Fix RandomSampleImputer and DropMissingData on non-pandas backends - #1024

_check_transform_input_and_state() now returns the narwhals frame, so
filter it directly instead of re-wrapping it with nw.from_native().

TransformXyMixin.transform_x_y() (rebased onto #1024) widens
n_features_in_ for the row-index tag column; also widen
feature_names_in_, since BaseOutlier reorders X to feature_names_in_ and
would otherwise drop the tag column on non-pandas backends.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

_check_transform_input_and_state() now returns the narwhals frame, so
filter it directly instead of re-wrapping it with nw.from_native().

TransformXyMixin.transform_x_y() (rebased onto #1024) widens
n_features_in_ for the row-index tag column; also widen
feature_names_in_, since BaseOutlier reorders X to feature_names_in_ and
would otherwise drop the tag column on non-pandas backends.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

_check_transform_input_and_state() now returns the narwhals frame, so
filter it directly instead of re-wrapping it with nw.from_native().

TransformXyMixin.transform_x_y() (rebased onto #1024) widens
n_features_in_ for the row-index tag column; also widen
feature_names_in_, since BaseOutlier reorders X to feature_names_in_ and
would otherwise drop the tag column on non-pandas backends.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

_check_transform_input_and_state() now returns the narwhals frame, so
filter it directly instead of re-wrapping it with nw.from_native().

TransformXyMixin.transform_x_y() (rebased onto #1024) widens
n_features_in_ for the row-index tag column; also widen
feature_names_in_, since BaseOutlier reorders X to feature_names_in_ and
would otherwise drop the tag column on non-pandas backends.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

_check_transform_input_and_state() now returns the narwhals frame, so
filter it directly instead of re-wrapping it with nw.from_native().

TransformXyMixin.transform_x_y() (rebased onto #1024) widens
n_features_in_ for the row-index tag column; also widen
feature_names_in_, since BaseOutlier reorders X to feature_names_in_ and
would otherwise drop the tag column on non-pandas backends.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

* Migrate OutlierTrimmer to narwhals, add polars support

transform() now filters rows via a single narwhals .filter() call built
from a combined boolean expression (AND of each variable's right/left
cap conditions), instead of a pandas .loc masking loop. Benchmarked
against pandas-native and a numpy boolean-mask extraction at 10k/50k/
100k rows x 1/2/10 columns: the narwhals filter is within 1.4-1.75x of
pandas-native at 10k rows (sub-millisecond absolute difference) and
becomes faster than pandas-native from 50k rows up (0.58x-0.97x),
so a single merged code path (no is_pandas branching) is the right
call here - unlike BaseOutlier's elementwise capping, which benefits
from numpy grouping, row-filtering is exactly what narwhals .filter()
already pushes down to the native backend efficiently.

Also fixes a latent bug in TransformXyMixin.transform_x_y()
(_base_transformers/mixins.py): the non-pandas branch added a
row-index marker column via with_row_index() and passed it straight
to self.transform(), but never widened feature_names_in_/
n_features_in_ to account for it. Any transform() that validates
column count (BaseOutlier._check_transform_input_and_state, via
_check_X_matches_training_df) then raised a ValueError on the extra
column. This was latent because no narwhals-migrated class on this
branch previously combined TransformXyMixin with a column-count-
checking transform() on a non-pandas backend - OutlierTrimmer is the
first. The fix (guarded widen/restore of feature_names_in_ around the
transform() call) is carried over verbatim from the same fix already
applied to this file on branch narwhals-drop-missing-data (commit
fd99caf), which hadn't been merged into this branch yet.

Tests rewritten to one parametrized test per behavior over
make_df in [pd.DataFrame, pl.DataFrame], plus a new test asserting
that caps on two different variables combine with AND (each variable
drops a distinct row) - a code path the old sequential-loop version
exercised implicitly but no test isolated directly.

Docs verified against live output: the class docstring's pandas
examples were already accurate; the user guide's Titanic-based
numbers had drifted from the current openml dataset (predates this
migration, e.g. the IQR section's age max was already wrong against
the old pandas-loop transform()) and are corrected here, plus a "With
polars" section is added.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

* Adapt OutlierTrimmer to narwhals-returning check_X

_check_transform_input_and_state() now returns the narwhals frame, so
filter it directly instead of re-wrapping it with nw.from_native().

TransformXyMixin.transform_x_y() (rebased onto #1024) widens
n_features_in_ for the row-index tag column; also widen
feature_names_in_, since BaseOutlier reorders X to feature_names_in_ and
would otherwise drop the tag column on non-pandas backends.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

* Use shared backend test fixtures and helpers in OutlierTrimmer tests

Replace the file-local data dicts and _cols/_to_list/_make_series helpers
with the shared test structure: make_df and data_normal_dist / data_na
fixtures, y built with make_series, isinstance(X, make_df) plus to_dict()
checks, and pytest.raises(match=...).

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

* Use frame_to_dict after the shared helper rename in #1045

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

* Document and test infinite caps for variables without variation

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

* Align OutlierTrimmer and its tests with the repo conventions

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

* Keep transform_x_y row alignment in OutlierTrimmer instead of the shared mixin

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

---------

Co-authored-by: Claude Sonnet 5 <noreply@anthropic.com>