BUG: dtype changes when assigning on a slice or list of labels with .loc or iloc

[arnaudlegout]

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
import numpy as np
s = pd.Series([10, 20, 30], index=list('abc'), dtype=np.uint8)
s.loc['a':'b'] = [100, 200]
print(s.dtype)
>>>
int32

s = pd.Series([10, 20, 30], index=list('abc'), dtype=np.uint8)
s.loc[['a', 'b']] = [100, 200]
print(s.dtype)
>>>
int32

s = pd.Series([10, 20, 30], index=list('abc'), dtype=np.uint8)
s.iloc[0:2] = [100, 200]
print(s.dtype)
>>>
int32

s = pd.Series([10, 20, 30], index=list('abc'), dtype=np.uint8)
s.iloc[[0, 1]] = [100, 200]
print(s.dtype)
>>>
int32

s = pd.Series([10, 20, 30], index=list('abc'), dtype=np.uint8)
s.loc[:] = [100, 200, 300]
print(s.dtype)
>>>
int32

Issue Description

Assigning with loc is supposed to always assign in-place. However, in all the given examples, the Series dtype is changed on the assingment, which should not be the case.

Expected Behavior

On each given assignment, the dtype must remains uint8

Installed Versions

INSTALLED VERSIONS

commit : 37ea63d
python : 3.11.2.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19044
machine : AMD64
processor : Intel64 Family 6 Model 140 Stepping 1, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : fr_FR.cp1252

pandas : 2.0.1
numpy : 1.23.5
pytz : 2022.7
dateutil : 2.8.2
setuptools : 65.6.3
pip : 23.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.9.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
IPython : 8.10.0
pandas_datareader: None
bs4 : 4.12.0
bottleneck : 1.3.5
brotli :
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : 3.7.1
numba : None
numexpr : 2.8.4
odfpy : None
openpyxl : 3.0.10
pandas_gbq : None
pyarrow : 10.0.1
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.10.0
snappy : None
sqlalchemy : None
tables : None
tabulate : 0.8.10
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.3
qtpy : 2.2.0
pyqt5 : None

​

[StefanieSenger]

Thanks for reporting, @arnaudlegout. :)
I think this issue is related to #52612, #52668 and #52593, but not solved yet.

[arnaudlegout]

@StefanieSenger it is possibly related, but not a duplicate. The issues you refer to concern the backward incompatible change in pandas that makes df.loc[:,'col'] = new_col assigning in place, so the col dtype cannot be changed with such an assignment.

My issue is the opposite problem: assigning on df.loc unexpectedly changes the dtype, whereas it is supposed to make in-place modifications and keep the original dtype.

[topper-123]

PDEP-6: Ban upcasting in setitem-like operations will def. fix this. PDEP-6 will be enforced in pandas 3.0.

A PR in the meantime would probably be warranted.

[jbrockmendel]

These all correctly raise on main. Could use a test (first check to see if one already exists)

[vijmeister]

Do not see a test, so one should be made.