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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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22 changes: 22 additions & 0 deletions pandas/tests/arrays/masked/test_arrow_compat.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,6 +3,7 @@

import pandas as pd
import pandas._testing as tm
from pandas.api.types import pandas_dtype

pa = pytest.importorskip("pyarrow", minversion="1.0.1")

Expand Down Expand Up @@ -36,6 +37,27 @@ def test_arrow_roundtrip(data):
tm.assert_frame_equal(result, df)


def test_dataframe_from_arrow_types_mapper(any_int_ea_dtype):
int_dtype = pandas_dtype(any_int_ea_dtype)

def types_mapper(arrow_type):
if pa.types.is_boolean(arrow_type):
return pd.BooleanDtype()
elif pa.types.is_integer(arrow_type):
return int_dtype
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Is it actually needed to parametrize this over all int EA dtypes? Could also just use pd.Int64Dtype() like you use BooleanDtype.

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Don't you want to make sure types_mapper works for any integer dtype?

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Alternatively, I could have a separate column in the arrow record batch for each integer data type, but that seemed too much.

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Don't you want to make sure types_mapper works for any integer dtype?

This is already implicitly tested in the test_arrow_roundtrip above (since that is parametrized for every dtype, and will under the hood make use of the same dtype.__from_arrow__)

But I am also fine with keeping the test as you have it now.

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Sounds good. I'll simplify, but add one column just so we test a code path that requires a copy because of differing data sizes.


bools_array = pa.array([True, None, False], type=pa.bool_())
ints_array = pa.array([1, None, 2], type=pa.int64())
record_batch = pa.RecordBatch.from_arrays(
[bools_array, ints_array], ["bools", "ints"]
)
result = record_batch.to_pandas(types_mapper=types_mapper)
bools = pd.Series([True, None, False], dtype="boolean")
ints = pd.Series([1, None, 2], dtype=int_dtype.name)
expected = pd.DataFrame({"bools": bools, "ints": ints})
tm.assert_frame_equal(result, expected)


def test_arrow_load_from_zero_chunks(data):
# GH-41040

Expand Down