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BUG: Should not raise error in concatenating Series with numpy scalar and tuple names (GH21015) #21132

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Merged
merged 10 commits into from
May 21, 2018
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BUG: Should not raise error in concatenating Series with numpy scalar and tuple names (GH21015) #21132

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GH21015
KalyanGokhale May 19, 2018
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GH21015
KalyanGokhale May 19, 2018
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GH21015
KalyanGokhale May 19, 2018
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KalyanGokhale May 19, 2018
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GH21015
KalyanGokhale May 19, 2018
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KalyanGokhale May 19, 2018
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KalyanGokhale May 20, 2018
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KalyanGokhale May 20, 2018
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6 changes: 5 additions & 1 deletion pandas/core/common.py
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Expand Up @@ -55,7 +55,11 @@ def flatten(l):
def _consensus_name_attr(objs):
name = objs[0].name
for obj in objs[1:]:
if obj.name != name:
name_check = (obj.name != name)
if not isinstance(name_check, (bool, np.bool_)):
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Can you use a try...except block here to catch the ValueError instead of type introspection?

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@WillAyd thanks - will make the changes. Should I add whatsnew note to v0.23.1?

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Yes that works

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Done

# 'If' test can also be a positive for 'np.ndarray'
return None
elif name_check:
return None
return name

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15 changes: 15 additions & 0 deletions pandas/tests/reshape/test_concat.py
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Expand Up @@ -2350,6 +2350,21 @@ def test_concat_datetime_timezone(self):

tm.assert_frame_equal(result, expected)

def test_concat_series_name_npscalar_tuple(self):
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this doesn't need to be in the class, it can just be a module level test (e.g. de-dent)

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parameterize this on a np.int64 and an int

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done - took the test out of class, parameterised as suggested, also actually removed the test case which took a row from DataFrame

# GH21015
s1 = pd.Series({'a': 1.5}, name=np.int64(190))
s2 = pd.Series([], name=(43, 0))
result = pd.concat([s1, s2])
expected = pd.Series({'a': 1.5})
tm.assert_series_equal(result, expected)

df1 = pd.DataFrame([[1, 2], [3, 4]], columns=['a', 'b'], index=[0, 1])
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this is very weird to construct a DataFrame to then take a row from it. Just directly construct the series.

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will update - had added the original test case from the issue #21015

df2 = pd.DataFrame([[5, 6], [7, 8]], columns=['c', 'd'],
index=pd.MultiIndex.from_tuples([(0, 0), (1, 1)]))
result = pd.concat([df1.iloc[0], df2.iloc[0]])
expected = pd.Series({'a': 1, 'b': 2, 'c': 5, 'd': 6})
tm.assert_series_equal(result, expected)


@pytest.mark.parametrize('pdt', [pd.Series, pd.DataFrame, pd.Panel])
@pytest.mark.parametrize('dt', np.sctypes['float'])
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