BUG: Series.astype is unable to handle NaN

Pandas version checks

• I have checked that this issue has not already been reported.

• I have confirmed this bug exists on the latest version of pandas.

• I have confirmed this bug exists on the main branch of pandas.

Reproducible Example

import pandas as pd
import numpy as np
df = pd.DataFrame({"a":[2.0, np.nan, 1.0]})
df = df.astype(object)
def iif(a, b, c):
    if a:
        return b
    else:
        return c

df["a"].astype(object).apply(lambda r: iif(np.isnan(r),None,r)).astype(object).apply(lambda r: iif(np.isnan(r),None,r))
df

Issue Description

The result is:

0    2.0
1    NaN
2    1.0

Expected Behavior

The result should be:

0    2.0
1    None
2    1.0

I checked BUG: Replacing NaN with None in Pandas 1.3 does not work But it seems astype doesn’t work here.

Installed Versions

INSTALLED VERSIONS

commit : 06d230151e6f18fdb8139d09abf539867a8cd481 python : 3.8.10.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.17763 machine : AMD64 processor : Intel64 Family 6 Model 79 Stepping 1, GenuineIntel byteorder : little LC_ALL : None LANG : None LOCALE : English_United States.950

pandas : 1.4.1 numpy : 1.22.3 pytz : 2021.3 dateutil : 2.8.2 pip : 21.1.1 setuptools : 56.0.0 Cython : None pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : None IPython : 8.1.1 pandas_datareader: None bs4 : None bottleneck : None fastparquet : None fsspec : None gcsfs : None matplotlib : 3.5.1 numba : None numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pyreadstat : None pyxlsb : None s3fs : None scipy : 1.8.0 sqlalchemy : 1.4.32 tables : None tabulate : None xarray : None xlrd : None xlwt : None zstandard : None

[rhshadrach]

Thanks for the report! I wonder if apply is coercing the result into a numeric dtype. I would agree this is not expected, further investigations and PRs to fix are welcome!

[rhshadrach]

@rhshadrach provided this information, would you agree that this behavior of .apply() with convert_dtype=True is unexpected?

At current state - there are various places where currently we treat None as np.nan and this is consistent with that.

df = pd.DataFrame({'a': [1, 2, None]})
print(df)

#      a
# 0  1.0
# 1  2.0
# 2  NaN

df = pd.DataFrame({'a': [1, np.nan, None], 'b': [2, 3, 4]})
gb = df.groupby('a', dropna=False)
print(gb.sum())

#      b
# a     
# 1.0  2
# NaN  7

That said, I have a hunch that it would be better if pandas were to treat None as a Python object instead of np.nan. However, this issue needs more study for me to feel more certain of this.