PERF: use Arrow native path in duration component calculation for Arrow-backed array #63470
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Pull request overview
This PR implements a significant performance optimization for duration component calculations on Arrow-backed timedelta arrays. Instead of converting to NumPy arrays and back, the new implementation uses native PyArrow compute functions to extract duration components directly, resulting in speedups of 200-1200x for various accessor operations.
Key changes:
- Implemented Arrow-native duration component extraction using PyArrow compute kernels
- Added floor division logic to correctly handle negative durations matching pandas semantics
- Introduced caching for the day remainder calculation with proper invalidation
Reviewed changes
Copilot reviewed 4 out of 4 changed files in this pull request and generated no comments.
| File | Description |
|---|---|
pandas/core/arrays/arrow/array.py |
Implements Arrow-native duration component extraction methods with floor division for negative values, cached day remainder calculation, and optimized _dt_components method |
pandas/core/indexes/accessors.py |
Updates the components property to use the new _dt_components method when available for Arrow arrays |
pandas/tests/extension/test_arrow.py |
Adds comprehensive tests covering positive/negative durations, null handling, component extraction, Python timedelta semantics, and cache invalidation |
asv_bench/benchmarks/timedelta.py |
Adds benchmarks comparing NumPy and PyArrow backends for duration component operations |
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doc/source/whatsnew/vX.X.X.rstfile if fixing a bug or adding a new feature.AGENTS.md..dt.days.dt.seconds.dt.microseconds.dt.nanoseconds.dt.componentsIt was run on an M1 MacBook Pro. Could someone verify this improvement on a more powerful machine?