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Reproducible Example

import pandas as pd

data1 = pd.Series([True, True, True], dtype=bool)
data2 = pd.Series([False, False, False], dtype=bool)
condition = pd.Series([False, True, False], dtype=bool)

data1[condition] = data2[condition] # > FutureWarning: Setting an item of incompatible dtype...

Issue Description

The assignment data1[condition] = data2[condition] results in warning claiming incompatible types:

FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[False]' has dtype incompatible with bool, please explicitly cast to a compatible dtype first. data1[condition] = data2[condition]

Which is clearly not true. This bug is somewhat related to the one reported in #56600, although the use case is different. Interestingly, the problem disappears when using another dtype, such as int (code below works as expected, no warnings) :

data1 = pd.Series([1 ,2, 3], dtype=int)
data2 = pd.Series([4, 5, 6], dtype=int)
condition = pd.Series([False, True, False], dtype=bool)

data1[condition] = data2[condition]

Note: Reproduced on pandas==2.2.0 and 3.0.0.dev0+292.g70f367194a

Expected Behavior

There should be no warning.

Installed Versions

INSTALLED VERSIONS ------------------ commit : 70f367194aff043f4f9ca549003df8a2a97f286a python : 3.10.12.final.0 python-bits : 64 OS : Linux OS-release : 5.15.133.1-microsoft-standard-WSL2 Version : #1 SMP Thu Oct 5 21:02:42 UTC 2023 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : None LOCALE : en_US.UTF-8 pandas : 3.0.0.dev0+292.g70f367194a numpy : 1.26.4 pytz : 2024.1 dateutil : 2.8.2 setuptools : 59.6.0 pip : 24.0 Cython : None pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : 3.1.3 IPython : 8.21.0 pandas_datareader : None adbc-driver-postgresql: None adbc-driver-sqlite : None bs4 : 4.12.3 bottleneck : None dataframe-api-compat : None fastparquet : None fsspec : None gcsfs : None matplotlib : 3.8.2 numba : None numexpr : None odfpy : None openpyxl : None pyarrow : None pyreadstat : None python-calamine : None pyxlsb : None s3fs : None scipy : 1.12.0 sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None zstandard : None tzdata : 2023.4 qtpy : None pyqt5 : None

Comment From: phofl

This actually upcasts to object, which means that the warning is kind of correct, but this should definitely work and continue to work.

I suspect that we do a align under the hood that causes this, cc @MarcoGorelli

Comment From: m0nzderr

@phofl I'm not familiar with the implementation and have no idea about the reasons for the upcast, but, intuitively, I see no reason for that to happen. Both series already have the same dtype, so any conversion would be unnecessary. It is also very counter-intuitive to have different behaviors among primitive types (i.e., no upcast in case of ints or floats, but an upcast in case of bools...).

Comment From: SpoopyPillow

I think what's happening is that when we mask the series, we eventually try to set the items at the index array([1]) to array([False]) (which is the second element of data2). However, this array is an object type. When we try to cast this array to a bool, pandas gives a LossySetitemError, which may be the problem.