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BUG: groupby with no non-empty groups, #21624 #21849

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BUG: groupby with no non-empty groups, #21624 #21849

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3543449
BUG: Fix groupby bug #21624.
lopez86 Jul 10, 2018
eddcbd1
Improving fix to bug #21624.
lopez86 Jul 11, 2018
59b3c45
Adding test to check that the issue is resolved.
lopez86 Jul 11, 2018
d47beea
Changing variable name to match established practices
lopez86 Jul 11, 2018
bda628c
Merge remote-tracking branch 'upstream/master' into fix_groupby_issue
lopez86 Jul 12, 2018
6d03bc9
Update to deal with refactor
lopez86 Jul 12, 2018
2fd0e73
Adding text to make sure we didn't break the behavior in non-edge cases
lopez86 Jul 13, 2018
6d2855c
Changed test to use parametrize decorator.
lopez86 Jul 13, 2018
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2 changes: 1 addition & 1 deletion pandas/core/groupby/ops.py
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Original file line number Diff line number Diff line change
Expand Up @@ -235,7 +235,7 @@ def size(self):
if ngroup:
out = np.bincount(ids[ids != -1], minlength=ngroup)
else:
out = ids
out = []
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Hmm something just seems strange to me about this. So your test example works fine, but what happens if we have a DataFrame that has other groups besides the all-NA one? Can you add a test for that and make sure that still works?

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Done. In that case, the else block should never be used

return Series(out,
index=self.result_index,
dtype='int64')
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13 changes: 13 additions & 0 deletions pandas/tests/groupby/test_transform.py
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Expand Up @@ -782,3 +782,16 @@ def test_any_all_np_func(func):

res = df.groupby('key')['val'].transform(func)
tm.assert_series_equal(res, exp)


@pytest.mark.parametrize("input_df, expected", [
(DataFrame({'groups': [np.nan, np.nan, np.nan], 'values': [1, 2, 3]}),
Series([np.nan, np.nan, np.nan], name='values')),
(DataFrame({'groups': [np.nan, 'A', 'A', 'B', 'B'], 'values': range(5)}),
Series([np.nan, 3, 3, 7, 7], name='values'))
])
def test_transform_with_all_nan(input_df, expected):
# GH 21624
grouped = input_df.groupby('groups')
result = grouped['values'].transform('sum')
tm.assert_series_equal(result, expected)