BUG: Fix bug in SeriesGroupBy.value_counts when DataFrame has one row (#42618) #42640
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rhshadrach
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doc/source/whatsnew/v1.4.0.rst
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| @@ -258,7 +258,7 @@ Groupby/resample/rolling | |||
| ^^^^^^^^^^^^^^^^^^^^^^^^ | |||
| - Fixed bug in :meth:`SeriesGroupBy.apply` where passing an unrecognized string argument failed to raise ``TypeError`` when the underlying ``Series`` is empty (:issue:`42021`) | |||
| - Bug in :meth:`Series.rolling.apply`, :meth:`DataFrame.rolling.apply`, :meth:`Series.expanding.apply` and :meth:`DataFrame.expanding.apply` with ``engine="numba"`` where ``*args`` were being cached with the user passed function (:issue:`42287`) | |||
| - | |||
| - Fixed bug in :meth:`SeriesGroupBy.value_counts` when the DataFrame/Series you are grouping has one row (:issue:`42618`) | |||
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Only Series, I think. Even if you start with a DataFrame/DataFrameGroupBy object and then subset, this method is only acting on a Series.
| result = dfg["B"].value_counts() | ||
| expected = df.value_counts() | ||
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| tm.assert_series_equal(result, expected, check_names=False) |
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Can you rename to the expected value instead of not checking
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| tm.assert_series_equal(result, expected, check_names=False) | ||
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| df = DataFrame([[1, 2, 3]], columns=["A", "B", "C"]) |
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Parametrize the test instead, e.g.
pytest.mark.parametrize("columns", [["A", "B"], ["A", "B", "C"]])
then use data=range(len(columns)), and groupby(columns)[:-1]
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…nts when DataFrame has one row (pandas-dev#42618)
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…ataFrame has one row (#42618) (#42696) Co-authored-by: neelmraman <[email protected]>
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Was introduced in v1.3.0 here. I couldn't find a reason why
len(lchanges)should be used and the unit tests still passed withlen(val)instead.