def take(self, indices):
from collections.abc import Sequence
from cudf import Series
from numbers import Integral
if isinstance(indices, (Integral, Sequence)):
indices = np.array(indices)
elif isinstance(indices, Series):
indices = indices.to_gpu_array()
elif isinstance(indices, slice):
start, stop, step, sln = utils.standard_python_slice(len(self),
indices)
indices = np.arange(start, stop, step)
codes = self.codes.take(indices)
result = MultiIndex(self.levels, codes)
result.names = self.names
return result
def __getitem__(self, arg):
rows = []
len_idx = len(self._sr)
if isinstance(arg, tuple):
for idx in arg:
rows.append(idx)
elif isinstance(arg, int):
rows.append(arg)
elif isinstance(arg, slice):
start, stop, step, sln = utils.standard_python_slice(len_idx, arg)
if sln > 0:
for idx in range(start, stop, step):
rows.append(idx)
else:
raise TypeError(type(arg))
# To check whether all the indices are valid.
for idx in rows:
if abs(idx) > len_idx or idx == len_idx:
raise IndexError("positional indexers are out-of-bounds")
for i in range(len(rows)):
if rows[i] < 0:
rows[i] = len_idx + rows[i]
# returns the single elem similar to pandas
if isinstance(arg, int) and len(rows) == 1:
return self._sr[rows[0]]
ret_list = []
for idx in rows:
ret_list.append(self._sr[idx])
col_data = columnops.as_column(ret_list,
dtype=self._sr.dtype,
nan_as_null=True)
return Series(col_data, index=as_index(np.asarray(rows)))
def __getitem__(self, index):
if isinstance(index, slice):
start, stop, step, sln = utils.standard_python_slice(
len(self), index)
start += self._start
stop += self._start
if sln == 0:
return RangeIndex(0)
else:
return index_from_range(start, stop, step)
elif isinstance(index, int):
index = utils.normalize_index(index, len(self))
index += self._start
return index
elif isinstance(index, (list, np.ndarray)):
index = np.array(index)
index = rmm.to_device(index)
if isinstance(index, (DeviceNDArray)):
return self.take(index)
else:
raise ValueError(index)