BUG: DataFrame.apply labelling index and columns asymmetrically if axis=0/1.

[attack68]

• I have checked that this issue has not already been reported.

• I have confirmed this bug exists on the latest version of pandas.

• (optional) I have confirmed this bug exists on the master branch of pandas.

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Currently DataFrame.apply works asymmetrically.

>>> df = pd.DataFrame([[1,2], [3,4]], index=["a", "b"], columns=["x", "y"])

>>> df.apply(lambda s: ["one", "two"], axis=0)
     x    y
a  one  one
b  two  two

>>> df.apply(lambda s: pd.Series(["one", "two"], index=["c", "d"]), axis=0)
     x    y
c  one  one
d  two  two

>>> df.apply(lambda s: ["one", "two", "three"], axis=0)
     x    y
0  one  one
1  two  two
2 three three

Along axis=0, the native index is maintained, if the shapes match and it is not overwritten by the function result object. When shapes don't match the created index is the default RangeIndex.

>>> df.apply(lambda s: ["one", "two"], axis=1, result_type="expand")
     0    1
a  one  two
b  one  two

>>> df.apply(lambda s: pd.Series(["one", "two"], index=["c", "d"]), axis=1, result_type="expand")
     c    d
a  one  two
b  one  two

>>> df.apply(lambda s: ["one", "two", "three"], axis=1, result_type="expand")
     0    1    2
a  one  two three
b  one  two three

Along axis=1 (with result_type=expand), the native columns are overwritten by a default RangeIndex (even if it has same shape) unless overwritten by the function result object index.

Expected Output

Would anyone object to a PR that looks to unify the two cases and apply the columns index where the shapes were the same?

Currently I use the workaround:

df.apply(func, axis=1, result_type="expand") -> df.T.apply(func, axis=0).T

Bug 1

Documentation states that for result_type arguments - These only act when axis=1 (columns):.
This is not true since:

>>> df = pd.DataFrame([[1, 2], [3, 4]], index=["a", "b"], columns=["a", "b"])
>>> df.apply(lambda s: [1,1,1], axis=0, result_type=None)  # or "expand"
	a	b
0	1	1
1	1	1
2	1	1
>>> df.apply(lambda s: [1,1,1], axis=0, result_type="broadcast")
ValueError: cannot broadcast result
>>> df.apply(lambda s: [1,1,1], axis=0, result_type="reduce")
a    [1, 1, 1]
b    [1, 1, 1]
dtype: object