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CLN: Add tets and fix docstring for concat with sort=True and outer join #47612

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Merged
merged 1 commit into from
Jul 7, 2022
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CLN: Add tets and fix docstring for concat with sort=True and outer join #47612

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1 change: 1 addition & 0 deletions pandas/core/indexes/api.py
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Expand Up @@ -233,6 +233,7 @@ def _unique_indices(inds, dtype) -> Index:
Parameters
----------
inds : list of Index or list objects
dtype : dtype to set for the resulting Index
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above, it still says The final dtype is inferred.

Is this still true? It is a positional arg and not an optional kwarg, but we can explicitly pass None to get inference?

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@phofl phofl Jul 8, 2022 •
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Sorry, yeah if None is passed then we infer, should update docstring to reflect this


Returns
-------
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31 changes: 27 additions & 4 deletions pandas/tests/reshape/concat/test_index.py
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Expand Up @@ -399,13 +399,36 @@ def test_concat_range_index_result(self):
expected_index = pd.RangeIndex(0, 2)
tm.assert_index_equal(result.index, expected_index, exact=True)

@pytest.mark.parametrize("dtype", ["Int64", "object"])
def test_concat_index_keep_dtype(self, dtype):
def test_concat_index_keep_dtype(self):
# GH#47329
df1 = DataFrame([[0, 1, 1]], columns=Index([1, 2, 3], dtype="object"))
df2 = DataFrame([[0, 1]], columns=Index([1, 2], dtype="object"))
result = concat([df1, df2], ignore_index=True, join="outer", sort=True)
expected = DataFrame(
[[0, 1, 1.0], [0, 1, np.nan]], columns=Index([1, 2, 3], dtype="object")
)
tm.assert_frame_equal(result, expected)

def test_concat_index_keep_dtype_ea_numeric(self, any_numeric_ea_dtype):
# GH#47329
df1 = DataFrame(
[[0, 1, 1]], columns=Index([1, 2, 3], dtype=any_numeric_ea_dtype)
)
df2 = DataFrame([[0, 1]], columns=Index([1, 2], dtype=any_numeric_ea_dtype))
result = concat([df1, df2], ignore_index=True, join="outer", sort=True)
expected = DataFrame(
[[0, 1, 1.0], [0, 1, np.nan]],
columns=Index([1, 2, 3], dtype=any_numeric_ea_dtype),
)
tm.assert_frame_equal(result, expected)

@pytest.mark.parametrize("dtype", ["Int8", "Int16", "Int32"])
def test_concat_index_find_common(self, dtype):
# GH#47329
df1 = DataFrame([[0, 1, 1]], columns=Index([1, 2, 3], dtype=dtype))
df2 = DataFrame([[0, 1]], columns=Index([1, 2], dtype=dtype))
df2 = DataFrame([[0, 1]], columns=Index([1, 2], dtype="Int32"))
result = concat([df1, df2], ignore_index=True, join="outer", sort=True)
expected = DataFrame(
[[0, 1, 1.0], [0, 1, np.nan]], columns=Index([1, 2, 3], dtype=dtype)
[[0, 1, 1.0], [0, 1, np.nan]], columns=Index([1, 2, 3], dtype="Int32")
)
tm.assert_frame_equal(result, expected)