Pandas BUG: inconsistent behaviour of the method .idxmin between timedelta datatype & int datatypes

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Reproducible Example

import pandas as pd

incomplete_df: pd.DataFrame = pd.DataFrame({'event1': [pd.Timedelta("1 days"),    pd.Timedelta("2 days"), pd.Timedelta("nat"),    pd.Timedelta("5 days") ,    pd.Timedelta("6 days"), pd.Timedelta("nat"),    pd.Timedelta("nat"),    pd.Timedelta("11 days"),    pd.Timedelta("nat"),    pd.Timedelta("15 days")],   
                              'event2': [pd.Timedelta("nat"),   pd.Timedelta("1 days") ,    pd.Timedelta("nat"),    pd.Timedelta("3 days") ,    pd.Timedelta("4 days"), pd.Timedelta("7 days") ,    pd.Timedelta("nat"),    pd.Timedelta("12 days"),    pd.Timedelta("nat"),    pd.Timedelta("17 days")],   
                              'event3': [pd.Timedelta("nat"),   pd.Timedelta("nat"),    pd.Timedelta("nat"),    pd.Timedelta("nat"),    pd.Timedelta("6 days"), pd.Timedelta("4 days") ,    pd.Timedelta("9 days") ,    pd.Timedelta("nat"),    pd.Timedelta("3 days") ,    pd.Timedelta("nat")]})

incomplete_df['reason'] = "Reason is " + incomplete_df.idxmin(axis="columns")

Issue Description

The above code will throw: ValueError: attempt to get argmin of an empty sequence

However, using this code:

import pandas as pd
import numpy as np
incomplete_df = pd.DataFrame({'event1': [1,     2     ,np.NAN,5     ,6,np.NAN,np.NAN,11    ,np.NAN,15],
                              'event2': [np.NAN,1     ,np.NAN,3     ,4,7     ,np.NAN,12    ,np.NAN,17],
                              'event3': [np.NAN,np.NAN,np.NAN,np.NAN,6,4     ,9     ,np.NAN,3     ,np.NAN]})

incomplete_df['reason'] = "Reason is " + incomplete_df.idxmin(axis="columns")

returns a dataframe (incomplete_df) with a fourth column with None when the three columns are np.NAN

Expected Behavior

The documentation states: Raises ValueError If the row/column is empty.

This is currently wrong when it comes to integers.

Expected behaviour: some consistency between the two datatypes.

With those two stackoverflow posts in mind (https://stackoverflow.com/questions/21188151/pandas-getting-the-name-of-the-minimum-column/73346957#73346957, and https://stackoverflow.com/questions/73354436/pandas-getting-the-name-of-the-minimum-column-with-timedelta-datatype), it is quite obvious, the expected behaviour should not be Raises ValueError If the row/column is empty but rather sets None in the created row/column when the row/column is empty. Or an optional argument should be added to determine what behaviour is desired by the user.

Installed Versions

INSTALLED VERSIONS

commit : e8093ba372f9adfe79439d90fe74b0b5b6dea9d6 python : 3.10.2.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.22000 machine : AMD64 processor : AMD64 Family 25 Model 80 Stepping 0, AuthenticAMD byteorder : little LC_ALL : None LANG : None LOCALE : English_United Kingdom.1252

pandas : 1.4.3 numpy : 1.22.0 pytz : 2021.3 dateutil : 2.8.2 setuptools : 58.1.0 pip : 22.2.2 Cython : None pytest : 7.1.2 hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : 3.0.3 IPython : 8.0.0 pandas_datareader: None bs4 : 4.11.1 bottleneck : None brotli : None fastparquet : None fsspec : 2022.5.0 gcsfs : None markupsafe : 2.0.1 matplotlib : 3.5.2 numba : 0.55.2 numexpr : None odfpy : None openpyxl : 3.0.9 pandas_gbq : None pyarrow : None pyreadstat : None pyxlsb : None s3fs : None scipy : 1.7.3 snappy : None sqlalchemy : 1.4.36 tables : None tabulate : None xarray : None xlrd : 2.0.1 xlwt : None zstandard : None

Comment From: josh-medorion

This method relies on numpy's core/fromnumeric.py, which contains a warning that if the object has a signature different from that of NumPy it will raise a TypeError. Since Timedelta is Pandas specific, I'm guessing that's the source of the problem.

Comment From: jreback

@JasonMendoza2008 bugs are fixed when the community does pull requests

Comment From: JasonMendoza2008

Update: The bug is still present in the new pandas version (pandas==1.5.2).

Comment From: jreback

@JasonMendoza2008 see my comment above

you are welcome to contribute a patch

bugs are fixed when someone does - pandas is all volunteer

Comment From: josh-medorion

Update: The bug is still present in the new pandas version (pandas==1.5.2).

@JasonMendoza2008 Please stop adding these comments, they only add noise and waste the time of the maintainers. As @jreback has said twice, bugs are not magically fixed, obviously a bug can be assumed to persist in future versions until a fix is contributed.