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- Remove include_groups parameter from all groupby().apply() calls - Add explicit column selection to prevent deprecation warnings - Update AUC function to work with pandas 1.5.3 - Update GRADE function to work with pandas 1.5.3 - Update episode_calculation function to work with pandas 1.5.3 - Bump version to 0.3.1 The include_groups parameter was introduced in pandas 2.0.0, causing 'unexpected keyword argument' errors in pandas 1.5.3. This fix: 1. Removes all include_groups=False parameters 2. Adds explicit column selection [['gl', 'gl_next']] to groupby operations 3. Maintains same functionality while ensuring compatibility All functions now work correctly with pandas 1.5.3: - iglu.auc() - AUC calculation - iglu.grade() - GRADE score calculation - iglu.episode_calculation() - Episode analysis Resolves compatibility issues for users with older pandas versions.
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Codecov Report❌ Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## main #12 +/- ##
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Coverage 91.16% 91.16%
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Files 46 46
Lines 1754 1754
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Hits 1599 1599
Misses 155 155 ☔ View full report in Codecov by Sentry. |
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Problem
Users with pandas 1.5.3 are experiencing errors when using IGLU-Python functions:
Root Cause
The
include_groupsparameter was introduced in pandas 2.0.0, but many users still have pandas 1.5.3 installed. This parameter doesn't exist in older pandas versions, causing the error.Solution
This PR fixes compatibility by:
include_groups=Falseparameters fromgroupby().apply()callsChanges Made
Files Modified
iglu_python/auc.py- Fixed 2 groupby operationsiglu_python/grade.py- Fixed 1 groupby operationiglu_python/episode_calculation.py- Fixed 3 groupby operationspyproject.toml- Version bump to 0.3.1Technical Details
data.groupby('id').apply(lambda x: ..., include_groups=False)data.groupby('id')[['gl']].apply(lambda x: ...)Compatibility Matrix
Testing
✅ All functions tested and working:
iglu.auc()- AUC calculationiglu.grade()- GRADE score calculationiglu.episode_calculation()- Episode analysisImpact
This fix ensures IGLU-Python works seamlessly across different pandas versions without requiring users to upgrade their pandas installation.