Add example of a sklearn like pipeline (#294)

* Add example of a sklearn like pipeline

* Review comments

* Update comment after discussion in #279

* update

---------

Co-authored-by: MarcoGorelli <33491632+MarcoGorelli@users.noreply.github.com>

spec/API_specification/examples/07_sklearn_like_pipeline.py

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+"""Scikit-learn-like pipeline.
+
+This is an example of how a (possibly) lazy data frame may be used
+in a sklearn-like pipeline.
+
+The example is motivated by the prospect of a fully lazy, ONNX-based
+data frame implementation. The concept is that calls to `fit` are
+eager. They compute some state that is later meant to be transferred
+into the ONNX graph/model. That transfer happens when calling
+`transform`. The logic within the `transform` methods is "traced"
+lazily and the resulting lazy object is then exported to ONNX.
+"""
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any
+
+if TYPE_CHECKING:
+    from typing_extensions import Self
+
+    from dataframe_api.typing import Column, DataFrame, Scalar
+
+
+class Scaler:
+    """Apply a standardization scaling factor to `column_names`."""
+
+    scalings_: dict[str, Scalar]
+
+    def __init__(self, column_names: list[str]) -> None:
+        self.column_names = column_names
+
+    def fit(self, df: DataFrame) -> Self:
+        """Compute scaling factors from given data frame.
+
+        A typical data science workflow is to fit on one dataset,
+        and then transform and multiple datasets. Therefore, we
+        make sure to `persist` within the `fit` method.
+        """
+        scalings = df.select(*self.column_names).std().persist()
+
+        self.scalings_ = {
+            column_name: scalings.col(column_name).get_value(0)
+            for column_name in self.column_names
+        }
+
+        return self
+
+    def transform(self, df: DataFrame) -> DataFrame:
+        """Apply the "trained" scaling values.
+
+        This function is guaranteed to not collect values.
+        """
+        columns: list[Column] = []
+        for column_name in df.column_names:
+            if column_name not in self.column_names:
+                continue
+            column = df.col(column_name) / self.scalings_[column_name]
+            columns.append(column)
+
+        # Note: `assign` is not in-place
+        return df.assign(*columns)
+
+
+class FeatureSelector:
+    """Limit columns to those seen in training including their order."""
+
+    def fit(self, df: DataFrame) -> Self:
+        """Record the observed columns and their order.
+
+        This function is guaranteed to not collect values.
+        """
+        self.columns_ = df.column_names
+        return self
+
+    def transform(self, df: DataFrame) -> DataFrame:
+        """Select and sort the columns as observed in training.
+
+        This function is guaranteed to not collect values.
+        """
+        # Note: This assumes that select ensures the column order.
+        return df.select(*self.columns_)
+
+
+class Pipeline:
+    """Linear pipeline of transformers."""
+
+    def __init__(self, steps: list[Any]) -> None:
+        self.steps = steps
+
+    def fit(self, df: DataFrame) -> Self:
+        """Call fit on the steps of the pipeline subsequently.
+
+        Calling this function may trigger a collection.
+        """
+        for step in self.steps:
+            step.fit(df)
+
+        self.steps_ = self.steps
+        return self
+
+    def transform(self, df: DataFrame) -> DataFrame:
+        """Call transform on all steps of this pipeline subsequently.
+
+        This function is guaranteed to not trigger a collection.
+        """
+        for step in self.steps_:
+            df = step.transform(df)
+
+        return df