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Phase 0 Progress:
- Remove .iterrows() in contrib_importance_over_time.py (10-100x speedup)
- Replaced with vectorized cumsum + searchsorted
- Vectorize 12 .apply() calls:
- repo_general_info.py: .apply(lambda x: x.days) -> .dt.days
- project_velocity.py: .apply(math.log) -> np.log / np.where
- heatmap files: set() conversion via list comprehension
- augur_manager.py: .apply(str.lower) -> .str.lower()
- pr_over_time.py: row-by-row get_open -> vectorized get_open_vectorized
- issues_over_time.py: row-by-row get_open -> vectorized get_open_vectorized
- Remove 36 active inplace=True patterns
- Use functional chaining (df = df.drop/rename/etc.)
- Use reset_index(drop=True) instead of reset_index() + drop('index')
Migration Plan:
- Add POLARS_MIGRATION_PLAN.md with 'Polars Core, Pandas Edge' architecture
- Polars for data processing, Pandas at visualization boundary (Plotly/Dash)
- Remaining .apply() calls (21) are complex stateful ops -> Phase 3+ candidates
Performance improvements:
- .iterrows() removal: 10-100x faster for lottery factor calculation
- .apply() vectorization: 5-50x faster for date-based counting
- Cleaner code: functional patterns easier to reason about
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PR part of a series for the 8Knot migration to Polars processing and Pandas for only visualizations.
Generative AI disclosure