, platform: Platform):
self.platform = platform
self.mc, self.pl, self.ins = utils.create_measurement_control('Calibration')
def load_settings(self):
# Load diagnostics settings
with open("calibration.yml", "r") as file:
return yaml.safe_load(file)
def run_resonator_spectroscopy(self):
platform = self.platform
platform.reload_settings()
mc = self.
|
7,807 | 223 | 1,687 |
qiboteam__qibolab
|
e4b0e8e6dd612e696a161da9972f4bb9b6bf8cd0
|
src/qibolab/calibration/calibration.py
|
inproject
|
add
| true |
function
| 8 | 8 | false | true |
[
"add",
"pulses",
"phase",
"qcm_pulses",
"qrm_pulses",
"add_measurement",
"add_u3",
"time",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "add",
"type": "function"
},
{
"name": "add_measurement",
"type": "function"
},
{
"name": "add_u3",
"type": "function"
},
{
"name": "phase",
"type": "statement"
},
{
"name": "pulses",
"type": "statement"
},
{
"name": "qcm_pulses",
"type": "statement"
},
{
"name": "qrm_pulses",
"type": "statement"
},
{
"name": "time",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import pathlib
import numpy as np
#import matplotlib.pyplot as plt
import utils
import yaml
import fitting
from qibolab import Platform
# TODO: Have a look in the documentation of ``MeasurementControl``
#from quantify_core.measurement import MeasurementControl
from quantify_core.measurement.control import Gettable, Settable
from quantify_core.data.handling import set_datadir
from scipy.signal import savgol_filter
from qibolab.pulses import Pulse, ReadoutPulse
from qibolab.circuit import PulseSequence
from qibolab.pulse_shapes import Rectangular, Gaussian
# TODO: Check why this set_datadir is needed
#set_datadir(pathlib.Path("data") / "quantify")
set_datadir(pathlib.Path(__file__).parent / "data" / "quantify")
class Calibration():
def __init__(self, platform: Platform):
self.platform = platform
self.mc, self.pl, self.ins = utils.create_measurement_control('Calibration')
def load_settings(self):
# Load diagnostics settings
with open("calibration.yml", "r") as file:
return yaml.safe_load(file)
def run_resonator_spectroscopy(self):
platform = self.platform
platform.reload_settings()
mc = self.mc
ps = platform.settings['settings']
qc_pulse_shape = eval(ps['qc_spectroscopy_pulse'].popitem()[1])
qc_pulse_settings = ps['qc_spectroscopy_pulse']
qc_pulse = Pulse(**qc_pulse_settings, shape = qc_pulse_shape)
ro_pulse_shape = eval(ps['readout_pulse'].popitem()[1])
ro_pulse_settings = ps['readout_pulse']
ro_pulse = ReadoutPulse(**ro_pulse_settings, shape = ro_pulse_shape)
sequence = PulseSequence()
sequence.
|
7,808 | 223 | 1,718 |
qiboteam__qibolab
|
e4b0e8e6dd612e696a161da9972f4bb9b6bf8cd0
|
src/qibolab/calibration/calibration.py
|
Unknown
|
add
| true |
function
| 8 | 8 | false | false |
[
"add",
"pulses",
"phase",
"qcm_pulses",
"qrm_pulses",
"add_measurement",
"add_u3",
"time",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "add",
"type": "function"
},
{
"name": "add_measurement",
"type": "function"
},
{
"name": "add_u3",
"type": "function"
},
{
"name": "phase",
"type": "statement"
},
{
"name": "pulses",
"type": "statement"
},
{
"name": "qcm_pulses",
"type": "statement"
},
{
"name": "qrm_pulses",
"type": "statement"
},
{
"name": "time",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import pathlib
import numpy as np
#import matplotlib.pyplot as plt
import utils
import yaml
import fitting
from qibolab import Platform
# TODO: Have a look in the documentation of ``MeasurementControl``
#from quantify_core.measurement import MeasurementControl
from quantify_core.measurement.control import Gettable, Settable
from quantify_core.data.handling import set_datadir
from scipy.signal import savgol_filter
from qibolab.pulses import Pulse, ReadoutPulse
from qibolab.circuit import PulseSequence
from qibolab.pulse_shapes import Rectangular, Gaussian
# TODO: Check why this set_datadir is needed
#set_datadir(pathlib.Path("data") / "quantify")
set_datadir(pathlib.Path(__file__).parent / "data" / "quantify")
class Calibration():
def __init__(self, platform: Platform):
self.platform = platform
self.mc, self.pl, self.ins = utils.create_measurement_control('Calibration')
def load_settings(self):
# Load diagnostics settings
with open("calibration.yml", "r") as file:
return yaml.safe_load(file)
def run_resonator_spectroscopy(self):
platform = self.platform
platform.reload_settings()
mc = self.mc
ps = platform.settings['settings']
qc_pulse_shape = eval(ps['qc_spectroscopy_pulse'].popitem()[1])
qc_pulse_settings = ps['qc_spectroscopy_pulse']
qc_pulse = Pulse(**qc_pulse_settings, shape = qc_pulse_shape)
ro_pulse_shape = eval(ps['readout_pulse'].popitem()[1])
ro_pulse_settings = ps['readout_pulse']
ro_pulse = ReadoutPulse(**ro_pulse_settings, shape = ro_pulse_shape)
sequence = PulseSequence()
sequence.add(qc_pulse)
sequence.
|
7,809 | 223 | 1,751 |
qiboteam__qibolab
|
e4b0e8e6dd612e696a161da9972f4bb9b6bf8cd0
|
src/qibolab/calibration/calibration.py
|
Unknown
|
load_settings
| true |
function
| 11 | 11 | false | true |
[
"platform",
"mc",
"pl",
"load_settings",
"ins",
"__init__",
"auto_calibrate_plaform",
"callibrate_qubit_states",
"run_qubit_spectroscopy",
"run_rabi_pulse_length",
"run_resonator_spectroscopy",
"run_t1",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "auto_calibrate_plaform",
"type": "function"
},
{
"name": "callibrate_qubit_states",
"type": "function"
},
{
"name": "ins",
"type": "statement"
},
{
"name": "load_settings",
"type": "function"
},
{
"name": "mc",
"type": "statement"
},
{
"name": "pl",
"type": "statement"
},
{
"name": "platform",
"type": "statement"
},
{
"name": "run_qubit_spectroscopy",
"type": "function"
},
{
"name": "run_rabi_pulse_length",
"type": "function"
},
{
"name": "run_resonator_spectroscopy",
"type": "function"
},
{
"name": "run_t1",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import pathlib
import numpy as np
#import matplotlib.pyplot as plt
import utils
import yaml
import fitting
from qibolab import Platform
# TODO: Have a look in the documentation of ``MeasurementControl``
#from quantify_core.measurement import MeasurementControl
from quantify_core.measurement.control import Gettable, Settable
from quantify_core.data.handling import set_datadir
from scipy.signal import savgol_filter
from qibolab.pulses import Pulse, ReadoutPulse
from qibolab.circuit import PulseSequence
from qibolab.pulse_shapes import Rectangular, Gaussian
# TODO: Check why this set_datadir is needed
#set_datadir(pathlib.Path("data") / "quantify")
set_datadir(pathlib.Path(__file__).parent / "data" / "quantify")
class Calibration():
def __init__(self, platform: Platform):
self.platform = platform
self.mc, self.pl, self.ins = utils.create_measurement_control('Calibration')
def load_settings(self):
# Load diagnostics settings
with open("calibration.yml", "r") as file:
return yaml.safe_load(file)
def run_resonator_spectroscopy(self):
platform = self.platform
platform.reload_settings()
mc = self.mc
ps = platform.settings['settings']
qc_pulse_shape = eval(ps['qc_spectroscopy_pulse'].popitem()[1])
qc_pulse_settings = ps['qc_spectroscopy_pulse']
qc_pulse = Pulse(**qc_pulse_settings, shape = qc_pulse_shape)
ro_pulse_shape = eval(ps['readout_pulse'].popitem()[1])
ro_pulse_settings = ps['readout_pulse']
ro_pulse = ReadoutPulse(**ro_pulse_settings, shape = ro_pulse_shape)
sequence = PulseSequence()
sequence.add(qc_pulse)
sequence.add(ro_pulse)
ds = self.
|
7,810 | 223 | 1,780 |
qiboteam__qibolab
|
e4b0e8e6dd612e696a161da9972f4bb9b6bf8cd0
|
src/qibolab/calibration/calibration.py
|
Unknown
|
pl
| true |
statement
| 11 | 11 | false | true |
[
"mc",
"platform",
"run_t1",
"pl",
"load_settings",
"__init__",
"auto_calibrate_plaform",
"callibrate_qubit_states",
"ins",
"run_qubit_spectroscopy",
"run_rabi_pulse_length",
"run_resonator_spectroscopy",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "auto_calibrate_plaform",
"type": "function"
},
{
"name": "callibrate_qubit_states",
"type": "function"
},
{
"name": "ins",
"type": "statement"
},
{
"name": "load_settings",
"type": "function"
},
{
"name": "mc",
"type": "statement"
},
{
"name": "pl",
"type": "statement"
},
{
"name": "platform",
"type": "statement"
},
{
"name": "run_qubit_spectroscopy",
"type": "function"
},
{
"name": "run_rabi_pulse_length",
"type": "function"
},
{
"name": "run_resonator_spectroscopy",
"type": "function"
},
{
"name": "run_t1",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import pathlib
import numpy as np
#import matplotlib.pyplot as plt
import utils
import yaml
import fitting
from qibolab import Platform
# TODO: Have a look in the documentation of ``MeasurementControl``
#from quantify_core.measurement import MeasurementControl
from quantify_core.measurement.control import Gettable, Settable
from quantify_core.data.handling import set_datadir
from scipy.signal import savgol_filter
from qibolab.pulses import Pulse, ReadoutPulse
from qibolab.circuit import PulseSequence
from qibolab.pulse_shapes import Rectangular, Gaussian
# TODO: Check why this set_datadir is needed
#set_datadir(pathlib.Path("data") / "quantify")
set_datadir(pathlib.Path(__file__).parent / "data" / "quantify")
class Calibration():
def __init__(self, platform: Platform):
self.platform = platform
self.mc, self.pl, self.ins = utils.create_measurement_control('Calibration')
def load_settings(self):
# Load diagnostics settings
with open("calibration.yml", "r") as file:
return yaml.safe_load(file)
def run_resonator_spectroscopy(self):
platform = self.platform
platform.reload_settings()
mc = self.mc
ps = platform.settings['settings']
qc_pulse_shape = eval(ps['qc_spectroscopy_pulse'].popitem()[1])
qc_pulse_settings = ps['qc_spectroscopy_pulse']
qc_pulse = Pulse(**qc_pulse_settings, shape = qc_pulse_shape)
ro_pulse_shape = eval(ps['readout_pulse'].popitem()[1])
ro_pulse_settings = ps['readout_pulse']
ro_pulse = ReadoutPulse(**ro_pulse_settings, shape = ro_pulse_shape)
sequence = PulseSequence()
sequence.add(qc_pulse)
sequence.add(ro_pulse)
ds = self.load_settings()
self.
|
7,811 | 223 | 3,549 |
qiboteam__qibolab
|
e4b0e8e6dd612e696a161da9972f4bb9b6bf8cd0
|
src/qibolab/calibration/calibration.py
|
inproject
|
lorentzian_fit
| true |
function
| 12 | 17 | false | true |
[
"t1_fit",
"rabi_fit",
"lorentzian_fit",
"ramsey_fit",
"curve_fit",
"BaseAnalysis",
"data_post",
"exp",
"rabi",
"ramsey",
"resonator_peak",
"set_datadir"
] |
[
{
"name": "BaseAnalysis",
"type": "module"
},
{
"name": "curve_fit",
"type": "module"
},
{
"name": "data_post",
"type": "function"
},
{
"name": "exp",
"type": "function"
},
{
"name": "lmfit",
"type": "module"
},
{
"name": "lorentzian_fit",
"type": "function"
},
{
"name": "np",
"type": "module"
},
{
"name": "os",
"type": "module"
},
{
"name": "pathlib",
"type": "module"
},
{
"name": "plt",
"type": "module"
},
{
"name": "rabi",
"type": "function"
},
{
"name": "rabi_fit",
"type": "function"
},
{
"name": "ramsey",
"type": "function"
},
{
"name": "ramsey_fit",
"type": "function"
},
{
"name": "resonator_peak",
"type": "function"
},
{
"name": "set_datadir",
"type": "module"
},
{
"name": "t1_fit",
"type": "function"
},
{
"name": "__doc__",
"type": "instance"
},
{
"name": "__file__",
"type": "instance"
},
{
"name": "__name__",
"type": "instance"
},
{
"name": "__package__",
"type": "instance"
}
] |
import pathlib
import numpy as np
#import matplotlib.pyplot as plt
import utils
import yaml
import fitting
from qibolab import Platform
# TODO: Have a look in the documentation of ``MeasurementControl``
#from quantify_core.measurement import MeasurementControl
from quantify_core.measurement.control import Gettable, Settable
from quantify_core.data.handling import set_datadir
from scipy.signal import savgol_filter
from qibolab.pulses import Pulse, ReadoutPulse
from qibolab.circuit import PulseSequence
from qibolab.pulse_shapes import Rectangular, Gaussian
# TODO: Check why this set_datadir is needed
#set_datadir(pathlib.Path("data") / "quantify")
set_datadir(pathlib.Path(__file__).parent / "data" / "quantify")
class Calibration():
def __init__(self, platform: Platform):
self.platform = platform
self.mc, self.pl, self.ins = utils.create_measurement_control('Calibration')
def load_settings(self):
# Load diagnostics settings
with open("calibration.yml", "r") as file:
return yaml.safe_load(file)
def run_resonator_spectroscopy(self):
platform = self.platform
platform.reload_settings()
mc = self.mc
ps = platform.settings['settings']
qc_pulse_shape = eval(ps['qc_spectroscopy_pulse'].popitem()[1])
qc_pulse_settings = ps['qc_spectroscopy_pulse']
qc_pulse = Pulse(**qc_pulse_settings, shape = qc_pulse_shape)
ro_pulse_shape = eval(ps['readout_pulse'].popitem()[1])
ro_pulse_settings = ps['readout_pulse']
ro_pulse = ReadoutPulse(**ro_pulse_settings, shape = ro_pulse_shape)
sequence = PulseSequence()
sequence.add(qc_pulse)
sequence.add(ro_pulse)
ds = self.load_settings()
self.pl.tuids_max_num(ds['max_num_plots'])
software_averages = ds['software_averages']
ds = ds['resonator_spectroscopy']
lowres_width = ds['lowres_width']
lowres_step = ds['lowres_step']
highres_width = ds['highres_width']
highres_step = ds['highres_step']
precision_width = ds['precision_width']
precision_step = ds['precision_step']
#Fast Sweep
scanrange = utils.variable_resolution_scanrange(lowres_width, lowres_step, highres_width, highres_step)
mc.settables(platform.LO_qrm.device.frequency)
mc.setpoints(scanrange + platform.LO_qrm.get_frequency())
mc.gettables(Gettable(ROController(platform, sequence)))
platform.start()
platform.LO_qcm.off()
dataset = mc.run("Resonator Spectroscopy Fast", soft_avg=1)
platform.stop()
platform.LO_qrm.set_frequency(dataset['x0'].values[dataset['y0'].argmax().values])
avg_min_voltage = np.mean(dataset['y0'].values[:(lowres_width//lowres_step)]) * 1e6
# Precision Sweep
scanrange = np.arange(-precision_width, precision_width, precision_step)
mc.settables(platform.LO_qrm.device.frequency)
mc.setpoints(scanrange + platform.LO_qrm.get_frequency())
mc.gettables(Gettable(ROController(platform, sequence)))
platform.start()
platform.LO_qcm.off()
dataset = mc.run("Resonator Spectroscopy Precision", soft_avg=software_averages)
platform.stop()
# Fitting
smooth_dataset = savgol_filter(dataset['y0'].values, 25, 2)
# resonator_freq = dataset['x0'].values[smooth_dataset.argmax()] + ro_pulse.frequency
max_ro_voltage = smooth_dataset.max() * 1e6
f0, BW, Q = fitting.
|
7,812 | 223 | 3,869 |
qiboteam__qibolab
|
e4b0e8e6dd612e696a161da9972f4bb9b6bf8cd0
|
src/qibolab/calibration/calibration.py
|
Unknown
|
platform
| true |
statement
| 11 | 11 | false | false |
[
"platform",
"mc",
"load_settings",
"pl",
"ins",
"__init__",
"auto_calibrate_plaform",
"callibrate_qubit_states",
"run_qubit_spectroscopy",
"run_rabi_pulse_length",
"run_resonator_spectroscopy",
"run_t1",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "auto_calibrate_plaform",
"type": "function"
},
{
"name": "callibrate_qubit_states",
"type": "function"
},
{
"name": "ins",
"type": "statement"
},
{
"name": "load_settings",
"type": "function"
},
{
"name": "mc",
"type": "statement"
},
{
"name": "pl",
"type": "statement"
},
{
"name": "platform",
"type": "statement"
},
{
"name": "run_qubit_spectroscopy",
"type": "function"
},
{
"name": "run_rabi_pulse_length",
"type": "function"
},
{
"name": "run_resonator_spectroscopy",
"type": "function"
},
{
"name": "run_t1",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import pathlib
import numpy as np
#import matplotlib.pyplot as plt
import utils
import yaml
import fitting
from qibolab import Platform
# TODO: Have a look in the documentation of ``MeasurementControl``
#from quantify_core.measurement import MeasurementControl
from quantify_core.measurement.control import Gettable, Settable
from quantify_core.data.handling import set_datadir
from scipy.signal import savgol_filter
from qibolab.pulses import Pulse, ReadoutPulse
from qibolab.circuit import PulseSequence
from qibolab.pulse_shapes import Rectangular, Gaussian
# TODO: Check why this set_datadir is needed
#set_datadir(pathlib.Path("data") / "quantify")
set_datadir(pathlib.Path(__file__).parent / "data" / "quantify")
class Calibration():
def __init__(self, platform: Platform):
self.platform = platform
self.mc, self.pl, self.ins = utils.create_measurement_control('Calibration')
def load_settings(self):
# Load diagnostics settings
with open("calibration.yml", "r") as file:
return yaml.safe_load(file)
def run_resonator_spectroscopy(self):
platform = self.platform
platform.reload_settings()
mc = self.mc
ps = platform.settings['settings']
qc_pulse_shape = eval(ps['qc_spectroscopy_pulse'].popitem()[1])
qc_pulse_settings = ps['qc_spectroscopy_pulse']
qc_pulse = Pulse(**qc_pulse_settings, shape = qc_pulse_shape)
ro_pulse_shape = eval(ps['readout_pulse'].popitem()[1])
ro_pulse_settings = ps['readout_pulse']
ro_pulse = ReadoutPulse(**ro_pulse_settings, shape = ro_pulse_shape)
sequence = PulseSequence()
sequence.add(qc_pulse)
sequence.add(ro_pulse)
ds = self.load_settings()
self.pl.tuids_max_num(ds['max_num_plots'])
software_averages = ds['software_averages']
ds = ds['resonator_spectroscopy']
lowres_width = ds['lowres_width']
lowres_step = ds['lowres_step']
highres_width = ds['highres_width']
highres_step = ds['highres_step']
precision_width = ds['precision_width']
precision_step = ds['precision_step']
#Fast Sweep
scanrange = utils.variable_resolution_scanrange(lowres_width, lowres_step, highres_width, highres_step)
mc.settables(platform.LO_qrm.device.frequency)
mc.setpoints(scanrange + platform.LO_qrm.get_frequency())
mc.gettables(Gettable(ROController(platform, sequence)))
platform.start()
platform.LO_qcm.off()
dataset = mc.run("Resonator Spectroscopy Fast", soft_avg=1)
platform.stop()
platform.LO_qrm.set_frequency(dataset['x0'].values[dataset['y0'].argmax().values])
avg_min_voltage = np.mean(dataset['y0'].values[:(lowres_width//lowres_step)]) * 1e6
# Precision Sweep
scanrange = np.arange(-precision_width, precision_width, precision_step)
mc.settables(platform.LO_qrm.device.frequency)
mc.setpoints(scanrange + platform.LO_qrm.get_frequency())
mc.gettables(Gettable(ROController(platform, sequence)))
platform.start()
platform.LO_qcm.off()
dataset = mc.run("Resonator Spectroscopy Precision", soft_avg=software_averages)
platform.stop()
# Fitting
smooth_dataset = savgol_filter(dataset['y0'].values, 25, 2)
# resonator_freq = dataset['x0'].values[smooth_dataset.argmax()] + ro_pulse.frequency
max_ro_voltage = smooth_dataset.max() * 1e6
f0, BW, Q = fitting.lorentzian_fit("last", max, "Resonator_spectroscopy")
resonator_freq = (f0*1e9 + ro_pulse.frequency)
print(f"\nResonator Frequency = {resonator_freq}")
return resonator_freq, avg_min_voltage, max_ro_voltage, smooth_dataset, dataset
def run_qubit_spectroscopy(self):
platform = self.
|
7,813 | 223 | 3,931 |
qiboteam__qibolab
|
e4b0e8e6dd612e696a161da9972f4bb9b6bf8cd0
|
src/qibolab/calibration/calibration.py
|
random
|
mc
| true |
statement
| 11 | 11 | false | false |
[
"mc",
"platform",
"load_settings",
"pl",
"ins",
"__init__",
"auto_calibrate_plaform",
"callibrate_qubit_states",
"run_qubit_spectroscopy",
"run_rabi_pulse_length",
"run_resonator_spectroscopy",
"run_t1",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "auto_calibrate_plaform",
"type": "function"
},
{
"name": "callibrate_qubit_states",
"type": "function"
},
{
"name": "ins",
"type": "statement"
},
{
"name": "load_settings",
"type": "function"
},
{
"name": "mc",
"type": "statement"
},
{
"name": "pl",
"type": "statement"
},
{
"name": "platform",
"type": "statement"
},
{
"name": "run_qubit_spectroscopy",
"type": "function"
},
{
"name": "run_rabi_pulse_length",
"type": "function"
},
{
"name": "run_resonator_spectroscopy",
"type": "function"
},
{
"name": "run_t1",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import pathlib
import numpy as np
#import matplotlib.pyplot as plt
import utils
import yaml
import fitting
from qibolab import Platform
# TODO: Have a look in the documentation of ``MeasurementControl``
#from quantify_core.measurement import MeasurementControl
from quantify_core.measurement.control import Gettable, Settable
from quantify_core.data.handling import set_datadir
from scipy.signal import savgol_filter
from qibolab.pulses import Pulse, ReadoutPulse
from qibolab.circuit import PulseSequence
from qibolab.pulse_shapes import Rectangular, Gaussian
# TODO: Check why this set_datadir is needed
#set_datadir(pathlib.Path("data") / "quantify")
set_datadir(pathlib.Path(__file__).parent / "data" / "quantify")
class Calibration():
def __init__(self, platform: Platform):
self.platform = platform
self.mc, self.pl, self.ins = utils.create_measurement_control('Calibration')
def load_settings(self):
# Load diagnostics settings
with open("calibration.yml", "r") as file:
return yaml.safe_load(file)
def run_resonator_spectroscopy(self):
platform = self.platform
platform.reload_settings()
mc = self.mc
ps = platform.settings['settings']
qc_pulse_shape = eval(ps['qc_spectroscopy_pulse'].popitem()[1])
qc_pulse_settings = ps['qc_spectroscopy_pulse']
qc_pulse = Pulse(**qc_pulse_settings, shape = qc_pulse_shape)
ro_pulse_shape = eval(ps['readout_pulse'].popitem()[1])
ro_pulse_settings = ps['readout_pulse']
ro_pulse = ReadoutPulse(**ro_pulse_settings, shape = ro_pulse_shape)
sequence = PulseSequence()
sequence.add(qc_pulse)
sequence.add(ro_pulse)
ds = self.load_settings()
self.pl.tuids_max_num(ds['max_num_plots'])
software_averages = ds['software_averages']
ds = ds['resonator_spectroscopy']
lowres_width = ds['lowres_width']
lowres_step = ds['lowres_step']
highres_width = ds['highres_width']
highres_step = ds['highres_step']
precision_width = ds['precision_width']
precision_step = ds['precision_step']
#Fast Sweep
scanrange = utils.variable_resolution_scanrange(lowres_width, lowres_step, highres_width, highres_step)
mc.settables(platform.LO_qrm.device.frequency)
mc.setpoints(scanrange + platform.LO_qrm.get_frequency())
mc.gettables(Gettable(ROController(platform, sequence)))
platform.start()
platform.LO_qcm.off()
dataset = mc.run("Resonator Spectroscopy Fast", soft_avg=1)
platform.stop()
platform.LO_qrm.set_frequency(dataset['x0'].values[dataset['y0'].argmax().values])
avg_min_voltage = np.mean(dataset['y0'].values[:(lowres_width//lowres_step)]) * 1e6
# Precision Sweep
scanrange = np.arange(-precision_width, precision_width, precision_step)
mc.settables(platform.LO_qrm.device.frequency)
mc.setpoints(scanrange + platform.LO_qrm.get_frequency())
mc.gettables(Gettable(ROController(platform, sequence)))
platform.start()
platform.LO_qcm.off()
dataset = mc.run("Resonator Spectroscopy Precision", soft_avg=software_averages)
platform.stop()
# Fitting
smooth_dataset = savgol_filter(dataset['y0'].values, 25, 2)
# resonator_freq = dataset['x0'].values[smooth_dataset.argmax()] + ro_pulse.frequency
max_ro_voltage = smooth_dataset.max() * 1e6
f0, BW, Q = fitting.lorentzian_fit("last", max, "Resonator_spectroscopy")
resonator_freq = (f0*1e9 + ro_pulse.frequency)
print(f"\nResonator Frequency = {resonator_freq}")
return resonator_freq, avg_min_voltage, max_ro_voltage, smooth_dataset, dataset
def run_qubit_spectroscopy(self):
platform = self.platform
platform.reload_settings()
mc = self.
|
7,814 | 223 | 4,425 |
qiboteam__qibolab
|
e4b0e8e6dd612e696a161da9972f4bb9b6bf8cd0
|
src/qibolab/calibration/calibration.py
|
Unknown
|
add
| true |
function
| 8 | 8 | false | false |
[
"add",
"pulses",
"phase",
"qcm_pulses",
"qrm_pulses",
"add_measurement",
"add_u3",
"time",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "add",
"type": "function"
},
{
"name": "add_measurement",
"type": "function"
},
{
"name": "add_u3",
"type": "function"
},
{
"name": "phase",
"type": "statement"
},
{
"name": "pulses",
"type": "statement"
},
{
"name": "qcm_pulses",
"type": "statement"
},
{
"name": "qrm_pulses",
"type": "statement"
},
{
"name": "time",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import pathlib
import numpy as np
#import matplotlib.pyplot as plt
import utils
import yaml
import fitting
from qibolab import Platform
# TODO: Have a look in the documentation of ``MeasurementControl``
#from quantify_core.measurement import MeasurementControl
from quantify_core.measurement.control import Gettable, Settable
from quantify_core.data.handling import set_datadir
from scipy.signal import savgol_filter
from qibolab.pulses import Pulse, ReadoutPulse
from qibolab.circuit import PulseSequence
from qibolab.pulse_shapes import Rectangular, Gaussian
# TODO: Check why this set_datadir is needed
#set_datadir(pathlib.Path("data") / "quantify")
set_datadir(pathlib.Path(__file__).parent / "data" / "quantify")
class Calibration():
def __init__(self, platform: Platform):
self.platform = platform
self.mc, self.pl, self.ins = utils.create_measurement_control('Calibration')
def load_settings(self):
# Load diagnostics settings
with open("calibration.yml", "r") as file:
return yaml.safe_load(file)
def run_resonator_spectroscopy(self):
platform = self.platform
platform.reload_settings()
mc = self.mc
ps = platform.settings['settings']
qc_pulse_shape = eval(ps['qc_spectroscopy_pulse'].popitem()[1])
qc_pulse_settings = ps['qc_spectroscopy_pulse']
qc_pulse = Pulse(**qc_pulse_settings, shape = qc_pulse_shape)
ro_pulse_shape = eval(ps['readout_pulse'].popitem()[1])
ro_pulse_settings = ps['readout_pulse']
ro_pulse = ReadoutPulse(**ro_pulse_settings, shape = ro_pulse_shape)
sequence = PulseSequence()
sequence.add(qc_pulse)
sequence.add(ro_pulse)
ds = self.load_settings()
self.pl.tuids_max_num(ds['max_num_plots'])
software_averages = ds['software_averages']
ds = ds['resonator_spectroscopy']
lowres_width = ds['lowres_width']
lowres_step = ds['lowres_step']
highres_width = ds['highres_width']
highres_step = ds['highres_step']
precision_width = ds['precision_width']
precision_step = ds['precision_step']
#Fast Sweep
scanrange = utils.variable_resolution_scanrange(lowres_width, lowres_step, highres_width, highres_step)
mc.settables(platform.LO_qrm.device.frequency)
mc.setpoints(scanrange + platform.LO_qrm.get_frequency())
mc.gettables(Gettable(ROController(platform, sequence)))
platform.start()
platform.LO_qcm.off()
dataset = mc.run("Resonator Spectroscopy Fast", soft_avg=1)
platform.stop()
platform.LO_qrm.set_frequency(dataset['x0'].values[dataset['y0'].argmax().values])
avg_min_voltage = np.mean(dataset['y0'].values[:(lowres_width//lowres_step)]) * 1e6
# Precision Sweep
scanrange = np.arange(-precision_width, precision_width, precision_step)
mc.settables(platform.LO_qrm.device.frequency)
mc.setpoints(scanrange + platform.LO_qrm.get_frequency())
mc.gettables(Gettable(ROController(platform, sequence)))
platform.start()
platform.LO_qcm.off()
dataset = mc.run("Resonator Spectroscopy Precision", soft_avg=software_averages)
platform.stop()
# Fitting
smooth_dataset = savgol_filter(dataset['y0'].values, 25, 2)
# resonator_freq = dataset['x0'].values[smooth_dataset.argmax()] + ro_pulse.frequency
max_ro_voltage = smooth_dataset.max() * 1e6
f0, BW, Q = fitting.lorentzian_fit("last", max, "Resonator_spectroscopy")
resonator_freq = (f0*1e9 + ro_pulse.frequency)
print(f"\nResonator Frequency = {resonator_freq}")
return resonator_freq, avg_min_voltage, max_ro_voltage, smooth_dataset, dataset
def run_qubit_spectroscopy(self):
platform = self.platform
platform.reload_settings()
mc = self.mc
ps = platform.settings['settings']
qc_pulse_shape = eval(ps['qc_spectroscopy_pulse'].popitem()[1])
qc_pulse_settings = ps['qc_spectroscopy_pulse']
qc_pulse = Pulse(**qc_pulse_settings, shape = qc_pulse_shape)
ro_pulse_shape = eval(ps['readout_pulse'].popitem()[1])
ro_pulse_settings = ps['readout_pulse']
ro_pulse = ReadoutPulse(**ro_pulse_settings, shape = ro_pulse_shape)
sequence = PulseSequence()
sequence.
|
7,815 | 223 | 4,456 |
qiboteam__qibolab
|
e4b0e8e6dd612e696a161da9972f4bb9b6bf8cd0
|
src/qibolab/calibration/calibration.py
|
Unknown
|
add
| true |
function
| 8 | 8 | false | false |
[
"add",
"pulses",
"phase",
"qcm_pulses",
"qrm_pulses",
"add_measurement",
"add_u3",
"time",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "add",
"type": "function"
},
{
"name": "add_measurement",
"type": "function"
},
{
"name": "add_u3",
"type": "function"
},
{
"name": "phase",
"type": "statement"
},
{
"name": "pulses",
"type": "statement"
},
{
"name": "qcm_pulses",
"type": "statement"
},
{
"name": "qrm_pulses",
"type": "statement"
},
{
"name": "time",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import pathlib
import numpy as np
#import matplotlib.pyplot as plt
import utils
import yaml
import fitting
from qibolab import Platform
# TODO: Have a look in the documentation of ``MeasurementControl``
#from quantify_core.measurement import MeasurementControl
from quantify_core.measurement.control import Gettable, Settable
from quantify_core.data.handling import set_datadir
from scipy.signal import savgol_filter
from qibolab.pulses import Pulse, ReadoutPulse
from qibolab.circuit import PulseSequence
from qibolab.pulse_shapes import Rectangular, Gaussian
# TODO: Check why this set_datadir is needed
#set_datadir(pathlib.Path("data") / "quantify")
set_datadir(pathlib.Path(__file__).parent / "data" / "quantify")
class Calibration():
def __init__(self, platform: Platform):
self.platform = platform
self.mc, self.pl, self.ins = utils.create_measurement_control('Calibration')
def load_settings(self):
# Load diagnostics settings
with open("calibration.yml", "r") as file:
return yaml.safe_load(file)
def run_resonator_spectroscopy(self):
platform = self.platform
platform.reload_settings()
mc = self.mc
ps = platform.settings['settings']
qc_pulse_shape = eval(ps['qc_spectroscopy_pulse'].popitem()[1])
qc_pulse_settings = ps['qc_spectroscopy_pulse']
qc_pulse = Pulse(**qc_pulse_settings, shape = qc_pulse_shape)
ro_pulse_shape = eval(ps['readout_pulse'].popitem()[1])
ro_pulse_settings = ps['readout_pulse']
ro_pulse = ReadoutPulse(**ro_pulse_settings, shape = ro_pulse_shape)
sequence = PulseSequence()
sequence.add(qc_pulse)
sequence.add(ro_pulse)
ds = self.load_settings()
self.pl.tuids_max_num(ds['max_num_plots'])
software_averages = ds['software_averages']
ds = ds['resonator_spectroscopy']
lowres_width = ds['lowres_width']
lowres_step = ds['lowres_step']
highres_width = ds['highres_width']
highres_step = ds['highres_step']
precision_width = ds['precision_width']
precision_step = ds['precision_step']
#Fast Sweep
scanrange = utils.variable_resolution_scanrange(lowres_width, lowres_step, highres_width, highres_step)
mc.settables(platform.LO_qrm.device.frequency)
mc.setpoints(scanrange + platform.LO_qrm.get_frequency())
mc.gettables(Gettable(ROController(platform, sequence)))
platform.start()
platform.LO_qcm.off()
dataset = mc.run("Resonator Spectroscopy Fast", soft_avg=1)
platform.stop()
platform.LO_qrm.set_frequency(dataset['x0'].values[dataset['y0'].argmax().values])
avg_min_voltage = np.mean(dataset['y0'].values[:(lowres_width//lowres_step)]) * 1e6
# Precision Sweep
scanrange = np.arange(-precision_width, precision_width, precision_step)
mc.settables(platform.LO_qrm.device.frequency)
mc.setpoints(scanrange + platform.LO_qrm.get_frequency())
mc.gettables(Gettable(ROController(platform, sequence)))
platform.start()
platform.LO_qcm.off()
dataset = mc.run("Resonator Spectroscopy Precision", soft_avg=software_averages)
platform.stop()
# Fitting
smooth_dataset = savgol_filter(dataset['y0'].values, 25, 2)
# resonator_freq = dataset['x0'].values[smooth_dataset.argmax()] + ro_pulse.frequency
max_ro_voltage = smooth_dataset.max() * 1e6
f0, BW, Q = fitting.lorentzian_fit("last", max, "Resonator_spectroscopy")
resonator_freq = (f0*1e9 + ro_pulse.frequency)
print(f"\nResonator Frequency = {resonator_freq}")
return resonator_freq, avg_min_voltage, max_ro_voltage, smooth_dataset, dataset
def run_qubit_spectroscopy(self):
platform = self.platform
platform.reload_settings()
mc = self.mc
ps = platform.settings['settings']
qc_pulse_shape = eval(ps['qc_spectroscopy_pulse'].popitem()[1])
qc_pulse_settings = ps['qc_spectroscopy_pulse']
qc_pulse = Pulse(**qc_pulse_settings, shape = qc_pulse_shape)
ro_pulse_shape = eval(ps['readout_pulse'].popitem()[1])
ro_pulse_settings = ps['readout_pulse']
ro_pulse = ReadoutPulse(**ro_pulse_settings, shape = ro_pulse_shape)
sequence = PulseSequence()
sequence.add(qc_pulse)
sequence.
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.