Narwhals mean median imputer - #1015
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Fit's mean()/median() computation is split by backend and, on the pandas branch, additionally rewritten to use NumPy directly. Benchmarked (10k-100k rows x 1-10 cols): narwhals-on-pandas vs pandas-native .mean()/.median() showed the same real, not minimal, loss (1.0-3.0x) already documented for BaseImputer's fillna and CategoricalImputer's mode(), so pandas keeps its own fast path. Going further, benchmarked a bulk NumPy nanmean/nanmedian pass (to_numpy() + axis=0 reduction, mirroring MathFeatures' reducer pattern) against pandas-native .mean()/.median() and found NumPy consistently as fast or faster (ratios 0.5-1.05x) - a real win, so the pandas branch now uses NumPy instead of pandas' own methods. For polars, the equivalent NumPy round-trip was benchmarked too and lost to narwhals' native per-column mean()/median() expressions (1.8-3.5x slower for mean; mixed but trending slower for median at scale), so the polars/narwhals branch computes stats with a single narwhals select() of one expression per variable instead - benchmarked against a per-column loop and against select()+to_native().to_dicts() and found select()+rows(named=True) is equal-or-faster and backend-agnostic (no reliance on a polars-only to_dicts() method). All-NaN/all-null columns produce matching values on both backends (verified directly): NumPy's nanmean/nanmedian warn on all-NaN slices where pandas' methods don't, so those warnings are suppressed the same way MathFeatures does. Nullable extension dtypes that would produce object arrays fall back to pandas' native .mean()/.median(), same guard as MathFeatures' dtype.kind check. Found and fixed a real crash: narwhals' select() with zero expressions collapses row count to 0 too, so stats.rows(named=True)[0] would IndexError when return_empty=True yields no numerical variables on polars input. Added an explicit empty-variables guard that skips the backend branch entirely instead of relying on backend-specific zero-column behaviour. Rewrote tests as one parametrized test per behaviour over pd.DataFrame/pl.DataFrame (a self-contained DATA dict replacing the pandas-only df_na fixture, matching the CategoricalImputer migration's pattern), keeping the MeanImputer/MeanMedianImputer deprecation-warning parametrization on top. Verified: tests/test_imputation full suite - 99 passed (up from 95 pre-migration, same tests plus new polars parametrizations), same 7 pre-existing failures in test_check_estimator_imputers.py (sklearn's check_estimator feeds raw numpy arrays, rejected by check_X's dataframe-only contract from the base migration - confirmed identical root cause against the pre-migration baseline via git stash). flake8 and mypy clean. mean_median.py's actual import chain (base_imputer, dataframe_checks, variable_handling) verified pandas-free with pandas blocked, using direct module loading to bypass the sibling not-yet-migrated imputers in imputation/__init__.py. sphinx -W build clean (only the pre-existing unrelated linkcode_resolve warning). Every doc example (docstring pandas/polars examples and the new "With polars" section in MeanImputer.rst) re-run against live output. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
check_X now returns a narwhals DataFrame; fit() passed it to find/check_numerical_variables, the is_pandas mean()/median() fast path and _get_feature_names_in, which then took their non-pandas path (spurious is_pandas_dataframe warning, hard failure on integer column names). check_X is pure validation, so stop rebinding X and keep working with the native input. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
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