BUG: Confusing error message when clipping datetimes

[deaglancrew]

• 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 master branch of pandas.

Reproducible Example

import pandas as pd


pd.Series(
    [1.0, 1.0],
    dtype='datetime64[ns]'
).clip(
    pd.Series(
        [None, 1.0],
        dtype='datetime64[ns]')
)

Issue Description

Clipping with columns that contain NaT values raises "TypeError: '>=' not supported between instances of 'Timestamp' and 'float'".

This can be quite confusing, particularly if you're starting with float data and casting to datetime.

Expected Behavior

Ideally the error message would read: "TypeError: '>=' not supported between instances of 'Timestamp' and 'NaT'".

Installed Versions

INSTALLED VERSIONS

commit : 3e1cfc5
python : 3.9.7.final.0
python-bits : 64
OS : Darwin
OS-release : 21.1.0
Version : Darwin Kernel Version 21.1.0: Wed Oct 13 17:33:23 PDT 2021; root:xnu-8019.41.5~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8

pandas : 1.4.0.dev0+1294.g3e1cfc528c
numpy : 1.21.2
pytz : 2021.3
dateutil : 2.8.2
pip : 21.2.4
setuptools : 58.0.4
Cython : 0.29.24
pytest : 6.2.5
hypothesis : 6.29.0
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.9.2 (dt dec pq3 ext lo64)
jinja2 : 3.0.2
IPython : 7.28.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 2021.11.0
fastparquet : None
gcsfs : None
matplotlib : 3.5.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 5.0.0
pyxlsb : None
s3fs : 2021.11.0
scipy : 1.7.3
sqlalchemy : None
tables : None
tabulate : None
xarray : 0.19.0
xlrd : None
xlwt : None
numba : None

[ms7463]

Looks like this bug was introduced here: #40927 (comment)

It replaces missing values with np.inf which is not a compatible comparison with pd.Timestamp.

Until that is fixed, I'd suggest doing this:

pd.Series(
    [1.0, 1.0],
    dtype='datetime64[ns]'
).clip(
    pd.Series(
        [None, 1.0],
        dtype='datetime64[ns]').fillna(pd.Timestamp.min)  # use pd.Timestamp.max for upper clip
)