BUG: Unclear FutureWarning regarding inplace iloc setitem

This issue has been tracked since 2022-09-21.

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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"] = new

Issue Description

FutureWarning: In a future version, df.iloc[:, i] = newvals will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either df[df.columns[i]] = newvals or, 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

mwaskom wrote this answer on 2022-09-21

Actually even looking at the release notes, I don't think I understand exactly what is deprecated here. The relevant section starts with

Most of the time setting values with DataFrame.iloc() attempts to set values inplace, only falling back to inserting a new array if necessary.

But then it says

This behavior is deprecated. In a future version, setting an entire column with iloc will attempt to operate inplace.

Isn't this what already happens ("most of the time"?) And if it's about different dtypes, how will that work? Do you mean to say it will coerce the dtype when setting the data?

anthonyaag wrote this answer on 2022-09-21

Seems like this warning was added in #45333 along with a some TODOs to get more info. Maybe @jbrockmendel has some guidance on what to do here.

More Details About Repo
Owner Name pandas-dev
Repo Name pandas
Full Name pandas-dev/pandas
Language Python
Created Date 2010-08-24
Updated Date 2022-10-04
Star Count 35430
Watcher Count 1120
Fork Count 15089
Issue Count 3589

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