Pandas version checks
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[X] I have confirmed this bug exists on the main branch of pandas.
Reproducible Example
import pandas as pd
df = pd.DataFrame({"A": [0,1], "B": [0,1]})
df = df.convert_dtypes() # this line should help working with NA
df["C"] = df["A"] / df["B"]
df.dropna() # behavior in question
Output:
A B C
0 0 0 NaN
1 1 1 1.0
Issue Description
According to docs, convert_dtypes() should be a better choice to work with NA data, but in the above example it makes column C with NA that cannot be dropped. Is it an expected behaviour? I believe it should work in the same way as if df is not dtypes converted?
Expected Behavior
import pandas as pd
df = pd.DataFrame({"A": [0,1], "B": [0,1]})
df["C"] = df["A"] / df["B"]
df.dropna() # correct behavior
Output:
A B C
1 1 1 1.0
Installed Versions
INSTALLED VERSIONS
------------------
commit : 37ea63d540fd27274cad6585082c91b1283f963d
python : 3.11.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19045
machine : AMD64
processor : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : English_United States.1251
pandas : 2.0.1
numpy : 1.24.3
pytz : 2023.3
dateutil : 2.8.2
setuptools : 66.0.0
pip : 23.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
brotli : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : None
snappy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.3
qtpy : None
pyqt5 : None
Comment From: phofl
Hi, thanks for your report. This is expected for now and tied to the discussion about NA vs NaN in FloatingArrays