Description
Pandas version checks
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I have checked that this issue has not already been reported.
-
I have confirmed this bug exists on the latest version of pandas.
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I have confirmed this bug exists on the main branch of pandas.
Reproducible Example
x = pd.DataFrame({
"A": pd.Categorical(["A", "B"], categories=["A", "B"]),
"B": [1,2],
"C": ["D", "E"]
})
print(x.dtypes)
x.loc[:, "C"] = pd.Categorical(x.loc[:, "C"], categories=["D", "E"])
print(x.dtypes)Issue Description
When setting an existing column to its categorical equivalent, the underlying dtypes stay the same.
Expected Behavior
Output now:
A category
B int64
C object
dtype: object
A category
B int64
C object
dtype: object
Expected output:
A category
B int64
C object
dtype: object
A category
B int64
C category <----
dtype: object
Installed Versions
INSTALLED VERSIONS
commit : 478d340
python : 3.9.14.final.0
python-bits : 64
OS : Darwin
OS-release : 22.4.0
Version : Darwin Kernel Version 22.4.0: Mon Mar 6 20:59:58 PST 2023; root:xnu-8796.101.5~3/RELEASE_ARM64_T6020
machine : arm64
processor : arm
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 2.0.0
numpy : 1.23.2
pytz : 2022.7.1
dateutil : 2.8.2
setuptools : 67.4.0
pip : 23.0.1
Cython : 0.29.33
pytest : 7.2.2
hypothesis : None
sphinx : 6.1.3
blosc : None
feather : None
xlsxwriter : 3.0.8
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.9.5
jinja2 : 3.0.3
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
brotli : None
fastparquet : None
fsspec : 2023.1.0
gcsfs : None
matplotlib : 3.7.0
numba : 0.56.4
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 11.0.0
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.8.1
snappy : None
sqlalchemy : 1.4.46
tables : None
tabulate : 0.9.0
xarray : None
xlrd : None
zstandard : None
tzdata : 2022.7
qtpy : None
pyqt5 : None