BUG: dropna throws error with arguments axis=1 and subset not None

[ochabloz]

Pandas version checks

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

• I have confirmed this bug exists on the main branch of pandas.

Reproducible Example

import pandas as pd
import numpy as np

df = pd.DataFrame({"name": ['Alfred', 'Batman', 'Catwoman'], "toy": [np.nan, 'Batmobile', 'Bullwhip'], "born": [pd.NaT, pd.NaT,pd.NaT]})
df.dropna(axis=1, subset=['born'])

Issue Description

Traceback (most recent call last):
File "/home/vscode/.local/lib/python3.10/site-packages/pandas/core/frame.py", line 6670, in dropna
raise KeyError(np.array(subset)[check].tolist())
KeyError: ['born']

Expected Behavior

dropna should return the dataframe without the born column.

Installed Versions

commit : 0691c5c
python : 3.10.15
python-bits : 64
OS : Linux
OS-release : 6.11.5
Version : #1-NixOS SMP PREEMPT_DYNAMIC Tue Oct 22 13:51:37 UTC 2024
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8

pandas : 2.2.3
numpy : 1.26.4
pytz : 2024.2
dateutil : 2.9.0.post0
pip : 23.0.1
Cython : None
sphinx : None
IPython : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
blosc : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : None
html5lib : None
hypothesis : None
gcsfs : None
jinja2 : 3.1.4
lxml.etree : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
psycopg2 : None
pymysql : None
pyarrow : None
pyreadstat : None
pytest : 8.3.3
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.14.1
sqlalchemy : 2.0.36
tables : None
tabulate : None
xarray : None
xlrd : None
xlsxwriter : None
zstandard : None
tzdata : 2024.2
qtpy : None
pyqt5 : None

[asishm]

Thanks for the report. The error is accurate.

The documentation for the subset argument states:

subset : column label or sequence of labels, optional
Labels along other axis to consider, e.g. if you are dropping rows
these would be a list of columns to include.

Note that it mentions other axis. In this case you specify axis=1 i.e. columns, therefore for subset you need to provide row labels (but that doesn't sound like what you're after).

df.dropna(axis=1, how='all') will drop all columns where every value is na

[rhshadrach]

Agreed @asishm - closing.