BUG: DataFrame.mask does not mask NaT using None

[StefanBrand]

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

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

• (optional) I have confirmed this bug exists on the master branch of pandas.

---

Code Sample, a copy-pastable example

import pandas as pd
import numpy as np
df = pd.DataFrame({"dates": ["2020-07-07", np.datetime64("Nat")]})
df.mask(df.isna(), None)

Problem description

df.mask does not replace NaT by None.

Expected Output

df.mask should properly mask NaT.

Output of pd.show_versions()

INSTALLED VERSIONS
------------------
commit           : 2cb96529396d93b46abab7bbc73a208e708c642e
python           : 3.8.8.final.0
python-bits      : 64
OS               : Linux
OS-release       : 4.14.214-160.339.amzn2.x86_64
Version          : #1 SMP Sun Jan 10 05:53:05 UTC 2021
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : en_US.UTF-8
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.2.4
numpy            : 1.20.1
pytz             : 2021.1
dateutil         : 2.8.1
pip              : 21.0.1
setuptools       : 49.6.0.post20210108
Cython           : 0.29.22
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : 4.6.2
html5lib         : None
pymysql          : None
psycopg2         : 2.8.6 (dt dec pq3 ext lo64)
jinja2           : 2.11.3
IPython          : 7.21.0
pandas_datareader: None
bs4              : 4.9.3
bottleneck       : 1.3.2
fsspec           : 0.8.7
fastparquet      : None
gcsfs            : None
matplotlib       : 3.3.4
numexpr          : 2.7.3
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : 3.0.0
pyxlsb           : None
s3fs             : 0.5.2
scipy            : 1.6.1
sqlalchemy       : 1.3.23
tables           : 3.6.1
tabulate         : None
xarray           : 0.17.0
xlrd             : 1.2.0
xlwt             : None
numba            : 0.52.0

[StefanBrand]

Seems to be the same issue as this: #15613

[StefanBrand]

My usecase is encoding as JSON:

def default(o):
    try:
        isodate = o.date().isoformat()
    except AttributeError:
        pass
    else:
        if isodate == "NaT":
            return None
        return isodate
#   Let the base class default method raise the TypeError
    return json.JSONEncoder.default(self, o)

json.JSONEncoder(default=default).encode(df.to_dict())

{"dates": {"0": "2020-07-07", "1": null}}

I just found out that there is df.to_json(), which sort of does the same:

df.to_json(date_format="iso", date_unit="s")

{"dates":{"0":"2020-07-07T00:00:00Z","1":null}}

I only wish I could set the date_unit precision to "day"...

[jreback]

closing as duplicate