Description
Pandas version checks
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I have checked that this issue has not already been reported.
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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
import pandas as pd
df = pd.DataFrame({'A': [0, 1, 2]})
print(df)
# A
# 0 0
# 1 1
# 2 2
df['A'].replace(to_replace=2, value=99, inplace=True)
print(df)
# A
# 0 0
# 1 1
# 2 99
df.at[0, 'A'] = pd.NA
df['A'].replace(to_replace=1, value=100, inplace=True)
print(df)
# A
# 0 <NA>
# 1 1 <-- should be 100
# 2 99Issue Description
Pandas replace function does not seem to work on a column if the column contains at least one pd.NA value
Expected Behavior
replace function should work even if pd.NA values are in the column
Installed Versions
INSTALLED VERSIONS
commit : 66e3805
python : 3.10.0.final.0
python-bits : 64
OS : Linux
OS-release : 5.16.19-76051619-generic
Version : #202204081339164969616120.04091f44bdev-Ubuntu SMP PREEMPT Tu
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.5
numpy : 1.21.2
pytz : 2021.3
dateutil : 2.8.2
pip : 21.2.4
setuptools : 58.0.4
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 3.0.3
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.2
IPython : 7.29.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.5.1
numexpr : None
odfpy : None
openpyxl : 3.0.9
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.8.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None