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Showing content from https://github.com/pandas-dev/pandas/issues/39111 below:

pd.apply returns a dataframe when empty dataframe instead of a series · Issue #39111 · pandas-dev/pandas · GitHub

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• 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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>>> import pandas as pd
>>> pd.DataFrame({'a': [1,2], 'b': [3, 0]})
   a  b
0  1  3
1  2  0
>>> df = pd.DataFrame({'a': [1,2], 'b': [3, 0]})
>>> df
   a  b
0  1  3
1  2  0
>>> df.apply(lambda x: max(x['a'], x['b']), axis = 1)
0    3
1    2
dtype: int64
>>> df.head(0).apply(lambda x: max(x['a'], x['b']), axis = 1)
Empty DataFrame
Columns: [a, b]
Index: []
>>>

When running .apply() to a dataframe, I'm expecting to get a pd.Series back to add as an additional column. If the dataframe is empty and I run .apply() on it, it will return a dataframe and I cannot add it as a column

>>> pd.show_versions()

INSTALLED VERSIONS
------------------
commit           : d9fff2792bf16178d4e450fe7384244e50635733
python           : 3.8.5.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 20.2.0
Version          : Darwin Kernel Version 20.2.0: Wed Dec  2 20:39:59 PST 2020; root:xnu-7195.60.75~1/RELEASE_X86_64
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.1.0
numpy            : 1.19.1
pytz             : 2020.5
dateutil         : 2.8.1
pip              : 20.2.2
setuptools       : 49.6.0.post20201009
Cython           : None
pytest           : 6.2.1
hypothesis       : None
sphinx           : None
blosc            : None
feather          : 0.4.1
xlsxwriter       : 1.3.7
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.11.2
IPython          : 7.19.0
pandas_datareader: None
bs4              : None
bottleneck       : None
fsspec           : 0.8.5
fastparquet      : None
gcsfs            : None
matplotlib       : 3.3.0
numexpr          : None
odfpy            : None
openpyxl         : 3.0.5
pandas_gbq       : None
pyarrow          : 2.0.0
pytables         : None
pyxlsb           : None
s3fs             : 0.5.1
scipy            : 1.5.2
sqlalchemy       : 1.3.19
tables           : None
tabulate         : 0.8.7
xarray           : None
xlrd             : 1.2.0
xlwt             : None
numba            : 0.50.1
>>>

[paste the output of pd.show_versions() here leaving a blank line after the details tag]

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