BUG: Using set_levels with a Categorical on a mutliindex isn't preserving the dtype · Issue #52125 · pandas-dev/pandas (original) (raw)

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Reproducible Example

import pandas as pd index = pd.MultiIndex.from_arrays([[1,2]]) index.set_levels(pd.Categorical([1,2]),level=0).levels[0]

Issue Description

When I set the level of a MultiIndex I expect it to be the type I set it to. But it gets overwritten to a more basic class:

In [24]: index = pd.MultiIndex.from_arrays([[1,2]]) ...: index.set_levels(pd.Categorical([1,2]),level=0).levels[0] Out[24]: Index([1, 2], dtype='int64')

Even overwriting with the exact same thing still does it:

In [43]: index.set_levels(pd.Categorical([1,2]),level=0).levels[0] Out[43]: Index([1, 2], dtype='int64')

Expected Behavior

index = pd.MultiIndex.from_arrays([pd.Categorical([1,2]),[3,4]]) index.set_levels(pd.Categorical([1,2]),level=0).levels[0]

Expected

CategoricalIndex([1, 2], categories=[1, 2], ordered=False, dtype='category')

Actual

Index([1, 2], dtype='int64')

Installed Versions

INSTALLED VERSIONS

commit : 5c15588
python : 3.10.9.final.0
python-bits : 64
OS : Linux
OS-release : 5.19.11-1rodete1-amd64
Version : #1 SMP PREEMPT_DYNAMIC Debian 5.19.11-1rodete1 (2022-10-31)
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 2.1.0.dev0+284.g5c155883fd
numpy : 1.21.5
pytz : 2022.6
dateutil : 2.8.2
setuptools : 65.6.3
pip : 22.3.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.9.1
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
IPython : 8.10.0
pandas_datareader: None
bs4 : 4.11.1
bottleneck : None
brotli : 1.0.9
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : None
snappy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None