diff --git a/examples/compose/plot_digits_pipe.py b/examples/compose/plot_digits_pipe.py
index ae9ce2e022df6..223fef687f65f 100644
--- a/examples/compose/plot_digits_pipe.py
+++ b/examples/compose/plot_digits_pipe.py
@@ -16,7 +16,7 @@
 
 import matplotlib.pyplot as plt
 import numpy as np
-import pandas as pd
+import polars as pl
 
 from sklearn import datasets
 from sklearn.decomposition import PCA
@@ -63,11 +63,15 @@
 ax0.legend(prop=dict(size=12))
 
 # For each number of components, find the best classifier results
-results = pd.DataFrame(search.cv_results_)
 components_col = "param_pca__n_components"
-best_clfs = results.groupby(components_col)[
-    [components_col, "mean_test_score", "std_test_score"]
-].apply(lambda g: g.nlargest(1, "mean_test_score"))
+is_max_test_score = pl.col("mean_test_score") == pl.col("mean_test_score").max()
+best_clfs = (
+    pl.LazyFrame(search.cv_results_)
+    .filter(is_max_test_score.over(components_col))
+    .unique(components_col)
+    .sort(components_col)
+    .collect()
+)
 ax1.errorbar(
     best_clfs[components_col],
     best_clfs["mean_test_score"],