Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
After https://github.com/pandas-dev/pandas/pull/55901, to_datetime with strings will now infer the resolution from the data, but the related pd.date_range to create datetime data still returns nanoseconds:
Should we update pd.date_range as well to infer the resulting resolution from the start/stop timestamp and freq ?
(I encountered this inconsistency in the pyarrow tests, where we essentially were using both idioms to create a result and expected data, but so that started failing because of a different dtype. I also opened https://github.com/pandas-dev/pandas/issues/58989 for that, but regardless of a possible default resolution, pd.date_range would still need to follow that as well)
After https://github.com/pandas-dev/pandas/pull/55901,
to_datetime
with strings will now infer the resolution from the data, but the relatedpd.date_range
to create datetime data still returns nanoseconds:Should we update
pd.date_range
as well to infer the resulting resolution from the start/stop timestamp and freq ?(I encountered this inconsistency in the pyarrow tests, where we essentially were using both idioms to create a result and expected data, but so that started failing because of a different dtype. I also opened https://github.com/pandas-dev/pandas/issues/58989 for that, but regardless of a possible default resolution,
pd.date_range
would still need to follow that as well)