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
>>> x = pd.Series(['90210', '60018-0123', '10010', 'text', '6.7', "<class 'object'>", '123456'], dtype="string")
>>> invalid = pd.Series([False, False, False, True, True, True, True])
>>> x[invalid] = None
>>> x[6] = pd.NA
>>> x[2] = None
>>> x
0 90210
1 60018-0123
2 <NA>
3 None
4 None
5 None
6 <NA>
dtype: stringIssue Description
There is inconsistent behavior when assigning missing values to a string series. As shown in the reproducible example, using an integer to assign None will result in a conversion to pd.NA, whereas using a boolean series to assign None will result in None with no conversion.
Expected Behavior
I'd expect there to be a single representation for missing values, so assigning x[invalid] = None would result in a conversion to pd.NA.
>>> import pandas as pd
>>> x = pd.Series(['90210', '60018-0123', '10010', 'text', '6.7', "<class 'object'>", '123456'], dtype="string")
>>> invalid = pd.Series([False, False, False, True, True, True, True])
>>> x[invalid] = None
>>> x
0 90210
1 60018-0123
2 10010
3 <NA>
4 <NA>
5 <NA>
6 <NA>
dtype: stringInstalled Versions
INSTALLED VERSIONS
commit : e8093ba
python : 3.9.7.final.0
python-bits : 64
OS : Darwin
OS-release : 21.3.0
Version : Darwin Kernel Version 21.3.0: Wed Jan 5 21:37:58 PST 2022; root:xnu-8019.80.24~20/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.4.3
numpy : 1.22.1
pytz : 2021.3
dateutil : 2.8.2
setuptools : 58.0.4
pip : 22.1
Cython : None
pytest : 7.0.1
hypothesis : None
sphinx : 4.2.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.3
IPython : 7.31.1
pandas_datareader: None
bs4 : 4.10.0
bottleneck : None
brotli : None
fastparquet : None
fsspec : 2022.01.0
gcsfs : None
markupsafe : 2.0.1
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 6.0.1
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.7.3
snappy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
zstandard : None