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BUG: grouby(axis=1) cannot select column names #27700

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Merged
merged 8 commits into from
Aug 2, 2019
Merged

BUG: grouby(axis=1) cannot select column names #27700

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7e461a1
remove \n from docstring
charlesdong1991 Dec 3, 2018
1314059
fix conflicts
charlesdong1991 Jan 19, 2019
8bcb313
Merge remote-tracking branch 'upstream/master'
charlesdong1991 Jul 30, 2019
a94eb4e
Fix bug in groupby when axis=1
charlesdong1991 Aug 1, 2019
e738e5f
Correct linting error
charlesdong1991 Aug 1, 2019
b5f95e6
Rephrase whatsnew note description
charlesdong1991 Aug 1, 2019
1413929
Add test case for MI columns
charlesdong1991 Aug 2, 2019
c3e1477
Fix doc issue
charlesdong1991 Aug 2, 2019
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v1.0.0.rst
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Expand Up @@ -166,6 +166,7 @@ Groupby/resample/rolling

-
-
- Bug in :meth:`groupby` does not offer implicit selection by column names when ``axis`` set to 1 (:issue:`27614)
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Should be DataFrame.groupby`

"does not offer" to "not offering"

"axis set to 1" to "axis=1"

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I'm also not sure what "implicit selection by column name" means here.

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I'm also not sure what "implicit selection by column name" means here.

ahh, my bad, i basically just copied and pasted the issue title, will change! @TomAugspurger thanks for your quick reply!


Reshaping
^^^^^^^^^
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4 changes: 2 additions & 2 deletions pandas/core/groupby/grouper.py
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Expand Up @@ -606,10 +606,10 @@ def is_in_obj(gpr):
elif is_in_axis(gpr): # df.groupby('name')
if gpr in obj:
if validate:
obj._check_label_or_level_ambiguity(gpr)
obj._check_label_or_level_ambiguity(gpr, axis=axis)
in_axis, name, gpr = True, gpr, obj[gpr]
exclusions.append(name)
elif obj._is_level_reference(gpr):
elif obj._is_level_reference(gpr, axis=axis):
in_axis, name, level, gpr = False, None, gpr, None
else:
raise KeyError(gpr)
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14 changes: 14 additions & 0 deletions pandas/tests/groupby/test_groupby.py
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Expand Up @@ -1860,3 +1860,17 @@ def test_groupby_groups_in_BaseGrouper():
result = df.groupby(["beta", pd.Grouper(level="alpha")])
expected = df.groupby(["beta", "alpha"])
assert result.groups == expected.groups


@pytest.mark.parametrize("group_name", ["x", ["x"]])
def test_groupby_axis_1(group_name):
# GH 27614
df = pd.DataFrame(
np.arange(12).reshape(3, 4), index=[0, 1, 0], columns=[10, 20, 10, 20]
)
df.index.name = "y"
df.columns.name = "x"

results = df.groupby(group_name, axis=1).sum()
expected = df.T.groupby(group_name).sum().T
assert_frame_equal(results, expected)