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GH-51696: Remove stale float16 guard in from_dataframe - #53727
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Asthenia0412 wants to merge 1 commit into
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Asthenia0412 wants to merge 1 commit into
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The guard rejecting float16 columns with NaN values was added because pc.is_nan didn't have a float16 kernel at the time. PR apache#46866 (apacheGH-45083) later added that kernel, making this guard obsolete. Removing the guard allows pandas float16 DataFrames to be imported via from_dataframe() properly. Also update test_pandas_to_pyarrow_float16_with_missing to verify the correct behavior instead of expecting NotImplementedError.
rok
reviewed
Oct 14, 2026
| @@ -319,14 +319,13 @@ def test_pandas_to_pyarrow_with_missing(np_float_str): | |||
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| @pytest.mark.pandas | |||
| def test_pandas_to_pyarrow_float16_with_missing(): | |||
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Perhaps it's better to just removetest_pandas_to_pyarrow_float16_with_missing test and make sure pc.is_nan on float16 is tested where all other types are tested.
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Background
A stale guard in
from_dataframe.pyrejects pandas float16 columns with NaN values, claimingpc.is_nanhas no float16 kernel.Problem
Every
from_dataframe()call with a float16 DataFrame raisesNotImplementedError.Solution
Removed the outdated guard block. PR #46866 already added float16 support to
pc.is_nan(GH-45083), so float16 now flows through the same code path as other float widths.Testing
Verified that
from_dataframe(pd.DataFrame({'h': np.array([1.5, np.nan], dtype='<f2')}))now succeeds and produces a table with correct null handling.Also updated the existing test
test_pandas_to_pyarrow_float16_with_missingto verify correct behavior instead of expectingNotImplementedError.