BUG: `TypeError` when mixing extension array dtypes in `mask`

[bollard]

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

# classic, numpy example
ser = pd.Series([0.0, 1.0, 2.0, 3.0])
other = pd.Series([True, False, True, False])
ser.mask(~ser.isna(), other).dtype  # bool

# extension array example
ser = pd.Series([0.0, 1.0, 2.0, 3.0], dtype=pd.Float64Dtype())
other = pd.Series([True, False, True, False], dtype=pd.BooleanDtype())
ser.mask(~ser.isna(), other).dtype # TypeError: object cannot be converted to FloatingDtype

Issue Description

Hi,

When using mask, in which other completely replaces self, the classic numpy approach works as expected (with resulting object of type bool). In the extension array example however, a TypeError is thrown.

A more complicated case is one in which other does not completely replace self, for example

ser = pd.Series([pd.NA, 1.0, 2.0, 3.0], dtype=pd.Float64Dtype())
other = pd.Series([True, False, True, False], dtype=pd.BooleanDtype())
ser.mask(~ser.isna(), other)

Here in the numpy case, the result is downcast to object, but in the extension array case the same TypeError is thrown. I can see how this could be a little trickier, but this could also downcast to object or (in this example) still hold the pd.NA and return a result of pd.BooleanDtype()

Expected Behavior

ser = pd.Series([0.0, 1.0, 2.0, 3.0], dtype=pd.Float64Dtype())
other = pd.Series([True, False, True, False], dtype=pd.BooleanDtype())
ser.mask(~ser.isna(), other) # would expect to return pd.Series([True, False, True, False], dtype=pd.BooleanDtype()), not a TypeError

Installed Versions

INSTALLED VERSIONS

commit : 8dab54d
python : 3.10.8.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19044
machine : AMD64
processor : Intel64 Family 6 Model 165 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : English_United Kingdom.1252

pandas : 1.5.2
numpy : 1.23.4
pytz : 2022.1
dateutil : 2.8.2
setuptools : 65.5.0
pip : 22.2.2
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.9.1
html5lib : None
pymysql : 1.0.2
psycopg2 : None
jinja2 : 3.1.2
IPython : 8.6.0
pandas_datareader: None
bs4 : 4.11.1
bottleneck : None
brotli :
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : 3.5.3
numba : None
numexpr : None
odfpy : None
openpyxl : 3.0.10
pandas_gbq : None
pyarrow : 9.0.0
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : None
snappy : None
sqlalchemy : 1.4.39
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
zstandard : None
tzdata : None

[phofl]

Hi, thanks for your report. this works on main, but keeps float dtype, which is what I would expect here. But probably needs tests

[ShashwatAgrawal20]

hey! @phofl I would like to work on this but don't know where to get started, it would be a great help if you can guide me from where can I get started

[ShashwatAgrawal20]

take

[ShashwatAgrawal20]

ser = pd.Series([pd.NA, 1.0, 2.0, 3.0], dtype=pd.Float64Dtype())
other = pd.Series([True, False, True, False], dtype=pd.BooleanDtype())
ser.mask(~ser.isna(), other)

The above might have shown an error as the mask method expects a series or scalar values with the same datatype as the original series for other parameters.

Might prevent this error by casting the other or the ser Series to the same as the original Series

ser = pd.Series([0.0, 1.0, 2.0, 3.0], dtype=pd.Float64Dtype())
other = pd.Series([True, False, True, False], dtype=pd.BooleanDtype())
ser = ser.astype(other.dtype)
result = ser.mask(~ser.isna(), other)
print(result)

Output