BUG: Binary operations with empty string arrays produce

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Reproducible Example

>>> import pandas as pd
>>> pd.DataFrame({'a': pd.Series([], dtype='str')}) + 1
Empty DataFrame
Columns: [a]
Index: []
>>> pd.DataFrame({'a': pd.Series([], dtype='str')}) | 1
Empty DataFrame
Columns: [a]
Index: []
>>> pd.DataFrame({'a': pd.Series([None], dtype='str')}) + 1
     a
0  NaN
>>> pd.DataFrame({'a': pd.Series([None], dtype='str')}) | 1
Traceback (most recent call last):
...
TypeError: unsupported operand type(s) for |: 'NoneType' and 'int'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
...
TypeError: Cannot perform 'or_' with a dtyped [object] array and scalar of type [bool]
>>> pd.DataFrame({'a': pd.Series([None, 'a'], dtype='str')}) + 1
Traceback (most recent call last):
...
TypeError: unsupported operand type(s) for +: 'NoneType' and 'int'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
...
TypeError: can only concatenate str (not "int") to str

Issue Description

When a DataFrame contains a column of dtype object that is either empty or contains only None, it may allow certain binary operations to occur that would otherwise be invalid.

Expected Behavior

Errors should occur consistently across different binary operations between string columns and scalars based on the dtype of the scalar irrespective of whether that column is empty, contains only None, or contains actual strings.

Installed Versions

pd.show_versions()

INSTALLED VERSIONS

commit : 66e3805b8cabe977f40c05259cc3fcf7ead5687d python : 3.8.12.final.0 python-bits : 64 OS : Linux OS-release : 4.15.0-76-generic Version : #86-Ubuntu SMP Fri Jan 17 17:24:28 UTC 2020 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : None LOCALE : en_US.UTF-8

pandas : 1.3.5 numpy : 1.21.5 pytz : 2021.3 dateutil : 2.8.2 pip : 22.0.4 setuptools : 59.8.0 Cython : 0.29.28 pytest : 7.0.1 hypothesis : 6.39.3 sphinx : 4.4.0 blosc : None feather : None xlsxwriter : None lxml.etree : 4.8.0 html5lib : None pymysql : None psycopg2 : None jinja2 : 3.0.3 IPython : 8.1.1 pandas_datareader: None bs4 : 4.10.0 bottleneck : None fsspec : 2022.02.0 fastparquet : None gcsfs : None matplotlib : None numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : 6.0.1 pyxlsb : None s3fs : None scipy : None sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None xlwt : None numba : 0.55.1

[vyasr]

Yup, that all sounds right to me!

[edwhuang23]

Gotcha, so it seems like you’re saying that the behavior of dtypes should not depend on content, which I absolutely agree with. Furthermore, I want to clarify: You are saying that performing a binary operation should work between two compatible columns according to their dtype (ex: integer column added to float column similar to the second commented example with two arrays added together) or that an operation between a column and a scalar value should be allowed if their dtypes are compatible, and that two incompatible dtypes should not work together (ex: integer added to a string as in your reproducible example) – is this correct?

In terms of proposed changes, I’m thinking of the following:

1. Changing the current implementation so that it does not check whether the column is empty or is nonempty and contains nulls, and instead check by dtypes according to the behavior I described above.
2. Ensuring consistency of valid binary operations between different dtypes, no matter if we are dealing with two columns or 1 column and a scalar value.

Let me know if I misunderstood anything.