Added usage code to filter for licenses

Signed-off-by: Andreas Florath <[email protected]>

README.md

@@ -175,7 +175,7 @@ theorem proving.
 * Tables: Three distinct tables: facts (definitions or notations),
   propositions (theorems and lemmas alongside proofs), and
   licensing/repository information.
-* Entries: 103,351 facts and 166,007 propositions with proofs.
+* Entries: 103,446 facts and 166,035 propositions with proofs.
 * Size: Character length ranging from as short as 11 to as long as
   177,585 characters.
 * Source and Collection Method: The Coq source files were collected
@@ -204,6 +204,31 @@ includes training or fine-tuning models to focus on proofs rather than
 definitions and notations. The dataset also allows for filtering based
 on specific licenses using the `info.parquet` file.
 
+```python
+import pandas as pd
+
+df_facts_raw = pd.read_parquet("facts.parquet")
+df_info = pd.read_parquet("info.parquet")
+
+# This is the list of licenses which might be seen as permissive
+permissive_licenses_list = [
+    'Apache-2.0', 'BSD-2-Clause', 'BSD-3-Clause',
+    'CECILL-B', 'CECILL-C', 
+    'LGPL-2.1-only', 'LGPL-2.1-or-later', 'LGPL-3.0-only', 'LGPL-3.0-or-later', 
+    'MIT', 'MPL-2.0', 'UniMath' ]
+
+# Set the license-type to permissive based on the list
+df_info['license-type'] = df_info['spdx-id'].apply(
+    lambda x: 'permissive' if x in permissive_licenses_list else 'not permissive')
+
+# Merge df_facts with df_info to get the license-type information
+# 'symbolic_name' is the common key in both DataFrames
+df_facts_merged = pd.merge(df_facts_raw, df_info, on='symbolic_name', how='left')
+
+# Filter the merged DataFrame to only include entries with a permissive license
+df_facts = df_facts_merged[df_facts_merged['license-type'] == 'permissive']
+```
+
 ## Experiments and Findings
 
 Initial experiments with the dataset have demonstrated its potential