address review feedback on dataframe_checks.py - #989
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solegalli merged 3 commits intoAug 24, 2026
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Follow-up to FBruzzesi's review on PR #965: - Clarify docstrings for check_X, check_y, check_X_y in terms of which dataframe libraries are safe to pass in (pandas, polars, PyArrow, modin, cuDF), instead of narwhals-specific "eager" terminology. - Use narwhals' IntoDataFrameT instead of IntoDataFrame for check_X and check_X_y, since both return the same concrete dataframe type they receive. - Fix a null/NaN detection bug: in polars, is_null() does not catch an explicit float("nan") value (only None counts as null), so check_y and _check_contains_na could silently miss NaNs in polars data. Now also check is_nan() for numeric columns/series, keeping numpy for the finite/inf checks since it benchmarks as fast or faster there. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Collapses the has_na/has_null/has_nan accumulator variables into a single short-circuiting if-condition, as suggested in review. This also avoids an unnecessary is_nan() call when is_null() already found a null value. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
The schema-based list comprehension rebuilt narwhals' full column schema on every single-column access, making it scale roughly quadratically with column count on pandas (benchmarked up to ~500x slower than necessary at 200 columns). Switch to the pandas fast-path / narwhals-selector pattern already used in variable_handling (find_numerical_variables, check_numerical_variables) for the same "which of these columns are numeric" problem. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
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* address review feedback on dataframe_checks.py Follow-up to FBruzzesi's review on PR #965: - Clarify docstrings for check_X, check_y, check_X_y in terms of which dataframe libraries are safe to pass in (pandas, polars, PyArrow, modin, cuDF), instead of narwhals-specific "eager" terminology. - Use narwhals' IntoDataFrameT instead of IntoDataFrame for check_X and check_X_y, since both return the same concrete dataframe type they receive. - Fix a null/NaN detection bug: in polars, is_null() does not catch an explicit float("nan") value (only None counts as null), so check_y and _check_contains_na could silently miss NaNs in polars data. Now also check is_nan() for numeric columns/series, keeping numpy for the finite/inf checks since it benchmarks as fast or faster there. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> * inline null/nan checks to match FBruzzesi's suggested one-liner Collapses the has_na/has_null/has_nan accumulator variables into a single short-circuiting if-condition, as suggested in review. This also avoids an unnecessary is_nan() call when is_null() already found a null value. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> * speed up numeric column detection in _check_contains_na The schema-based list comprehension rebuilt narwhals' full column schema on every single-column access, making it scale roughly quadratically with column count on pandas (benchmarked up to ~500x slower than necessary at 200 columns). Switch to the pandas fast-path / narwhals-selector pattern already used in variable_handling (find_numerical_variables, check_numerical_variables) for the same "which of these columns are numeric" problem. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> --------- Co-authored-by: Claude Sonnet 5 <noreply@anthropic.com>
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Follow-up to @FBruzzesi review on PR #965:
Numpy seems to be faster respect to narwhals for null and inf check, particularly for pandas, so at the moment I am inclined to leave as is.