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TST: check compatibility with pyarrow types_mapper parameter #44369

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Merged
merged 12 commits into from
Dec 1, 2021
Merged

TST: check compatibility with pyarrow types_mapper parameter #44369

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017803f
BUG: BooleanDtype compatibility with pyarrow types_mapper parameter
tswast Nov 9, 2021
325c0bf
add whatsnew
tswast Nov 9, 2021
88d6972
use dtype fixture
tswast Nov 9, 2021
49ad892
really use dtype fixture
tswast Nov 9, 2021
09fe48e
revert dtype changes
tswast Nov 9, 2021
ece974c
remove unnecessary date_as_object
tswast Nov 9, 2021
2c80f74
move tests to test_arrow_compat.py
tswast Nov 10, 2021
12a092e
Merge branch 'issue44368-BooleanDtype' of github.com:tswast/pandas in…
tswast Nov 10, 2021
da2873f
cleanup tests
tswast Nov 10, 2021
0762fd7
Merge remote-tracking branch 'upstream/master' into issue44368-Boolea…
tswast Nov 22, 2021
1ede256
Merge remote-tracking branch 'upstream/master' into issue44368-Boolea…
tswast Nov 30, 2021
31b0065
simplify types_mapper tests
tswast Nov 30, 2021
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18 changes: 18 additions & 0 deletions pandas/tests/extension/test_boolean.py
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Expand Up @@ -397,3 +397,21 @@ class TestParsing(base.BaseParsingTests):

class Test2DCompat(base.Dim2CompatTests):
pass


def test_from_arrow(dtype):
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you might need to use a min version here in the skip and use the decorator version like
td.skip_if_no("pyarrow", min_version="1.0.0")

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Can you also move the test to /tests/arrays/boolean/ ?

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Or more specifically, we have tests for arrow compat in tests/arrays/masked/test_arrow_compat.py. I would add it there (or first check if it's not actually already covered)

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tests/arrays/masked/test_arrow_compat.py looks more low-level than what I'm aiming to test here, but a combo test there could make sense.

pyarrow = pytest.importorskip("pyarrow")

def types_mapper(arrow_type):
if pyarrow.types.is_boolean(arrow_type):
return dtype

pyarrow_array = pyarrow.array([True, None, False], type=pyarrow.bool_())
expected = pd.Series([True, None, False], dtype=dtype.name)

# Convert to RecordBatch because types_mapper argument is ignored when
# using a pyarrow.Array. https://issues.apache.org/jira/browse/ARROW-9664
record_batch = pyarrow.RecordBatch.from_arrays([pyarrow_array], ["test_col"])
dataframe = record_batch.to_pandas(types_mapper=types_mapper)
series = dataframe["test_col"]
tm.assert_series_equal(series, expected, check_names=False)