diff --git a/py-polars/polars/dataframe/frame.py b/py-polars/polars/dataframe/frame.py
index ddae3ce337e7..7c9b69d80836 100644
--- a/py-polars/polars/dataframe/frame.py
+++ b/py-polars/polars/dataframe/frame.py
@@ -6871,6 +6871,9 @@ def pivot(
         """
         Create a spreadsheet-style pivot table as a DataFrame.
 
+        Only available in eager mode. See "Examples" section below for how to do a
+        "lazy pivot" if you know the unique column values in advance.
+
         Parameters
         ----------
         values
@@ -6969,6 +6972,36 @@ def pivot(
         │ b    ┆ 0.964028 ┆ 0.999954 │
         └──────┴──────────┴──────────┘
 
+        Note that `pivot` is only available in eager mode. If you know the unique
+        column values in advance, you can use :meth:`polars.LazyFrame.groupby` to
+        get the same result as above in lazy mode:
+
+        >>> index = pl.col("col1")
+        >>> columns = pl.col("col2")
+        >>> values = pl.col("col3")
+        >>> unique_column_values = ["x", "y"]
+        >>> aggregate_function = lambda col: col.tanh().mean()
+        >>> (
+        ...     df.lazy()
+        ...     .group_by(index)
+        ...     .agg(
+        ...         *[
+        ...             aggregate_function(values.filter(columns == value)).alias(value)
+        ...             for value in unique_column_values
+        ...         ]
+        ...     )
+        ...     .collect()
+        ... )  # doctest: +IGNORE_RESULT
+        shape: (2, 3)
+        ┌──────┬──────────┬──────────┐
+        │ col1 ┆ x        ┆ y        │
+        │ ---  ┆ ---      ┆ ---      │
+        │ str  ┆ f64      ┆ f64      │
+        ╞══════╪══════════╪══════════╡
+        │ a    ┆ 0.998347 ┆ null     │
+        │ b    ┆ 0.964028 ┆ 0.999954 │
+        └──────┴──────────┴──────────┘
+
         """  # noqa: W505
         values = _expand_selectors(self, values)
         index = _expand_selectors(self, index)