Migrate CyclicalFeatures to narwhals, add polars support - #991
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fit(): unified across backends via .to_numpy().max(axis=0) instead of pandas' .max().to_dict() (~1.55x faster for pandas, ~1.28x for polars, benchmarked). .tolist() keeps the returned dict's values as plain Python int/float, matching the old .to_dict() dtype. transform(): kept as two branches rather than one narwhals-only path - benchmarked running narwhals expressions against a pandas-backed frame and it was consistently 1.24x-2.06x slower than the pandas-native loop across variable counts and row counts, worse at small scale. The pandas branch is therefore left as the original, unmodified loop (an earlier numpy-vectorized version of it was only a 1.0x-1.4x gain, not worth it once the branches stay separate anyway). The narwhals branch uses column expressions, the only approach that stayed competitive with pandas-native as variable count grows (a numpy-array round-trip loses to expressions on polars once there is more than 1 variable). Verified no legacy numpy-array-input code remains in this file or its base classes. Tests rewritten to parametrize pandas and polars via make_df; error-matching tightened per AGENTS.md except where the message legitimately differs by backend. Docstring and user-guide example gained a polars walkthrough per the new AGENTS.md doc-sync rule.
Style: removed the now-unused narwhals.dependencies import (flake8 F401) left over from dropping the is_pandas_dataframe branch. Also fixed 7 pre-existing flake8 issues (line length, unused variable) in test_get_feature_names_out_mixin.py that predate this branch. Docs: docs/user_guide/creation/CyclicalFeatures.rst's polars output block was under `.. code:: python`, and Sphinx's Pygments highlighter can't lex the box-drawing table as Python (misc.highlighting_failure), which -W promotes to a build error. Switched to `.. code:: text`, matching the convention already used elsewhere (PowerTransformer.rst, MeanImputer.rst) for output-only blocks. Pre-existing bug in my own doc addition, unrelated to the branch unification. Two correctness issues surfaced by testing the unification: - max_values_ lost its .tolist() call, so it held numpy scalars (np.int64) instead of plain Python int/float - restored. - narwhals' .select([]) collapses row count to 0 (not just columns), so routing pandas through the narwhals numpy path broke return_empty=True (empty variables_) with a "zero-size array to reduction operation maximum" error. Guarded for it explicitly, since return_empty=True is a real, designed-for case, not a hypothetical.
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