BUG: Series.replace doesn't update CategoricalDtype when inplace=True in pandas-2.0

[galipremsagar]

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

In [1]: import pandas as pd

In [2]: s = pd.Series(["one", "two", "three"], dtype="category")

In [3]: s
Out[3]: 
0      one
1      two
2    three
dtype: category
Categories (3, object): ['one', 'three', 'two']

In [4]: new_s = s.replace(to_replace="one", value="two")

In [5]: new_s
Out[5]: 
0      two
1      two
2    three
dtype: category
Categories (2, object): ['three', 'two']

In [6]: new_s.dtype
Out[6]: CategoricalDtype(categories=['three', 'two'], ordered=False)     # Correct

In [7]: s.replace(to_replace="one", value="two", inplace=True)

In [8]: s
Out[8]: 
0      two
1      two
2    three
dtype: category
Categories (2, object): ['three', 'two']          # Correct __repr__ output

In [9]: s.dtype
Out[9]: CategoricalDtype(categories=['one', 'three', 'two'], ordered=False). # BUG: Incorrect dtype, dtype used to be updated pre pandas-2.0

Issue Description

When we replace values in a categorical Series, with inplace=True, the dtype is not being updated.

Expected Behavior

In [9]: s.dtype
Out[9]: CategoricalDtype(categories=['three', 'two'], ordered=False)

Installed Versions

INSTALLED VERSIONS

commit : 37ea63d
python : 3.10.11.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-76-generic
Version : #86-Ubuntu SMP Fri Jan 17 17:24:28 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 2.0.1
numpy : 1.23.5
pytz : 2023.3
dateutil : 2.8.2
setuptools : 67.7.2
pip : 23.1.2
Cython : 0.29.34
pytest : 7.3.1
hypothesis : 6.75.3
sphinx : 5.3.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
IPython : 8.13.2
pandas_datareader: None
bs4 : 4.12.2
bottleneck : None
brotli :
fastparquet : None
fsspec : 2023.5.0
gcsfs : None
matplotlib : None
numba : 0.56.4
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 11.0.0
pyreadstat : None
pyxlsb : None
s3fs : 2023.5.0
scipy : 1.10.1
snappy :
sqlalchemy : 2.0.15
tables : None
tabulate : 0.9.0
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.3
qtpy : None
pyqt5 : None

[mroeschke]

This looks to be fixed on main (and I think will be fixed in 2.1). Could use a unit test

In [1]: In [1]: import pandas as pd
   ...: 
   ...: In [2]: s = pd.Series(["one", "two", "three"], dtype="category")

In [2]: s.replace(to_replace="one", value="two", inplace=True)

In [3]: s
Out[3]: 
0      two
1      two
2    three
dtype: category
Categories (2, object): ['three', 'two']

In [4]: s.dtype
Out[4]: CategoricalDtype(categories=['three', 'two'], ordered=False, categories_dtype=object)

[Bsvelandiac]

take

[yusharth]

Hey @Bsvelandiac can I help you with this task?

[happyvignesh]

Hi @galipremsagar,
Thanks for the clear reproducible example.
I tried to reproduce it however I do get different output when I print Series.dtype.(i.e. it prints only "category" ).

`s.replace(to_replace="one", value="two", inplace=True)
print('value of s after replace')
print(s)
print(s.dtype)

Output:
value of s after replace
0 two
1 two
2 three
dtype: category
Categories (2, object) : ['three', 'two']
category
`

please refer the below details.
pandas version: 2.0.1
Branch: main

[galipremsagar]

@happyvignesh Like @mroeschke pointed here:#53358 (comment), it's now fixed in 2.0.2 too but probably needs some pytests in pandas code-base.

[aadeshmodak13]

it's now fixed in 2.0.2

[mohammedouahman]

Good to hear that!!

[natmokval]

Hi @Bsvelandiac , I'd like to create a pr for this issue if you don't mind.