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Explicit about bitwidth difference between cudf boolean and arrow boolean #9192

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merged 4 commits into from
Sep 22, 2021
Merged

Explicit about bitwidth difference between cudf boolean and arrow boolean #9192

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e8c805a
Initial
isVoid Sep 8, 2021
b481d74
documentation, comments
isVoid Sep 8, 2021
0f638a3
Add test
isVoid Sep 16, 2021
6146ae5
Fix broken test case
isVoid Sep 16, 2021
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20 changes: 18 additions & 2 deletions python/cudf/cudf/_lib/utils.pyx
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Original file line number Diff line number Diff line change
@@ -1,5 +1,6 @@
# Copyright (c) 2020-2021, NVIDIA CORPORATION.

import numpy as np
import pyarrow as pa

import cudf
Expand Down Expand Up @@ -98,7 +99,14 @@ cpdef generate_pandas_metadata(Table table, index):
):
types.append(col.dtype.to_arrow())
else:
types.append(np_to_pa_dtype(col.dtype))
# A boolean element takes 8 bits in cudf and 1 bit in
# pyarrow. To make sure the cudf format is interperable
# in arrow, we use `int8` type when converting from a
# cudf boolean array.
if col.dtype.type == np.bool_:
types.append(pa.int8())
else:
types.append(np_to_pa_dtype(col.dtype))
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# Indexes
if index is not False:
Expand Down Expand Up @@ -142,7 +150,15 @@ cpdef generate_pandas_metadata(Table table, index):
elif is_list_dtype(idx):
types.append(col.dtype.to_arrow())
else:
types.append(np_to_pa_dtype(idx.dtype))
# A boolean element takes 8 bits in cudf and 1 bit in
# pyarrow. To make sure the cudf format is interperable
# in arrow, we use `int8` type when converting from a
# cudf boolean array.
if idx.dtype.type == np.bool_:
types.append(pa.int8())
else:
types.append(np_to_pa_dtype(idx.dtype))

index_levels.append(idx)
col_names.append(name)
index_descriptors.append(descr)
Expand Down
5 changes: 1 addition & 4 deletions python/cudf/cudf/core/column/column.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2084,10 +2084,7 @@ def as_column(
data
)
np_type = np.dtype(dtype).type
if np_type == np.bool_:
pa_type = pa.bool_()
else:
pa_type = np_to_pa_dtype(np.dtype(dtype))
pa_type = np_to_pa_dtype(np.dtype(dtype))
data = as_column(
pa.array(
arbitrary,
Expand Down
7 changes: 6 additions & 1 deletion python/cudf/cudf/utils/dtypes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -35,6 +35,11 @@
from cudf.core._compat import PANDAS_GE_120

_NA_REP = "<NA>"

"""Map numpy dtype to pyarrow types.
Note that np.bool_ bitwidth (8) is different from pa.bool_ (1). Special
handling is required when converting a Boolean column into arrow.
"""
_np_pa_dtypes = {
np.float64: pa.float64(),
np.float32: pa.float32(),
Expand All @@ -43,7 +48,7 @@
np.int32: pa.int32(),
np.int16: pa.int16(),
np.int8: pa.int8(),
np.bool_: pa.int8(),
np.bool_: pa.bool_(),
np.uint64: pa.uint64(),
np.uint32: pa.uint32(),
np.uint16: pa.uint16(),
Expand Down