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31 changes: 27 additions & 4 deletions pandas/core/ops/__init__.py
Original file line number Diff line number Diff line change
Expand Up @@ -43,6 +43,7 @@
ABCSeries,
ABCSparseArray,
ABCSparseSeries,
ABCTimedeltaArray,
)
from pandas.core.dtypes.missing import isna, notna

Expand Down Expand Up @@ -1716,10 +1717,32 @@ def wrapper(left, right):
# Note: we cannot use dispatch_to_index_op because
# that may incorrectly raise TypeError when we
# should get NullFrequencyError
result = op(pd.Index(left), right)
return construct_result(
left, result, index=left.index, name=res_name, dtype=result.dtype
)
orig_right = right
if is_scalar(right):
# broadcast and wrap in a TimedeltaIndex
# TODO: possible perf improvement by using NaTD scalar
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I don't think this this is a worthwhile comment here as NaTD is not likely to happen

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I think there's a decent chance we'll implement it for internal use to de-duplicate logic like this.

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Removed comment

# here (GH#24983)
assert np.isnat(right)
right = np.broadcast_to(right, left.shape)
right = pd.TimedeltaIndex(right)

assert isinstance(right, (pd.TimedeltaIndex, ABCTimedeltaArray, ABCSeries))
try:
result = op(left._values, right)
except NullFrequencyError:
if orig_right is not right:
# i.e. scalar timedelta64('NaT')
# We get a NullFrequencyError because we broadcast to
# TimedeltaIndex, but this should be TypeError.
raise TypeError(
"incompatible type for a datetime/timedelta "
"operation [{name}]".format(name=op.__name__)
)
raise

# We do not pass dtype to ensure that the Series constructor
# does inference in the case where `result` has object-dtype.
return construct_result(left, result, index=left.index, name=res_name)

lvalues = left.values
rvalues = right
Expand Down