BUG: DatetimeIndex.get_indexer wrong result on mixed-type argument

[batterseapower]

• 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 datetime
import pandas as pd

dates = pd.bdate_range('2020-04-20', '2020-04-25')
print(dates.get_indexer([datetime.date(2020, 4, 20), datetime.date(2020, 4, 21)]))
# [0, 1]
print(dates.get_indexer([pd.Timestamp('2020-04-20'), datetime.date(2020, 4, 21)]))
# [0, -1]

Problem description

As a general rule I would expect that index.get_indexer(as + bs) == np.concat([index.get_indexer(as), index.get_indexer(bs)]) for any lists as and bs, i.e. index.get_indexer operates elementwise. However in this case the type of a previous argument is surprisingly affecting the index that is assigned to a later one.

Expected Output

[0, 1] in both cases

Output of pd.show_versions()

INSTALLED VERSIONS ------------------ commit : None python : 3.8.2.final.0 python-bits : 64 OS : Windows OS-release : 10 machine : AMD64 processor : Intel64 Family 6 Model 63 Stepping 2, GenuineIntel byteorder : little LC_ALL : None LANG : None LOCALE : English_United Kingdom.936

pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200330
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None

[dsaxton]

@batterseapower Thanks, the issue is on master as well and appears to be caused by a bug in the type inference for Index itself:

[ins] In [21]: pd.Index([datetime.date(2020, 4, 20), pd.Timestamp("2020-04-21")]).inferred_type                                                                                                              
Out[21]: 'date'

[ins] In [22]: pd.Index([pd.Timestamp("2020-04-21"), datetime.date(2020, 4, 20)]).inferred_type                                                                                                              
Out[22]: 'mixed'

I think fixing that would make get_indexer work as expected (if you try just changing the order of the list in the original example it should give the right output).

[simonjayhawkins]

This was giving the expected result in 0.25.3 so will mark as regression.

>>> import numpy as np
>>> import pandas as pd
>>>
>>> print(pd.__version__)
0.25.3
>>>
>>> import datetime
>>>
>>> dates = pd.bdate_range("2020-04-20", "2020-04-25")
>>> dates
DatetimeIndex(['2020-04-20', '2020-04-21', '2020-04-22', '2020-04-23',
               '2020-04-24'],
              dtype='datetime64[ns]', freq='B')
>>>
>>> print(dates.get_indexer([datetime.date(2020, 4, 20), datetime.date(2020, 4, 21)]))
[0 1]
>>>
>>> print(dates.get_indexer([pd.Timestamp("2020-04-20"), datetime.date(2020, 4, 21)]))
[0 1]
>>>
>>>

[simonjayhawkins]

This was giving the expected result in 0.25.3 so will mark as regression.

not always. and running code sample on master many times sometimes produces the correct answer.

had a quick go at creating a test and using pytest-repeat, but either always passes or always fails.

unable to bisect, so will remove the regression tag.