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 numpy as np, pandas as pd
values = np.arange(4).reshape(2, 2)
df = pd.DataFrame(values, columns=["a", "b"])
new = np.array([10, 11]).astype(np.int16)
df.loc[:, "a"] = newIssue Description
FutureWarning: In a future version,
df.iloc[:, i] = newvalswill attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use eitherdf[df.columns[i]] = newvalsor, if columns are non-unique,df.isetitem(i, newvals)
This is confusing because I did not do df.iloc, I did df.loc. In the release notes, the subsection header mentions .loc, but the text only talks about .iloc.
Additionally, it was very difficult to put together a reproducible example, until I found a related issue demonstrating that it matters whether the old/new series have different dtypes. This is reasonably clear from the release notes themselves, but not the warning message.
Expected Behavior
I assume that this change does affect both .loc and .iloc so the warning message could be updated to be more clear, but in the event it's a false alarm on .loc, it would be good to suppress it.
The warning message could also be a little bit more clear about why the warning got triggered (even if in a general sense).
Installed Versions
INSTALLED VERSIONS
commit : 87cfe4e
python : 3.10.0.final.0
python-bits : 64
OS : Darwin
OS-release : 21.6.0
Version : Darwin Kernel Version 21.6.0: Mon Aug 22 20:20:07 PDT 2022; root:xnu-8020.140.49~2/RELEASE_ARM64_T8110
machine : arm64
processor : arm
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.5.0
numpy : 1.23.3
pytz : 2022.2.1
dateutil : 2.8.2
setuptools : 63.4.1
pip : 22.1.2
Cython : None
pytest : 7.1.3
hypothesis : None
sphinx : 5.1.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.9.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
IPython : 8.5.0
pandas_datareader: None
bs4 : 4.11.1
bottleneck : None
brotli : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : 3.6.0
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.9.1
snappy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
zstandard : None
tzdata : None