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TST: Test to verify behavior of groupby.std with no numeric columns and numeric_only=True #51761

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Merged
merged 9 commits into from
Mar 6, 2023
Merged

TST: Test to verify behavior of groupby.std with no numeric columns and numeric_only=True #51761

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183bb42
Added tests for std calculation on groups with numeric_only option
Mar 2, 2023
1d35bea
Merge branch 'main' of https://github.com/pandas-dev/pandas into grou…
Mar 3, 2023
618efb1
Added tests for std calculation on groups with numeric_only option
Mar 2, 2023
d1813f3
Merge branch 'groupby-std-numeric-only-tests' of https://github.com/N…
Mar 3, 2023
d75cdcb
removed unnecessary test and adjusted documentation for numeric_only
Mar 3, 2023
9026941
Merge remote-tracking branch 'upstream/main' into groupby-std-numeric…
Mar 3, 2023
10edb72
Parameterized the tests and removed doc changes
Mar 5, 2023
ea3bcc4
Merge remote-tracking branch 'upstream/main' into groupby-std-numeric…
Mar 5, 2023
1390fa4
revert missed doc
Mar 5, 2023
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8 changes: 4 additions & 4 deletions pandas/core/groupby/groupby.py
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Expand Up @@ -1915,7 +1915,7 @@ def std(
.. versionadded:: 1.4.0

numeric_only : bool, default False
Include only `float`, `int` or `boolean` data.
Include only `float`, `int` or `boolean` columns.
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I believe data is used here because this docstring is shared by DataFrameGroupBy and SeriesGroupBy; the latter does not have columns. I think this should be reverted.

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Makes sense. I have reverted these changes. But should't we have separate docstrings for DataFrameGroupBy and SeriesGroupBy for more clarity?

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We could template these docstrings or just separate them out entirely; but that should be a separate PR.


.. versionadded:: 1.5.0

Expand Down Expand Up @@ -1998,7 +1998,7 @@ def var(
.. versionadded:: 1.4.0

numeric_only : bool, default False
Include only `float`, `int` or `boolean` data.
Include only `float`, `int` or `boolean` columns.

.. versionadded:: 1.5.0

Expand Down Expand Up @@ -2167,7 +2167,7 @@ def sem(self, ddof: int = 1, numeric_only: bool = False):
Degrees of freedom.

numeric_only : bool, default False
Include only `float`, `int` or `boolean` data.
Include only `float`, `int` or `boolean` columns.

.. versionadded:: 1.5.0

Expand Down Expand Up @@ -3093,7 +3093,7 @@ def quantile(
interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}
Method to use when the desired quantile falls between two points.
numeric_only : bool, default False
Include only `float`, `int` or `boolean` data.
Include only `float`, `int` or `boolean` columns.

.. versionadded:: 1.5.0

Expand Down
19 changes: 19 additions & 0 deletions pandas/tests/groupby/test_groupby.py
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Expand Up @@ -2843,3 +2843,22 @@ def test_obj_with_exclusions_duplicate_columns():
result = gb._obj_with_exclusions
expected = df.take([0, 2, 3], axis=1)
tm.assert_frame_equal(result, expected)


def test_groupby_numeric_only_std_no_result():
# GH 51080
dicts_non_numeric = [{"a": "foo", "b": "bar"}, {"a": "car", "b": "dar"}]
df = DataFrame(dicts_non_numeric)
dfgb = df.groupby("a")
result = dfgb.std(numeric_only=True)

assert result.empty


def test_groupby_std_raises_error():
# GH 51080
dicts_non_numeric = [{"a": "foo", "b": "bar"}, {"a": "car", "b": "dar"}]
df = DataFrame(dicts_non_numeric)
dfgb = df.groupby("a")
with pytest.raises(ValueError, match="could not convert string to float: 'bar'"):
dfgb.std()