def __from_arrow__(
self, array: pyarrow.Array | pyarrow.ChunkedArray) -> PeriodArray:
"""
Construct PeriodArray from pyarrow Array/ChunkedArray.
"""
import pyarrow
from pandas.core.arrays import PeriodArray
from pandas.core.arrays.arrow._arrow_utils import (
pyarrow_array_to_numpy_and_mask, )
if isinstance(array, pyarrow.Array):
chunks = [array]
else:
chunks = array.chunks
results = []
for arr in chunks:
data, mask = pyarrow_array_to_numpy_and_mask(arr,
dtype=np.dtype(
np.int64))
parr = PeriodArray(data.copy(), freq=self.freq, copy=False)
parr[~mask] = NaT
results.append(parr)
if not results:
return PeriodArray(np.array([], dtype="int64"),
freq=self.freq,
copy=False)
return PeriodArray._concat_same_type(results)
def __from_arrow__(
self, array: pyarrow.Array | pyarrow.ChunkedArray
) -> PeriodArray:
"""
Construct PeriodArray from pyarrow Array/ChunkedArray.
"""
import pyarrow
from pandas.core.arrays import PeriodArray
from pandas.core.arrays.arrow._arrow_utils import (
pyarrow_array_to_numpy_and_mask,
)
if isinstance(array, pyarrow.Array):
chunks = [array]
else:
chunks = array.chunks
results = []
for arr in chunks:
data, mask = pyarrow_array_to_numpy_and_mask(arr, dtype=np.dtype(np.int64))
parr = PeriodArray(data.copy(), freq=self.freq, copy=False)
# error: Invalid index type "ndarray[Any, dtype[bool_]]" for "PeriodArray";
# expected type "Union[int, Sequence[int], Sequence[bool], slice]"
parr[~mask] = NaT # type: ignore[index]
results.append(parr)
if not results:
return PeriodArray(np.array([], dtype="int64"), freq=self.freq, copy=False)
return PeriodArray._concat_same_type(results)
def __from_arrow__(
self, array: pyarrow.Array | pyarrow.ChunkedArray
) -> BaseMaskedArray:
"""
Construct IntegerArray/FloatingArray from pyarrow Array/ChunkedArray.
"""
import pyarrow
from pandas.core.arrays.arrow._arrow_utils import (
pyarrow_array_to_numpy_and_mask,
)
array_class = self.construct_array_type()
pyarrow_type = pyarrow.from_numpy_dtype(self.type)
if not array.type.equals(pyarrow_type):
# test_from_arrow_type_error raise for string, but allow
# through itemsize conversion GH#31896
rt_dtype = pandas_dtype(array.type.to_pandas_dtype())
if rt_dtype.kind not in ["i", "u", "f"]:
# Could allow "c" or potentially disallow float<->int conversion,
# but at the moment we specifically test that uint<->int works
raise TypeError(
f"Expected array of {self} type, got {array.type} instead"
)
array = array.cast(pyarrow_type)
if isinstance(array, pyarrow.Array):
chunks = [array]
else:
# pyarrow.ChunkedArray
chunks = array.chunks
results = []
for arr in chunks:
data, mask = pyarrow_array_to_numpy_and_mask(arr, dtype=self.numpy_dtype)
num_arr = array_class(data.copy(), ~mask, copy=False)
results.append(num_arr)
if not results:
return array_class(
np.array([], dtype=self.numpy_dtype), np.array([], dtype=np.bool_)
)
elif len(results) == 1:
# avoid additional copy in _concat_same_type
return results[0]
else:
return array_class._concat_same_type(results)
def test_pyarrow_array_to_numpy_and_mask(np_dtype_to_arrays):
"""
Test conversion from pyarrow array to numpy array.
Modifies the pyarrow buffer to contain padding and offset, which are
considered valid buffers by pyarrow.
Also tests empty pyarrow arrays with non empty buffers.
See https://github.com/pandas-dev/pandas/issues/40896
"""
np_dtype, pa_array, np_expected, mask_expected = np_dtype_to_arrays
data, mask = pyarrow_array_to_numpy_and_mask(pa_array, np_dtype)
tm.assert_numpy_array_equal(data[:3], np_expected)
tm.assert_numpy_array_equal(mask, mask_expected)
mask_buffer = pa_array.buffers()[0]
data_buffer = pa_array.buffers()[1]
data_buffer_bytes = pa_array.buffers()[1].to_pybytes()
# Add trailing padding to the buffer.
data_buffer_trail = pa.py_buffer(data_buffer_bytes + b"\x00")
pa_array_trail = pa.Array.from_buffers(
type=pa_array.type,
length=len(pa_array),
buffers=[mask_buffer, data_buffer_trail],
offset=pa_array.offset,
)
pa_array_trail.validate()
data, mask = pyarrow_array_to_numpy_and_mask(pa_array_trail, np_dtype)
tm.assert_numpy_array_equal(data[:3], np_expected)
tm.assert_numpy_array_equal(mask, mask_expected)
# Add offset to the buffer.
offset = b"\x00" * (pa_array.type.bit_width // 8)
data_buffer_offset = pa.py_buffer(offset + data_buffer_bytes)
mask_buffer_offset = pa.py_buffer(b"\x0E")
pa_array_offset = pa.Array.from_buffers(
type=pa_array.type,
length=len(pa_array),
buffers=[mask_buffer_offset, data_buffer_offset],
offset=pa_array.offset + 1,
)
pa_array_offset.validate()
data, mask = pyarrow_array_to_numpy_and_mask(pa_array_offset, np_dtype)
tm.assert_numpy_array_equal(data[:3], np_expected)
tm.assert_numpy_array_equal(mask, mask_expected)
# Empty array
np_expected_empty = np.array([], dtype=np_dtype)
mask_expected_empty = np.array([], dtype=np.bool_)
pa_array_offset = pa.Array.from_buffers(
type=pa_array.type,
length=0,
buffers=[mask_buffer, data_buffer],
offset=pa_array.offset,
)
pa_array_offset.validate()
data, mask = pyarrow_array_to_numpy_and_mask(pa_array_offset, np_dtype)
tm.assert_numpy_array_equal(data[:3], np_expected_empty)
tm.assert_numpy_array_equal(mask, mask_expected_empty)