Perf: avoid fallback to NumPy in value_counts for Arrow-backed array #63389
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… arrays Previously, value_counts_internal would convert Arrow array counts to NumPy just to call .sum() for normalization. This is unnecessary since Series.sum() works correctly for all backends. Changes: - Remove unnecessary np.asarray(counts) conversion for Arrow arrays - Remove unused counts variable assignments from bins and MultiIndex branches - Use result.sum() instead of counts.sum() for normalization This eliminates a performance bottleneck where Arrow-backed Series would fall back to NumPy during value_counts operations. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <[email protected]>
… arrays Previously, value_counts_internal would convert Arrow array counts to NumPy just to call .sum() for normalization. This is unnecessary since Series.sum() works correctly for all backends. Changes: - Remove unnecessary np.asarray(counts) conversion for Arrow arrays - Remove unused counts variable assignments from bins and MultiIndex branches - Use result.sum() instead of counts.sum() for normalization This eliminates a performance bottleneck where Arrow-backed Series would fall back to NumPy during value_counts operations. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <[email protected]>
The previous change to avoid unnecessary to_numpy conversion broke normalization when bins is used. Bins normalization should divide by the total input length, not the sum of counts in bins. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <[email protected]>
mroeschke
requested changes
Dec 17, 2025
mroeschke
added
Performance
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