def get_indexer_dict(
label_list: List[np.ndarray], keys: List["Index"]
) -> Dict[Union[str, Tuple], np.ndarray]:
"""
Returns
-------
dict:
Labels mapped to indexers.
"""
shape = [len(x) for x in keys]
group_index = get_group_index(label_list, shape, sort=True, xnull=True)
if np.all(group_index == -1):
# When all keys are nan and dropna=True, indices_fast can't handle this
# and the return is empty anyway
return {}
ngroups = (
((group_index.size and group_index.max()) + 1)
if is_int64_overflow_possible(shape)
else np.prod(shape, dtype="i8")
)
sorter = get_group_index_sorter(group_index, ngroups)
sorted_labels = [lab.take(sorter) for lab in label_list]
group_index = group_index.take(sorter)
return lib.indices_fast(sorter, group_index, keys, sorted_labels)
def get_indexer_dict(
label_list: list[np.ndarray], keys: list[Index]
) -> dict[Hashable, npt.NDArray[np.intp]]:
"""
Returns
-------
dict:
Labels mapped to indexers.
"""
shape = tuple(len(x) for x in keys)
group_index = get_group_index(label_list, shape, sort=True, xnull=True)
if np.all(group_index == -1):
# Short-circuit, lib.indices_fast will return the same
return {}
ngroups = (
((group_index.size and group_index.max()) + 1)
if is_int64_overflow_possible(shape)
else np.prod(shape, dtype="i8")
)
sorter = get_group_index_sorter(group_index, ngroups)
sorted_labels = [lab.take(sorter) for lab in label_list]
group_index = group_index.take(sorter)
return lib.indices_fast(sorter, group_index, keys, sorted_labels)
def get_indexer_dict(label_list, keys):
""" return a dict of {labels} -> {indexers} """
shape = [len(x) for x in keys]
group_index = get_group_index(label_list, shape, sort=True, xnull=True)
ngroups = (((group_index.size and group_index.max()) +
1) if is_int64_overflow_possible(shape) else np.prod(
shape, dtype="i8"))
sorter = get_group_index_sorter(group_index, ngroups)
sorted_labels = [lab.take(sorter) for lab in label_list]
group_index = group_index.take(sorter)
return lib.indices_fast(sorter, group_index, keys, sorted_labels)
def get_indexer_dict(label_list, keys):
""" return a diction of {labels} -> {indexers} """
shape = list(map(len, keys))
group_index = get_group_index(label_list, shape, sort=True, xnull=True)
ngroups = ((group_index.size and group_index.max()) + 1) \
if is_int64_overflow_possible(shape) \
else np.prod(shape, dtype='i8')
sorter = get_group_index_sorter(group_index, ngroups)
sorted_labels = [lab.take(sorter) for lab in label_list]
group_index = group_index.take(sorter)
return lib.indices_fast(sorter, group_index, keys, sorted_labels)
