BUG: Boolean Column Indexing Issue in Pandas

[tanjt107]

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

data = {"A": [1, 2, 3], "B": [4, 5, 6], True: [7, 8, 9]}

df = pd.DataFrame(data)

cols = ["A"]
df[cols]
#    A
# 0  1
# 1  2
# 2  3

cols = [True]
df[cols]  # ValueError: Item wrong length 1 instead of 3.

Issue Description

The issue arises when attempting to access a pandas.DataFrame using a list of boolean values as column names.

Expected Behavior

   A
0  7
1  8
2  9

Installed Versions

INSTALLED VERSIONS

commit : 0691c5c
python : 3.12.4
python-bits : 64
OS : Darwin
OS-release : 24.5.0
Version : Darwin Kernel Version 24.5.0: Tue Apr 22 19:54:49 PDT 2025; root:xnu-11417.121.6~2/RELEASE_ARM64_T6000
machine : arm64
processor : arm
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 2.2.3
numpy : 2.2.4
pytz : 2025.2
dateutil : 2.9.0.post0
pip : 25.0.1
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 : 3.1.6
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 : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlsxwriter : None
zstandard : None
tzdata : 2025.2
qtpy : None
pyqt5 : None

[rhshadrach]

Thanks for the report. Unfortunately because there are multiple acceptable inputs similar to [True] (Boolean mask or list of columns), pandas has to make some choice as to what takes priority. It isn't clear to me that we should do one or the other, and because of that I'd be hesitant to change behavior here.

This is a great example of why I think pandas should only accept strings as columns (but of course, that would involve a significant amount of API changes and backwards incompatibility issues).

[arthurlw]

-1 for the reasons above as well. [True] is ambiguous between a mask and a label, and changing the behavior risks breaking existing usage.

Thanks for raising this edge case though!