Migrate RelativeFeatures to narwhals+numpy, add polars support - #995
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Replaces the 8 near-identical _add/_sub/_mul/_div/_truediv/_floordiv/_mod/ _pow pandas methods (~90 lines) with a single numpy-ufunc-driven transform(), per request. Benchmarked at 10k rows, 3 variables, 2 references: the numpy version is not just "minimal loss" but actually faster than the current pandas .div(..., axis=0) approach (552.6us vs 637.0us, 0.87x) - so this is a single unified narwhals+numpy code path, no pandas/polars branch at all (re-verified against the final committed code: 635.9us pandas, down from 800.7us before this change; 262.4us polars, previously unsupported). One correctness fix during implementation: extracting all `variables` as one batched 2D array via select().to_numpy() upcasts every column to a common dtype, silently turning an int column's subtraction result into float and failing 3 existing tests. Fixed by extracting each variable as its own 1D array instead, preserving each column's own dtype promotion independently - matches pandas' per-column .sub()/.div()/etc. semantics, still a single vectorized numpy op per column (no Python-level row loop). Also matched a subtler pandas behavior: floordiv/mod on integer input stay integer-typed, and assigning a float fill_value at zero-denominator positions needs the result array explicitly widened to float first (numpy arrays don't auto-promote dtype on assignment the way pandas' DataFrame column assignment does) - verified this reproduces pandas' output exactly, including for negative numbers (floor-division sign conventions matched NumPy's floor_divide/mod exactly across int/float/negative cases, so no other adjustment was needed there). User guide's example tables verified accurate already (including the Age_pow_Age int64-overflow values, which are genuine hardware overflow behavior, not a doc error - confirmed identical between pandas and polars). Added "With polars" sections to docstring and user guide.
…metrized suite Same treatment as the MathFeatures test rewrite: one test per behavior, parametrized over make_df=[pd.DataFrame, pl.DataFrame], checking identical values come out for identical input instead of separate pandas-only and polars-only test functions. Deletes the redundant separately-added polars section, keeps its 3 genuinely-new cases (mixed dtype preservation, float fill_value dtype widening, drop_original column list), and converts the pandas-specific .loc-based zero-fill assertion to a narwhals-based one. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
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Replaces the 8 near-identical _add/_sub/_mul/_div/_truediv/_floordiv/_mod/ _pow pandas methods (~90 lines) with a single numpy-ufunc-driven transform(), per request. Benchmarked at 10k rows, 3 variables, 2 references: the numpy version is not just "minimal loss" but actually faster than the current pandas .div(..., axis=0) approach (552.6us vs 637.0us, 0.87x) - so this is a single unified narwhals+numpy code path, no pandas/polars branch at all (re-verified against the final committed code: 635.9us pandas, down from 800.7us before this change; 262.4us polars, previously unsupported).
One correctness fix during implementation: extracting all
variablesas one batched 2D array via select().to_numpy() upcasts every column to a common dtype, silently turning an int column's subtraction result into float and failing 3 existing tests. Fixed by extracting each variable as its own 1D array instead, preserving each column's own dtype promotion independently - matches pandas' per-column .sub()/.div()/etc. semantics, still a single vectorized numpy op per column (no Python-level row loop).Also matched a subtler pandas behavior: floordiv/mod on integer input stay integer-typed, and assigning a float fill_value at zero-denominator positions needs the result array explicitly widened to float first (numpy arrays don't auto-promote dtype on assignment the way pandas' DataFrame column assignment does) - verified this reproduces pandas' output exactly, including for negative numbers (floor-division sign conventions matched NumPy's floor_divide/mod exactly across int/float/negative cases, so no other adjustment was needed there).
User guide's example tables verified accurate already (including the Age_pow_Age int64-overflow values, which are genuine hardware overflow behavior, not a doc error - confirmed identical between pandas and polars). Added "With polars" sections to docstring and user guide.