BUG: `pd.to_numeric` has an inconsistent behavior for `datetime` objects

[hec10r]

• 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
>>> from datetime import datetime
>>> pd.to_numeric(datetime(2021, 8, 22), errors="coerce")
nan
>>> pd.to_numeric(pd.Series(datetime(2021, 8, 22)), errors="coerce")
0    1629590400000000000
dtype: int64
>>> pd.Series([datetime(2021, 8, 22)]).apply(partial(pd.to_numeric), errors="coerce")
0   NaN
dtype: float64
>>>
>>> pd.to_numeric(pd.NaT, errors="coerce")
nan
>>> pd.to_numeric(pd.Series(pd.NaT), errors="coerce")
0   -9223372036854775808
dtype: int64
>>> pd.Series([pd.NaT]).apply(partial(pd.to_numeric), errors="coerce")
0   NaN
dtype: float64

Problem description

When using pd.to_numeric to convert a pd.Series with dtype datetime64[ns], it returns different values than converting the series value by value

Expected Output

Converting a pd.Series as a whole should be the same than converting it value by value.
I am not sure about what the correct output should be, but IMO the output should be consistent in these two scenarios.

What I suggest:

• For no-null values, returns the same value. Maybe the integer?
• For pd.NaT, always returns np.NaN

Output of pd.show_versions()

I am using the latest version of master until today

INSTALLED VERSIONS

commit : e39ea30
python : 3.8.3.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Tue Jun 22 19:49:55 PDT 2021; root:xnu-6153.141.35~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8

pandas : 1.4.0.dev0+517.gc3761e24d8
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 49.2.0.post20200714
Cython : 0.29.21
pytest : 5.4.3
hypothesis : None
sphinx : 3.1.2
blosc : None
feather : None
xlsxwriter : 1.2.9
lxml.etree : 4.5.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fsspec : 0.7.4
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.4
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.5.0
sqlalchemy : 1.3.18
tables : 3.6.1
tabulate : 0.8.9
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.50.1

[DAKSHA2001]

Assign me this issue. I will solve it. I have to make an open source contribution as part of Microsoft Research Intern Role so assign this to me

[ShreyasPatel031]

take

[Navaneethan2503]

this PR will close this issue #43289 , thanks for this issue @hec10r .
closes #43280

[hec10r]

take

[hec10r]

After some investigation, it looks like this behavior is explained by: numpy/numpy#19782, so not sure if this is a pandas issue or needs to be fixed in numpy. Before creating a PR here I would like to have the input from the maintainers.

[mroeschke]

The key point to note is that datetime objects in pandas get converted to datetime64[ns] (this has been the convention for a while) and not explicitly an object unless dtype=object is specified. So that being said:

Incorrect, should be 1629590400000000000

>>> pd.to_numeric(datetime(2021, 8, 22), errors="coerce")
nan

Correct

>>> pd.to_numeric(pd.Series(datetime(2021, 8, 22)), errors="coerce")
0    1629590400000000000
dtype: int64

Incorrect, should be 1629590400000000000

>>> pd.Series([datetime(2021, 8, 22)]).apply(partial(pd.to_numeric), errors="coerce")
0   NaN
dtype: float64

Correct

>>> pd.to_numeric(pd.NaT, errors="coerce")
nan

Incorrect, probably related to #16674

>>> pd.to_numeric(pd.Series(pd.NaT), errors="coerce")
0   -9223372036854775808
dtype: int64

Correct

>>> pd.Series([pd.NaT]).apply(partial(pd.to_numeric), errors="coerce")
0   NaN

[hec10r]

Hi Matthew, thanks for answering.

That's the other approach that I considered, but since there weren't documentation of the pd.to_numeric behavior for date-like objects and I didn't find it very intuitive, I thought that changing the whole behavior for something more intuitive would be good.

The use case where I am struggling with is this one:

>>> import pandas as pd
>>> from datetime import datetime
>>> pd.to_numeric(pd.Series([datetime(2021,8,22)]), errors="raise") # Same for errors="ignore"/"coerce"
0    1629590400000000000
dtype: int64
>>> pd.to_numeric(pd.Series(["apple", 1, datetime(2021,8,22)]), errors="coerce")
0    NaN
1    1.0
2    NaN
dtype: float64
>>> pd.to_numeric(pd.Series(["apple", 1, datetime(2021,8,22)]), errors="ignore")
0                  apple
1                      1
2    2021-08-22 00:00:00
dtype: object

Is this desired/expected? Should we return 1629590400000000000 in the three cases?

[mroeschke]

Sorry, yes the documentation could use improvement to address how datetime-like objects (e.g. np.datetime64, datetime.datetime, etc) are treated.

I would expect 1629590400000000000 in all 3 cases, thought this case is a bit tricky since the entire Series has dtype=object, but certain elements in that Series can be converted to numbers as it aligns with what you mentioned in your OP

Converting a pd.Series as a whole should be the same as converting it value by value.