datetimelike indexes add/sub zero-dim integer arrays

[jbrockmendel]

Setup:

dti = pd.date_range('2016-01-01', periods=3, freq='H')
one = np.array(1)

0.21.1:

>>> dti + one
DatetimeIndex(['2016-01-01 00:00:00.000000001',
               '2016-01-01 01:00:00.000000001',
               '2016-01-01 02:00:00.000000001'],
              dtype='datetime64[ns]', freq='H')
>>> dti.freq = None
>>> dti + one
DatetimeIndex(['2016-01-01 00:00:00.000000001',
               '2016-01-01 01:00:00.000000001',
               '2016-01-01 02:00:00.000000001'],
              dtype='datetime64[ns]', freq=None)

Master: (See #19011)

>>> dti + one
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "pandas/core/indexes/datetimelike.py", line 685, in __add__
    elif is_offsetlike(other):
  File "pandas/core/dtypes/common.py", line 294, in is_offsetlike
    elif (is_list_like(arr_or_obj) and len(arr_or_obj) and
TypeError: len() of unsized object

After

>>> dti + one
DatetimeIndex(['2016-01-01 01:00:00', '2016-01-01 02:00:00',
               '2016-01-01 03:00:00'],
              dtype='datetime64[ns]', freq='H')
>>> dti.freq = None
>>> dti + one
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "pandas/core/indexes/datetimelike.py", line 683, in __add__
    return self.shift(other)
  File "pandas/core/indexes/datetimelike.py", line 821, in shift
    raise ValueError("Cannot shift with no freq")
ValueError: Cannot shift with no freq

• closes DatetimeIndex/TimedeltaIndex add/sub zero-dim arrays incorrect #19012
• tests added / passed
• passes git diff upstream/master -u -- "*.py" | flake8 --diff
• whatsnew entry

[jreback]

this already exists

[jreback]

we have item_from_zerodim. so need to reconcile this. you generally don't need to actually check, rather you just unbox.

[jbrockmendel]

where?

[jreback]

In [4]: pd.api.types.is_scalar(np.int64(1))
Out[4]: True

[jbrockmendel]

Wow, yah, just calling other = lib.item_from_zero_dim(other) at the top of __sub__ and __add__ is way simpler.

[codecov]

Codecov Report

Merging #19013 into master will increase coverage by 0.02%.
The diff coverage is 100%.

@@            Coverage Diff             @@
##           master   #19013      +/-   ##
==========================================
+ Coverage   91.55%   91.57%   +0.02%     
==========================================
  Files         150      150              
  Lines       48939    48941       +2     
==========================================
+ Hits        44805    44817      +12     
+ Misses       4134     4124      -10

Flag	Coverage Δ
#multiple	89.93% <100%> (+0.02%)	⬆️
#single	41.75% <0%> (-0.01%)	⬇️

Impacted Files	Coverage Δ
pandas/core/indexes/datetimelike.py	97.05% <100%> (+0.01%)	⬆️
pandas/util/testing.py	84.74% <0%> (-0.22%)	⬇️
pandas/plotting/_converter.py	66.95% <0%> (+1.73%)	⬆️

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[jreback]

thanks!