BUG: numpy.maximum(pandas.Timestamp(...), pandas.Series(..., dtype='datetime64')) fails with TypeError

[hottwaj]

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, numpy
ts = pandas.Timestamp('2022-02-01')
dates_sr = pandas.to_datetime(pandas.Series(['2022-01-01', '2022-02-01', '2022-03-01']))

# note that comparison operators work
pandas.testing.assert_series_equal(ts < dates_sr, pandas.Series([False, False, True]))

# numpy.maximum fails
numpy.maximum(ts, dates_sr)

Issue Description

numpy.maximum fails when given args of type TimeStamp and Series(with dtype datetime64)

Stacktrace follows:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Input In [2], in <module>
      2 dates_sr = pandas.to_datetime(pandas.Series(["2022-01-01", "2022-02-01", "2022-03-01"]))
      3 pandas.testing.assert_series_equal(ts < dates_sr, pandas.Series([False, False, True]))
----> 4 numpy.maximum(ts, dates_sr)

File ~/.../site-packages/pandas/core/generic.py:2032, in NDFrame.__array_ufunc__(self, ufunc, method, *inputs, **kwargs)
   2029 def __array_ufunc__(
   2030     self, ufunc: np.ufunc, method: str, *inputs: Any, **kwargs: Any
   2031 ):
-> 2032     return arraylike.array_ufunc(self, ufunc, method, *inputs, **kwargs)

File ~/.../site-packages/pandas/core/arraylike.py:364, in array_ufunc(self, ufunc, method, *inputs, **kwargs)
    361 elif self.ndim == 1:
    362     # ufunc(series, ...)
    363     inputs = tuple(extract_array(x, extract_numpy=True) for x in inputs)
--> 364     result = getattr(ufunc, method)(*inputs, **kwargs)
    365 else:
    366     # ufunc(dataframe)
    367     if method == "__call__" and not kwargs:
    368         # for np.<ufunc>(..) calls
    369         # kwargs cannot necessarily be handled block-by-block, so only
    370         # take this path if there are no kwargs

TypeError: '>=' not supported between instances of 'Timestamp' and 'int'

Expected Behavior

Expected return value is:

pandas.to_datetime(pandas.Series(['2022-02-01', '2022-02-01', '2022-03-01']))

Installed Versions

INSTALLED VERSIONS

commit : 2e218d1
python : 3.10.2.final.0
python-bits : 64
OS : Linux
OS-release : 5.15.0-57-generic
Version : #63~20.04.1-Ubuntu SMP Wed Nov 30 13:40:16 UTC 2022
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8

pandas : 1.5.3
numpy : 1.24.1
pytz : 2021.3
dateutil : 2.8.2
setuptools : 59.6.0
pip : 22.3.1
Cython : 3.0.0a10
pytest : 7.1.0
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 3.0.2
lxml.etree : 4.7.1
html5lib : None
pymysql : None
psycopg2 : 2.9.3
jinja2 : 3.0.3
IPython : 8.0.1
pandas_datareader: None
bs4 : 4.8.2
bottleneck : None
brotli : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : 3.5.1
numba : None
numexpr : 2.8.1
odfpy : None
openpyxl : 3.0.9
pandas_gbq : None
pyarrow : 6.0.1
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.10.0
snappy : None
sqlalchemy : None
tables : 3.7.0
tabulate : None
xarray : 2022.3.0
xlrd : 1.2.0
xlwt : 1.3.0
zstandard : None
tzdata : 2021.5

[rhshadrach]

Thanks for the report - by calling np.maximum, pandas is doing the following:

np.maximum(ts, np.asarray(dates_sr))

This gives the same error, but no real traceback:

Traceback (most recent call last):
  File "scratch.py", line 37, in <module>
    print(np.maximum(ts, np.asarray(dates_sr)))
TypeError: '>=' not supported between instances of 'Timestamp' and 'int'

The dtype of np.asarray(dates_sr) is datetime64[ns]. So not sure where the int in the error message is coming from.

[mroeschke]

Looks like this is correctly working on main now

In [6]: import pandas, numpy
   ...: ts = pandas.Timestamp('2022-02-01')
   ...: dates_sr = pandas.to_datetime(pandas.Series(['2022-01-01', '2022-02-01', '2022-03-01']))
   ...: 
   ...: # note that comparison operators work
   ...: pandas.testing.assert_series_equal(ts < dates_sr, pandas.Series([False, False, True]))
   ...: 
   ...: # numpy.maximum fails
   ...: numpy.maximum(ts, dates_sr)
Out[6]: 
0   2022-02-01
1   2022-02-01
2   2022-03-01
dtype: datetime64[us]

I suppose we could use a test for this

[tirthrajj]

take

[Prabhat-Thapa45]

If it works on main shouldn't this be closed @Tirthraj93 @hottwaj ?

[rhshadrach]

@Prabhat-Thapa45 - when there is a bug that happens to be fixed by other changes, we prefer to add a test so that a regression doesn't happen.

[Maverick1905]

just tried the code above and receiving: No module named 'pandas._libs.pandas_parser'

and got inside of pandas:

#Below imports needs to happen first to ensure pandas top level
#module gets monkeypatched with the pandas_datetime_CAPI
#see pandas_datetime_exec in pd_datetime.c

import pandas._libs.pandas_parser # isort: skip # type: ignore[reportUnusedImport]
import pandas._libs.pandas_datetime # noqa: F401 # isort: skip # type: ignore[reportUnusedImport]
from pandas._libs.interval import Interval
from pandas._libs.tslibs import (
NaT,
NaTType,
OutOfBoundsDatetime,
Period,
Timedelta,
Timestamp,
iNaT,
)