API: dont do type inference on arithmetic results #49714
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jbrockmendel
added
API Design
Numeric Operations
mroeschke
reviewed
Nov 16, 2022
doc/source/whatsnew/v2.0.0.rst
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| @@ -341,6 +341,7 @@ 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`) | |||
| - Changed behavior of :class:`Index` constructor with an object-dtype ``numpy.ndarray`` containing all-``bool`` values or all-complex values, this will now retain object dtype, consistent with the :class:`Series` behavior (:issue:`49594`) | |||
| - 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:`??`) | |||
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Suggested change
| - 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:`??`) | |
| - 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 and the resulting type will be ``object`` (:issue:`49714`) |
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AFAICT that isnt 100% accurate, though we could take this a step further to make it work that way
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IMO the resulting type will be object would be a nicer, more predictable guarantee for the user. Would it be difficult to taking it a step further?
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im a bit skeptical of this for two reasons:
- with 3rd party EAs we can't control what dtype they return (or we could, but it would be hacky)
- for our own ops, there are a lot of places we do
result = np.array([op(x, y) for x, y in zip(self, other)])where numpy will do inference.
5 tasks
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more comments here? |
mroeschke
approved these changes
Dec 27, 2022
|
Thanks @jbrockmendel |
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doc/source/whatsnew/vX.X.X.rstfile if fixing a bug or adding a new feature.