API: dont do inference on object-dtype arithmetic results #49999
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doc/source/whatsnew/v2.0.0.rst
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| @@ -348,6 +348,7 @@ Other API changes | |||
| - Passing a sequence containing a type that cannot be converted to :class:`Timedelta` to :func:`to_timedelta` or to the :class:`Series` or :class:`DataFrame` constructor with ``dtype="timedelta64[ns]"`` or to :class:`TimedeltaIndex` now raises ``TypeError`` instead of ``ValueError`` (:issue:`49525`) | |||
| - Changed behavior of :class:`Index` constructor with sequence containing at least one ``NaT`` and everything else either ``None`` or ``NaN`` to infer ``datetime64[ns]`` dtype instead of ``object``, matching :class:`Series` behavior (:issue:`49340`) | |||
| - :func:`read_stata` with parameter ``index_col`` set to ``None`` (the default) will now set the index on the returned :class:`DataFrame` to a :class:`RangeIndex` instead of a :class:`Int64Index` (:issue:`49745`) | |||
| - Changed behavior of :class:`Index`, :class:`Series`, and :class:`DataFrame` arithmetic methods when working with object-dtypes, the results no longer do type inference on the result of the array operations (:issue:`49714`) | |||
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So users would now call convert_dtypes to get close to the old behavior?
Might be good to mention because while this is more consistent behavior it's probably a little more inconvenient for users to work with object type
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infer_objects, will update
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i think adding this note will have to wait until 1) i add infer_objects to Index and possibly 2) a copy=False option is added to infer_objects
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Just merged the infer_objects(copy=) PR, does that need to be included here?
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ill add the copy=False in my next "assorted cleanups" branch
mroeschke
added
Dtype Conversions
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Thanks @jbrockmendel |
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Looks like this caused a slowdown. Is this expected? |
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best guess is #50306 will address that |
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Sounds reasonable, thanks for the quick response |
doc/source/whatsnew/vX.X.X.rstfile if fixing a bug or adding a new feature.Broken off from #49714, which also changes TimedeltaArray inference behavior. This is exclusively Index/Series/DataFrame.