Agg functions for df not respecting numeric only with level #40683
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doc/source/whatsnew/v1.3.0.rst
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| @@ -720,7 +720,7 @@ Other | |||
| - Bug in :meth:`DataFrame.equals`, :meth:`Series.equals`, :meth:`Index.equals` with object-dtype containing ``np.datetime64("NaT")`` or ``np.timedelta64("NaT")`` (:issue:`39650`) | |||
| - Bug in :func:`pandas.util.show_versions` where console JSON output was not proper JSON (:issue:`39701`) | |||
| - Bug in :meth:`DataFrame.convert_dtypes` incorrectly raised ValueError when called on an empty DataFrame (:issue:`40393`) | |||
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| - Bug in aggregation functions for :class:`DataFrame` not respecting ``numeric_only`` argument when ``level`` keyword was given (:issue:`40660`) | |||
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level is really doing a groupby, I think it make senses to move the note there.
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At least, I've always thought about level as being an alias for groupby(level=...).
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yeah, generally try to avoid the 'Other' section.
nb. we have a PR somewhere deprecating level= to these
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Thx, was not sure if groupby was the correct place.
This bug is actually a good argument for the deprecation and removal...
doc/source/whatsnew/v1.3.0.rst
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| @@ -720,7 +720,7 @@ Other | |||
| - Bug in :meth:`DataFrame.equals`, :meth:`Series.equals`, :meth:`Index.equals` with object-dtype containing ``np.datetime64("NaT")`` or ``np.timedelta64("NaT")`` (:issue:`39650`) | |||
| - Bug in :func:`pandas.util.show_versions` where console JSON output was not proper JSON (:issue:`39701`) | |||
| - Bug in :meth:`DataFrame.convert_dtypes` incorrectly raised ValueError when called on an empty DataFrame (:issue:`40393`) | |||
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| - Bug in aggregation functions for :class:`DataFrame` not respecting ``numeric_only`` argument when ``level`` keyword was given (:issue:`40660`) | |||
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yeah, generally try to avoid the 'Other' section.
nb. we have a PR somewhere deprecating level= to these
pandas/tests/groupby/test_groupby.py
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| @@ -2190,3 +2190,18 @@ def test_groupby_mean_duplicate_index(rand_series_with_duplicate_datetimeindex): | |||
| result = dups.groupby(level=0).mean() | |||
| expected = dups.groupby(dups.index).mean() | |||
| tm.assert_series_equal(result, expected) | |||
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| @pytest.mark.parametrize("meth", ["max", "min", "sum", "mean", "median"]) | |||
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hmm can you put these with similar tests, maybe in test_reductions?
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thanks @phofl |
stumbled accross this one