BUG: Replace changes the dtype of other columns #46634
Description
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
df = pd.DataFrame(
{
"id": pd.Series([5, 4, 3, 2], dtype="float64"),
"col": pd.Series(["b", "b", "b", "d"], dtype="category"),
}
)
df = df.replace({3: None})Issue Description
In version 1.4.1, df.replace would change the id column to the object type and leave col with the category dtype. However, in pandas 1.4.2, this now replaces both the dtypes with object.
Expected Behavior
I would expect all columns that are unimpacted/unchanged by replace to continue to exhibit the same dtype as previously assigned.
Installed Versions
INSTALLED VERSIONS
commit : 4bfe3d0
python : 3.8.0.final.0
python-bits : 64
OS : Darwin
OS-release : 21.1.0
Version : Darwin Kernel Version 21.1.0: Wed Oct 13 17:33:23 PDT 2021; root:xnu-8019.41.5~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.4.2
numpy : 1.21.2
pytz : 2021.1
dateutil : 2.8.2
pip : 22.0.4
setuptools : 41.2.0
Cython : 0.29.28
pytest : 7.0.1
hypothesis : None
sphinx : 4.2.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.31.1
pandas_datareader: None
bs4 : 4.10.0
bottleneck : None
brotli : None
fastparquet : None
fsspec : 2021.08.1
gcsfs : None
markupsafe : 2.0.1
matplotlib : 3.5.1
numba : 0.55.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 5.0.0
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.7.1
snappy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
zstandard : None