BUG: Series.mul silently returns wrong values with UInt8 dtype when overflowing the max value range

[Timost]

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Reproducible Example

import pandas as pd
s = pd.Series([36], dtype='UInt8')
100 * s # This returns a series with one value: 16 (!!)
s.mul(100) # Also returns a wrong value

# But
7 * s # This returns the correct value

8 * s # This returns the wrong value (overflow)

100. * s # returns the correct value (due to casting I guess)

Issue Description

When multiplying a UInt8 series by a python integer, for which the result overflows the dtype range, the resulting values are wrong.

AFAICT this only happens with the UInt8 dtype (I tested UInt8, UInt16, UInt32 and UInt64).
Other UInt dtypes are properly casted to the "next" dtype.

Anybody computing percentages on UInt8 dtypes will easily hit this issue.

This was introduced in the 2.1.0 Release.

Expected Behavior

I expect the series to be casted to UInt16 when the results overflow the UInt8 range and return the correct values.

Installed Versions

INSTALLED VERSIONS

commit : fd3f571
python : 3.11.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.15.153.1-microsoft-standard-WSL2
Version : #1 SMP Fri Mar 29 23:14:13 UTC 2024
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8

pandas : 2.2.0
numpy : 1.26.4
pytz : 2024.1
dateutil : 2.9.0.post0
setuptools : 70.3.0
pip : 23.3.1
Cython : None
pytest : 7.4.4
hypothesis : None
sphinx : 7.3.7
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : 1.4.6
psycopg2 : None
jinja2 : None
IPython : 8.26.0
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.12.3
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : 2024.6.1
gcsfs : None
matplotlib : 3.9.1
numba : None
numexpr : None
odfpy : None
openpyxl : 3.1.5
pandas_gbq : None
pyarrow : 16.1.0
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : 2024.6.1
scipy : None
sqlalchemy : 2.0.31
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2024.1
qtpy : None
pyqt5 : None

[simran2jain]

take

[BobChuckyJoe]

Hi, wouldn't 16 be the correct value returned for 100 * 36 mod 256 ?

[Timost]

Hi,

yes, 16 is the value corresponding to the overflow, so in a sense it is correct.

However, AFAICT, other UInt dtypes do not behave this way and are casted to the "next bigger" dtype when computation results overflow.

I think the behavior should be coherent across the various UInt flavours.

[rhshadrach]

I believe pandas will overflow if integer being multiplied can be stored in the given dtype, but otherwise will upcast.

s = pd.Series([36], dtype='Int16')
print((s * 1000).dtype, (s * 1000).iloc[0])
# Int16 -29536
print((s * 32767).dtype, (s * 32767).iloc[0])
# Int16 -36
print((s * 32768).dtype, (s * 32768).iloc[0])
# Int32 1179648