[SPARK-56041][PS][TESTS] Normalize ndarray values in apply_batch typed result comparison for pandas 3#54873
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ueshin wants to merge 1 commit intoapache:masterfrom
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[SPARK-56041][PS][TESTS] Normalize ndarray values in apply_batch typed result comparison for pandas 3#54873ueshin wants to merge 1 commit intoapache:masterfrom
ueshin wants to merge 1 commit intoapache:masterfrom
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HyukjinKwon
approved these changes
Mar 17, 2026
| def test_apply_batch_with_type(self): | ||
| using_pandas3 = LooseVersion(pd.__version__) >= "3.0.0" | ||
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| def normalize_array_values(pdf: pd.DataFrame) -> pd.DataFrame: |
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Is this method only usable here (for now and for the future), @ueshin ? It's just a question because this looks like a general and useful function.
dongjoon-hyun
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Mar 17, 2026
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dongjoon-hyun
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+1, LGTM (Pending CIs).
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What changes were proposed in this pull request?
This PR updates the pandas-on-Spark
apply_batchtyped test inpython/pyspark/pandas/tests/computation/test_apply_func.py.With pandas 3.x, the typed
apply_batchcases that return array-like values can produce pandas results whose nested values arenp.ndarray, while the expected pandas DataFrame still contains Python lists. The values are equivalent, but the comparison is stricter and the test fails.To make the test compare the intended semantics instead of the container representation, this PR normalizes nested
np.ndarrayvalues to lists before asserting equality intest_apply_batch_with_type. The normalization is scoped to pandas 3.x and only applied in the affected test cases.Why are the changes needed?
The failure is caused by stricter comparison behavior in pandas 3.x rather than a functional regression in pandas-on-Spark
apply_batch.In the failing cases, the expected and actual results contain the same data, but one side may use Python lists and the other may use
np.ndarrayfor nested array values. Relaxing the production code path would broaden behavior beyond the test. Updating the test keeps runtime behavior unchanged and makes the assertion reflect the intended equivalence.Does this PR introduce any user-facing change?
No.
How was this patch tested?
Ran the related pandas-on-Spark computation test module in both the default Python 3.12 environment and the pandas 3 environment, and both passed.
Was this patch authored or co-authored using generative AI tooling?
Generated-by: Codex GPT-5