CLN: Remove unnecessary .view(dtype) in nanops and take#65178
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jbrockmendel wants to merge 3 commits intopandas-dev:mainfrom
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CLN: Remove unnecessary .view(dtype) in nanops and take#65178jbrockmendel wants to merge 3 commits intopandas-dev:mainfrom
jbrockmendel wants to merge 3 commits intopandas-dev:mainfrom
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Replace roundabout .astype("m8[ns]").view(dtype) and .astype("i8").view(dtype)
patterns with direct .astype(dtype) conversions in _wrap_results and
_mask_datetimelike_result. Use explicit np.datetime64/np.timedelta64 constructors
for scalar results instead of .view() byte reinterpretation.
In take.py _view_wrapper, replace hardcoded .astype("m8[ns]")/.astype("M8[ns]")
with resolution-agnostic .view("i8").
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
…uctors Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
ravarnax
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Apr 13, 2026
ravarnax
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I'm currently learning about the performance trade-offs in pandas internals. Could you help me understand why astype() is preferred over the previous astype("i8").view() approach here? Is it primarily for code clarity, or does it also handle edge cases with different datetime resolutions more safely?
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In this context it doesn't make a perf difference, just a simplifying for its own sake |
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Summary
.astype("m8[ns]").view(dtype)and.astype("i8").view(dtype)patterns in_wrap_resultsand_mask_datetimelike_resultwith direct.astype(dtype)conversionsnp.datetime64/np.timedelta64constructors for scalar results instead of.view()byte reinterpretationtake.py_view_wrapper, replace hardcoded.astype("m8[ns]")/.astype("M8[ns]")with resolution-agnostic.view("i8")🤖 Generated with Claude Code