def _replace_with_indices(
cls,
chunk: pa.Array,
indices: npt.NDArray[np.intp],
value: npt.NDArray[Any],
) -> pa.Array:
"""
Replace items selected with a set of positional indices.
Analogous to pyarrow.compute.replace_with_mask, except that replacement
positions are identified via indices rather than a mask.
Parameters
----------
chunk : pa.Array
indices : npt.NDArray[np.intp]
value : npt.NDArray[Any]
Replacement value(s).
Returns
-------
pa.Array
"""
n = len(indices)
if n == 0:
return chunk
start, stop = indices[[0, -1]]
if (stop - start) == (n - 1):
# fast path for a contiguous set of indices
arrays = [
chunk[:start],
pa.array(value, type=chunk.type, from_pandas=True),
chunk[stop + 1:],
]
arrays = [arr for arr in arrays if len(arr)]
if len(arrays) == 1:
return arrays[0]
return pa.concat_arrays(arrays)
mask = np.zeros(len(chunk), dtype=np.bool_)
mask[indices] = True
if pa_version_under5p0:
arr = chunk.to_numpy(zero_copy_only=False)
arr[mask] = value
return pa.array(arr, type=chunk.type)
if isna(value).all():
return pc.if_else(mask, None, chunk)
return pc.replace_with_mask(chunk, mask, value)
def _set_via_chunk_iteration(self, indices: npt.NDArray[np.intp],
value: npt.NDArray[Any]) -> pa.ChunkedArray:
"""
Loop through the array chunks and set the new values while
leaving the chunking layout unchanged.
"""
chunk_indices = self._within_chunk_indices(indices)
new_data = []
for i, chunk in enumerate(self._data.iterchunks()):
c_ind = chunk_indices[i]
n = len(c_ind)
c_value, value = value[:n], value[n:]
if n == 1:
# fast path
chunk = self._set_single_index_in_chunk(
chunk, c_ind[0], c_value[0])
elif n > 0:
mask = np.zeros(len(chunk), dtype=np.bool_)
mask[c_ind] = True
if not pa_version_under5p0:
if c_value is None or isna(np.array(c_value)).all():
chunk = pc.if_else(mask, None, chunk)
else:
chunk = pc.replace_with_mask(chunk, mask, c_value)
else:
# The pyarrow compute functions were added in
# version 5.0. For prior versions we implement
# our own by converting to numpy and back.
chunk = chunk.to_numpy(zero_copy_only=False)
chunk[mask] = c_value
chunk = pa.array(chunk, type=pa.string())
new_data.append(chunk)
return pa.chunked_array(new_data)
