id
int64 | number
int64 | title
string | state
string | created_at
timestamp[s] | updated_at
timestamp[s] | closed_at
timestamp[s] | html_url
string | is_pull_request
bool | pull_request_url
string | pull_request_html_url
string | user_login
string | comments_count
int64 | body
string | labels
list | reactions_plus1
int64 | reactions_minus1
int64 | reactions_laugh
int64 | reactions_hooray
int64 | reactions_confused
int64 | reactions_heart
int64 | reactions_rocket
int64 | reactions_eyes
int64 | comments
list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2,119,827,045
| 57,272
|
PERF: reindex default fill_value dtype
|
closed
| 2024-02-06T02:45:31
| 2024-02-09T21:07:25
| 2024-02-09T21:06:00
|
https://github.com/pandas-dev/pandas/pull/57272
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57272
|
https://github.com/pandas-dev/pandas/pull/57272
|
mroeschke
| 1
|
- [ ] closes #45857 (Replace xxxx with the GitHub issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
|
[
"Missing-data",
"Dtype Conversions"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"thx @mroeschke "
] |
2,119,658,132
| 57,271
|
Backport PR #57265 on branch 2.2.x (COMPAT: Numpy 2.0 casting compat)
|
closed
| 2024-02-05T23:31:53
| 2024-02-06T02:28:17
| 2024-02-06T02:28:17
|
https://github.com/pandas-dev/pandas/pull/57271
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57271
|
https://github.com/pandas-dev/pandas/pull/57271
|
meeseeksmachine
| 0
|
Backport PR #57265: COMPAT: Numpy 2.0 casting compat
|
[
"Compat"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[] |
2,119,654,694
| 57,270
|
CoW: Remove cow branches from more tests
|
closed
| 2024-02-05T23:28:32
| 2024-02-06T10:15:28
| 2024-02-06T02:29:12
|
https://github.com/pandas-dev/pandas/pull/57270
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57270
|
https://github.com/pandas-dev/pandas/pull/57270
|
phofl
| 1
|
- [ ] closes #xxxx (Replace xxxx with the GitHub issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
|
[
"Copy / view semantics"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @phofl "
] |
2,119,653,927
| 57,269
|
CoW: Finish deprecation enforcal on block level
|
closed
| 2024-02-05T23:27:50
| 2024-02-09T22:07:17
| 2024-02-09T22:07:10
|
https://github.com/pandas-dev/pandas/pull/57269
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57269
|
https://github.com/pandas-dev/pandas/pull/57269
|
phofl
| 1
|
- [ ] closes #xxxx (Replace xxxx with the GitHub issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
|
[
"Copy / view semantics"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @phofl "
] |
2,119,521,723
| 57,268
|
ENH: Preserve input `start`/`end` type in `interval_range`
|
closed
| 2024-02-05T21:53:16
| 2024-02-15T17:35:08
| 2024-02-15T17:35:07
|
https://github.com/pandas-dev/pandas/issues/57268
| true
| null | null |
mroeschke
| 1
|
```python
In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: pd.interval_range(start=np.int8(1), end=np.int8(10))
Out[3]:
IntervalIndex([(1, 2], (2, 3], (3, 4], (4, 5], (5, 6], (6, 7], (7, 8], (8, 9],
(9, 10]],
dtype='interval[int64, right]')
```
It would be ideal if the resulting dtype was `'interval[int8, right]'`
|
[
"Enhancement",
"Dtype Conversions",
"Interval"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"In the interval.py, I made it retain the same datatype if both start and end has same datatype\r\n\r\n```python\r\ndtype = start.dtype if start.dtype == end.dtype else np.dtype(\"int64\")\r\nbreaks = maybe_downcast_numeric(\r\n breaks, # type: ignore[arg-type]\r\n dtype,\r\n)\r\nreturn IntervalIndex.from_breaks(breaks, name=name, closed=closed, dtype=IntervalDtype(subtype=dtype, closed=closed))\r\n```\r\nAnd I am getting the desired results\r\n\r\n\r\n"
] |
2,119,392,019
| 57,267
|
BUG: Series attrs disappear after modifcation to the DataFrame that contains them.
|
open
| 2024-02-05T20:21:47
| 2024-03-08T23:04:48
| null |
https://github.com/pandas-dev/pandas/issues/57267
| true
| null | null |
lendres
| 3
|
### Pandas version checks
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this bug exists on the [main branch](https://pandas.pydata.org/docs/dev/getting_started/install.html#installing-the-development-version-of-pandas) of pandas.
### Reproducible Example
```python
import pandas as pd
# Example 1 - changing the data type.
df = pd.DataFrame(dict(column1=[1,2,3,4], column2=[1,2,3,4]))
df["column1"].attrs['name'] = 'hello'
print("\nExample 1 before:")
print(df)
print("Column1 attributes:", df["column1"].attrs)
# Changing the data type of the DataFrame drops the series attributes.
df1 = df.astype(float)
print("\nAfter type change:")
print(df1)
print("Column1 attributes:", df1["column1"].attrs)
# Example 2 - assignment to a DataFrame.
df = pd.DataFrame(dict(column1=[1,2,3,4], column2=[1,2,3,4]))
df["column1"].attrs['name'] = 'hello1'
df["column2"].attrs['name'] = 'hello2'
print("\n\n\nExample 2 before:")
print(df)
print("Column1 attributes:", df["column1"].attrs)
print("Column2 attributes:", df["column2"].attrs)
# Assigning a column to the DataFrame drops the attributes from the Series in DataFrame.
df["column1"] = df["column1"] + 5
print("\nAfter assignment to DataFrame:")
print(df)
print("Column1 attributes:", df["column1"].attrs)
print("Column2 attributes:", df["column2"].attrs)
```
### Issue Description
# Issue
The attrs dictionary of Series are not carried through when operations are performed on a DataFrame.
# Example 1
```python
# Example 1 - changing the data type.
df = pd.DataFrame(dict(column1=[1,2,3,4], column2=[1,2,3,4]))
df["column1"].attrs['name'] = 'hello'
print("\nExample 1 before:")
print(df)
print("Column1 attributes:", df["column1"].attrs)
"""
OUTUPUT:
Example 1 before:
column1 column2
0 1 1
1 2 2
2 3 3
3 4 4
Column1 attributes: {'name': 'hello'}
"""
```
The first series has an attr stored. However, after changing the data types, the attribute is lost.
```python
# Changing the data type of the DataFrame drops the series attributes.
df1 = df.astype(float)
print("\nAfter type change:")
print(df1)
print("Column1 attributes:", df1["column1"].attrs)
"""
OUTUPUT:
After type change:
column1 column2
0 1.0 1.0
1 2.0 2.0
2 3.0 3.0
3 4.0 4.0
Column1 attributes: {} <-- NO ATTRS
"""
```
# Example 2
```python
# Example 2 - assignment to a DataFrame.
df = pd.DataFrame(dict(column1=[1,2,3,4], column2=[1,2,3,4]))
df["column1"].attrs['name'] = 'hello1'
df["column2"].attrs['name'] = 'hello2'
print("\n\n\nExample 2 before:")
print(df)
print("Column1 attributes:", df["column1"].attrs)
print("Column2 attributes:", df["column2"].attrs)
"""
OUTPUT:
Example 2 before:
column1 column2
0 1 1
1 2 2
2 3 3
3 4 4
Column1 attributes: {'name': 'hello1'}
Column2 attributes: {'name': 'hello2'}
"""
```
The first series has an attr stored. However, reassigning a column drops the attributes.
```python
# Assigning a column to the DataFrame drops the attributes from the Series in DataFrame.
df["column1"] = df["column1"] + 5
print("\nAfter assignment to DataFrame:")
print(df)
print("Column1 attributes:", df["column1"].attrs)
print("Column2 attributes:", df["column2"].attrs)
"""
OUTPUT:
After assignment to DataFrame:
column1 column2
0 6 1
1 7 2
2 8 3
3 9 4
Column1 attributes: {} <-- NO ATTRS
Column2 attributes: {} <-- NO ATTRS
"""
```
### Expected Behavior
The attributes (attrs) should be maintained in the DataFrame when operations are performed on it.
Tested on 2.1.1 and 2.2.0
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : f538741432edf55c6b9fb5d0d496d2dd1d7c2457
python : 3.11.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19044
machine : AMD64
processor : Intel64 Family 6 Model 165 Stepping 2, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en
LOCALE : English_United States.1252
pandas : 2.2.0
numpy : 1.26.3
pytz : 2024.1
dateutil : 2.8.2
setuptools : 68.0.0
pip : 23.2.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.9.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
IPython : 8.15.0
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.12.2
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : 3.7.1
numba : None
numexpr : 2.8.4
odfpy : None
openpyxl : 3.1.2
pandas_gbq : None
pyarrow : None
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.11.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.4
qtpy : None
pyqt5 : None
</details>
|
[
"Bug",
"metadata"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks for the report; attr propagation is only partially implemented currently. PRs to fix are welcome!",
"take",
"On main this type of Series.attr propagation is basically nonexistent. Practically, only DataFrame.attrs are accessible and get attached to the Series when the Series representation of a column is retrieved.\r\n\r\nI would try to implement a solution that introduces hidden column_attrs to the DataFrame that would support this type of Series.attrs propagation. Solutions that only make use of the DataFrame.attrs by design either lack propagation functionality or lead to confusing behaviour."
] |
2,119,352,918
| 57,266
|
DOC: fix pr02 errors in docstrings - plot.bar, plot.barh, plot.line, plot.pie
|
closed
| 2024-02-05T19:55:15
| 2024-02-07T18:12:40
| 2024-02-07T18:12:33
|
https://github.com/pandas-dev/pandas/pull/57266
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57266
|
https://github.com/pandas-dev/pandas/pull/57266
|
nkasing
| 1
|
Methods for `pandas.Series.plot.bar` and `pandas.DataFrame.plot.bar` have the same underlying usage of `PlotAccessor`.
### validate_docstrings script output
1a. scripts/validate_docstrings.py --format=actions --errors=PR02 pandas.Series.plot.bar
```
################################################################################
################################## Validation ##################################
################################################################################
Docstring for "pandas.Series.plot.bar" correct. :)
```
1b. scripts/validate_docstrings.py --format=actions --errors=PR02 pandas.DataFrame.plot.bar
```
################################################################################
################################## Validation ##################################
################################################################################
Docstring for "pandas.DataFrame.plot.bar" correct. :)
```
2a. scripts/validate_docstrings.py --format=actions --errors=PR02 pandas.Series.plot.barh
```
################################################################################
################################## Validation ##################################
################################################################################
Docstring for "pandas.Series.plot.barh" correct. :)
```
2b. scripts/validate_docstrings.py --format=actions --errors=PR02 pandas.DataFrame.plot.barh
```
################################################################################
################################## Validation ##################################
################################################################################
Docstring for "pandas.DataFrame.plot.barh" correct. :)
```
3a. scripts/validate_docstrings.py --format=actions --errors=PR02 pandas.Series.plot.line
```
################################################################################
################################## Validation ##################################
################################################################################
Docstring for "pandas.Series.plot.line" correct. :)
```
3b. scripts/validate_docstrings.py --format=actions --errors=PR02 pandas.DataFrame.plot.line
```
################################################################################
################################## Validation ##################################
################################################################################
Docstring for "pandas.DataFrame.plot.line" correct. :)
```
4a. scripts/validate_docstrings.py --format=actions --errors=PR02 pandas.Series.plot.pie
```
################################################################################
################################## Validation ##################################
################################################################################
Docstring for "pandas.Series.plot.pie" correct. :)
4b. scripts/validate_docstrings.py --format=actions --errors=PR02 pandas.DataFrame.plot.pie
```
################################################################################
################################## Validation ##################################
################################################################################
Docstring for "pandas.DataFrame.plot.pie" correct. :)
- [x] cross ref #57111 (Replace xxxx with the GitHub issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
|
[
"Docs",
"Visualization"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @noah-asing "
] |
2,119,197,960
| 57,265
|
COMPAT: Numpy 2.0 casting compat
|
closed
| 2024-02-05T18:24:56
| 2024-02-06T02:26:36
| 2024-02-05T23:31:27
|
https://github.com/pandas-dev/pandas/pull/57265
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57265
|
https://github.com/pandas-dev/pandas/pull/57265
|
mroeschke
| 1
| null |
[
"Compat"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"thx"
] |
2,119,139,804
| 57,264
|
BUG: pandas.tseries.offsets.Second() division
|
open
| 2024-02-05T17:56:03
| 2025-06-02T17:09:47
| null |
https://github.com/pandas-dev/pandas/issues/57264
| true
| null | null |
famenzel
| 2
|
### Pandas version checks
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this bug exists on the [main branch](https://pandas.pydata.org/docs/dev/getting_started/install.html#installing-the-development-version-of-pandas) of pandas.
### Reproducible Example
```python
import pandas.tseries.offsets as tso
a = tso.Second()/10
b = tso.Second()*.1
```
### Issue Description
In contrast to tso.Minute(), tso.Day(), division with tso.Second() with any integer always results in tso.Day(0) (a). This is not the case with the corresponding multiplication (b). In previous pandas versions this did not happen.
### Expected Behavior
tso.Second()/10 should return tso.Milli(100) and not tso.Day(0).
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : f538741432edf55c6b9fb5d0d496d2dd1d7c2457
python : 3.10.11.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.22621
machine : AMD64
processor : Intel64 Family 6 Model 154 Stepping 4, GenuineIntel
byteorder : little
LC_ALL : None
LANG : de_DE.UTF-8
LOCALE : de_DE.cp1252
pandas : 2.2.0
numpy : 1.25.2
pytz : 2023.3.post1
dateutil : 2.8.2
setuptools : 65.5.0
pip : 23.0.1
Cython : None
pytest : 7.4.0
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
IPython : 8.14.0
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : 3.7.3
numba : 0.58.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.11.3
sqlalchemy : 2.0.19
tables : None
tabulate : 0.9.0
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.3
qtpy : None
pyqt5 : None
</details>
|
[
"Bug",
"Regression",
"Frequency"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks for the report. Result of a git bisect:\r\n\r\n```\r\ncommit 8b8f0d04795e80a9298688e55bb4164b597392bc\r\nAuthor: jbrockmendel\r\nDate: Tue Dec 19 15:35:22 2023 -0800\r\n\r\n DEPR: Tick.delta (#56558)\r\n```\r\n\r\ncc @jbrockmendel ",
"Looks like the difference is with the ``_value`` attr on Timedelta\r\n\r\n```\r\n>>> pd.Timedelta(a)._value\r\n1\r\n>>> a.delta._value\r\n<stdin>:1: FutureWarning: Second.delta is deprecated and will be removed in a future version. Use pd.Timedelta(obj) instead\r\n1000000000\r\n\r\n```\r\n\r\nTaking a look"
] |
2,118,959,453
| 57,263
|
[pre-commit.ci] pre-commit autoupdate
|
closed
| 2024-02-05T16:29:57
| 2024-02-06T02:46:51
| 2024-02-06T02:46:48
|
https://github.com/pandas-dev/pandas/pull/57263
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57263
|
https://github.com/pandas-dev/pandas/pull/57263
|
pre-commit-ci[bot]
| 1
|
<!--pre-commit.ci start-->
updates:
- [github.com/astral-sh/ruff-pre-commit: v0.1.6 → v0.2.0](https://github.com/astral-sh/ruff-pre-commit/compare/v0.1.6...v0.2.0)
- [github.com/pylint-dev/pylint: v3.0.1 → v3.0.3](https://github.com/pylint-dev/pylint/compare/v3.0.1...v3.0.3)
- [github.com/PyCQA/isort: 5.12.0 → 5.13.2](https://github.com/PyCQA/isort/compare/5.12.0...5.13.2)
<!--pre-commit.ci end-->
|
[] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Since https://github.com/pandas-dev/pandas/pull/57182 is bumping ruff and the other updates seem minor, closing in favor of that PR"
] |
2,118,015,266
| 57,262
|
BUG: Importing `Series` with latest `pytest` and Python 3.12 generates DeprecationWarning
|
closed
| 2024-02-05T09:04:29
| 2024-02-05T15:29:14
| 2024-02-05T15:29:13
|
https://github.com/pandas-dev/pandas/issues/57262
| true
| null | null |
adamnsch
| 2
|
### Pandas version checks
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this bug exists on the [main branch](https://pandas.pydata.org/docs/dev/getting_started/install.html#installing-the-development-version-of-pandas) of pandas.
### Reproducible Example
Create a file:
```python
from pandas import Series
```
Then on the cmd line run:
```sh
python3.12 -m pytest <path-to-file>
```
### Issue Description
Running the command above generates the following output for me:
```sh
======================================= warnings summary ========================================
../../.pyenv/versions/3.12.0/lib/python3.12/site-packages/dateutil/tz/tz.py:37
XXXXXXX/versions/3.12.0/lib/python3.12/site-packages/dateutil/tz/tz.py:37: DeprecationWarning: datetime.datetime.utcfromtimestamp() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.fromtimestamp(timestamp, datetime.UTC).
EPOCH = datetime.datetime.utcfromtimestamp(0)
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
====================================== 1 warning in 0.40s =======================================
```
### Expected Behavior
No DeprecationWarnining
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : fd3f57170aa1af588ba877e8e28c158a20a4886d
python : 3.12.0.final.0
python-bits : 64
OS : Darwin
OS-release : 23.3.0
Version : Darwin Kernel Version 23.3.0: Wed Dec 20 21:31:00 PST 2023; root:xnu-10002.81.5~7/RELEASE_ARM64_T6020
machine : arm64
processor : arm
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 2.2.0
numpy : 1.26.3
pytz : 2023.3.post1
dateutil : 2.8.2
setuptools : None
pip : 23.2.1
Cython : None
pytest : 7.4.4
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 14.0.2
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.4
qtpy : None
pyqt5 : None
</details>
|
[
"Warnings",
"Closing Candidate",
"Upstream issue"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
[
"This looks like an issue with dateutil.\r\n\r\nxref https://github.com/dateutil/dateutil/issues/1314.",
"> This looks like an issue with dateutil.\r\n> \r\n> xref [dateutil/dateutil#1314](https://github.com/dateutil/dateutil/issues/1314).\r\n\r\nAh ok 👍 Closing this then"
] |
2,117,997,374
| 57,261
|
DOC: fix PR02 errors in docstrings of Index subclasses
|
closed
| 2024-02-05T08:55:52
| 2024-02-06T03:27:31
| 2024-02-05T17:24:55
|
https://github.com/pandas-dev/pandas/pull/57261
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57261
|
https://github.com/pandas-dev/pandas/pull/57261
|
yuanx749
| 2
|
To fix PR02 errors, this PR adds `__signature__` to the methods, so that the docs show the correct signature instead of `*args, **kwargs`.
- [x] xref #57111(Replace xxxx with the GitHub issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
|
[
"Docs"
] | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
|
[
"Thanks @yuanx749 ",
"Awesome!! Thank you "
] |
2,117,960,265
| 57,260
|
QST: The concept behind how hashes are combined in pandas?
|
open
| 2024-02-05T08:35:11
| 2024-02-05T22:07:00
| null |
https://github.com/pandas-dev/pandas/issues/57260
| true
| null | null |
phphuc612
| 2
|
### Research
- [X] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [X] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
### Link to question on StackOverflow
https://stackoverflow.com/questions/77784124/the-concept-behind-how-hashes-are-combined-in-pandas
### Question about pandas
I came across the `combine_hash_arrays` functions in pandas library [here](https://github.com/pandas-dev/pandas/blob/main/pandas/core/util/hashing.py). How does it keep good collision rate after combination ? Does anyone have the mathematical proof for this code ?
The below code is retrieved from pandas implementation.
```
def combine_hash_arrays(
arrays: Iterator[np.ndarray], num_items: int
) -> npt.NDArray[np.uint64]:
"""
Parameters
----------
arrays : Iterator[np.ndarray]
num_items : int
Returns
-------
np.ndarray[uint64]
Should be the same as CPython's tupleobject.c
"""
try:
first = next(arrays)
except StopIteration:
return np.array([], dtype=np.uint64)
arrays = itertools.chain([first], arrays)
mult = np.uint64(1000003)
out = np.zeros_like(first) + np.uint64(0x345678)
last_i = 0
for i, a in enumerate(arrays):
inverse_i = num_items - i
out ^= a
out *= mult
mult += np.uint64(82520 + inverse_i + inverse_i)
last_i = i
assert last_i + 1 == num_items, "Fed in wrong num_items"
out += np.uint64(97531)
return out
```
I have already checked whether they work well by checking their combined hashes' collision rate.
|
[
"Usage Question",
"hashing"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"#15227 indicates it was taken from https://github.com/python/cpython/blob/main/Objects/tupleobject.c; perhaps researching how CPython does this may be fruitful. I'd recommend searching the Python dev mailing list.",
"Here is a more specific link: https://github.com/python/cpython/blob/c32bae52904723d99e1f98e2ef54570268d86467/Objects/tupleobject.c#L291"
] |
2,117,910,542
| 57,259
|
DOC: `CategoricalDtype` equality semantics aren't completely described
|
closed
| 2024-02-05T08:03:57
| 2024-02-17T12:25:58
| 2024-02-17T12:25:58
|
https://github.com/pandas-dev/pandas/issues/57259
| true
| null | null |
VladimirFokow
| 12
|
### Pandas version checks
- [X] I have checked that the issue still exists on the latest versions of the docs on `main` [here](https://pandas.pydata.org/docs/dev/)
### Location of the documentation
https://pandas.pydata.org/docs/user_guide/categorical.html#equality-semantics
https://github.com/pandas-dev/pandas/blob/main/pandas/core/dtypes/dtypes.py#L407
### Documentation problem
Problematic [statement 1](https://pandas.pydata.org/docs/user_guide/categorical.html#equality-semantics
):
> Two instances of `CategoricalDtype` compare equal whenever they have the same categories and order.
Problematic [statement 2](https://github.com/pandas-dev/pandas/blob/main/pandas/core/dtypes/dtypes.py#L407):
> 5) A CDT with ordered={False, None} is only equal to another CDT with
ordered={False, None} and identical categories.
Counter-example:
```py
>>> a = pd.Categorical(np.full(2, np.nan, dtype=object))
>>> b = pd.Categorical(np.full(2, np.nan))
>>> a, b
([NaN, NaN]
Categories (0, object): [],
[NaN, NaN]
Categories (0, float64): [])
>>> a.dtype, b.dtype
(CategoricalDtype(categories=[], ordered=False, categories_dtype=object),
CategoricalDtype(categories=[], ordered=False, categories_dtype=float64))
>>> a.dtype == b.dtype
False
```
As we can see, they both have `ordered=False`, and their `categories` are same.
Following the documentation, they should be equal.
### Suggested fix for documentation
to have accurate and exhaustive descriptions
|
[
"Docs",
"Categorical",
"good first issue"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks for the report.\r\n\r\n> As we can see, they both have ordered=False, and their categories are same.\r\n\r\nThough both empty, two arrays of different dtypes are not the same.\r\n\r\nI suppose \"including their dtypes\" would be okay to add.",
"take",
"thanks @rhshadrach !\r\nI don't think this is a good first issue, because someone with an accurate and full conceptual understanding of `Categorical` and `CategoricalDtype` should help edit these descriptions.\r\nI expand on this in [PR #57273](https://github.com/pandas-dev/pandas/pull/57273#issuecomment-1929049214)\r\n(but on the other hand, maybe it is good - for people to figure this out and then write their findings)",
"\r\n#57259",
"@Mehnaz02 \r\ndtype in `b` in my example is `float64`\r\n(the whole point is `a` and `b` having different `dtype`s in their `CategoricalDtype`s):\r\n```py\r\n# when\r\na.categories.dtype\r\n# is different from:\r\nb.categories.dtype\r\n```\r\n\r\n(btw, in the latest pandas version `a.dtype` also prints the `categories_dtype` at the end)",
"I can try starting to fix it, but definitely much later.. \r\nand would need a technical check for accuracy.",
"> I don't think this is a good first issue, because someone with an accurate and full conceptual understanding of `Categorical` and `CategoricalDtype` should help edit these descriptions.\r\n\r\nI disagree with this assessment. I believe one just needs to modify the documentation to state that the categories must be the equal as indexes, and that in particular includes their dtype. Am I missing something?\r\n\r\nIn addition, all PRs are reviewed, and so help can be given as part of that review.",
"> all PRs are reviewed, and so help can be given as part of that review.\r\n\r\nAs a regular contributor with little experience in the pandas community, I've observed that the label 'good first issue' extends beyond mere simplicity in solving a problem. Typically, it's assigned to documentation-related tasks, serving as a helpful starting point for newcomers eager to engage with the community. \r\n\r\nHowever, this doesn't diminish the significance of documentation enhancements in the community's eyes. Every pull request, regardless of its nature, undergoes review and suggestions. Nevertheless, complex documentation improvements might demand more effort from reviewers, particularly as they often originate from contributors unfamiliar with the pandas community.\r\n\r\n> I believe one just needs to modify the documentation to state that the categories must be the equal as indexes, and that in particular includes their dtype.\r\n\r\nIn issues #57273 and #57281, I believe @VladimirFokow intended to convey that not only this specific aspect of the documentation, but the documentation as a whole, seems somewhat perplexing to him.",
"thank you so much @luke396 !!\r\nOkay, help on the review sounds good👍",
"Hey, I hope you're doing well team.\r\nLooking for an interesting issue to contribute, any reco please?\r\n",
"Hello @eaedk !\r\nHow about this one?",
"Hello @VladimirFokow, oh I thought it was solved. \r\nI would like to, of course I will ask questions for guidance."
] |
2,117,835,719
| 57,258
|
BUG: CategoricalDtype.__init__() got an unexpected keyword argument 'categories_dtype'
|
closed
| 2024-02-05T07:12:58
| 2024-02-05T10:10:05
| 2024-02-05T10:10:05
|
https://github.com/pandas-dev/pandas/issues/57258
| true
| null | null |
VladimirFokow
| 1
|
### Pandas version checks
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the [main branch](https://pandas.pydata.org/docs/dev/getting_started/install.html#installing-the-development-version-of-pandas) of pandas.
### Reproducible Example
```python
from pandas.api.types import CategoricalDtype
CategoricalDtype(categories=['a', 'b', 'c'], ordered=False, categories_dtype=object)
```
### Issue Description
[Example from the docs](https://pandas.pydata.org/docs/user_guide/categorical.html#categoricaldtype) produces an error:
```
TypeError: CategoricalDtype.__init__() got an unexpected keyword argument 'categories_dtype'
```
there is no `categories_dtype` argument 🫤: https://github.com/pandas-dev/pandas/blob/main/pandas/core/dtypes/dtypes.py#L218
### Expected Behavior
doc examples should work
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : fd3f57170aa1af588ba877e8e28c158a20a4886d
python : 3.11.6.final.0
python-bits : 64
OS : Darwin
OS-release : 23.2.0
Version : Darwin Kernel Version 23.2.0: Wed Nov 15 21:53:18 PST 2023; root:xnu-10002.61.3~2/RELEASE_ARM64_T6000
machine : arm64
processor : arm
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 2.2.0
numpy : 1.26.3
pytz : 2024.1
dateutil : 2.8.2
setuptools : 69.0.3
pip : 23.3.2
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 8.21.0
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.4
qtpy : None
pyqt5 : None
</details>
|
[
"Bug",
"Needs Triage"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"my bad, this code is the output, not the input"
] |
2,117,537,179
| 57,257
|
error_bad_lines not work, but how do I read data successfully?
|
closed
| 2024-02-05T02:29:38
| 2024-05-19T18:36:05
| 2024-05-19T18:36:04
|
https://github.com/pandas-dev/pandas/issues/57257
| true
| null | null |
zysNLP
| 4
|
Since I use panda as version=2.2 I found "error_bad_lines" para was dropped, but I use pd.read_csv("unknown.csv"), Got an Error:
Traceback (most recent call last):
File "D:\work\email_reply\data_process.py", line 11, in <module>
df = pd.read_csv('./data/data_0101.csv', on_bad_lines="warn")
File "D:\miniconda3\envs\py310\lib\site-packages\pandas\io\parsers\readers.py", line 1024, in read_csv
return _read(filepath_or_buffer, kwds)
File "D:\miniconda3\envs\py310\lib\site-packages\pandas\io\parsers\readers.py", line 624, in _read
return parser.read(nrows)
File "D:\miniconda3\envs\py310\lib\site-packages\pandas\io\parsers\readers.py", line 1921, in read
) = self._engine.read( # type: ignore[attr-defined]
File "D:\miniconda3\envs\py310\lib\site-packages\pandas\io\parsers\c_parser_wrapper.py", line 234, in read
chunks = self._reader.read_low_memory(nrows)
File "parsers.pyx", line 838, in pandas._libs.parsers.TextReader.read_low_memory
File "parsers.pyx", line 905, in pandas._libs.parsers.TextReader._read_rows
File "parsers.pyx", line 874, in pandas._libs.parsers.TextReader._tokenize_rows
File "parsers.pyx", line 891, in pandas._libs.parsers.TextReader._check_tokenize_status
File "parsers.pyx", line 2061, in pandas._libs.parsers.raise_parser_error
pandas.errors.ParserError: Error tokenizing data. C error: Buffer overflow caught - possible malformed input file.
So how could I read this data sucessfully now ? If there is a better way to deal with this?
|
[
"Usage Question",
"IO CSV",
"Needs Info",
"Closing Candidate"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"You can look into the ``on_bad_lines`` parameter.",
"I'm seeing the identical error [nearly to the same line numbers] on pip 2.2.0. I tried updating to 2.2.1 before reading this (obviously didn't help). Has anyone found a solution??",
"Can you provide a reproducible example?",
"Closing as a usage question and no reproducible example to act on"
] |
2,117,400,649
| 57,256
|
TYP: misc Index return types
|
closed
| 2024-02-05T00:07:29
| 2024-02-10T20:34:29
| 2024-02-05T23:14:29
|
https://github.com/pandas-dev/pandas/pull/57256
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57256
|
https://github.com/pandas-dev/pandas/pull/57256
|
twoertwein
| 2
| null |
[
"Typing"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"> Looks good just a linting failure\r\n\r\nThanks, I didn't notice that! I put IndexT on the ignore list for codespell.",
"Thanks @twoertwein "
] |
2,117,394,916
| 57,255
|
CoW: Remove copy-on-write conditions from frame tests
|
closed
| 2024-02-04T23:56:23
| 2024-02-05T09:06:40
| 2024-02-05T02:01:35
|
https://github.com/pandas-dev/pandas/pull/57255
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57255
|
https://github.com/pandas-dev/pandas/pull/57255
|
phofl
| 1
|
Getting started here as well
|
[
"Copy / view semantics"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @phofl "
] |
2,117,388,529
| 57,254
|
CoW: Enforce some deprecations on the datafame level
|
closed
| 2024-02-04T23:44:11
| 2024-02-09T22:01:37
| 2024-02-09T22:01:01
|
https://github.com/pandas-dev/pandas/pull/57254
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57254
|
https://github.com/pandas-dev/pandas/pull/57254
|
phofl
| 2
|
- [ ] closes #xxxx (Replace xxxx with the GitHub issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
Got most usages on the frame/series level
|
[
"Copy / view semantics"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"this should take care of the failures",
"Thanks @phofl "
] |
2,117,374,328
| 57,253
|
CoW: Enforce some deprecations on the block level
|
closed
| 2024-02-04T23:25:18
| 2024-02-05T09:06:33
| 2024-02-05T02:00:07
|
https://github.com/pandas-dev/pandas/pull/57253
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57253
|
https://github.com/pandas-dev/pandas/pull/57253
|
phofl
| 1
|
This starts ripping the branches out of blocks.py, remaining ones will follow in another pr
|
[
"Copy / view semantics"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @phofl "
] |
2,117,302,922
| 57,252
|
PERF: RangeIndex.append with the same index
|
closed
| 2024-02-04T21:02:50
| 2024-02-09T21:31:13
| 2024-02-09T21:07:41
|
https://github.com/pandas-dev/pandas/pull/57252
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57252
|
https://github.com/pandas-dev/pandas/pull/57252
|
mroeschke
| 1
|
```python
In [1]: import pandas as pd, numpy as np
In [2]: idx = pd.RangeIndex(1000)
In [3]: %timeit idx.append([idx] * 100_000)
362 ms ± 4.39 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) # main
342 ms ± 4.07 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) # pr
```
|
[
"Performance",
"Index"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"thx @mroeschke "
] |
2,117,134,898
| 57,251
|
DOC: DeprecationWarning:
|
closed
| 2024-02-04T14:45:33
| 2024-02-04T14:48:48
| 2024-02-04T14:48:20
|
https://github.com/pandas-dev/pandas/issues/57251
| true
| null | null |
goldenage75
| 1
|
### Pandas version checks
- [X] I have checked that the issue still exists on the latest versions of the docs on `main` [here](https://pandas.pydata.org/docs/dev/)
### Location of the documentation
C:\Users\Lord\AppData\Local\Temp\ipykernel_10296\2035458946.py:1: DeprecationWarning
### Documentation problem
C:\Users\Lord\AppData\Local\Temp\ipykernel_10296\2035458946.py:1: DeprecationWarning:
Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),
(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)
but was not found to be installed on your system.
If this would cause problems for you
### Suggested fix for documentation
DeprecationWarning:
|
[
"Duplicate Report"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @goldenage75 for the report. Please report in #54466. closing as duplicate"
] |
2,117,112,223
| 57,250
|
REGR/DOC: pd.concat should special case DatetimeIndex to sort even when sort=False
|
closed
| 2024-02-04T14:06:07
| 2024-02-10T09:51:19
| 2024-02-09T22:07:01
|
https://github.com/pandas-dev/pandas/pull/57250
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57250
|
https://github.com/pandas-dev/pandas/pull/57250
|
lukemanley
| 3
|
- [x] closes #57006
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [x] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [x] Added an entry in the latest `doc/source/whatsnew/v2.2.1.rst` file if fixing a bug or adding a new feature.
Reverts the changes from #54769 due to long-standing behavior and documents the behavior instead.
See discussion regarding the inconsistency of the long-standing behavior here: https://github.com/pandas-dev/pandas/issues/57006#issuecomment-1913562404
|
[
"Docs",
"Reshaping",
"Regression"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"thx @lukemanley ",
"IMO I think this case should still be deprecated. Carve-outs like this ultimately lead to confusion",
"> IMO I think this case should still be deprecated. Carve-outs like this ultimately lead to confusion\r\n\r\nI opened https://github.com/pandas-dev/pandas/issues/57335 for discussion."
] |
2,117,105,605
| 57,249
|
Update type hint in io/formats/format.py
|
closed
| 2024-02-04T13:49:24
| 2024-02-04T16:07:31
| 2024-02-04T16:05:20
|
https://github.com/pandas-dev/pandas/pull/57249
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57249
|
https://github.com/pandas-dev/pandas/pull/57249
|
MarkBekooy
| 2
|
The brackets around the type hint broke the libcst parser. Since the brackets are not necessary for the type hint, they have been removed
- [x] closes #57248
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [x] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
|
[
"Typing"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"@twoertwein i've milestoned 3.0 since we normally don't backport typing changes and the problem code has not been modified recently but if the superfluous parenthesis breaks things feel feel to change to 2.2.1 as it would probably do no harm.",
"Thank you @FrostMegaByte!\r\n\r\nI'm with you, @simonjayhawkins, on not backporting (it doesn't impact major typecheckers and seems more like a libcst issue), but I have no strong opinion."
] |
2,117,103,075
| 57,248
|
BUG: breaking type annotation of `format_percentiles` function in `io/formats/format.py`
|
closed
| 2024-02-04T13:42:42
| 2024-02-04T16:05:21
| 2024-02-04T16:05:21
|
https://github.com/pandas-dev/pandas/issues/57248
| true
| null | null |
MarkBekooy
| 1
|
### Pandas version checks
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the [main branch](https://pandas.pydata.org/docs/dev/getting_started/install.html#installing-the-development-version-of-pandas) of pandas.
### Reproducible Example
```python
Open `io/formats/format.py` file and see that the following:
def format_percentiles(
percentiles: (np.ndarray | Sequence[float]),
) -> list[str]:
```
### Issue Description
The brackets of `(np.ndarray | Sequence[float])` should be removed as they break the libcst parser and they are unneccesary
### Expected Behavior
Removed brackets
### Installed Versions
<details>
...
</details>
|
[
"Bug",
"good first issue",
"Typing"
] | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Since it's valid Python, it sounds like this should also be reported to libcst."
] |
2,117,080,252
| 57,247
|
Fix errors in docstring for `pandas.PeriodIndex`
|
closed
| 2024-02-04T12:43:50
| 2024-02-05T04:04:48
| 2024-02-04T18:19:04
|
https://github.com/pandas-dev/pandas/pull/57247
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57247
|
https://github.com/pandas-dev/pandas/pull/57247
|
yuanx749
| 1
|
Fix errors in `pandas.PeriodIndex`.
While working on it, I also add docstrings to the two associated new methods.
- [x] xref #57111(Replace xxxx with the GitHub issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
|
[
"Docs",
"Period"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @yuanx749 "
] |
2,117,042,290
| 57,246
|
DEPR: Remove first and last from DataFrame
|
closed
| 2024-02-04T11:16:04
| 2024-02-04T15:20:56
| 2024-02-04T15:17:30
|
https://github.com/pandas-dev/pandas/pull/57246
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57246
|
https://github.com/pandas-dev/pandas/pull/57246
|
phofl
| 3
|
- [ ] closes #xxxx (Replace xxxx with the GitHub issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
xref #53710
|
[
"Deprecate",
"DataFrame"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @phofl ",
"@rhshadrach you can’t add the deprecation label, otherwise the bot will pick it up \r\n\r\nwe would have to add a new label if we want to group the enforcals somehow",
"Ah - thanks"
] |
2,116,944,893
| 57,245
|
DOC: Pandas Case_When
|
closed
| 2024-02-04T08:11:55
| 2024-05-31T22:13:05
| 2024-05-31T22:13:04
|
https://github.com/pandas-dev/pandas/issues/57245
| true
| null | null |
MesumRaza
| 3
|
### Pandas version checks
- [X] I have checked that the issue still exists on the latest versions of the docs on `main` [here](https://pandas.pydata.org/docs/dev/)
### Location of the documentation
https://pandas.pydata.org/docs/dev/reference/api/pandas.Series.case_when.html
### Documentation problem
The example provide is not intuitive.
### Suggested fix for documentation
I found the example of pd.Series.case_when very non intuitive hence here is the updated example
Examples
--------
>>> # Real-world example: Updating product prices based on a dynamic pricing strategy
>>> current_prices = pd.Series([50, 75, 100, 120], name='current_prices')
>>> demand_forecast = pd.Series([100, 80, 50, 120], name='demand_forecast')
>>> # Conditions for price adjustments based on demand forecast
>>> conditions = [
... (demand_forecast > 90, current_prices * 1.2), # Increase by 20% for high demand
... (demand_forecast < 60, current_prices * 0.8), # Decrease by 20% for low demand
... (current_prices > 100, current_prices * 0.95) # Apply a 5% discount for high-priced items
... ]
>>> # Applying the dynamic pricing strategy
>>> updated_prices = current_prices.case_when(caselist=conditions)
>>> updated_prices
0 60.0
1 75.0
2 80.0
3 114.0
Name: current_prices, dtype: float64
|
[
"Docs",
"Needs Info",
"Conditionals"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks for the suggestion.\r\n\r\n> The example provide is not intuitive.\r\n\r\nCan you expand on this?",
"I think the minimal example in the docs is good - because it is the simplest case, intended to demonstrate the mechanics as clearly as possible.\r\n\r\nThat said, for me another example - with the application of this method - would be also good to have. But perhaps even better to have - would be a reference to [`np.select`](https://numpy.org/doc/stable/reference/generated/numpy.select.html#numpy-select) - because I didn't get it at first, but essentially it's like `np.select`, right? - just for a Series (and in-place ?). Can there be a reference to `np.select` in \"See also\", or is linking to methods outside of pandas prohibited?\r\n\r\nSome additional thoughts on improving that page:\r\n\r\n- good: It gives an example where both `a` and `b` compete for the `.gt(0)`, but bad: the order of dominance isn't described explicitly with words (as I understood, the higher replacement (closer to the start of the list) dominates in case of collisions). But since this isn't stated clearly, maybe there is some other logic of dominance..\r\n\r\n- purpose of `, name='c'` isn't clear.. Is this required? If not, why not remove it to have less confusion\r\n\r\n- returns this series edited in place, or a new series?",
"Seems like the example is OK so closing"
] |
2,116,942,815
| 57,244
|
QST: How to become a Collaborator?
|
closed
| 2024-02-04T08:10:32
| 2024-02-04T11:09:07
| 2024-02-04T11:09:07
|
https://github.com/pandas-dev/pandas/issues/57244
| true
| null | null |
MesumRaza
| 1
|
### Research
- [X] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [X] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
### Link to question on StackOverflow
https://
### Question about pandas
I want to contribute to documentation and docstrings for Real world examples
|
[
"Usage Question",
"Needs Triage"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"See https://pandas.pydata.org/docs/development/contributing.html"
] |
2,116,931,843
| 57,243
|
DOC: fix PR02 errors in docstring for pandas.core.groupby.DataFrameGroupBy.corrwith
|
closed
| 2024-02-04T07:51:27
| 2024-02-07T01:31:22
| 2024-02-06T17:38:57
|
https://github.com/pandas-dev/pandas/pull/57243
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57243
|
https://github.com/pandas-dev/pandas/pull/57243
|
jordan-d-murphy
| 2
|
All PR02 Errors resolved in the following cases:
1. scripts/validate_docstrings.py --format=actions --errors=PR02 pandas.core.groupby.DataFrameGroupBy.corrwith
2. scripts/validate_docstrings.py --format=actions --errors=PR02 pandas.DataFrame.corrwith
OUTPUT:
1. scripts/validate_docstrings.py --format=actions --errors=PR02 pandas.core.groupby.DataFrameGroupBy.corrwith
```
################################################################################
################################## Validation ##################################
################################################################################
Docstring for "pandas.core.groupby.DataFrameGroupBy.corrwith" correct. :)
```
2. scripts/validate_docstrings.py --format=actions --errors=PR02 pandas.DataFrame.corrwith
```
################################################################################
################################## Validation ##################################
################################################################################
Docstring for "pandas.DataFrame.corrwith" correct. :)
```
- [x] xref https://github.com/pandas-dev/pandas/issues/57111
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
|
[
"Docs",
"Groupby"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"@mroeschke can you take a look at this PR when you get a chance? ",
"Thanks keep em coming!"
] |
2,116,884,401
| 57,242
|
DOC: Fix typo in merging.rst
|
closed
| 2024-02-04T04:55:59
| 2024-02-04T05:21:19
| 2024-02-04T05:21:06
|
https://github.com/pandas-dev/pandas/pull/57242
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57242
|
https://github.com/pandas-dev/pandas/pull/57242
|
thadguidry
| 1
|
- [ ] closes #xxxx (Replace xxxx with the GitHub issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
|
[
"Docs"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @thadguidry "
] |
2,116,847,445
| 57,241
|
DEPR: Remove sql.execute, *args/**kwargs in resample, unrecognized timezones
|
closed
| 2024-02-04T03:07:01
| 2024-02-06T18:44:29
| 2024-02-06T18:20:07
|
https://github.com/pandas-dev/pandas/pull/57241
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57241
|
https://github.com/pandas-dev/pandas/pull/57241
|
mroeschke
| 1
| null |
[
"Clean"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @mroeschke "
] |
2,116,793,240
| 57,240
|
Backport PR #57232 on branch 2.2.x (REGR: to_json converting nullable ints to floats)
|
closed
| 2024-02-04T01:20:35
| 2024-02-04T03:08:15
| 2024-02-04T03:08:15
|
https://github.com/pandas-dev/pandas/pull/57240
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57240
|
https://github.com/pandas-dev/pandas/pull/57240
|
meeseeksmachine
| 0
|
Backport PR #57232: REGR: to_json converting nullable ints to floats
|
[
"Regression",
"IO JSON"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[] |
2,116,792,327
| 57,239
|
DOC: fix PR02 errors in docstring for pandas.Series.rename_axis
|
closed
| 2024-02-04T01:19:50
| 2024-02-04T18:21:34
| 2024-02-04T18:21:23
|
https://github.com/pandas-dev/pandas/pull/57239
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57239
|
https://github.com/pandas-dev/pandas/pull/57239
|
jordan-d-murphy
| 2
|
All PR02 Errors resolved in the following cases:
1. scripts/validate_docstrings.py --format=actions --errors=PR02 pandas.Series.rename_axis
2. scripts/validate_docstrings.py --format=actions --errors=PR02 pandas.DataFrame.rename_axis
OUTPUT:
1. scripts/validate_docstrings.py --format=actions --errors=PR02 pandas.Series.rename_axis
```
################################################################################
################################## Validation ##################################
################################################################################
1 Errors found for `pandas.Series.rename_axis`:
ES01 No extended summary found
```
2. scripts/validate_docstrings.py --format=actions --errors=PR02 pandas.DataFrame.rename_axis
```
################################################################################
################################## Validation ##################################
################################################################################
1 Errors found for `pandas.DataFrame.rename_axis`:
ES01 No extended summary found
```
- [x] xref https://github.com/pandas-dev/pandas/issues/57111
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
|
[
"Docs"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"@mroeschke I've pushed a fix for your suggested improvements, can you take another look?",
"Thanks @jordan-d-murphy "
] |
2,116,754,130
| 57,238
|
Remove Copy-on-Write builds
|
closed
| 2024-02-04T00:14:33
| 2024-02-04T01:17:56
| 2024-02-04T01:17:41
|
https://github.com/pandas-dev/pandas/pull/57238
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57238
|
https://github.com/pandas-dev/pandas/pull/57238
|
phofl
| 1
|
- [ ] closes #xxxx (Replace xxxx with the GitHub issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
|
[
"CI",
"Copy / view semantics"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @phofl "
] |
2,116,744,899
| 57,237
|
Remove Copy-on-Write warning mode from tests
|
closed
| 2024-02-04T00:05:54
| 2024-02-16T15:20:41
| 2024-02-04T01:14:14
|
https://github.com/pandas-dev/pandas/pull/57237
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57237
|
https://github.com/pandas-dev/pandas/pull/57237
|
phofl
| 1
|
- [ ] closes #xxxx (Replace xxxx with the GitHub issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
|
[
"Copy / view semantics"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @phofl "
] |
2,116,744,441
| 57,236
|
DOC: Clarifying brackets vs. parentheses
|
closed
| 2024-02-04T00:05:26
| 2024-02-04T18:25:47
| 2024-02-04T15:24:14
|
https://github.com/pandas-dev/pandas/pull/57236
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57236
|
https://github.com/pandas-dev/pandas/pull/57236
|
nkasing
| 1
|
- [x] closes #56972
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit). (Doc edits only)
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions. (Doc edits only)
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature. (Doc edits only)
In addition to the clarification requested in #56972, I adjusted the language where square brackets were specified elsewhere in the documentation to match "square brackets ``[]``", with the exception of one file.
In the data subsetting tutorial guide (`doc/source/getting_started/intro_tutorials/03_subset_data.rst`), square brackets are specified towards the beginning of the guide, and "selection brackets" was used elsewhere, often to specify outer vs. inner square brackets (i.e. `df[['ColA', 'ColB']]`)
|
[
"Docs"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @noah-asing!"
] |
2,116,717,762
| 57,235
|
DOC: Updated docstring for set_option
|
closed
| 2024-02-03T23:42:37
| 2024-02-05T21:53:52
| 2024-02-05T17:29:52
|
https://github.com/pandas-dev/pandas/pull/57235
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57235
|
https://github.com/pandas-dev/pandas/pull/57235
|
jrmylow
| 3
|
- [X] closes #57148
- [X] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
|
[
"Docs"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Also related: https://github.com/pandas-dev/pandas/issues/57111",
"pre-commit.ci autofix",
"Thanks @jrmylow "
] |
2,116,716,353
| 57,234
|
Remove Copy-on-Write warning mode
|
closed
| 2024-02-03T23:36:58
| 2024-02-04T01:11:49
| 2024-02-04T01:11:11
|
https://github.com/pandas-dev/pandas/pull/57234
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57234
|
https://github.com/pandas-dev/pandas/pull/57234
|
phofl
| 1
|
This removes the warning mode from the code, tests will follow in a separate PR
cc @mroeschke
|
[
"Copy / view semantics"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @phofl "
] |
2,116,710,632
| 57,233
|
REGR: Fix to_numpy conversion for arrow ea with float dtype given
|
closed
| 2024-02-03T23:12:42
| 2024-02-07T18:22:31
| 2024-02-07T16:52:08
|
https://github.com/pandas-dev/pandas/pull/57233
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57233
|
https://github.com/pandas-dev/pandas/pull/57233
|
phofl
| 3
|
- [ ] closes #xxxx (Replace xxxx with the GitHub issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
xref #57121
|
[
"Arrow"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"This should fix #57093 as well. \r\n\r\nI started something similar in #57170. Do you want to copy the test from #57170 into this PR and I'll close mine?\r\n\r\nyou might also want to include the `NaT` cases I have in #57170 as it will fix this which currently raises:\r\n\r\n```\r\nimport pandas as pd\r\n\r\nser = pd.Series([1, None], dtype='Int64')\r\nser.astype(\"m8[ns]\")\r\n```",
"Does this address https://github.com/pandas-dev/pandas/pull/57233#issuecomment-1925737224 as well?",
"Yikes I missed that comment, will follow up"
] |
2,116,707,301
| 57,232
|
REGR: to_json converting nullable ints to floats
|
closed
| 2024-02-03T22:59:47
| 2024-02-04T01:34:23
| 2024-02-04T01:20:05
|
https://github.com/pandas-dev/pandas/pull/57232
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57232
|
https://github.com/pandas-dev/pandas/pull/57232
|
phofl
| 1
|
- [ ] closes #57224 (Replace xxxx with the GitHub issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
|
[
"Regression",
"IO JSON"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @phofl "
] |
2,116,701,137
| 57,231
|
DEPR: Remove Index type checking attributes
|
closed
| 2024-02-03T22:35:19
| 2024-02-09T17:34:02
| 2024-02-08T22:09:38
|
https://github.com/pandas-dev/pandas/pull/57231
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57231
|
https://github.com/pandas-dev/pandas/pull/57231
|
mroeschke
| 1
|
- [ ] closes #xxxx (Replace xxxx with the GitHub issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
|
[
"Index"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Going to merge this one in. Happy to follow up."
] |
2,116,686,374
| 57,230
|
DOC: fix PR02 errors in docstring for pandas.io.formats.style.Styler.to_excel
|
closed
| 2024-02-03T21:37:36
| 2024-02-04T01:20:36
| 2024-02-04T01:07:01
|
https://github.com/pandas-dev/pandas/pull/57230
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57230
|
https://github.com/pandas-dev/pandas/pull/57230
|
jordan-d-murphy
| 1
|
All PR02 Errors resolved in the following cases:
1. scripts/validate_docstrings.py --format=actions --errors=PR02 pandas.io.formats.style.Styler.to_excel
OUTPUT:
1. scripts/validate_docstrings.py --format=actions --errors=PR02 pandas.io.formats.style.Styler.to_excel
```
################################################################################
################################## Validation ##################################
################################################################################
1 Errors found for `pandas.io.formats.style.Styler.to_excel`:
PR01 Parameters {'verbose', 'encoding'} not documented
```
- [x] xref https://github.com/pandas-dev/pandas/issues/57111
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
|
[
"Docs",
"IO Excel"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @jordan-d-murphy "
] |
2,116,685,958
| 57,229
|
BUG: A Warning is emitted in Pandas 2.2.0 when I change the type of a column using `df.iloc[:, i]=...`
|
closed
| 2024-02-03T21:35:59
| 2025-05-20T07:47:38
| 2024-02-03T22:49:01
|
https://github.com/pandas-dev/pandas/issues/57229
| true
| null | null |
mwouts
| 3
|
### Pandas version checks
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this bug exists on the [main branch](https://pandas.pydata.org/docs/dev/getting_started/install.html#installing-the-development-version-of-pandas) of pandas.
### Reproducible Example
```python
import pandas as pd
df = pd.DataFrame({'a': [1]})
# Change the type of colum 0.
# NB I Use column indices as sometimes
# df has duplicated column names
i = 0
x = df.iloc[:, i]
df.iloc[:,i] = x.astype(str)
```
### Issue Description
When I replace an integer column by its string representation, the following warning is emitted (see https://github.com/mwouts/itables/issues/223):
```
FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '0 1
Name: a, dtype: object' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
df.iloc[:,i] = x.astype(str)
```
### Expected Behavior
I think the warning makes sense when I replace only a fraction of the rows, but here I replace the full column, so I am not sure the warning is legit. Do you think we could drop the warning when the full column is replaced, or is there a better way to replace the i-th column?
Thanks!
### Installed Versions
<details>
<summary><code>pd.show_versions()</code></summary>
<code>
INSTALLED VERSIONS
------------------
commit : fd3f57170aa1af588ba877e8e28c158a20a4886d
python : 3.11.7.final.0
python-bits : 64
OS : Linux
OS-release : 6.5.0-14-generic
Version : #14~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Mon Nov 20 18:15:30 UTC 2
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 2.2.0
numpy : 1.26.3
pytz : 2023.3.post1
dateutil : 2.8.2
setuptools : 68.2.2
pip : 23.3.1
Cython : None
pytest : 7.4.0
hypothesis : None
sphinx : 7.2.6
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
IPython : 8.20.0
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.12.2
bottleneck : 1.3.7
dataframe-api-compat : None
fastparquet : None
fsspec : 2023.12.2
gcsfs : 2023.12.2post1
matplotlib : 3.8.2
numba : None
numexpr : 2.8.7
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 11.0.0
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 2.0.25
tables : None
tabulate : 0.9.0
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.3
qtpy : 2.4.1
pyqt5 : None
</code>
</details>
|
[
"Bug",
"Needs Triage"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"You should use isetitem if you don't want to modify your object inplace and thus avoid the warning\r\n\r\n```\r\ndf.isetitem(i, df.iloc[:,i].astype(str))\r\n```",
"Sounds great! Thank you for pointing out at isetitem!",
"`df.loc[:, \"COL\"] = df[\"COL\"].astype(str) ` issues `FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas.` too. I've been struggling for hours :("
] |
2,116,645,064
| 57,228
|
TYP: misc IO return types
|
closed
| 2024-02-03T19:09:18
| 2024-02-10T20:34:31
| 2024-02-03T21:24:50
|
https://github.com/pandas-dev/pandas/pull/57228
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57228
|
https://github.com/pandas-dev/pandas/pull/57228
|
twoertwein
| 1
|
mostly for the CSV methods
|
[
"Typing"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @twoertwein "
] |
2,116,631,918
| 57,227
|
Update style.ipynb http -> https
|
closed
| 2024-02-03T18:25:44
| 2024-02-03T21:38:54
| 2024-02-03T20:01:01
|
https://github.com/pandas-dev/pandas/pull/57227
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57227
|
https://github.com/pandas-dev/pandas/pull/57227
|
partev
| 1
|
http://seaborn.pydata.org/ -> https://seaborn.pydata.org/
- [ ] closes #xxxx (Replace xxxx with the GitHub issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
|
[
"Docs"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @partev "
] |
2,116,622,677
| 57,226
|
WEB: Using Bootstrap icon instead of font awesome icons
|
closed
| 2024-02-03T18:11:13
| 2024-02-05T17:33:00
| 2024-02-05T17:32:53
|
https://github.com/pandas-dev/pandas/pull/57226
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57226
|
https://github.com/pandas-dev/pandas/pull/57226
|
koushik-rout-samsung
| 3
|
Closes https://github.com/pandas-dev/pandas/issues/57165
|
[
"Web"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"/preview",
"Website preview of this PR available at: https://pandas.pydata.org/preview/pandas-dev/pandas/57226/",
"Thanks @koushik-rout-samsung "
] |
2,116,520,702
| 57,225
|
BUG: Raise if an aggregation function other than mean is used with ewm and times
|
closed
| 2024-02-03T14:57:55
| 2024-02-03T21:55:04
| 2024-02-03T21:54:36
|
https://github.com/pandas-dev/pandas/pull/57225
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57225
|
https://github.com/pandas-dev/pandas/pull/57225
|
stevenschaerer
| 1
|
Ran into this recently and found an issue was reported a while ago without any progress (#51695)
- [x] closes #51695
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [x] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
|
[
"Window"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @stevenschaerer "
] |
2,116,463,645
| 57,224
|
BUG: `DataFrame#to_json` in pandas 2.2.0 outputs `Int64` values containing `NA` as floats
|
closed
| 2024-02-03T12:29:50
| 2024-02-04T10:01:59
| 2024-02-04T01:20:06
|
https://github.com/pandas-dev/pandas/issues/57224
| true
| null | null |
skatsuta
| 0
|
### Pandas version checks
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this bug exists on the [main branch](https://pandas.pydata.org/docs/dev/getting_started/install.html#installing-the-development-version-of-pandas) of pandas.
### Reproducible Example
```python
import pandas as pd
df = pd.DataFrame({"a": [1, pd.NA]}, dtype="Int64")
json = df.to_json(orient="records", lines=True)
print(json)
# Output:
# {"a":1.0} <- float!
# {"a":null}
```
### Issue Description
In pandas 2.1.4, `DataFrame#to_json` outputs `Int64` integers as JSON integers even if the column contains `NA`, as shown below. This is an expected behavior.
```python
# pandas 2.1.4
import pandas as pd
df = pd.DataFrame({"a": [1, pd.NA]}, dtype="Int64")
json = df.to_json(orient="records", lines=True)
print(json)
# Output:
# {"a":1} <- int
# {"a":null}
```
However, in pandas 2.2.0, `DataFrame#to_json` outputs `Int64` integers as JSON *floats* for columns containing `NA`, as shown below.
```python
# pandas 2.2.0
import pandas as pd
df = pd.DataFrame({"a": [1, pd.NA]}, dtype="Int64")
json = df.to_json(orient="records", lines=True)
print(json)
# Output:
# {"a":1.0} <- float!
# {"a":null}
```
Note that even in 2.2.0, `Int64` column values that do *not* contain any `NA` are output as JSON integers as expected.
```python
# pandas 2.2.0
import pandas as pd
df = pd.DataFrame({"a": [1, 2]}, dtype="Int64")
json = df.to_json(orient="records", lines=True)
print(json)
# Output:
# {"a":1} <- int
# {"a":2} <- int
```
This problem also occurs in `Series` as follows.
```python
s = pd.Series([1, pd.NA], dtype="Int64")
print(s.to_json(orient="records"))
# Output:
# [1.0,null]
# ^
# float
```
### Expected Behavior
As in pandas 2.1.4, `Int64` column values should be output as JSON integers even if they contain `NA`.
```python
import pandas as pd
df = pd.DataFrame({"a": [1, pd.NA]}, dtype="Int64")
json = df.to_json(orient="records", lines=True)
print(json)
# Expected output:
# {"a":1} <- int
# {"a":null}
```
### Installed Versions
<details>
```
INSTALLED VERSIONS
------------------
commit : fd3f57170aa1af588ba877e8e28c158a20a4886d
python : 3.12.1.final.0
python-bits : 64
OS : Darwin
OS-release : 22.6.0
Version : Darwin Kernel Version 22.6.0: Sun Dec 17 22:12:45 PST 2023; root:xnu-8796.141.3.703.2~2/RELEASE_ARM64_T6000
machine : arm64
processor : arm
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 2.2.0
numpy : 1.26.3
pytz : 2024.1
dateutil : 2.8.2
setuptools : 69.0.3
pip : 23.3.1
Cython : None
pytest : 7.4.4
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : 2023.12.2
gcsfs : 2023.12.2post1
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : 0.20.0
pyarrow : 15.0.0
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.12.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.4
qtpy : None
pyqt5 : None
```
</details>
|
[
"Regression",
"IO JSON"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[] |
2,116,463,548
| 57,223
|
DOC: Replace pandas logo with a conditional image for light and dark …
|
closed
| 2024-02-03T12:29:31
| 2024-02-04T18:11:28
| 2024-02-04T18:11:18
|
https://github.com/pandas-dev/pandas/pull/57223
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57223
|
https://github.com/pandas-dev/pandas/pull/57223
|
LakshmanKishore
| 2
|
...themes
Replaced the static pandas logo with a `picture` tag that displays a different logo based on the color scheme. In light mode, the original logo is shown, while in dark mode, a white version of the logo is displayed for better visibility.
In GitHub Dark Theme
In GitHub Light Theme
|
[
"Docs"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thank you for the feedback, I'm glad you found the change helpful! \r\nLooking forward to do more contributions!",
"Thanks @LakshmanKishore "
] |
2,116,253,943
| 57,222
|
ENH: Add all warnings check to the assert_produces_warnings, and separate messages for each warning.
|
closed
| 2024-02-03T05:30:15
| 2024-04-14T19:18:34
| 2024-04-14T19:18:21
|
https://github.com/pandas-dev/pandas/pull/57222
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57222
|
https://github.com/pandas-dev/pandas/pull/57222
|
Jorewin
| 9
|
The function in question gets used in multiple places, that's why I felt it would be safer to preserve current behavior and API, and add an additional, optional parameter, which alters the behavior, enabling checking if all passed warning types get detected.
- [x] closes #56555 (Replace xxxx with the GitHub issue number)
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [x] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
|
[
"Testing",
"Warnings"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"@rhshadrach Could you review this? Or direct me to someone who can? It's my first contribution here, so I don't know who to ask.",
"I should be able to get to this tomorrow.",
"@rhshadrach Could you take another look at this, when you have time? I addressed all change requests and resolved all merge conflicts. The docstring checks seem to still be failing, but it looks unrelated to the changes introduced in this PR.",
"@rhshadrach I just applied a patch that fixed the python 3.9 unit test not passing. With this, this should be ready for review.",
"This pull request is stale because it has been open for thirty days with no activity. Please [update](https://pandas.pydata.org/pandas-docs/stable/development/contributing.html#updating-your-pull-request) and respond to this comment if you're still interested in working on this.",
"@Jorewin - are you interested in finishing this up? If not, I can add some tests to get it over the finish line.",
"@rhshadrach If you want to, then go ahead. I just didn't have much time lately.",
"@mroeschke - tests added for various combinations. Let me know if you see any others to add.",
"Thanks @Jorewin and @rhshadrach "
] |
2,116,237,901
| 57,221
|
TST: Reduce parameterization of test_str_find_e2e
|
closed
| 2024-02-03T05:09:15
| 2024-02-03T18:00:18
| 2024-02-03T18:00:15
|
https://github.com/pandas-dev/pandas/pull/57221
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57221
|
https://github.com/pandas-dev/pandas/pull/57221
|
mroeschke
| 0
|
This recently added test creates 25k tests and looks to add an extra 6-7 minutes in the CI. Reducing the coverage here to speed the test up
|
[
"Testing"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[] |
2,116,151,590
| 57,220
|
CI: Fix using "python" instead of "sys.executable"
|
closed
| 2024-02-03T03:25:52
| 2024-02-03T22:21:13
| 2024-02-03T11:30:00
|
https://github.com/pandas-dev/pandas/pull/57220
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57220
|
https://github.com/pandas-dev/pandas/pull/57220
|
jrmylow
| 0
|
- [x] closes #57219
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
|
[
"Bug",
"CI"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[] |
2,116,151,440
| 57,219
|
BUG: validate_docstring uses main python executable instead of sys.executable
|
closed
| 2024-02-03T03:25:24
| 2024-02-03T11:30:01
| 2024-02-03T11:30:01
|
https://github.com/pandas-dev/pandas/issues/57219
| true
| null | null |
jrmylow
| 0
|
### Pandas version checks
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the [main branch](https://pandas.pydata.org/docs/dev/getting_started/install.html#installing-the-development-version-of-pandas) of pandas.
### Reproducible Example
```python
Check:
pip freeze > t.out and make sure flake8 is uninstalled from the main python environment
Run:
python scripts/validate_docstrings.py pandas.set_option()
Error:
Traceback (most recent call last):
File "...\scripts\validate_docstrings.py", line 474, in <module>
main(
File "...\scripts\validate_docstrings.py", line 417, in main
print_validate_one_results(func_name)
File "...\scripts\validate_docstrings.py", line 386, in print_validate_one_results
result = pandas_validate(func_name)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "...\scripts\validate_docstrings.py", line 262, in pandas_validate
for error_code, error_message, line_number, col_number in doc.validate_pep8():
File "...\scripts\validate_docstrings.py", line 211, in validate_pep8
line_number, col_number, error_code, message = error_message.split(
```
### Issue Description
When trying to run docstring tests in `scripts/validate_docstring` the script would fail in calling `validate_pep8`. The error message would say that flake8 was not installed and the function would fail because this error message is not in the expected format to be parsed with `.split()`.
The root cause is that the function passes `"python"` instead of `sys.executable` to `subprocess.run`. Because my main python environment is barebones, it doesn't include flake8.
Note that in other instances where `subprocess.run` is used in pandas, `sys.executable` is passed in.
### Expected Behavior
Script should output the docstrings/validation errors as per normal behaviour.
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 1d1672d1ecb5d108e45338de837d0604edba2859
python : 3.12.1.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19045
machine : AMD64
processor : Intel64 Family 6 Model 140 Stepping 2, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : English_Australia.1252
pandas : 2.2.0.dev0+1177.g1d1672d1ec
numpy : 1.26.3
pytz : 2024.1
dateutil : 2.8.2
setuptools : 69.0.3
pip : 23.3.2
Cython : 3.0.8
pytest : 8.0.0
hypothesis : 6.97.4
sphinx : 7.2.6
blosc : None
feather : None
xlsxwriter : 3.1.9
lxml.etree : 5.1.0
html5lib : 1.1
pymysql : 1.4.6
psycopg2 : 2.9.9
jinja2 : 3.1.3
IPython : 8.21.0
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.12.3
bottleneck : 1.3.7
dataframe-api-compat : None
fastparquet : 2023.10.1
fsspec : 2023.12.2
gcsfs : 2023.12.2post1
matplotlib : 3.8.2
numba : 0.59.0
numexpr : 2.9.0
odfpy : None
openpyxl : 3.1.2
pandas_gbq : None
pyarrow : 15.0.0
pyreadstat : 1.2.6
python-calamine : None
pyxlsb : 1.0.10
s3fs : 2023.12.2
scipy : 1.12.0
sqlalchemy : 2.0.25
tables : 3.9.2
tabulate : 0.9.0
xarray : 2024.1.1
xlrd : 2.0.1
zstandard : 0.22.0
tzdata : 2023.4
qtpy : None
pyqt5 : None
</details>
|
[
"Bug",
"CI"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[] |
2,116,101,792
| 57,218
|
BUG: Fixing broken link in getting_started.md
|
closed
| 2024-02-03T01:17:04
| 2024-02-03T01:23:06
| 2024-02-03T01:23:00
|
https://github.com/pandas-dev/pandas/pull/57218
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57218
|
https://github.com/pandas-dev/pandas/pull/57218
|
chrish935
| 1
|
- [x] closes #57217
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit)
|
[
"Web"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @chrish935 "
] |
2,115,946,954
| 57,217
|
WEB: Broken link to Anaconda
|
closed
| 2024-02-02T22:48:44
| 2024-02-03T01:23:01
| 2024-02-03T01:23:01
|
https://github.com/pandas-dev/pandas/issues/57217
| true
| null | null |
rhshadrach
| 1
|
At https://pandas.pydata.org/getting_started.html, the link to Anaconda in step 1 is broken and should point to https://www.anaconda.com/download.
|
[
"good first issue",
"Web"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"take"
] |
2,115,839,284
| 57,216
|
ENH: In sort methods, accept sequence or dict as key
|
closed
| 2024-02-02T21:37:00
| 2024-08-24T19:47:24
| 2024-08-24T19:47:24
|
https://github.com/pandas-dev/pandas/issues/57216
| true
| null | null |
gsehrlich
| 2
|
### Feature Type
- [X] Adding new functionality to pandas
- [ ] Changing existing functionality in pandas
- [ ] Removing existing functionality in pandas
### Problem Description
Pretty much every time I use the `key` kwarg of a pandas sort method, I use it to tell pandas the desired order of the unique values. For example:
```python
import pandas as pd
my_index = pd.Index(['one', 'two', 'three', 'three', 'two', 'one', 'three'])
my_index.sort_values(
key=lambda idx: (idx
.to_series()
.rename(index={'one': 1, 'two': 2, 'three': 3})
.index
)
)
```
This is all a bit redundant, and I suspect it's a common use case.
Relatedly, sometimes I want to use a different key function for different columns/levels. In order to do that, I actually need to call the sort method multiple times.
### Feature Description
Accept sequences like `['one', 'two', 'three']` specifying the ascending order of the values.
EDIT: Example equivalent to the `sort_values` call above:
```python
my_index.sort_values(key=['one', 'two', 'three'])
```
Also accept dictionaries like `{'col_1': ['one', 'two', 'three'], 'col_3': my_sort_fn}` to sort different columns or levels with different keys in any format.
### Alternative Solutions
Make sure all data that I want to lexsort is always an ordered Categorical.
### Additional Context
_No response_
|
[
"Enhancement",
"Index",
"Needs Triage",
"Sorting"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks for the request. You can accomplish this with map, argsort, and take:\r\n\r\n```\r\nmy_index = pd.Index(['one', 'two', 'three', 'three', 'two', 'one', 'three'])\r\nprint(my_index.take(my_index.map({'one': 1, 'two': 2, 'three': 3}).argsort()))\r\n\r\n# Result:\r\n# Index(['one', 'one', 'two', 'two', 'three', 'three', 'three'], dtype='object')\r\n```",
"Thanks for the issue, but it appears this hasn't gotten traction in a while so closing"
] |
2,115,764,905
| 57,215
|
BUG: Error when trying to use `pd.date_range`
|
open
| 2024-02-02T20:39:36
| 2025-06-02T17:12:13
| null |
https://github.com/pandas-dev/pandas/issues/57215
| true
| null | null |
khider
| 8
|
### Pandas version checks
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the [main branch](https://pandas.pydata.org/docs/dev/getting_started/install.html#installing-the-development-version-of-pandas) of pandas.
### Reproducible Example
Edit[rhshadrach]: On pandas >= 3.0, you need to use `freq='1000000000YS-JAN'` below.
```python
import pandas as pd
import numpy as np
pd.date_range(start = np.datetime64('500000000-01-01', 's'), end = np.datetime64('1500000000-01-01', 's'), freq='1000000000AS-JAN', unit='s')
```
### Issue Description
Error:
raise ValueError(f"Offset {offset} did not increment date")
ValueError: Offset <YearBegin: month=1> did not increment date
### Expected Behavior
Create my date range
### Installed Versions
INSTALLED VERSIONS
------------------
commit : fd3f57170aa1af588ba877e8e28c158a20a4886d
python : 3.11.7.final.0
python-bits : 64
OS : Darwin
OS-release : 23.1.0
Version : Darwin Kernel Version 23.1.0: Mon Oct 9 21:32:11 PDT 2023; root:xnu-10002.41.9~7/RELEASE_ARM64_T6030
machine : arm64
processor : arm
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 2.2.0
numpy : 1.26.3
pytz : 2023.3.post1
dateutil : 2.8.2
setuptools : 68.2.2
pip : 23.3.1
Cython : None
pytest : 8.0.0
hypothesis : None
sphinx : 5.0.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
IPython : 8.20.0
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.12.2
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : 3.8.2
numba : 0.58.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 15.0.0
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.12.0
sqlalchemy : None
tables : None
tabulate : 0.9.0
xarray : None
xlrd : 2.0.1
zstandard : None
tzdata : 2023.4
qtpy : 2.4.1
pyqt5 : None
|
[
"Bug",
"Regression",
"datetime.date"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks for the report; it's not clear to me if `date_range` should be able to handle values at this scale. Further investigations are welcome.\r\n\r\ncc @MarcoGorelli ",
"This error came up as part of CI testing of a package. See here: https://github.com/LinkedEarth/Pyleoclim_util/blob/bf9716c22329b6bdefc2c9c2a39dc4982289ad5a/pyleoclim/tests/test_core_Series.py#L1274\r\n\r\nThe tests used to work so it was supported in previous versions of Pandas.\r\n\r\nJust to confirm, it works with dates in the ~1500. ",
"Thanks for adding that, best to always indicate it's a regression! That said, I'm seeing this fail in pandas 2.1.0 and pandas 2.0.0. Prior to that, `pd.date_range` did not take the `unit` argument.\r\n\r\nCan you produce an environment where the example is successful, and report the output of `pd.show_versions()`?",
"Tried it on our JupyterHub and it worked (although note that this was before `AS` deprecation warning).\r\n\r\n\r\n\r\nOutput of `pd.show_versions()`:\r\n\r\n\r\n```INSTALLED VERSIONS\r\n------------------\r\ncommit : 965ceca9fd796940050d6fc817707bba1c4f9bff\r\npython : 3.10.11.final.0\r\npython-bits : 64\r\nOS : Linux\r\nOS-release : 5.15.120+\r\nVersion : #1 SMP Fri Jul 21 03:39:30 UTC 2023\r\nmachine : x86_64\r\nprocessor : x86_64\r\nbyteorder : little\r\nLC_ALL : en_US.UTF-8\r\nLANG : en_US.UTF-8\r\nLOCALE : en_US.UTF-8\r\n\r\npandas : 2.0.2\r\nnumpy : 1.23.5\r\npytz : 2023.3\r\ndateutil : 2.8.2\r\nsetuptools : 68.0.0\r\npip : 23.1.2\r\nCython : 0.29.35\r\npytest : None\r\nhypothesis : None\r\nsphinx : None\r\nblosc : None\r\nfeather : None\r\nxlsxwriter : None\r\nlxml.etree : 4.9.2\r\nhtml5lib : None\r\npymysql : None\r\npsycopg2 : None\r\njinja2 : 3.1.2\r\nIPython : 8.14.0\r\npandas_datareader: None\r\nbs4 : 4.12.2\r\nbottleneck : None\r\nbrotli : \r\nfastparquet : None\r\nfsspec : 2023.6.0\r\ngcsfs : None\r\nmatplotlib : 3.7.1\r\nnumba : 0.57.1\r\nnumexpr : None\r\nodfpy : None\r\nopenpyxl : None\r\npandas_gbq : None\r\npyarrow : 12.0.0\r\npyreadstat : None\r\npyxlsb : None\r\ns3fs : 2023.6.0\r\nscipy : 1.10.1\r\nsnappy : None\r\nsqlalchemy : 2.0.15\r\ntables : None\r\ntabulate : 0.9.0\r\nxarray : 2023.5.0\r\nxlrd : 2.0.1\r\nzstandard : None\r\ntzdata : 2023.3\r\nqtpy : 2.3.1\r\npyqt5 : None\r\nNone\r\n```\r\nThe container is available here: https://quay.io/repository/linkedearth/linkedearthhub?tab=tags\r\n\r\nLatest tag should work. The problem with the GitHub tests is very new. It was passing as of a week ago. Unfortunately, we don't pin versions for CI so I have no idea what was actually installed then. ",
"Thanks - I've edited the OP to be `AS` for now. I was able to confirm on 2.0.2 but could not run a git-bisect due to the change in build systems. @phofl - I recall you had a solve for this?\r\n\r\nFurther investigations and PRs to fix are welcome!",
"Inside `pandas/core/arrays/datetimes.py:_generate_range`, when applying the offset to the current date\r\n\r\n```py\r\nnext_date = offset._apply(cur)\r\n```\r\n\r\nAs `Timestamp cur` is converted to cython object `_Timestamp`, its year is set to 1972. See `pandas/_libs/tslibs/timestamps.pyx:create_timestamp_from_ts`\r\n\r\nThe change originates from https://github.com/pandas-dev/pandas/pull/47720. Don't really know where to go next tho. Maybe depends on how we want to handle out-of-bound pydatetime\r\n\r\n",
"cc @jbrockmendel",
"Looks like the issue is in shift_month. Changing `year = stamp.year + dy` to `year = (<object>stamp).year + dy` seems to fix it. That isn't a great solution though. Might be preferable to change the declaration on shift_month from datetime to _Timestamp, though i expect that will take more work."
] |
2,115,685,556
| 57,214
|
ENH: pd.Series.str.isempty() to check for empty strings
|
closed
| 2024-02-02T20:03:53
| 2024-02-07T21:23:03
| 2024-02-07T17:34:31
|
https://github.com/pandas-dev/pandas/issues/57214
| true
| null | null |
michaelquinn32
| 3
|
### Feature Type
- [X] Adding new functionality to pandas
- [ ] Changing existing functionality in pandas
- [ ] Removing existing functionality in pandas
### Problem Description
The current pattern for empty strings is to do an equals comparion
```python
strings = pd.Series(['a', '', 'b'])
strings == ''
```
There are often recommendations to check truthiness of strings with implicit boolean values, e.g.
https://pylint.readthedocs.io/en/latest/user_guide/messages/convention/use-implicit-booleaness-not-comparison.html
This method would be similar to other string checking patterns, like `pd.Series.str.isspace()`. While this does deviate from the existing str api that the methods seek to follow, the approach from the str api to checking strings fails because pd.Series objects don't have an implicit truth value.
This would also make more fluid to combine operations:
```python
strings.mask(strings.str.isempty() | strings.str.isspace(), 'empty')
```
### Feature Description
In [pandas/core/strings/object_array.py](https://github.com/pandas-dev/pandas/blob/4f0870efecff989c211dd8cfe975ef2127cc86b8/pandas/core/strings/object_array.py#L4)
```python
def _str_isempty(self):
return self._str_map(lambda s: s == '', dtype="bool")
```
### Alternative Solutions
N/A
### Additional Context
_No response_
|
[
"Enhancement",
"Strings",
"Closing Candidate"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks for the request. The link to pylint warns about behavior differences using `==` or `!=` as compared to implicit truthiness of objects. If I'm understanding this request correctly, it is asking for another way to test equality to the empty string. Users would be able to do:\r\n\r\n strings == ''\r\n strings.eq('') # Great for method chaining\r\n strings.str.len() == 0\r\n strings.str.len().eq(0)\r\n \r\n # And now\r\n strings.str.isempty()\r\n\r\nand these would all result in precisely the same behavior. It would expand the size of the API and the maintenance that comes along with it, without providing any new behavior users could take advantage of. For this reason, I'm rather negative on the proposal.",
"Thanks for getting back to me, and thanks for taking them time to look through this in detail.\r\n\r\n`.eq('')` is probably close enough in intent to the original ask, as it can clarify to the reader (human or machine) that this isn't exactly the same as the standard string comparison. I'm a little embarrassed to say that I wasn't aware of this option when making the request.\r\n\r\nI'll close this issue.",
"> I'm a little embarrassed to say that I wasn't aware of this option\r\n\r\nYou shouldn't be! The pandas API is quite large, core maintainers do not know it all - and we certainly don't expect users to!"
] |
2,115,536,364
| 57,213
|
BUG: to_numeric loses precision when converting decimal type to integer
|
open
| 2024-02-02T18:34:30
| 2024-02-27T10:06:14
| null |
https://github.com/pandas-dev/pandas/issues/57213
| true
| null | null |
sfc-gh-aalam
| 3
|
### Pandas version checks
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this bug exists on the [main branch](https://pandas.pydata.org/docs/dev/getting_started/install.html#installing-the-development-version-of-pandas) of pandas.
### Reproducible Example
```python
from decimal import *
import pandas as pd
df = pd.DataFrame({'column1': [Decimal('1'*19)]})
print(pd.to_numeric(df['column1'], downcast='integer'))
print(df['column1'].astype('int64'))
"""
0 1111111111111111168
Name: column1, dtype: int64
0 1111111111111111111
Name: column1, dtype: int64
"""
```
### Issue Description
When converting data from decimal type to integer using `to_numeric`, we see loss of precision where as doing the same conversion using `astype('int64')` works as expected without any precision loss.
### Expected Behavior
Perform the conversion without a precision loss
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : fd3f57170aa1af588ba877e8e28c158a20a4886d
python : 3.9.16.final.0
python-bits : 64
OS : Darwin
OS-release : 23.2.0
Version : Darwin Kernel Version 23.2.0: Wed Nov 15 21:53:18 PST 2023; root:xnu-10002.61.3~2/RELEASE_ARM64_T6000
machine : arm64
processor : arm
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 2.2.0
numpy : 1.24.2
pytz : 2022.7.1
dateutil : 2.8.2
setuptools : 65.6.3
pip : 23.0.1
Cython : 0.29.34
pytest : None
hypothesis : None
sphinx : 5.0.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
IPython : None
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : 3.8.2
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 10.0.1
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.11.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.4
qtpy : None
pyqt5 : None
</details>
|
[
"Bug",
"Dtype Conversions"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks for the report. We likely go through float64. Further investigations and PRs to fix are welcome!",
"I'll be attempting this.",
"take"
] |
2,114,982,768
| 57,212
|
BUG: ensure_string_array might modify read-only array inplace
|
closed
| 2024-02-02T13:51:24
| 2024-02-04T18:16:05
| 2024-02-04T18:15:37
|
https://github.com/pandas-dev/pandas/pull/57212
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57212
|
https://github.com/pandas-dev/pandas/pull/57212
|
phofl
| 1
|
We ran into this in Dask, it's quite the edge case...
|
[
"Bug",
"Strings"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @phofl "
] |
2,114,361,376
| 57,211
|
DOC: fix PR02 errors in docstrings - pandas.core.window.rolling.Rolling.quantile, pandas.core.window.expanding.Expanding.quantile
|
closed
| 2024-02-02T08:14:40
| 2024-02-02T17:59:49
| 2024-02-02T11:59:14
|
https://github.com/pandas-dev/pandas/pull/57211
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57211
|
https://github.com/pandas-dev/pandas/pull/57211
|
jordan-d-murphy
| 1
|
All PR02 Errors resolved in the following cases:
1. scripts/validate_docstrings.py --format=actions --errors=PR02 pandas.core.window.rolling.Rolling.quantile
2. scripts/validate_docstrings.py --format=actions --errors=PR02 pandas.core.window.expanding.Expanding.quantile
OUTPUT:
1. scripts/validate_docstrings.py --format=actions --errors=PR02 pandas.core.window.rolling.Rolling.quantile
```
################################################################################
################################## Validation ##################################
################################################################################
5 Errors found for `pandas.core.window.rolling.Rolling.quantile`:
ES01 No extended summary found
SA05 pandas.Series.rolling in `See Also` section does not need `pandas` prefix, use Series.rolling instead.
SA05 pandas.DataFrame.rolling in `See Also` section does not need `pandas` prefix, use DataFrame.rolling instead.
SA05 pandas.Series.quantile in `See Also` section does not need `pandas` prefix, use Series.quantile instead.
SA05 pandas.DataFrame.quantile in `See Also` section does not need `pandas` prefix, use DataFrame.quantile instead.
```
2. scripts/validate_docstrings.py --format=actions --errors=PR02 pandas.core.window.expanding.Expanding.quantile
```
################################################################################
################################## Validation ##################################
################################################################################
5 Errors found for `pandas.core.window.expanding.Expanding.quantile`:
ES01 No extended summary found
SA05 pandas.Series.expanding in `See Also` section does not need `pandas` prefix, use Series.expanding instead.
SA05 pandas.DataFrame.expanding in `See Also` section does not need `pandas` prefix, use DataFrame.expanding instead.
SA05 pandas.Series.quantile in `See Also` section does not need `pandas` prefix, use Series.quantile instead.
SA05 pandas.DataFrame.quantile in `See Also` section does not need `pandas` prefix, use DataFrame.quantile instead.
```
- [x] xref https://github.com/pandas-dev/pandas/issues/57111
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
|
[
"Docs",
"Clean"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @jordan-d-murphy, nice update"
] |
2,114,232,620
| 57,210
|
DOC: fix PR02 errors in docstrings - pandas.core.groupby.SeriesGroupBy.transform and pandas.core.groupby.DataFrameGroupBy.transform
|
closed
| 2024-02-02T06:55:29
| 2024-02-02T17:59:26
| 2024-02-02T11:51:19
|
https://github.com/pandas-dev/pandas/pull/57210
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57210
|
https://github.com/pandas-dev/pandas/pull/57210
|
jordan-d-murphy
| 1
|
All PR02 Errors resolved in the following cases:
1. scripts/validate_docstrings.py --format=actions --errors=PR02 pandas.core.groupby.SeriesGroupBy.transform
2. scripts/validate_docstrings.py --format=actions --errors=PR02 pandas.core.groupby.DataFrameGroupBy.transform
OUTPUT:
1. scripts/validate_docstrings.py --format=actions --errors=PR02 pandas.core.groupby.SeriesGroupBy.transform
```
################################################################################
################################## Validation ##################################
################################################################################
1 Errors found for `pandas.core.groupby.SeriesGroupBy.transform`:
RT03 Return value has no description
```
2. scripts/validate_docstrings.py --format=actions --errors=PR02 pandas.core.groupby.DataFrameGroupBy.transform
```
################################################################################
################################## Validation ##################################
################################################################################
1 Errors found for `pandas.core.groupby.DataFrameGroupBy.transform`:
RT03 Return value has no description
```
- [x] xref https://github.com/pandas-dev/pandas/issues/57111
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
|
[
"Docs",
"Clean"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @jordan-d-murphy, very nice"
] |
2,114,190,022
| 57,209
|
BUG: qcut bins error
|
closed
| 2024-02-02T06:30:59
| 2024-02-06T02:13:25
| 2024-02-03T11:59:20
|
https://github.com/pandas-dev/pandas/issues/57209
| true
| null | null |
0x30
| 6
|
### Pandas version checks
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the [main branch](https://pandas.pydata.org/docs/dev/getting_started/install.html#installing-the-development-version-of-pandas) of pandas.
### Reproducible Example
```python
res, bins = pd.qcut([500, 600, 600, 500, 600, 1000, 2000, 3000], q=2, retbins=True)
print(res, bins)
res = pd.cut([500, 600, 600, 500, 600, 1000, 2000, 3000], bins=bins)
print(res)
```
### Issue Description
```python
res, bins = pd.qcut([500, 600, 600, 500, 600, 1000, 2000, 3000], q=2, retbins=True)
res, bins
# ([(499.999, 600.0], (499.999, 600.0], (499.999, 600.0], (499.999, 600.0], (499.999, 600.0], (600.0, 3000.0], (600.0, 3000.0], (600.0, 3000.0]]
# Categories (2, interval[float64, right]): [(499.999, 600.0] < (600.0, 3000.0]],
# array([ 500., 600., 3000.]))
```
```python
pd.cut([500, 600, 600, 500, 600, 1000, 2000, 3000], bins=bins)
# [NaN, (500.0, 600.0], (500.0, 600.0], NaN, (500.0, 600.0], (600.0, 3000.0], (600.0, 3000.0], (600.0, 3000.0]]
# Categories (2, interval[float64, right]): [(500.0, 600.0] < (600.0, 3000.0]]
```
### Expected Behavior
`bins` should start `499.999 `
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : fd3f57170aa1af588ba877e8e28c158a20a4886d
python : 3.11.3.final.0
python-bits : 64
OS : Darwin
OS-release : 23.3.0
Version : Darwin Kernel Version 23.3.0: Wed Dec 20 21:28:58 PST 2023; root:xnu-10002.81.5~7/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : zh_CN.UTF-8
LOCALE : zh_CN.UTF-8
pandas : 2.2.0
numpy : 1.26.3
pytz : 2023.3.post1
dateutil : 2.8.2
setuptools : 66.0.0
pip : 23.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : 1.0.3
psycopg2 : None
jinja2 : None
IPython : 8.13.2
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : 3.8.2
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 2.0.13
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.4
qtpy : None
pyqt5 : None
</details>
|
[
"Bug",
"Duplicate Report",
"cut"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks for the report. Closing as a duplicate of #51532.",
"hello @rhshadrach . I'm sorry, personally I think it's not that the cut accuracy is inaccurate, but rather that qcut gave the wrong bins. I'm not very sure if this is the same mistake\r\n\r\nI think what qcut should return in the above example is `array([ 499.99., 600., 3000.]))`\r\n",
"From that issue:\r\n\r\n> NOTE 1: The same problem exists with `pd.qcut` which cuts the data into buckets based on data quantiles. In that case it could be argued that that is desirable, so that you have the correct proportion of data in each bin. E.g. if using the quartiles, then the way it currently works means that 25% of the data will get into each bucket. Whereas the way I am suggesting, you can get more or less data in each bucket. However that argument isn't very strong with pd.cut. And in any case, I think that correctly binning data should always be the primary consideration, and size of bins secondary to that.\r\n\r\nYour result separates the data into two bins each of size 3 - satisfying `q=2` but at the expense of not binning certain data points. I think that makes this the same issue.\r\n\r\nLet me know if you still disagree!",
"Sorry to bother you\r\n\r\nPerhaps there are also issues with cut, but I think the problem I am currently facing should be due to the incorrect bins returned by qcut, or the fact that pandas were originally designed in this way is that I misunderstood the usage method and caused the error\r\n\r\nin [qcut document](https://github.com/pandas-dev/pandas/blob/46fd7114d6ded7dcd58499776175d4ccfd04d9da/pandas/core/reshape/tile.py#L319)\r\n\r\n```\r\nExamples\r\n--------\r\n>>> pd.qcut(range(5), 4)\r\n... # doctest: +ELLIPSIS\r\n[(-0.001, 1.0], (-0.001, 1.0], (1.0, 2.0], (2.0, 3.0], (3.0, 4.0]]\r\nCategories (4, interval[float64, right]): [(-0.001, 1.0] < (1.0, 2.0] ...\r\n```\r\nuse qcut will return right close interval `Categories (4, interval[float64, right]): [(-0.001, 1.0] < (1.0, 2.0]` \r\n\r\nHere is what I want to do. I have a batch of data here, and i hope `quct` group data and give me `bins`, i use `bins` to group other data\r\n\r\n```python\r\nres, bins = pd.qcut([1, 2], q=2, retbins=True)\r\n\r\n# get result\r\n# ([(0.999, 1.5], (1.5, 2.0]]\r\n# Categories (2, interval[float64, right]): [(0.999, 1.5] < (1.5, 2.0]],\r\n# array([1. , 1.5, 2. ]))\r\n```\r\n\r\nand use qcut return bins to exec `pd.cut([1,2], bins = bins)` get\r\n\r\n```\r\npd.cut([1, 2], bins=bins)\r\n\r\n# get result\r\n# [NaN, (1.5, 2.0]]\r\n# Categories (2, interval[float64, right]): [(1.0, 1.5] < (1.5, 2.0]]\r\n```\r\n\r\nI didn't get the result I wanted until I used `include_lowest`\r\n\r\n```python\r\npd.cut([1, 2], bins=bins, include_lowest=True)\r\n\r\n# get result\r\n# [(0.999, 1.5], (1.5, 2.0]]\r\n# Categories (2, interval[float64, right]): [(0.999, 1.5] < (1.5, 2.0]]\r\n```\r\n\r\nI hope qcut will return me `[0.999, 1.5, 2.0]` by default.\r\n\r\nI'm not sure if this is a problem or if Pandas was originally designed this way\r\n",
"> Perhaps there are also issues with cut, but I think the problem I am currently facing should be due to the incorrect bins returned by qcut\r\n\r\nNo disagreement there. Issue #51532 is also about qcut, as I quoted above.",
"👌"
] |
2,114,019,485
| 57,208
|
REF: Remove internals.base
|
closed
| 2024-02-02T04:16:15
| 2024-02-02T18:15:12
| 2024-02-02T17:53:30
|
https://github.com/pandas-dev/pandas/pull/57208
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57208
|
https://github.com/pandas-dev/pandas/pull/57208
|
rhshadrach
| 1
|
- [x] closes #57187 (Replace xxxx with the GitHub issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
|
[
"Refactor",
"Internals"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @rhshadrach "
] |
2,113,973,484
| 57,207
|
DEPR: Enforce deprecation of groupby(...).grouper
|
closed
| 2024-02-02T03:32:27
| 2024-02-02T23:00:53
| 2024-02-02T22:54:29
|
https://github.com/pandas-dev/pandas/pull/57207
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57207
|
https://github.com/pandas-dev/pandas/pull/57207
|
rhshadrach
| 1
|
- [ ] closes #xxxx (Replace xxxx with the GitHub issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
Enforces #56521
|
[
"Groupby"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @rhshadrach "
] |
2,113,964,521
| 57,206
|
DOC: Fix minor typo in indexing.rst
|
closed
| 2024-02-02T03:24:56
| 2024-02-04T03:49:02
| 2024-02-02T12:00:41
|
https://github.com/pandas-dev/pandas/pull/57206
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57206
|
https://github.com/pandas-dev/pandas/pull/57206
|
chris-hewitt
| 1
|
- [ ] closes #xxxx (Replace xxxx with the GitHub issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
|
[
"Docs"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks for fixing this @chris-hewitt "
] |
2,113,909,202
| 57,205
|
PERF: Avoid Series constructor in DataFrame(dict(...), columns=)
|
closed
| 2024-02-02T02:36:52
| 2024-02-24T00:13:55
| 2024-02-24T00:13:52
|
https://github.com/pandas-dev/pandas/pull/57205
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57205
|
https://github.com/pandas-dev/pandas/pull/57205
|
mroeschke
| 3
|
- [ ] closes #24368 (Replace xxxx with the GitHub issue number)
- [ ] closed #25887
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
```python
import pandas as pd, numpy as np
a = pd.Series(np.array([1, 2, 3]))
columns = range(10_000)
data = {i: a for i in columns}
%timeit pd.DataFrame(data, columns=columns)
51.3 ms ± 96 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) # PR
102 ms ± 1.8 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) # main
```
```python
In [1]: import pandas as pd
In [2]: %timeit pd.DataFrame([], columns=['a'])
37.3 µs ± 231 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each) # PR
183 µs ± 584 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each) # main
```
|
[
"Performance",
"DataFrame"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"When you have a chance, could you take a look @jbrockmendel ",
"will take a look. i like the idea of getting Series out of here",
"LGTM"
] |
2,113,870,822
| 57,204
|
PDEP-14: Publish translations of pandas.pydata.org
|
closed
| 2024-02-02T01:53:51
| 2024-02-27T19:09:50
| 2024-02-27T19:09:50
|
https://github.com/pandas-dev/pandas/pull/57204
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57204
|
https://github.com/pandas-dev/pandas/pull/57204
|
steppi
| 16
|
Following up on the discussion in #56301, this PR adds a first draft for a PDEP proposing creation and publication of translations of the core website content.
To summarize, the [Scientific Python community and communications infrastructure grant](https://scientific-python.org/doc/scientific-python-community-and-communications-infrastructure-2022.pdf) is supporting work to facilitate official translation of content from the websites of [Scientific Python core projects](https://scientific-python.org/specs/core-projects/) and publication of these translations. This PDEP proposes that Pandas participates in these efforts. The hope is that the bulk of the work would be taken on by myself and/or a colleague at Quansight, and the work required by Pandas maintainers would be minimal.
There's more information in the PDEP below. Thank you. Looking forward to hearing your feedback.
|
[
"PDEP"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"cc @pandas-dev/pandas-core ",
"Thanks for putting this together @steppi, nice start. I'm fine with the general idea, but being honest, not a fan of the PDEP as it is now.\r\n\r\nI may be wrong, but I don't think we need so much context on why translations are useful. This is going to be reviewed by many people, and to me feels like everything in the PDEP except the implementation part could be summarized in a paragraph, making the review of the PDEP much faster.\r\n\r\nAlso, I don't think the PDEP should be opinionated on what exact pages should be translated. In my opinion we want to define what's made available to be translated, and that's it. Then if you work on some of the translations as part of the grant, surely feel free to skip the roadmap or whatever it doesn't make sense.\r\n\r\nI don't fully understand about the mirror, maybe you can expand on why a mirror is useful.\r\n\r\nFinally, feels like your idea is to create files such as `web/pandas/it/getting_started.md` or `web/pandas/zh/about/team.md` in this same repo with the content translated. I'm a strong -1 on this. We can synchronize the translations in our production repository directly from somewhere else. I'm -1 on anything that involves having translated content in this repo.\r\n\r\nNot sure if the idea is to generate `.po` / `.mo` files or to have a full copy of the markdown files for each translation, but whatever it is it can be kept separately, and we can fetch it from somewhere else when we build our website, so we have the translated files published. I don't think there is added value on moving translated context to this repo.",
"Thanks for the pointed criticism @datapythonista. I’m just trying to get the ball rolling and would have been surprised to produce a good proposal on the first go honestly. I’ll take your feedback into account and make revisions.",
"> Finally, feels like your idea is to create files such as `web/pandas/it/getting_started.md` or `web/pandas/zh/about/team.md` in this same repo with the content translated. I'm a strong -1 on this. We can synchronize the translations in our production repository directly from somewhere else. I'm -1 on anything that involves having translated content in this repo.\r\n\r\nyep, at first glance, this appears to be a major stumbling block.\r\n\r\nI see many web sites that warn of external content, so I guess that any links from the pandas web site to translated content would need something similar.\r\n\r\nI don't think it is realistic to expect pandas core maintainers to approve changes/ merge PRs they do not understand. Even if we have some maintainers that are comfortable with some other languages, this still reduces the bandwidth available for approval and discussion.",
"To be clear, I'm fine to have the translations in for example https://pandas.pydata.org/it/ for Italian.\r\n\r\nWhat I think it's a bad idea that adds zero value and a significant maintenance cost is to have translated texts in this repo. The CI process can download translations from `.po` files, from translated markdown files or from whatever, render the website with them, and publish the website with the translations. I don't see how having translated content in this repo if the pandas maintainers and the translators are different groups of people is helpful in any way.",
"Thank you @datapythonista and @simonjayhawkins for your feedback. I've produced an updated version of the proposal incorporating your suggestions. I've removed all of the extra-fluff and replaced it with a one paragraph summary that I hope hits upon the key points, removed the opinions on what should be translated, removed the suggestions to host translated content on the Pandas website itself and removed everything else that would be onerous for Pandas maintainers.",
"Thanks for the update and the flexibility @steppi, this looks much better.\r\n\r\nNot sure if you agree, but seems like the proposal goes into many technical details in some things (like already deciding to use crowdin over other options, the need to clone the repo...). And at the same time the process is very unclear. I tried to watch a video on how is the workflow in crowdin, but in their website they'll make me register, and in youtube I couldn't find a short video to understand the process. I watched few, but they all cover the crowdin dashboard.\r\n\r\nDo you think in this PDEP we want to already agree in the technical solution, or we just want to agree on moving forward with the project? Feels like it'd be good to either remove the implementation details, or add a much clearer explanation on what exactly is going to be implemented.\r\n\r\nOther than that, Imy personal preference is to leave the translators parts more open. If there is abuse or problems with the translations, I would look for solutions, but so far I'd start with letting anyone translate anything. I assume the translation software will let us block or revert if ever needed.",
"Thanks @datapythonista \r\n\r\n> Not sure if you agree, but seems like the proposal goes into many technical details in some things (like already deciding to use crowdin over other options, the need to clone the repo...). And at the same time the process is very unclear. I tried to watch a video on how is the workflow in crowdin, but in their website they'll make me register, and in youtube I couldn't find a short video to understand the process. I watched few, but they all cover the crowdin dashboard.\r\n\r\nUnderstood. I just saw that the template section heading is **Detailed Implementation** and thought the expectation was to give a high level of detail. If we don't need to settle down the technical details, I think that would be better. The strong bias towards choosing Crowdin is motivated by wanting to have a uniform platform among the different core projects, and the quality of the free support they've been offering. Since it's only been used for NumPy so far, there's still an opportunity to change platform if there's something that would be significantly better. I didn't really understand how Crowdin works until I set up a test project and played around with it. If you'd like, I could do a short call to run through the workflow, or make a short screencast video.\r\n\r\n> Other than that, my personal preference is to leave the translators parts more open. If there is abuse or problems with the translations, I would look for solutions, but so far I'd start with letting anyone translate anything. I assume the translation software will let us block or revert if ever needed.\r\n\r\nI could look into how that would work. I know that it's possible to suggest translations without overriding existing ones. I think having things invite-only doesn't have to add that much friction if approval is prompt and we guaranteed.\r\n",
"> Thanks @steppi for the changes.\r\n> \r\n> My current thinking is if this is a funded project with limited scope and duration that maybe a PDEP is not needed after all.\r\n> \r\n> PDEP are for enhancements to the pandas project and processes. It appears that most of this work will be outside of pandas\r\n\r\nThanks @simonjayhawkins. That would be nice if we could forgo the PDEP process. Yes, the plan would be for most of the work to be outside of Pandas.",
"> That would be nice if we could forgo the PDEP process.\r\n\r\nhopefully others will give some input on this. If the pandas core maintainers are only required to create some sort of synchronization to the pandas web site then IMHO a PDEP is not needed.\r\n\r\nAnother concern I have that I don't think has yet been mentioned is ensuring that users of the translated documentation are aware where to report issues/suggestions. I assume this is not going to be the main pandas repo.",
"> Another concern I have that I don't think has yet been mentioned is ensuring that users of the translated documentation are aware where to report issues/suggestions. I assume this is not going to be the main pandas repo\r\n\r\nGood question. I think such issues/suggestions should be made in the repo hosting the translations. There could be a link to create an issue in that repo among the things listed when someone clicks new issue in pandas. \r\n",
"> There could be a link to create an issue in that repo among the things listed when someone clicks new issue in pandas.\r\n\r\nsgtm",
"Thanks @Dr-Irv.\r\n\r\n> 1. What work by the pandas team is required up front (i.e., just one time) to support these translations? For example, changes to how the web site is built, or an additional button/menu, etc.\r\n\r\nYes, I think you have this right. It would require changes to how the web site is built and addition of some kind of UI element to switch languages.\r\n\r\n> 2\\. What ongoing support, if any, is required by the pandas team to support these translations?\r\n\r\nAt the moment I can't think of anything that goes beyond my answers to the following questions.\r\n\r\n> 3\\. If someone updates the English version of a page on the pandas web site, how would the translations get updated?\r\nAs I envision it, the repo hosting the translated content will have a github action polling for changes to the English content, and merging them. These changes would then be synced to Crowdin and translators would receive a notification that there are new strings to translate. Pandas maintainers should not be responsible for finding or communicating with translators. We're going to try to build an active translation community that works across the Scientific Python projects.\r\n\r\n> 4\\. Will the translations clearly indicate that issues with the translations should be reported elsewhere?\r\n\r\nYes, I think that's doable. Also, one of the preset options when trying to open a new issue on Pandas could be for issues with translations, and it would link elsewhere.\r\n\r\n> 5\\. Suppose at some point the pandas team decides to reorganize the pandas documentation? Does this need to be coordinated with the translations project?\r\n\r\nPotentially, but I'd hope to make the need for coordination minimal. If its just a reorganization, without significant changes to any of the content selected for translation, this could be sorted out on the translation project side with a few hours work (provided that the content selected for translation is kept reasonably small). It would be good to get a heads up that you're planning a reorganization, but I think I could just set up email filters to notify me about issues or pull requests related to website updates.\r\n\r\nI think you could potentially feel constrained with respect to major or complete changes to the content that's selected for translation though. In that all translations would be lost and it may take time for new ones to be made.",
"Hi everyone. While this is still up in the air, is it OK if I get started setting up a mirrored repo with the content and syncing it with Crowdin? There's an open source license request form, https://crowdin.com/page/open-source-project-setup-request, that they'll let me fill out in your place with your permission. The decision for whether or not you want to publish the translations on your website can wait, but in any case I think it would still be valuable for quality translations to exist.",
"Thanks for the continued work on this @steppi. This sounds good. I'm personally fine to close this PDEP and have you working on this independently from the main pandas repository. Also, if you want to publish the translated content in for example GitHub pages, I can add a rule to our web server configuration so for example pandas.pydata.org/zh/ serves the Chinese translation you publish there. I guess there may be few challenges here and there, but if this makes sense to you, I personally don't think we need further discussion, build whatever you think it's best, and when things are ready, just let us know how things work, so we can make sure there are no significant concerns in terms of the translations becoming obsolete, people abusing and using the translations to spam...\r\n\r\nOf course if you prefer to continue iterating on this PDEP you are welcome to, but since my understanding is that this is a grant you want to be working on regardless of the decisions here, I personally think it'll make your life easier to build the first version in the best way to you personally, and we give you feedback once we can comment on the specific implementation.\r\n\r\nIf you can get the website translated without core devs having to review PRs with translations or have significant extra maintenance work, it'll be amazing, and I don't think there'll be blockers from anyone.",
"Thanks @datapythonista! That sounds good. Feel free to close this issue now. I'll get to work on setting things up for Pandas translations.\r\n\r\n> If you can get the website translated without core devs having to review PRs with translations or have significant extra maintenance work, it'll be amazing, and I don't think there'll be blockers from anyone.\r\n\r\nThat's the goal. The discussion with you and other Pandas devs was very valuable in helping me put together a concrete plan for moving this forward. Thank you to everyone who was involved.\r\n"
] |
2,113,847,176
| 57,203
|
Backport PR #57174 on branch 2.2.x (BUG: Interchange protocol implementation allows non-string column names)
|
closed
| 2024-02-02T01:25:51
| 2024-02-02T02:38:20
| 2024-02-02T02:38:20
|
https://github.com/pandas-dev/pandas/pull/57203
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57203
|
https://github.com/pandas-dev/pandas/pull/57203
|
meeseeksmachine
| 0
|
Backport PR #57174: BUG: Interchange protocol implementation allows non-string column names
|
[
"Interchange"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[] |
2,113,817,417
| 57,202
|
Backport PR #57163 on branch 2.2.x (CI: Add macOS M1 CI)
|
closed
| 2024-02-02T00:53:33
| 2024-02-02T02:37:49
| 2024-02-02T02:37:48
|
https://github.com/pandas-dev/pandas/pull/57202
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57202
|
https://github.com/pandas-dev/pandas/pull/57202
|
meeseeksmachine
| 0
|
Backport PR #57163: CI: Add macOS M1 CI
|
[
"Build",
"CI",
"OS X"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[] |
2,113,806,810
| 57,201
|
CLN: pyproject.toml
|
closed
| 2024-02-02T00:42:31
| 2024-02-02T17:45:03
| 2024-02-02T17:45:00
|
https://github.com/pandas-dev/pandas/pull/57201
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57201
|
https://github.com/pandas-dev/pandas/pull/57201
|
mroeschke
| 0
| null |
[
"Clean"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[] |
2,113,432,446
| 57,200
|
STY: Enable some passing rules
|
closed
| 2024-02-01T20:34:35
| 2024-02-02T17:43:25
| 2024-02-02T17:43:22
|
https://github.com/pandas-dev/pandas/pull/57200
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57200
|
https://github.com/pandas-dev/pandas/pull/57200
|
mroeschke
| 0
| null |
[
"Code Style"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[] |
2,113,365,130
| 57,199
|
TST/CLN: Assorted
|
closed
| 2024-02-01T20:01:12
| 2024-02-02T17:43:35
| 2024-02-02T17:43:33
|
https://github.com/pandas-dev/pandas/pull/57199
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57199
|
https://github.com/pandas-dev/pandas/pull/57199
|
mroeschke
| 0
|
Only notable change is bumping up the tolerance in `test_dti_constructor_with_non_nano_now_today` xref https://github.com/pandas-dev/pandas/actions/runs/7746334432/job/21124335580
|
[
"Testing",
"Clean"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[] |
2,113,334,460
| 57,198
|
Backport PR #57173: BUG: pandas int extension dtypes has no attribute…
|
closed
| 2024-02-01T19:45:08
| 2024-02-01T21:15:40
| 2024-02-01T21:15:39
|
https://github.com/pandas-dev/pandas/pull/57198
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57198
|
https://github.com/pandas-dev/pandas/pull/57198
|
MarcoGorelli
| 0
|
… byteorder
- [ ] closes #xxxx (Replace xxxx with the GitHub issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
|
[] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[] |
2,113,253,877
| 57,197
|
BUG: ValueError: Invalid frequency: ME
|
closed
| 2024-02-01T19:02:39
| 2024-05-02T19:55:28
| 2024-02-09T09:36:43
|
https://github.com/pandas-dev/pandas/issues/57197
| true
| null | null |
celestinoxp
| 2
|
### Pandas version checks
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this bug exists on the [main branch](https://pandas.pydata.org/docs/dev/getting_started/install.html#installing-the-development-version-of-pandas) of pandas.
### Reproducible Example
```python
.
```
### Issue Description
I'm testing a pull-request on pycaret https://github.com/pycaret/pycaret/pull/3857 to support new scikit-learn 1.4, and new pandas 2.2.0 also.
I fixed/removed deprecated ALIAS of time frequencies, as you can see here: https://github.com/pycaret/pycaret/pull/3857/commits/039f159ab25f22f68d3f0d6820a94c40e3f4173f
However as i can see in pandas doccumentation of pandas 2.2 https://pandas.pydata.org/docs/whatsnew/v2.2.0.html#deprecate-aliases-m-q-y-etc-in-favour-of-me-qe-ye-etc-for-offsets, the alias M is changed to ME, but something is wrong because i receive an error because ME is in use... i give you an picture of my vscode:
As you can see, have an error ValueError: Invalid frequency: ME, failed to parse with error message: ValueError("for Period, please use 'M' instead of 'ME'")
but i have pandas 2.2 as you can see (i used terminal to show pandas version)
### Expected Behavior
No errors
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : f538741432edf55c6b9fb5d0d496d2dd1d7c2457
python : 3.10.13.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.22631
machine : AMD64
processor : AMD64 Family 23 Model 96 Stepping 1, AuthenticAMD
byteorder : little
LC_ALL : None
LANG : pt_BR.UTF-8
LOCALE : Portuguese_Portugal.1252
pandas : 2.2.0
numpy : 1.26.3
pytz : 2022.7.1
dateutil : 2.8.2
setuptools : 69.0.3
pip : 23.3.2
Cython : 0.29.36
pytest : 7.4.3
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.9.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.3
IPython : 8.20.0
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.12.2
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : 2023.10.0
gcsfs : None
matplotlib : 3.8.2
numba : 0.58.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.11.4
sqlalchemy : None
tables : None
tabulate : 0.9.0
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.3
qtpy : None
pyqt5 : None
</details>
|
[
"Bug",
"Needs Info"
] | 2
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks for the report. Could you provide a reproducible example.",
"I'm going to close this issue because it's possible that the problem is with third-party packages"
] |
2,112,713,037
| 57,196
|
BUG: Dataframe's `to_sql` method fails while trying to persist Dataframe to Postgtres in Version 2.2.0
|
open
| 2024-02-01T14:50:31
| 2025-07-16T09:16:43
| null |
https://github.com/pandas-dev/pandas/issues/57196
| true
| null | null |
damianr13
| 4
|
### Pandas version checks
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the [main branch](https://pandas.pydata.org/docs/dev/getting_started/install.html#installing-the-development-version-of-pandas) of pandas.
### Reproducible Example
```python
import pandas as pd
from sqlalchemy import create_engine
from .settings import get_settings # pydantic way of reading the env variables
conn_string = get_settings().db_conn_string
df = pd.DataFrame(range(5), columns=["id"])
db = create_engine(conn_string)
conn = db.connect()
nr_rows = df.to_sql(
"temp_pandas_test", con=conn, if_exists="replace", index=False, chunksize=1000
)
```
### Issue Description
Exporting a pandas dataframe to Postgres doesn't work anymore in version 2.2.0. The code above works well with version 2.1.2, but in version 2.2.0 it fails with the following error:
```
Traceback (most recent call last):
File "/Users/robert-andreidamian/Workspace/panprices/shelf_analytics_update_products/demo.py", line 14, in <module>
nr_rows = df.to_sql(
File "/Users/robert-andreidamian/.local/share/virtualenvs/shelf_analytics_update_products-BwnpeEoI/lib/python3.9/site-packages/pandas/util/_decorators.py", line 333, in wrapper
return func(*args, **kwargs)
File "/Users/robert-andreidamian/.local/share/virtualenvs/shelf_analytics_update_products-BwnpeEoI/lib/python3.9/site-packages/pandas/core/generic.py", line 3059, in to_sql
return sql.to_sql(
File "/Users/robert-andreidamian/.local/share/virtualenvs/shelf_analytics_update_products-BwnpeEoI/lib/python3.9/site-packages/pandas/io/sql.py", line 858, in to_sql
return pandas_sql.to_sql(
File "/Users/robert-andreidamian/.local/share/virtualenvs/shelf_analytics_update_products-BwnpeEoI/lib/python3.9/site-packages/pandas/io/sql.py", line 2867, in to_sql
table.create()
File "/Users/robert-andreidamian/.local/share/virtualenvs/shelf_analytics_update_products-BwnpeEoI/lib/python3.9/site-packages/pandas/io/sql.py", line 1000, in create
if self.exists():
File "/Users/robert-andreidamian/.local/share/virtualenvs/shelf_analytics_update_products-BwnpeEoI/lib/python3.9/site-packages/pandas/io/sql.py", line 986, in exists
return self.pd_sql.has_table(self.name, self.schema)
File "/Users/robert-andreidamian/.local/share/virtualenvs/shelf_analytics_update_products-BwnpeEoI/lib/python3.9/site-packages/pandas/io/sql.py", line 2882, in has_table
return len(self.execute(query, [name]).fetchall()) > 0
File "/Users/robert-andreidamian/.local/share/virtualenvs/shelf_analytics_update_products-BwnpeEoI/lib/python3.9/site-packages/pandas/io/sql.py", line 2689, in execute
cur = self.con.cursor()
AttributeError: 'Connection' object has no attribute 'cursor'
```
I noticed that [a new driver](https://pandas.pydata.org/docs/whatsnew/v2.2.0.html#adbc-driver-support-in-to-sql-and-read-sql) was added in Version 2.2.0, which might be related to my issue.
### Expected Behavior
The code above should execute the same way it used to do with the previous version, and export the dataframe to a table in my Postgres Database.
### Installed Versions
INSTALLED VERSIONS
------------------
commit : c3014abb3bf2e15fa66d194ebd2867161527c497
python : 3.9.13.final.0
python-bits : 64
OS : Darwin
OS-release : 23.1.0
Version : Darwin Kernel Version 23.1.0: Mon Oct 9 21:32:52 PDT 2023; root:xnu-10002.41.9~7/RELEASE_ARM64_T8122
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_GB.UTF-8
pandas : 3.0.0.dev0+210.gc3014abb3b
numpy : 1.26.3
pytz : 2023.4
dateutil : 2.8.2
setuptools : 68.2.2
pip : 23.3.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 5.1.0
html5lib : None
pymysql : None
psycopg2 : 2.9.9
jinja2 : 3.1.3
IPython : None
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : 3.1.2
pandas_gbq : None
pyarrow : None
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.4
qtpy : None
pyqt5 : None
|
[
"Bug",
"IO SQL",
"Needs Triage"
] | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"cc @WillAyd related to your changes?",
"maybe related #57049 and #57178",
"Same error here with an **sqlite** database (I get the error with pandas 2.2.0 but not with 2.1.4)",
"The issue continues to exist in 2.3 as well. "
] |
2,112,687,872
| 57,195
|
ENH: Remove value dependent dtype behavior for grouped aggregation for masked and pyarrow dtypes
|
closed
| 2024-02-01T14:38:38
| 2024-02-02T01:24:17
| 2024-02-02T01:24:17
|
https://github.com/pandas-dev/pandas/issues/57195
| true
| null | null |
rohanjain101
| 1
|
### Feature Type
- [ ] Adding new functionality to pandas
- [ ] Changing existing functionality in pandas
- [X] Removing existing functionality in pandas
### Problem Description
```
>>> a = pd.DataFrame({"A": pd.Series([True, True], dtype=pd.BooleanDtype()), "B": pd.Series([1,2], dtype="Int8")})
>>> a.groupby("A").sum().dtypes
B Int8
dtype: object
>>>
```
But if I increase the values in "B" such that the sum can't fit in an Int8:
```
>>> a = pd.DataFrame({"A": pd.Series([True, True], dtype=pd.BooleanDtype()), "B": pd.Series([124,126], dtype="Int8")})
>>> a.groupby("A").sum().dtypes
B Int64
dtype: object
>>>
```
For the same input type (Int8), sum returns either an Int8 or an Int64 depending on the values in the original series.
I think value dependent type behavior should be avoided, and in this example, sum(Int8) should always return Int64, irrespective of the values in the series. This would have more predictable behavior for downstream consumers of the aggregate column, and also, probably would improve the performance of the aggregate itself, if it had a pre-determined return type.
### Feature Description
All aggregate should have a pre-determined return type for the given input type, and this behavior should not be affected by the values in the column.
### Alternative Solutions
None.
### Additional Context
_No response_
|
[
"Enhancement",
"Needs Triage"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"I think there is already an issue about this, a little bit older though"
] |
2,112,546,833
| 57,194
|
fix typo
|
closed
| 2024-02-01T13:40:54
| 2024-02-01T20:14:06
| 2024-02-01T17:32:42
|
https://github.com/pandas-dev/pandas/pull/57194
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57194
|
https://github.com/pandas-dev/pandas/pull/57194
|
mdeff
| 1
|
- [ ] closes #xxxx (Replace xxxx with the GitHub issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
|
[
"Docs"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @mdeff "
] |
2,112,219,935
| 57,193
|
BUG: fix bug in DataFrame.diff when dataframe has bool dtype and axis=1
|
closed
| 2024-02-01T11:04:52
| 2024-06-28T06:27:49
| 2024-06-17T16:24:03
|
https://github.com/pandas-dev/pandas/pull/57193
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57193
|
https://github.com/pandas-dev/pandas/pull/57193
|
yuanx749
| 2
|
supersede #53248
- [x] closes #52579 (Replace xxxx with the GitHub issue number)
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
|
[
"Bug",
"DataFrame",
"Transformations"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"@jbrockmendel @mroeschke gentle ping for review.",
"Thanks for the pull request, but it appears to have gone stale. If interested in continuing, please merge in the main branch, address any review comments and/or failing tests, and we can reopen."
] |
2,112,052,708
| 57,192
|
BUG: pandas 2.2.0 `DataFrame.groupby` regression when group labels of type `pd.Timestamp`
|
open
| 2024-02-01T09:52:11
| 2025-06-02T17:10:20
| null |
https://github.com/pandas-dev/pandas/issues/57192
| true
| null | null |
maread99
| 3
|
### Pandas version checks
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this bug exists on the [main branch](https://pandas.pydata.org/docs/dev/getting_started/install.html#installing-the-development-version-of-pandas) of pandas.
### Reproducible Example
```python
import pandas as pd
assert pd.__version__ == "2.2.0"
index = pd.date_range("2024-02-01", "2024-02-03", freq="8h", tz="UTC")
# index
# DatetimeIndex(['2024-02-01 00:00:00+00:00', '2024-02-01 08:00:00+00:00',
# '2024-02-01 16:00:00+00:00', '2024-02-02 00:00:00+00:00',
# '2024-02-02 08:00:00+00:00', '2024-02-02 16:00:00+00:00',
# '2024-02-03 00:00:00+00:00'],
# dtype='datetime64[ns, UTC]', freq='8h')
df = pd.DataFrame([1] * len(index), index)
def f(ts: pd.Timestamp) -> pd.Timestamp:
"""Group by date, i.e. as timezone naive timestamp."""
return ts.normalize().tz_convert(None)
grouped = df.groupby(by=f)
keys = list(grouped.groups.keys())
print(keys)
# [Timestamp('2024-02-01 00:00:00+0000', tz='UTC'),
# Timestamp('2024-02-02 00:00:00+0000', tz='UTC'),
# Timestamp('2024-02-03 00:00:00+0000', tz='UTC')]
```
### Issue Description
Although the return from the 'by' function is timezone naive, the group labels are timezone aware (UTC).
(I've tried every combination of the `as_index` and `group_keys` options, always get the same group keys).
### Expected Behavior
Would have expected behaviour as pandas 2.1.4 and prior, i.e,. group keys as returned from the 'by' function...
```python
import pandas as pd
assert pd.__version__ == "2.1.4"
index = pd.date_range("2024-02-01", "2024-02-03", freq="8h", tz="UTC")
df = pd.DataFrame([1] * len(index), index)
def f(ts: pd.Timestamp) -> pd.Timestamp:
return ts.normalize().tz_convert(None)
grouped = df.groupby(by=f)
keys = list(grouped.groups.keys())
print(keys)
```
```
[Timestamp('2024-02-01 00:00:00'),
Timestamp('2024-02-02 00:00:00'),
Timestamp('2024-02-03 00:00:00')]
```
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : f538741432edf55c6b9fb5d0d496d2dd1d7c2457
python : 3.9.13.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.22631
machine : AMD64
processor : Intel64 Family 6 Model 141 Stepping 1, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United Kingdom.1252
pandas : 2.2.0
numpy : 1.26.3
pytz : 2023.4
dateutil : 2.8.2
setuptools : 58.1.0
pip : 23.3.2
Cython : None
pytest : 8.0.0
hypothesis : 6.97.3
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.9.4
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 8.14.0
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.12.3
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : None
numba : None
numexpr : 2.9.0
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 15.0.0
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : 3.9.2
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.4
qtpy : None
pyqt5 : None
</details>
|
[
"Bug",
"Regression",
"Timezones",
"Index"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks for the report. Result of a git bisect:\r\n\r\n```\r\ncommit 746e5eee860b6e143c33c9b985e095dac2e42677\r\nAuthor: jbrockmendel\r\nDate: Mon Oct 16 11:50:28 2023 -0700\r\n\r\n ENH: EA._from_scalars (#53089)\r\n```\r\n\r\ncc @jbrockmendel ",
"yah i think thats a problem in DTA._from_scalars. _from_scalars itself was a mistake xref #56430",
"This can be reproduced just using `index.map`, so taking off the groupby label. "
] |
2,111,600,616
| 57,191
|
ENH: deprecate reading from clipboard only supports utf-8 encoding
|
open
| 2024-02-01T05:41:04
| 2024-02-04T14:48:38
| null |
https://github.com/pandas-dev/pandas/issues/57191
| true
| null | null |
PaleNeutron
| 1
|
### Feature Type
- [ ] Adding new functionality to pandas
- [X] Changing existing functionality in pandas
- [ ] Removing existing functionality in pandas
### Problem Description
Code following will raise `NotImplementedError: reading from clipboard only supports utf-8 encoding`
```python
df = pd.read_clipboard(encoding="gbk")
```
BUT, if user do it with:
```python
pd.io.clipboard.ENCODING = "gbk"
df = pd.read_clipboard()
pd.io.clipboard.ENCODING = "utf8"
```
It works well.
### Feature Description
I am not sure why `encoding` in pandas is fixed to 'utf8', but in wsl2 user's windows system default encoding is usually not Unicode and very hard to migrate this settings.
Allow user change encoding of `read_clipboard` will be very helpful.
### Alternative Solutions
```python
pd.io.clipboard.ENCODING = "gbk"
df = pd.read_clipboard()
pd.io.clipboard.ENCODING = "utf8"
```
### Additional Context
data for testting:
这 | 1
-- | --
是 | 2
一 | 3
个 | 4
表 | 5
```
# stdout[:-2]
data = b'\xd5\xe2\t1\r\n\xca\xc7\t2\r\n\xd2\xbb\t3\r\n\xb8\xf6\t4\r\n\xb1\xed\t5\r\n'
```
|
[
"Enhancement",
"IO Data",
"Needs Discussion"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks for the request. pandas implements this by vendoring [pyperclip](https://github.com/asweigart/pyperclip). It does not appear to me that pyperclip (explicitly) supports anything but UTF-8, I would be hesistant to support other encodings unless pyperclip supports them or there is extensive testing."
] |
2,111,566,090
| 57,190
|
CI: Pinning doc previewer action to current release
|
closed
| 2024-02-01T05:07:12
| 2024-02-01T17:15:04
| 2024-02-01T17:14:56
|
https://github.com/pandas-dev/pandas/pull/57190
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57190
|
https://github.com/pandas-dev/pandas/pull/57190
|
datapythonista
| 1
|
I don't think we'll have any doc previewer release that won't be done for pandas, but pinning the action to avoid automatic upgrades anyway.
|
[
"Docs",
"CI"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks for the follow up @datapythonista "
] |
2,111,162,868
| 57,189
|
BUG: Series.map() coerces Int64Dtype and int64[pyarrow] series which contain missing values to float64
|
open
| 2024-01-31T23:26:59
| 2024-02-02T09:21:57
| null |
https://github.com/pandas-dev/pandas/issues/57189
| true
| null | null |
weltenseglr
| 6
|
### Pandas version checks
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the [main branch](https://pandas.pydata.org/docs/dev/getting_started/install.html#installing-the-development-version-of-pandas) of pandas.
### Reproducible Example
```python
import pandas as pd
import pyarrow as pa
ser1 = pd.Series([1,2,3,None,10], dtype=pd.Int64Dtype())
ser2 = pd.Series([1,2,3,pd.NA,10], dtype=pd.Int64Dtype())
ser3 = pd.Series([1,2,3,pd.NA,10], dtype=pd.ArrowDtype(pa.int64()))
for ser in ser1, ser2, ser3:
print(f"initial dtype: {ser.dtype}")
ser_mapped = ser.map(lambda x: x)
print(f"post map dtype: {ser_mapped.dtype}")
ser_filtered = ser.dropna().map(lambda x: x)
print(f"dropna map dtype: {ser_mapped.dtype}")
ser2.map(type)
ser2.dropna().map(type)
"""
output:
for ser1:
initial dtype: Int64
post map dtype: float64
dropna map dtype: float64
for ser2:
initial dtype: Int64
post map dtype: float64
dropna map dtype: float64
for ser3:
initial dtype: int64[pyarrow]
post map dtype: float64
dropna map dtype: float64
"""
```
### Issue Description
Using map on a Series with dtype Int64Dtype or int64[dtype] will coerce values to float if it contains any missing values.
```
>>> ser2.map(type)
0 <class 'float'>
1 <class 'float'>
2 <class 'float'>
3 <class 'float'>
4 <class 'float'>
dtype: object
>>> ser2.dropna().map(type)
0 <class 'int'>
1 <class 'int'>
2 <class 'int'>
4 <class 'int'>
dtype: object
```
### Expected Behavior
Series.map() should not coerce into float64 with these dtypes.
As stated in the documentation on [working with missing data](https://pandas.pydata.org/docs/user_guide/missing_data.html#values-considered-missing):
> [NA](https://pandas.pydata.org/docs/reference/api/pandas.NA.html#pandas.NA) for [StringDtype](https://pandas.pydata.org/docs/reference/api/pandas.StringDtype.html#pandas.StringDtype), [Int64Dtype](https://pandas.pydata.org/docs/reference/api/pandas.Int64Dtype.html#pandas.Int64Dtype) (and other bit widths), Float64Dtype`(and other bit widths), :class:`BooleanDtype and [ArrowDtype](https://pandas.pydata.org/docs/reference/api/pandas.ArrowDtype.html#pandas.ArrowDtype). **These types will maintain the original data type of the data.**
### Installed Versions
<details>
>>> pd.show_versions()
/usr/lib/python3.12/site-packages/_distutils_hack/__init__.py:33: UserWarning: Setuptools is replacing distutils.
warnings.warn("Setuptools is replacing distutils.")
INSTALLED VERSIONS
------------------
commit : db11e25d2b1175fdf85d963a88ff5a1d4bdb6fd8
python : 3.12.1.final.0
python-bits : 64
OS : Linux
OS-release : 6.6.13-200.fc39.x86_64
Version : #1 SMP PREEMPT_DYNAMIC Sat Jan 20 18:03:28 UTC 2024
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_IE.UTF-8
LOCALE : en_IE.UTF-8
pandas : 3.0.0.dev0+197.gdb11e25d2b
numpy : 1.26.3
pytz : 2023.3.post1
dateutil : 2.8.2
setuptools : 67.7.2
pip : 23.2.1
Cython : None
pytest : 7.4.3
hypothesis : None
sphinx : 7.2.6
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 5.0.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
IPython : 8.19.0
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 15.0.0
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.4
qtpy : None
pyqt5 : None
</details>
|
[
"Bug",
"NA - MaskedArrays",
"Arrow"
] | 2
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"take",
"https://github.com/pandas-dev/pandas/issues/56606\r\n\r\nI had the same issue, based on the issue above, it seemed to be intentional change in 2.2.X",
"@rohanjain101 Yeah you're right. I believe it changed from v2.1.x\r\n\r\nhttps://github.com/pandas-dev/pandas/blob/a671b5a8bf5dd13fb19f0e88edc679bc9e15c673/pandas/core/arrays/base.py#L2188\r\n\r\nto \r\n\r\nhttps://github.com/pandas-dev/pandas/blob/c3014abb3bf2e15fa66d194ebd2867161527c497/pandas/core/arrays/masked.py#L1332-L1333\r\n\r\nhttps://github.com/pandas-dev/pandas/blob/c3014abb3bf2e15fa66d194ebd2867161527c497/pandas/core/arrays/arrow/array.py#L1426-L1430\r\n \r\nin v2.2.0. Guess you might have to cast it back to your wanted dtype @weltenseglr ",
"Thanks for your feedback, @rohanjain101 @remiBoudreau. I think the documentation is outdated then?\r\n[Series.map()](https://pandas.pydata.org/docs/reference/api/pandas.Series.map.html#pandas.Series.map) does not warn about this matter and [missing data](https://pandas.pydata.org/docs/user_guide/missing_data.html#values-considered-missing) states that\r\n> [NA](https://pandas.pydata.org/docs/reference/api/pandas.NA.html#pandas.NA) for [StringDtype](https://pandas.pydata.org/docs/reference/api/pandas.StringDtype.html#pandas.StringDtype), [Int64Dtype](https://pandas.pydata.org/docs/reference/api/pandas.Int64Dtype.html#pandas.Int64Dtype) (and other bit widths), Float64Dtype`(and other bit widths), :class:`BooleanDtype and [ArrowDtype](https://pandas.pydata.org/docs/reference/api/pandas.ArrowDtype.html#pandas.ArrowDtype). **These types will maintain the original data type of the data.**\r\n\r\nCasting back won't help in my case, as my mapper must not receive coerced values...\r\n\r\nI'm trying to figure out how to maintain the original data type. Any ideas?\r\n\r\n\r\n\r\n",
"@weltenseglr You could use the workaround described in https://github.com/pandas-dev/pandas/issues/56606#issuecomment-1870453754\r\n\r\nUsing the python map operator instead of Series.map. Atleast for now, this preserves the original type.",
"thanks, @rohanjain101.\r\n\r\nI guess I will have to implement a workaround based on your suggestion or revert to a 2.1 release.\r\nHowever, it still feels wrong that pandas' map casts nullable numerical dtypes to float64.\r\n\r\nIs this going to change in pandas 3.0? I think this doesn't play well with the project's intentions regarding PDEP-10 and its benefits?"
] |
2,111,108,717
| 57,188
|
ENH: align timedelta fractional seconds
|
open
| 2024-01-31T22:42:03
| 2024-05-15T14:22:00
| null |
https://github.com/pandas-dev/pandas/issues/57188
| true
| null | null |
wjandrea
| 7
|
### Feature Type
- [X] Adding new functionality to pandas
- [ ] Changing existing functionality in pandas
- [ ] Removing existing functionality in pandas
### Problem Description
It's hard to read a column of timedeltas with fractional seconds of different precisions because it's not aligned, for example:
```
0 0 days 00:00:01
1 0 days 00:00:00.500000
2 0 days 00:00:00.333333333
dtype: timedelta64[ns]
```
It'd be easier to read like this:
```
0 0 days 00:00:01
1 0 days 00:00:00.500000
2 0 days 00:00:00.333333333
dtype: timedelta64[ns]
```
or
```
0 0 days 00:00:01.000000000
1 0 days 00:00:00.500000000
2 0 days 00:00:00.333333333
dtype: timedelta64[ns]
```
To reproduce:
```python
s = 1 / pd.Series(np.arange(3)+1)
td = pd.to_timedelta(s, unit='s')
td
```
### Feature Description
Add padding on the right.
In this case, it can be done by left-justifying to the largest size:
```python
n = td.astype(str).apply(len).max()
td.astype(str).str.ljust(n)
```
```
0 0 days 00:00:01
1 0 days 00:00:00.500000
2 0 days 00:00:00.333333333
dtype: object
```
But of course, this wouldn't work for different numbers of days. For example:
```python
td1 = pd.concat([td, pd.Series(pd.Timedelta(days=4000))])
```
```
0 0 days 00:00:01
1 0 days 00:00:00.500000
2 0 days 00:00:00.333333333
0 4000 days 00:00:00
dtype: object
```
If timedeltas had a `.replace()` method so you could zero out the days, then it would work, but AFAIK such a method doesn't exist.
BTW, the same thing is already implemented for floats and datetimes.
```python
>>> s*10
0 10.000000
1 5.000000
2 3.333333
dtype: float64
>>> pd.Timestamp(0) + td
0 1970-01-01 00:00:01.000000000
1 1970-01-01 00:00:00.500000000
2 1970-01-01 00:00:00.333333333
dtype: datetime64[ns]
```
### Alternative Solutions
n/a
### Additional Context
_No response_
|
[
"Enhancement",
"Output-Formatting",
"Timedelta"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks for the report - agreed this would be an improvement. PRs to fix are welcome.",
"> PRs to fix are welcome.\r\n\r\nWorking on one :) but this functionality is buried in some Cython code (`_Timedelta._repr_base`), which I've never written before, so it's a bit of a slog.",
"still working on this @wjandrea ?",
"Hi @wjandrea , just a friendly follow-up on my previous comment. Since I haven't heard back, I wanted to let you know that I'm planning to start working on this soon. Your input would be valuable before I begin. Thanks!",
"take",
"take",
"@mafaldam Yeah, I actually made quite a bit of progress on this, but it's setting up the build system that I'm stuck on right now. Let me plug away at it a little while and I'll let you know. Sorry for not replying sooner :)"
] |
2,111,103,979
| 57,187
|
REF: Remove internals.base
|
closed
| 2024-01-31T22:37:28
| 2024-02-02T17:53:31
| 2024-02-02T17:53:31
|
https://github.com/pandas-dev/pandas/issues/57187
| true
| null | null |
rhshadrach
| 0
|
Ref: https://github.com/pandas-dev/pandas/pull/57118#discussion_r1469860926
|
[
"Refactor",
"Internals"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[] |
2,111,037,661
| 57,186
|
DEPR: Enforce deprecation of groupby(..., axis=1)
|
closed
| 2024-01-31T21:55:30
| 2024-02-01T21:37:20
| 2024-02-01T17:39:51
|
https://github.com/pandas-dev/pandas/pull/57186
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57186
|
https://github.com/pandas-dev/pandas/pull/57186
|
rhshadrach
| 1
|
- [ ] closes #xxxx (Replace xxxx with the GitHub issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
Enforcing #51203
|
[
"Groupby",
"Deprecate"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @rhshadrach "
] |
2,111,026,264
| 57,185
|
CI: Fix generating requirements-dev.txt
|
closed
| 2024-01-31T21:47:11
| 2024-01-31T22:17:58
| 2024-01-31T22:10:02
|
https://github.com/pandas-dev/pandas/pull/57185
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57185
|
https://github.com/pandas-dev/pandas/pull/57185
|
rhshadrach
| 2
|
- [ ] closes #xxxx (Replace xxxx with the GitHub issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
Fix for #56993
|
[
"Bug",
"CI",
"Dependencies"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Docker build job pulls from main's requirements-dev.txt. It will fail until this is merged.",
"Thanks @rhshadrach "
] |
2,110,992,084
| 57,184
|
Use ruff to detect banned import
|
closed
| 2024-01-31T21:22:40
| 2024-03-17T07:53:18
| 2024-02-05T17:31:50
|
https://github.com/pandas-dev/pandas/pull/57184
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57184
|
https://github.com/pandas-dev/pandas/pull/57184
|
tqa236
| 1
|
There are several other potential replacement, I just open this PR first to check if this replacement is good or not. The newly added `noqa` in `pandas/io/common.py` shows that the check works.
- [ ] closes #xxxx (Replace xxxx with the GitHub issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
|
[
"Code Style"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @tqa236 "
] |
2,110,877,862
| 57,183
|
BUG: df.reset_index(names=[None]) names index column NaN instead of None
|
closed
| 2024-01-31T20:06:36
| 2024-02-03T23:00:57
| 2024-02-03T23:00:57
|
https://github.com/pandas-dev/pandas/issues/57183
| true
| null | null |
mvashishtha
| 1
|
### Pandas version checks
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this bug exists on the [main branch](https://pandas.pydata.org/docs/dev/getting_started/install.html#installing-the-development-version-of-pandas) of pandas.
### Reproducible Example
```python
import pandas as pd
df = pd.DataFrame([1])
result = df.reset_index(names=[None])
assert list(result.columns) == [None, 0], result.columns
```
### Issue Description
`reset_index(names=[None])` should add a column with name `None` containing the values of the index. Instead it adds a column with name `np.nan`.
### Expected Behavior
This should be not raise an assertion:
```python
import pandas as pd
df = pd.DataFrame([1])
result = df.reset_index(names=[None])
assert list(result.columns) == [None, 0], result.columns
```
### Installed Versions
<details>
```
INSTALLED VERSIONS
------------------
commit : fd3f57170aa1af588ba877e8e28c158a20a4886d
python : 3.9.18.final.0
python-bits : 64
OS : Darwin
OS-release : 23.3.0
Version : Darwin Kernel Version 23.3.0: Wed Dec 20 21:31:00 PST 2023; root:xnu-10002.81.5~7/RELEASE_ARM64_T6020
machine : arm64
processor : arm
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 2.2.0
numpy : 1.26.3
pytz : 2023.3.post1
dateutil : 2.8.2
setuptools : 68.2.2
pip : 23.3.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 8.18.1
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.4
qtpy : None
pyqt5 : None
```
</details>
|
[
"Dtype Conversions",
"Closing Candidate"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Hi, thanks for your report.\r\n\r\nInserting None into an Integer Index casts to float not to object, so this behavior is as expected"
] |
2,110,869,914
| 57,182
|
Migrate pylint to ruff
|
closed
| 2024-01-31T20:03:19
| 2024-04-09T21:15:39
| 2024-04-08T21:24:19
|
https://github.com/pandas-dev/pandas/pull/57182
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57182
|
https://github.com/pandas-dev/pandas/pull/57182
|
tqa236
| 10
|
Hi @mroeschke, while looking into the `pylint` rule, I noticed that most of them are currently gated behind the `preview` flag. Do you think we should enable the `preview` flag or wait some rules to be promoted to stable in `ruff==0.2.0` (should be released in the next few weeks)
Other than that, this PR is ready for review.
- [ ] closes #xxxx (Replace xxxx with the GitHub issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
|
[
"Code Style"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Is it possible to opt in to preview mode with just pylint?",
"@mroeschke can you take a look at the latest commit and the 177 errors that it detects? I think we can achieve that with `pre-commit`, although we need to explicitly pass all necessary.\r\n\r\nThis new hook replaces the one below, which is disabled by default\r\n\r\n```\r\n- repo: https://github.com/pylint-dev/pylint\r\n rev: v3.0.1\r\n hooks:\r\n - id: pylint\r\n stages: [manual]\r\n args: [--load-plugins=pylint.extensions.redefined_loop_name]\r\n```\r\n\r\nGiven that I can't fix all the errors within 1 PR, let me know how should I proceed",
"My main curiosity is how many of the 177 errors are ruff-pylint checks that are not enabled in the current pylint checks? https://github.com/pandas-dev/pandas/blob/cad0d87c63a914ebc6e33fb1b5e22c9068d75710/pyproject.toml#L369\r\n\r\nIdeally this PR should just bump ruff and enable ruff-pylint, and all modifications are updates from other rules.\r\n\r\nThen in a follow up PR, the old pylint can be removed (and optional ruff-pylint rules can be enabled too)",
"hi @mroeschke, please take a look at the last commit, where I manage to make an automatic translation of `pylint` config to `ruff` config with the help of [`pylint-to-ruff`](https://github.com/akx/pylint-to-ruff).\r\n\r\nGiven the list of `pylint` rules that has not been implemented in `ruff` (the one that's commented out), let me know if it's ok to drop `pylint` in favor of `ruff`",
"@mroeschke thanks for your review. I rebased on `main`",
"hi @mroeschke, I wonder if we should split the version bump of `ruff` with the switch from `pylint` to `ruff` so that we can use the latest `ruff` version first. \r\n\r\nI can also take a look at the rather messy bump from `0.2.0` to `0.3.0` if it's good for you",
"Yes I think that's a good idea to bump ruff to 0.3.0 first before doing a pylint migration",
"Coming back around to this; to simplify the transition I think we can just enable the pylint rule in ruff (and ignore the non-fix rules that don't pass currently) and remove the pylint config in pre-commit-config. I think it's OK to not exactly have a 1:1 transition between the old and new pylint configuration",
"Thanks @tqa236 ",
"thank you for finalizing this PR, I was on vacation and didn't have time to check your last message."
] |
2,110,729,553
| 57,181
|
ENH: Add a functionality to scan csv files and show some useful info (eg-the record count) of a CSV file without loading entire file.
|
closed
| 2024-01-31T18:36:43
| 2024-02-01T00:43:27
| 2024-02-01T00:43:27
|
https://github.com/pandas-dev/pandas/issues/57181
| true
| null | null |
KelumPerera
| 3
|
### Feature Type
- [X] Adding new functionality to pandas
- [ ] Changing existing functionality in pandas
- [ ] Removing existing functionality in pandas
### Problem Description
There are many instances where users want to know how many records are in their large CSV files before loading them.
Good if pandas have some functionality to scan CSV files (without reading the entire file to memory at once), and provide some useful information to the user.
-Number of records in the file by counting new lines
-What are the column names
-What is the delimiter
-Are there non-standard characters within the data, Show them to decide suitable encoding method
-What is the optimal record chunk size when reading the file using pandas
-Memory requirement to hold the entire file in memory and memory requirement to hold data in each column
### Feature Description
# Sample code to show number of rows by using Numpy library,
#copied from https://stackoverflow.com/a/64744699
import numpy as np
chunk = 1024 * 1024 * 512 # Process 500 MB at a time.
f = np.memmap(r"C:\path\to\csvfile\somefile.csv")
num_newlines = sum(np.sum(f[i:i+chunk] == ord('\n'))
for i in range(0, len(f), chunk))
del f
print(num_newlines)
### Alternative Solutions
It is hard to find alternative solutions especially when the file size is 10+ gigabytes
### Additional Context
_No response_
|
[
"Enhancement",
"IO CSV",
"Needs Discussion"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks for the request. This indeed seems useful to me, but outside the scope of pandas.",
"@KelumPerera take a look at the csv package, especially [Sniffer](https://docs.python.org/3/library/csv.html#csv.Sniffer) and [chardet](https://chardet.readthedocs.io/en/latest/usage.html) for charset detection.",
"Agreed I think this is outside the scope of pandas. I would recommend using the standard library `csv` module as described above as a starting point. Closing as out of scope"
] |
2,110,701,228
| 57,180
|
Backport PR #57169 on branch 2.2.x (REGR: DataFrame.sort_index not producing stable sort)
|
closed
| 2024-01-31T18:20:19
| 2024-01-31T19:20:42
| 2024-01-31T19:20:42
|
https://github.com/pandas-dev/pandas/pull/57180
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57180
|
https://github.com/pandas-dev/pandas/pull/57180
|
meeseeksmachine
| 0
|
Backport PR #57169: REGR: DataFrame.sort_index not producing stable sort
|
[
"Regression",
"Index"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[] |
2,110,677,561
| 57,179
|
Backport PR #57175 on branch 2.2.x (BUG: Interchange protocol implementation handles empty dataframes incorrectly)
|
closed
| 2024-01-31T18:05:45
| 2024-01-31T19:06:36
| 2024-01-31T19:06:36
|
https://github.com/pandas-dev/pandas/pull/57179
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57179
|
https://github.com/pandas-dev/pandas/pull/57179
|
meeseeksmachine
| 0
|
Backport PR #57175: BUG: Interchange protocol implementation handles empty dataframes incorrectly
|
[
"Interchange"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[] |
2,110,602,795
| 57,178
|
ENH: Not silently ignore violation of minimal required version of SQLAlchemy
|
open
| 2024-01-31T17:21:53
| 2024-03-19T08:32:58
| null |
https://github.com/pandas-dev/pandas/issues/57178
| true
| null | null |
bebach
| 5
|
### Feature Type
- [ ] Adding new functionality to pandas
- [X] Changing existing functionality in pandas
- [ ] Removing existing functionality in pandas
### Problem Description
#### Observation
After upgrading to Pandas 2.2.0 calling `read_sql_query` with a SQLAlchemy query on an SQLAlchemy connection yielded `"Query must be a string unless using sqlalchemy."` without any indication why or what may have broken with the upgrade.
#### Analysis
Tracking the problem down it was found that `pandasSQL_builder` [(code)](https://github.com/pandas-dev/pandas/blob/f538741432edf55c6b9fb5d0d496d2dd1d7c2457/pandas/io/sql.py#L900) calls `sqlalchemy = import_optional_dependency("sqlalchemy", errors="ignore")` to resolve the optional SQLAlchemy package (which was installed in my case)
Due to `errors="ignore"` the violation of the minimal required version for SQLAlchemy does not lead to a warn or raise [(code)](https://github.com/pandas-dev/pandas/blob/f538741432edf55c6b9fb5d0d496d2dd1d7c2457/pandas/compat/_optional.py#L156) but just silently returns with `None`.
This in turn lets `pandasSQL_builder` silently default to `SQLiteDatabase(con)`.
### Feature Description
#### Proposed improvement
Do not ignore the minimal version violation in `import_optional_dependency` in this context but make it obvious. For example by introducing an additional "errors"-mode like `import_optional_dependency("sqlalchemy", errors="raise-on-version-violation")`.
### Alternative Solutions
./.
### Additional Context
_No response_
|
[
"Enhancement",
"IO SQL",
"Dependencies"
] | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks for the report! It looks like this was changed in #45679 to allow for other dependencies. Perhaps the logic could be changed there so that when SQLAlchemy is used, we do check that the version is satisfied.\r\n\r\nFurther investigations and PRs to fix are welcome!",
"I have attempted to recreate the issue in a fresh environment with `pandas==2.2.0; sqlalchemy==2.0.25`, and the output seems to be correct. I may require assistance in reproducing the issue.\r\n\r\n```python\r\nfrom sqlalchemy import create_engine\r\nimport pandas as pd\r\n\r\nengine = create_engine(\"sqlite:///:memory:\")\r\n\r\ndf = pd.DataFrame({\r\n \"id\": [1, 2, 3],\r\n \"name\": [\"Alice\", \"Bob\", \"Charlie\"]\r\n})\r\ndf.to_sql(\"Person\", engine, index=False)\r\n\r\nsql_query = \"SELECT * FROM Person\"\r\nresult = pd.read_sql_query(sql_query, engine)\r\nprint(result)\r\n```\r\n```\r\n id name\r\n0 1 Alice\r\n1 2 Bob\r\n2 3 Charlie\r\n```\r\n",
"The issue is not that it works with SQLAlchemy 2 but that it gives no clue why it does not work with SQLAlchemy < 2.x\r\n\r\nTo replicate install SQLAlchemy==1.4 and rerun the code-snippet from above. This results in..\r\n\r\n```\r\n$ python pandas_test.py\r\nD:\\temp\\pandas-issue-57178\\pandas_test.py:10: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\r\n df.to_sql(\"Person\", engine, index=False)\r\nTraceback (most recent call last):\r\n File \"D:\\temp\\pandas-issue-57178\\pandas_test.py\", line 10, in <module>\r\n df.to_sql(\"Person\", engine, index=False)\r\n File \"D:\\temp\\pandas-issue-57178\\venv\\lib\\site-packages\\pandas\\util\\_decorators.py\", line 333, in wrapper\r\n return func(*args, **kwargs)\r\n File \"D:\\temp\\pandas-issue-57178\\venv\\lib\\site-packages\\pandas\\core\\generic.py\", line 3081, in to_sql\r\n return sql.to_sql(\r\n File \"D:\\temp\\pandas-issue-57178\\venv\\lib\\site-packages\\pandas\\io\\sql.py\", line 842, in to_sql\r\n return pandas_sql.to_sql(\r\n File \"D:\\temp\\pandas-issue-57178\\venv\\lib\\site-packages\\pandas\\io\\sql.py\", line 2851, in to_sql\r\n table.create()\r\n File \"D:\\temp\\pandas-issue-57178\\venv\\lib\\site-packages\\pandas\\io\\sql.py\", line 984, in create\r\n if self.exists():\r\n File \"D:\\temp\\pandas-issue-57178\\venv\\lib\\site-packages\\pandas\\io\\sql.py\", line 970, in exists\r\n return self.pd_sql.has_table(self.name, self.schema)\r\n File \"D:\\temp\\pandas-issue-57178\\venv\\lib\\site-packages\\pandas\\io\\sql.py\", line 2866, in has_table\r\n return len(self.execute(query, [name]).fetchall()) > 0\r\n File \"D:\\temp\\pandas-issue-57178\\venv\\lib\\site-packages\\pandas\\io\\sql.py\", line 2673, in execute\r\n cur = self.con.cursor()\r\nAttributeError: 'Engine' object has no attribute 'cursor'\r\n\r\n```\r\n\r\nwhich is surprising given that we just created the engine with SQLAlchemy. And it gives the user no clue that this is due to violation of version requirement.\r\n\r\nIt would be obvious what is the issue if the version check in `import_optional_dependency` raises the\r\n\r\n```\r\n msg = (\r\n f\"Pandas requires version '{minimum_version}' or newer of '{parent}' \"\r\n f\"(version '{version}' currently installed).\"\r\n )\r\n```\r\nhttps://github.com/pandas-dev/pandas/blob/f538741432edf55c6b9fb5d0d496d2dd1d7c2457/pandas/compat/_optional.py#L152\r\n\r\n... instead of just silently returning None (which only later leads to the miss-leading error from above.)\r\n",
"Seems related #57049, whether to support Sqlalchemy 1.4 is under discussion.",
"I think this is related. It should state that sqlalchemy <2.0 is not supported instead of \"not installed\".\r\n\r\n```python\r\nimport sqlalchemy\r\nprint(\"sqlalchemy Version:\", sqlalchemy.__version__)\r\npd.read_sql(\"select * from osm_point limit 1\", connection_string)\r\n```\r\n```sh\r\nsqlalchemy Version: 1.4.52\r\n...\r\nImportError: Using URI string without sqlalchemy installed.\r\n```"
] |
2,110,496,397
| 57,177
|
ENH: Expose integer formatting also for default display, not only styler
|
open
| 2024-01-31T16:29:42
| 2024-02-15T05:16:52
| null |
https://github.com/pandas-dev/pandas/issues/57177
| true
| null | null |
enryH
| 14
|
### Feature Type
- [X] Adding new functionality to pandas
- [ ] Changing existing functionality in pandas
- [ ] Removing existing functionality in pandas
### Problem Description
I noticed that in Pandas version `2.2` the internal implementation of `IntArrayFormatter` was changed (or at least renamed), marking the classes as private using one underscore. I used the implementation as described [here](https://stackoverflow.com/a/29663750/9684872) to format integers in a DataFrame on each display.
See changes between `2.1` and `2.2` to make the linked patch work: [link](https://github.com/RasmussenLab/njab/pull/4/commits/10b0c84ddb28a1dd9c2bd58e9b6af148dbd6c9dc)
I see that the styler supports formatting thousands:
```python
import pandas as pd
pd.options.styler.format.thousands = ','
s = pd.Series([1_000_000]).to_frame()
s.style # does change display of s to 1,000,000
```
however
```
s # will display 1000000
```
Would it be possible to add the same to the display options?
### Feature Description
pd.options.format.thousands = ','
would need to pass:
```python
if pd.options.format.thousands:
format_str = f':{pd.options.format.thousands}d'.format
else:
format_str = ':d'.format
```
### Alternative Solutions
Change the default [here](https://github.com/pandas-dev/pandas/blob/main/pandas/io/formats/format.py#L1475)
```python
class IntArrayFormatter(pf._GenericArrayFormatter):
def _format_strings(self):
formatter = self.formatter or '{:,d}'.format
fmt_values = [formatter(x) for x in self.values]
return fmt_values
pf._IntArrayFormatter = IntArrayFormatter
```
Using the above patch I get the display I would prefer:
```
>>> pd.Series([1_000_000])
0 1,000,000
dtype: int64
```
### Additional Context
Working with large integers in tables is made easier if thousands are visually distinguished. Maybe there is already a way to specify that formatter function (`str.format`) for display, which would be perfect.
|
[
"Enhancement",
"Output-Formatting"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks for the request. From your example:\r\n\r\n> s.style # does not change display of s\r\n\r\nI'm seeing that this does display the comma separator, are you not? Or perhaps you meant that `s` without `.style` still does not show the commas?",
"i dont think this feature is about Styler. It is about adding functionality to the default DataFrame display that is demonstrated by Styler, i.e. which Styler already possesses.",
"Yes exactly, currently I have to use the internals to make `s` already represent with the commas. \r\n\r\n```python\r\n>>> import pandas as pd\r\n>>> pd.options.styler.format.thousands = ',' # here it would be great to have the option pd.options.display.thousands \r\n>>> s = pd.Series([1_000_000])\r\n\r\n>>> s\r\n0 1000000\r\ndtype: int64\r\n>>>s.to_frame().style\r\n 0\r\n0\t1,000,000\r\n```\r\n\r\nI see that I put a \"not\" at the wrong place intially... Sorry about that. I'll clarify my example above by modifying it.",
"I would personally find this feature convenient, I'm positive on it. @attack68 do you have any thoughts?\r\n\r\nIf we do implement this, I think it should only be implemented for integer/float dtypes, and documented as such. Users should not expect pandas to infer whether to insert commas in object dtype values.",
"Hello @enryH,\r\n\r\nMy project team is looking for Pandas enhancement features for our grad school semester long project. We saw this task and would like to contribute if possible!",
"i think its generally quite handy to have configurable options for precision, thousands and decimal separator. Styler avoided using the locale package, if I recall correctly, but it was a bit tricky in the end to make sure the european decimal-comma was compatible with the us comma-decimal style. \r\n\r\nTo make it easier this should definitely only be applied to float/int dtypes.",
"i think its generally quite handy to have configurable options for precision, thousands and decimal separator. Styler avoided using the locale package, if I recall correctly, but it was a bit tricky in the end to make sure the european decimal-comma was compatible with the us comma-decimal style. \r\n\r\nTo make it easier this should definitely only be applied to float/int dtypes.",
"take",
"I guess it will boil down to figuring out how the formatting function is set (see `IntArrayFormatter` [here](https://github.com/pandas-dev/pandas/blob/main/pandas/io/formats/format.py#L1475)):\r\n\r\n```python\r\nformatter = self.formatter or '{:d}'.format # how to set self.formatter from pandas.options\r\n```",
"I ran into the same problem too (down to relying on the same StackOverflow code to modify display of integers). I also would like a cleaner way to do it.\r\n\r\nAdditionally, what is the recommended way to to handle the name change? I have some tools that need to support both `pd.io.formats.format.IntArrayFormatter` and `pd.io.formats.format._IntArrayFormatter`. Thank you.",
"> I ran into the same problem too (down to relying on the same StackOverflow code to modify display of integers). I also would like a cleaner way to do it.\r\n\r\nPRs welcome!\r\n\r\n> Additionally, what is the recommended way to to handle the name change? I have some tools that need to support both `pd.io.formats.format.IntArrayFormatter` and `pd.io.formats.format._IntArrayFormatter`. Thank you.\r\n\r\nIntArrayFormatter is not intended to be public. If you have a need to use it, can you open a new issue detailing it.\r\n\r\n",
"Hey @rhshadrach is the goal to create a new class or to improve the _IntArrayFormatter class and change it back to public? \r\n\r\nJust wanted to understand what was considered in scope before tackling this issue. \r\n\r\nIf this is not the right forum/area to ask questions, please let know!",
"> IntArrayFormatter is not intended to be public. If you have a need to use it, can you open a new issue detailing it.\r\n\r\nMaybe it's easier to talk about what I am trying to do in order to avoid an XY problem. What I need is a way to automatically always show commas in integers. As `IntArrayFormatter` is not intended to be public, I'd be happy to use any different method to accomplish the same goal.\r\n",
"Thanks @saiwing-yeung - we're open for PRs to add an option (e.g. `pd.set_option(\"display.numeric_separator\")` - could probably use a better name!). It would default to `''`, but users could change to e.g. `'_'` or `','`.\r\n\r\n@Meadiocre \r\n\r\n> is the goal to create a new class or to improve the _IntArrayFormatter class and change it back to public?\r\n\r\nNo - just expose a new option for users."
] |
2,110,257,577
| 57,176
|
BUG: pandas.core.groupby.SeriesGroupBy.idxmax (and idxmin) - unexpected behavior with groups containing single NaN value
|
closed
| 2024-01-31T14:44:23
| 2024-02-01T20:42:52
| 2024-01-31T22:47:12
|
https://github.com/pandas-dev/pandas/issues/57176
| true
| null | null |
semyonbok
| 4
|
### Pandas version checks
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this bug exists on the [main branch](https://pandas.pydata.org/docs/dev/getting_started/install.html#installing-the-development-version-of-pandas) of pandas.
### Reproducible Example
```python
import numpy as np
import pandas as pd
df = pd.DataFrame({
"var": ["A", "B", "C", "C", "D", "D"],
"val": [11, np.nan, 12, 13, 14, np.nan]
})
df.groupby("var")["val"].idxmax()
```
### Issue Description
The provided code snippet yields the following output:
```python
var
A 0
B 0
C 3
D 4
Name: val, dtype: int64
```
Group 'B' with single `np.nan` value yields 0, which corresponds to the index of the first entry in the frame, relating to group "A".
### Expected Behavior
The output with pandas 2.1.0
```python
var
A 0.0
B NaN
C 3.0
D 4.0
Name: val, dtype: float64
```
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : f538741432edf55c6b9fb5d0d496d2dd1d7c2457
python : 3.9.16.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19045
machine : AMD64
processor : Intel64 Family 6 Model 140 Stepping 1, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United Kingdom.1252
pandas : 2.2.0
numpy : 1.26.3
pytz : 2023.3.post1
dateutil : 2.8.2
setuptools : 69.0.3
pip : 23.3.2
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 3.1.9
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.3
IPython : 8.18.1
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.12.3
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : 3.8.2
numba : None
numexpr : None
odfpy : None
openpyxl : 3.1.2
pandas_gbq : None
pyarrow : None
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.12.0
sqlalchemy : None
tables : 3.9.2
tabulate : None
xarray : None
xlrd : 2.0.1
zstandard : None
tzdata : 2023.4
qtpy : None
pyqt5 : None
</details>
|
[
"Bug",
"Groupby",
"Regression",
"Duplicate Report",
"Reduction Operations"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"I'm unable to produce regardless of version on Ubuntu 22.04.3 LTS and Docker container",
"@remiBoudreau, thank you for giving it a try. I can add that I use Miniconda package manager; and I installed the latest pandas through conda-forge channel. I've just tried it on another Windows machine, and observed the same behaviour. Furthermore, groups with single `np.nan` are not just zeroed-out, but assigned with the first object from the index in my case.\r\n```python\r\nimport numpy as np\r\nimport pandas as pd\r\n\r\ndf = pd.DataFrame({\r\n \"var\": [\"A\", \"B\", \"C\", \"C\", \"D\", \"D\", \"E\"],\r\n \"val\": [11, np.nan, 12, 13, 14, np.nan, np.nan]\r\n}, index=list(\"abcdefg\"))\r\n\r\ndf.groupby(\"var\")[\"val\"].idxmax()\r\n```\r\noutputs:\r\n```python\r\nvar\r\nA a\r\nB a\r\nC d\r\nD e\r\nE a\r\nName: val, dtype: object\r\n```\r\nFinally, I see that [`_idxmax_idxmin`](https://github.com/pandas-dev/pandas/blob/bc09d5765197c39daae8326fc9ee44bb2c3153c4/pandas/core/groupby/groupby.py#L5792C9-L5792C23) logic is quite new. Please let me know if there anything useful I can provide from my end. ",
"Thanks for the report. This was fixed in #57046 and has the same root cause. The fix will be part of 2.2.1.",
"@rhshadrach, great news, thank you very much! I'll do my best to try the main branch prior to raising an issue next time :)"
] |
2,110,228,091
| 57,175
|
BUG: Interchange protocol implementation handles empty dataframes incorrectly
|
closed
| 2024-01-31T14:31:00
| 2024-01-31T18:05:22
| 2024-01-31T18:05:14
|
https://github.com/pandas-dev/pandas/pull/57175
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57175
|
https://github.com/pandas-dev/pandas/pull/57175
|
MarcoGorelli
| 1
|
- [ ] closes #56700 (Replace xxxx with the GitHub issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
|
[
"Interchange"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @MarcoGorelli "
] |
2,110,209,791
| 57,174
|
BUG: Interchange protocol implementation allows non-string column names
|
closed
| 2024-01-31T14:22:15
| 2024-02-02T01:26:35
| 2024-02-02T01:25:43
|
https://github.com/pandas-dev/pandas/pull/57174
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57174
|
https://github.com/pandas-dev/pandas/pull/57174
|
MarcoGorelli
| 1
|
- [ ] closes #56701 (Replace xxxx with the GitHub issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
|
[
"Interchange"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @MarcoGorelli "
] |
2,110,169,168
| 57,173
|
BUG: pandas int extension dtypes has no attribute byteorder
|
closed
| 2024-01-31T14:04:12
| 2024-03-01T13:54:29
| 2024-02-01T19:04:29
|
https://github.com/pandas-dev/pandas/pull/57173
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57173
|
https://github.com/pandas-dev/pandas/pull/57173
|
MarcoGorelli
| 2
|
- [ ] closes #55069 (Replace xxxx with the GitHub issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
|
[
"NA - MaskedArrays",
"Interchange"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.