BUG: fillna(0) does not fill NaNs for Float64Arrays

[evyasonov]

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

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

---

Code Sample, a copy-pastable example

f = pandas.DataFrame(data = [[0, 0]], columns = ['A', 'B']).astype('Int64')
f['C'] = f['A'] / f['B']
f.fillna(0)

Output:

	A	B	C
0	0	0	NaN

Problem description

fillna does not work as expected. It do not fill na

Expected Output

	A	B	C
0	0	0	0

Output of pd.show_versions()

INSTALLED VERSIONS

commit : 7d32926
python : 3.7.9.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None

pandas : 1.2.2
numpy : 1.18.3
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.3.3.post20210118
Cython : 0.29.21
pytest : 6.2.1
hypothesis : None
sphinx : 3.4.3
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.3
fastparquet : 0.3.3
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.2
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : 0.16.0
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.21
tables : 3.6.1
tabulate : 0.8.7
xarray : None
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.51.2

[phofl]

I don't think that fillna is the issue here. The vaue in C should be pd.NA not np.nan. I am not sure if fillna is supposed to work for np.nan in a Float64 Array. Maybe @jorisvandenbossche can help here?

[evyasonov]

@phofl
The point that according to documentation I expect that fillna will fill all nans regardless of the origin. It's really confusing when it does not do its job..

[phofl]

I am not sure if this is the expected behavior here, hence I pinged joris.

If this is the expected behavior in case of np.nan in Float64Arrays, then we should adjust the documnetation, if not then we got 2 unrelated bugs here.

[simonjayhawkins]

the discussion on how to interpret np.nan in a nullable float array is ongoing. #32265

so for now this is not yet a bug. but indeed as part of #32265 the documentation may need to be updated to clarify the behavior for nullable float arrays.

[phofl]

Thx, then let's repurpose this:

f = pandas.DataFrame(data = [[0, 0]], columns = ['A', 'B']).astype('Int64')
f['C'] = f['A'] / f['B']

this should return

   A  B     C
0  0  0  <NA>

and not

   A  B     C
0  0  0  NaN

[phofl]

Could maybe someone rename the issue?

[simonjayhawkins]

I think the return of f['C'] = f['A'] / f['B'] is correct. A and B are not missing values and therefore C is not missing but correctly np.NAN.