def test_isin(self, array: np.ndarray) -> None:
container_factory = (list, set, np.array)
result = None
if array.ndim == 1:
sample = array[0]
if np.array(sample).dtype.kind in DTYPE_INEXACT_KINDS and np.isnan(
sample):
pass
else:
for factory in container_factory:
result = util.isin(array, factory((sample, )))
self.assertTrue(result[0])
elif array.ndim == 2:
sample = array[0, 0]
if np.array(sample).dtype.kind in DTYPE_INEXACT_KINDS and np.isnan(
sample):
pass
else:
for factory in container_factory:
result = util.isin(array, factory((sample, )))
self.assertTrue(result[0, 0])
if result is not None:
self.assertTrue(array.shape == result.shape)
self.assertTrue(result.dtype == bool)
def isin(self, other: tp.Iterable[tp.Iterable[tp.Hashable]]) -> np.ndarray:
'''
Return a Boolean array showing True where one or more of the passed in iterable of labels is found in the index.
'''
if self._recache:
self._update_array_cache()
matches = []
for seq in other:
if not hasattr(seq, '__iter__'):
raise RuntimeError(
'must provide one or more iterables within an iterable')
# Coerce to hashable type
as_tuple = tuple(seq)
if len(as_tuple) == self.depth:
# can pre-filter if iterable matches to length
matches.append(as_tuple)
if not matches:
return np.full(self._length, False, dtype=bool)
return isin(self.flat().values, matches)
def isin(self, other: tp.Iterable[tp.Any]) -> np.ndarray:
'''
Return a Boolean array showing True where a label is found in other. If other is a multidimensional array, it is flattened.
'''
return isin(self.values, other, array_is_unique=True)