Series[datetime64/timedelta64] bool ops broken

[jbrockmendel]

dti = pd.date_range('2016-01-01', periods=3)
ser = pd.Series(dti)
tr = np.array([True, True, True])

>>> ser & tr
0    False
1    False
2    False
dtype: bool

>>> ser | ser
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "pandas/core/ops.py", line 1047, in wrapper
    other = fill_int(other) if is_other_int_dtype else fill_bool(other)
  File "pandas/core/ops.py", line 1036, in <lambda>
    fill_bool = lambda x: x.fillna(False).astype(bool)
  File "pandas/util/_decorators.py", line 138, in wrapper
    return func(*args, **kwargs)
  File "pandas/core/generic.py", line 4443, in astype
    **kwargs)
  File "pandas/core/internals.py", line 3606, in astype
    return self.apply('astype', dtype=dtype, **kwargs)
  File "pandas/core/internals.py", line 3473, in apply
    applied = getattr(b, f)(**kwargs)
  File "pandas/core/internals.py", line 569, in astype
    **kwargs)
  File "pandas/core/internals.py", line 2603, in _astype
    return super(DatetimeBlock, self)._astype(dtype=dtype, **kwargs)
  File "pandas/core/internals.py", line 657, in _astype
    values = astype_nansafe(values.ravel(), dtype, copy=True)
  File "pandas/core/dtypes/cast.py", line 668, in astype_nansafe
    to_dtype=dtype))
TypeError: cannot astype a datetimelike from [datetime64[ns]] to [bool]

It isn't clear to me what the desired behavior is here.

Also operating with an Index falls through to a case that I think is intended to only catch scalars:

tdi = pd.TimedeltaIndex(['1 Day', '2 Days', 'NaT'])
ser = pd.Series(tdi)
>>> ser & tdi
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "pandas/core/ops.py", line 1062, in wrapper
    res_values = na_op(self.values, other)
  File "pandas/core/ops.py", line 1020, in na_op
    if not isna(y):
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

[jreback]

there are some issues about this already
see if u can find them

[jbrockmendel]

There are a few that are broadly related but I didn't see any about dt64/td64 dtypes. AFAICT the issue that needs to be nailed down before I can fix is: what are these operations supposed to represent for these dtypes? Since bitwise ops don't make a lot of sense for these dtypes it may just be a matter of making Series.astype(bool) work for datetime64 and timedelta64 dtypes (or ideally an implementation with less far-reaching effects)

[mroeschke]

FWIW the TDI/Series case returns a value now:

In [73]: tdi = pd.TimedeltaIndex(['1 Day', '2 Days', 'NaT'])
    ...: ser = pd.Series(tdi)

In [74]: ser & tdi
Out[74]:
0    False
1    False
2    False
dtype: bool```

[jbrockmendel]

I would have expected [True, True, False]; wouldn't you?

[mroeschke]

Agreed. Just noting that this doesn't raise a ValueError anymore

[jorisvandenbossche]

An option is also to simply raise an error stating that logical ops are not supported for timedelta/datetime dtypes ?

[jbrockmendel]

These all now raise in ways that make sense to me. Closing.