BUG: fix read_parquet failing with TimedeltaIndex column (#59692)#65117
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tianlangqin wants to merge 2 commits intopandas-dev:mainfrom
Open
BUG: fix read_parquet failing with TimedeltaIndex column (#59692)#65117tianlangqin wants to merge 2 commits intopandas-dev:mainfrom
tianlangqin wants to merge 2 commits intopandas-dev:mainfrom
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jbrockmendel
reviewed
Apr 9, 2026
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| from pandas._libs.tslibs import is_unitless | ||
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| if is_unitless(dtype): |
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Thanks for the review. Since the error originates from pyarrow calling level.astype(dtype) with a unitless dtype during column reconstruction, where would you suggest handling this instead?
jbrockmendel
reviewed
Apr 9, 2026
| return DatetimeArray._from_sequence(self, dtype=dtype, copy=copy) | ||
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| elif lib.is_np_dtype(dtype, "m"): | ||
| from pandas._libs.tslibs import is_unitless |
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doc/source/whatsnew/vX.X.X.rstfile if fixing a bug or adding a new feature.AGENTS.md.When writing a dataframe with
TimedeltaIndexas column names to parquet with pyarrow and then reading it back, pyarrow reconstructs the column index by callinglevel.astype(dtype)with a unitlesstimedelta64dtype, which causesValueErrorto be thrown since Pandas doesn't know the unit.I fixed it by default replacing the unitless
timedelta64withtimedelta64[ns]inExtensionArray.astypebefore callingTimedeltaArray._from_sequence. Then I added two short circuit checks in_astype_nansafeandTimedeltaArray.astypeso if the source array already has a valid timedelta or datetime resolution and the requested target is unitless, it now returns the array as it is instead of attempting an invalid conversion.The change in
TimedeltaArray.astyperelaxes the previous behavior whereidx.astype("timedelta64")automatically raised aValueError. Which was introduced in #13149 to prevent silent data corruptions. This concern doesn't apply here since the cast request fromtimedelta64[ns]totimedelta64results in the identical array to be returned. The validation for the opposite direction remains unchanged.