BUG: mapping categorical with single category to boolean returns category instead of bool dtype

[kdebrab]

Pandas version checks

• I have checked that this issue has not already been reported.

• 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

pd.Series(["a", "a"]).astype("category").map(lambda x: x == "c")

Issue Description

The above snippet erroneously returns category dtype:

0    False
1    False
dtype: category
Categories (1, bool): [False]

Expected Behavior

As soon as there are at least two categories, one gets the expected bool dtype:

pd.Series(["a", "b"]).astype("category").map(lambda x: x == "c")

returns:

0    False
1    False
dtype: bool

I would expect the same result if there is only one category involved.

Installed Versions

INSTALLED VERSIONS

commit : 2cc3762
python : 3.12.9
python-bits : 64
OS : Windows
OS-release : 11
Version : 10.0.26100
machine : AMD64
processor : Intel64 Family 6 Model 140 Stepping 1, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_Belgium.1252
pandas : 2.3.0
numpy : 2.3.1
pytz : 2025.2
dateutil : 2.9.0.post0
pip : None
Cython : None
sphinx : None
IPython : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
blosc : None
bottleneck : 1.5.0
dataframe-api-compat : None
fastparquet : None
fsspec : 2025.5.1
html5lib : None
hypothesis : None
gcsfs : None
jinja2 : None
lxml.etree : 5.4.0
matplotlib : 3.10.3
numba : None
numexpr : 2.11.0
odfpy : None
openpyxl : 3.1.5
pandas_gbq : None
psycopg2 : None
pymysql : None
pyarrow : 20.0.0
pyreadstat : None
pytest : 8.4.1
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.15.3
sqlalchemy : None
tables : None
tabulate : 0.9.0
xarray : 2025.3.1
xlrd : None
xlsxwriter : 3.2.5
zstandard : None
tzdata : 2025.2
qtpy : None
pyqt5 : None

[kernelism]

take

[kernelism]

@kdebrab
The issue is happening here:

pandas/pandas/core/arrays/categorical.py

Lines 1583 to 1585 in 35b0d1d

	if new_categories.is_unique and not new_categories.hasnans and na_val is np.nan:
	new_dtype = CategoricalDtype(new_categories, ordered=self.ordered)
	return self.from_codes(self._codes.copy(), dtype=new_dtype, validate=False)

In the first case, new_categories would be Index([False], dtype='bool') and since its unique, it ends up returning a CategoricalDtype. Should note that issue depends on unique categories after the condition is applied. For example in this code snippet:

pd.Series(["a", "a", "a", "b"]).astype("category").map(lambda x: x == "b")

even though there are at least 2 categories, the result is still:

0    False
1    False
2    False
3     True
dtype: category
Categories (2, bool): [False, True]

This is because the mapping condition does not return duplicate categories. I think this specific code block was added for efficiency purposes by checking a 1:1 mapping.

A simple fix to this would be to instead use:

pd.Series(["a", "a"]).astype("category") == "c"

or

pd.Series(["a", "a"]).astype("category").eq("c")

which correctly returns:

0    False
1    False
dtype: bool

[jbrockmendel]

dtype inference at the end of map calls is a really tricky problem that has come up before. Maybe someone will find an elegant solution, but this is a "don't get your hopes up" situation

[kernelism]

Yes I agree.