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 pandas as pd
s1 = pd.Series({"a": 0.0, "b": 1, "c": 1, "d": 0})
s2 = pd.Series({"a": 0.0, "b": 2, "c": 2, "d": 2})
s3 = s1/s2
#display(s3)
s4 = s1.convert_dtypes()/s2.convert_dtypes()
#display(s4)
s5 = pd.Series([None,0.5,0.5,0]).convert_dtypes()
#display(s5)
s3.mean(skipna=True), s4.mean(skipna=True), s5.mean(skipna=True)Issue Description
Following #59961, I understand that series/dataframes of FloatingArrays cointaing np.NaN values are possible and meant to exists. These very same dataframes/series, however, fail to skip NaN values when asked to. The above examples outputs:
(np.float64(0.3333333333333333), <NA>, np.float64(0.3333333333333333))
Expected Behavior
>>> s4.mean(skipna=True)
np.float64(0.3333333333333333)
Installed Versions
INSTALLED VERSIONS
commit : 139def2
python : 3.12.3
python-bits : 64
OS : Linux
OS-release : 6.8.0-41-generic
Version : #41-Ubuntu SMP PREEMPT_DYNAMIC Fri Aug 2 20:41:06 UTC 2024
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 3.0.0.dev0+1545.g139def2145
numpy : 2.2.0.dev0+git20240930.3ee9e6a
dateutil : 2.9.0.post0
pip : 24.0
Cython : None
sphinx : None
IPython : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
blosc : None
bottleneck : None
fastparquet : None
fsspec : None
html5lib : None
hypothesis : None
gcsfs : None
jinja2 : None
lxml.etree : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : None
psycopg2 : None
pymysql : None
pyarrow : None
pyreadstat : None
pytest : None
python-calamine : None
pytz : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlsxwriter : None
zstandard : None
tzdata : 2024.2
qtpy : None
pyqt5 : None