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fix: support converting empty time Series to pyarrow Array #11

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Sep 29, 2021
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3 changes: 2 additions & 1 deletion db_dtypes/__init__.py
Original file line number Diff line number Diff line change
Expand Up @@ -98,7 +98,8 @@ def astype(self, dtype, copy=True):

def __arrow_array__(self, type=None):
return pyarrow.array(
self.to_numpy(), type=type if type is not None else pyarrow.time64("ns"),
self.to_numpy(dtype="object"),
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This might have performance implications, but it does seem to prevent the cast to float64 for empty arrays. Also, the dtype seems to be object whenever there are any values in the array, anyway.

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I approached this by adding the missing to_numpy() for pandas <1, that just uses astype('object')

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type=type if type is not None else pyarrow.time64("ns"),
)


Expand Down
163 changes: 163 additions & 0 deletions tests/unit/test_arrow.py
Original file line number Diff line number Diff line change
@@ -0,0 +1,163 @@
# Copyright 2021 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import datetime as dt

import pandas
import pyarrow
import pytest

# To register the types.
import db_dtypes # noqa


@pytest.mark.parametrize(
("series", "expected"),
(
(pandas.Series([], dtype="date"), pyarrow.array([], type=pyarrow.date32())),
(
pandas.Series([None, None, None], dtype="date"),
pyarrow.array([None, None, None], type=pyarrow.date32()),
),
(
pandas.Series(
[dt.date(2021, 9, 27), None, dt.date(2011, 9, 27)], dtype="date"
),
pyarrow.array(
[dt.date(2021, 9, 27), None, dt.date(2011, 9, 27)],
type=pyarrow.date32(),
),
),
(
pandas.Series(
[dt.date(1677, 9, 22), dt.date(1970, 1, 1), dt.date(2262, 4, 11)],
dtype="date",
),
pyarrow.array(
[dt.date(1677, 9, 22), dt.date(1970, 1, 1), dt.date(2262, 4, 11)],
type=pyarrow.date32(),
),
),
(pandas.Series([], dtype="time"), pyarrow.array([], type=pyarrow.time64("ns"))),
(
pandas.Series([None, None, None], dtype="time"),
pyarrow.array([None, None, None], type=pyarrow.time64("ns")),
),
(
pandas.Series(
[dt.time(0, 0, 0, 0), None, dt.time(23, 59, 59, 999_999)], dtype="time"
),
pyarrow.array(
[dt.time(0, 0, 0, 0), None, dt.time(23, 59, 59, 999_999)],
type=pyarrow.time64("ns"),
),
),
(
pandas.Series(
[
dt.time(0, 0, 0, 0),
dt.time(12, 30, 15, 125_000),
dt.time(23, 59, 59, 999_999),
],
dtype="time",
),
pyarrow.array(
[
dt.time(0, 0, 0, 0),
dt.time(12, 30, 15, 125_000),
dt.time(23, 59, 59, 999_999),
],
type=pyarrow.time64("ns"),
),
),
),
)
def test_to_arrow(series, expected):
array = pyarrow.array(series)
assert array.equals(expected)


@pytest.mark.parametrize(
("series", "expected"),
(
(pandas.Series([], dtype="date"), pyarrow.array([], type=pyarrow.date64())),
(
pandas.Series([None, None, None], dtype="date"),
pyarrow.array([None, None, None], type=pyarrow.date64()),
),
(
pandas.Series(
[dt.date(2021, 9, 27), None, dt.date(2011, 9, 27)], dtype="date"
),
pyarrow.array(
[dt.date(2021, 9, 27), None, dt.date(2011, 9, 27)],
type=pyarrow.date64(),
),
),
(
pandas.Series(
[dt.date(1677, 9, 22), dt.date(1970, 1, 1), dt.date(2262, 4, 11)],
dtype="date",
),
pyarrow.array(
[dt.date(1677, 9, 22), dt.date(1970, 1, 1), dt.date(2262, 4, 11)],
type=pyarrow.date64(),
),
),
(pandas.Series([], dtype="time"), pyarrow.array([], type=pyarrow.time32("ms"))),
(
pandas.Series([None, None, None], dtype="time"),
pyarrow.array([None, None, None], type=pyarrow.time32("ms")),
),
(
pandas.Series(
[dt.time(0, 0, 0, 0), None, dt.time(23, 59, 59, 999_000)], dtype="time"
),
pyarrow.array(
[dt.time(0, 0, 0, 0), None, dt.time(23, 59, 59, 999_000)],
type=pyarrow.time32("ms"),
),
),
(
pandas.Series(
[dt.time(0, 0, 0, 0), None, dt.time(23, 59, 59, 999_999)], dtype="time"
),
pyarrow.array(
[dt.time(0, 0, 0, 0), None, dt.time(23, 59, 59, 999_999)],
type=pyarrow.time64("us"),
),
),
(
pandas.Series(
[
dt.time(0, 0, 0, 0),
dt.time(12, 30, 15, 125_000),
dt.time(23, 59, 59, 999_999),
],
dtype="time",
),
pyarrow.array(
[
dt.time(0, 0, 0, 0),
dt.time(12, 30, 15, 125_000),
dt.time(23, 59, 59, 999_999),
],
type=pyarrow.time64("us"),
),
),
),
)
def test_to_arrow_w_arrow_type(series, expected):
array = pyarrow.array(series, type=expected.type)
assert array.equals(expected)
Comment on lines +1 to +163

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Your tests are nicer than mine. :)

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Thanks! They did catch a bug with empty arrays, so I'm glad I wrote them