BUG: pd.DataFrame.sum with min_count changes dtype if result contains NaNs · Issue #46947 · pandas-dev/pandas (original) (raw)

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Reproducible Example

import numpy as np import pandas as pd df = pd.DataFrame({'a': [1., 2.3, 4.4], 'b': [2.2, 3, np.nan]}, dtype='float32') df.sum(axis=1, min_count=1).dtype dtype('float32') # OK df.sum(axis=1, min_count=2).dtype dtype('float64') # not OK df.sum(axis=1, skipna=False).dtype dtype('float32') # OK

Issue Description

When using min_count, the dtype of the result depends on whether the result contains a NaN or not.

Expected Behavior

The resulting dtype should be the same as the dtype of the original DataFrame, also when the result contains NaNs.

Installed Versions

INSTALLED VERSIONS

commit : 4bfe3d0
python : 3.10.2.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19043
machine : AMD64
processor : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United Kingdom.1252
pandas : 1.4.2
numpy : 1.22.3
pytz : 2022.1
dateutil : 2.8.2
pip : 22.0.4
setuptools : 58.1.0
Cython : None
pytest : 7.0.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 3.0.3
lxml.etree : 4.8.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.1
IPython : 8.1.0
pandas_datareader: None
bs4 : 4.10.0
bottleneck : 1.3.4
brotli : None
fastparquet : None
fsspec : 2022.02.0
gcsfs : None
markupsafe : 2.1.0
matplotlib : 3.5.1
numba : None
numexpr : 2.8.1
odfpy : None
openpyxl : 3.0.9
pandas_gbq : None
pyarrow : 7.0.0
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.8.0
snappy : None
sqlalchemy : 1.4.32
tables : None
tabulate : 0.8.9
xarray : 2022.3.0
xlrd : None
xlwt : None
zstandard : None