Selection and explicit passthrough¶

Use FeaturePassthrough(columns) when a pipeline layer should simply carry named columns forward. It returns just those columns in the specified order, with their Polars values, names, dtypes, nulls, and row order preserved. There is no renaming or casting option. Selected columns must exist; input order and dtypes may differ between calls. It supports the same verbosity, pandas conversion, fitted feature names, and output controls as the other transformers.

import polars as pl
from sklearn.pipeline import Pipeline

from polars_learn import FeaturePassthrough

X = pl.DataFrame({"id": [7, 8], "flag": [True, False], "unused": [1, 2]})
pipeline = Pipeline([("unchanged", FeaturePassthrough(["id", "flag"]))])
result = pipeline.fit_transform(X.lazy())
assert isinstance(result, pl.LazyFrame)
assert result.collect().equals(X.select("id", "flag"))

A sequential Pipeline passes each step's output to the next step, so a passthrough step selecting two columns omits all the others. To keep unchanged columns alongside transformed columns, put the operations in parallel ColumnTransformer branches:

import polars as pl
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler

from polars_learn import FeaturePassthrough

X = pl.DataFrame({"value": [10.0, 20.0], "id": [7, 8], "flag": [True, False]})
preprocessing = ColumnTransformer(
    [
        ("scaled", StandardScaler(), ["value"]),
        ("unchanged", FeaturePassthrough(["id", "flag"]), ["id", "flag"]),
    ],
    verbose_feature_names_out=False,
).set_output(transform="polars")
result = preprocessing.fit_transform(X)
assert result["value"].to_list() == [-1.0, 1.0]
assert result.select("id", "flag").equals(X.select("id", "flag"))
assert result.columns == ["value", "id", "flag"]

The outer column lists tell ColumnTransformer which input to send to each branch; FeaturePassthrough declares its own selected-column contract. Use eager input at this boundary (lazy_X.collect() for a lazy query). Polars output keeps the separate column dtypes. verbose_feature_names_out=False prevents branch prefixes on output names; final names must then be unique. For branch behavior, see the scikit-learn ColumnTransformer reference. Use ColumnSelector when you also need optional renaming.

Selecting and renaming¶

Use ColumnSelector(["quantity", "price"], rename={"quantity": "qty"}) to establish output order and optionally rename selected columns. Use FeaturePassthrough to communicate that selected values require no transformation. Both drop unselected columns; neither checks dtype drift or casts values. Combine them with a schema validator when dtype enforcement is required.

ColumnTransformer examples use eager frames. Its outer output setting is a scikit-learn setting, while native transformers' "polars-lazy" setting belongs to this package. See the selection API and passthrough API.