diff --git a/src/tabmat/categorical_matrix.py b/src/tabmat/categorical_matrix.py
index 60bc53d5..e8200ab8 100644
--- a/src/tabmat/categorical_matrix.py
+++ b/src/tabmat/categorical_matrix.py
@@ -244,8 +244,8 @@ def _extract_codes_and_categories(cat_vec):
     elif _is_polars(cat_vec):
         if not _is_polars(cat_vec.dtype):
             cat_vec = cat_vec.cast(pl.Categorical)
-        categories = cat_vec.cat.get_categories().to_numpy()
-        indices = cat_vec.to_physical().fill_null(-1).to_numpy()
+        categories = cat_vec.cat.to_local().cat.get_categories().to_numpy()
+        indices = cat_vec.cat.to_local().to_physical().fill_null(-1).to_numpy()
     else:
         indices, categories = pd.factorize(cat_vec, sort=True)
     return indices, categories
diff --git a/tests/test_categorical_matrix.py b/tests/test_categorical_matrix.py
index 5d74d676..1c102a61 100644
--- a/tests/test_categorical_matrix.py
+++ b/tests/test_categorical_matrix.py
@@ -2,9 +2,10 @@
 
 import numpy as np
 import pandas as pd
+import polars as pl
 import pytest
 
-from tabmat.categorical_matrix import CategoricalMatrix
+from tabmat.categorical_matrix import CategoricalMatrix, _extract_codes_and_categories
 
 
 @pytest.fixture
@@ -202,3 +203,13 @@ def test_categorical_indexing(drop_first, missing, cat_missing_method):
         dummy_na=cat_missing_method == "convert" and missing,
     ).to_numpy()[:, [0, 1]]
     np.testing.assert_allclose(mat[:, [0, 1]].toarray(), expected)
+
+
+def test_polars_non_contiguous_codes():
+    str_series = ["labrador", "boxer", "beagle"]
+    with pl.StringCache():
+        _ = pl.Series(["beagle", "poodle", "labrador"], dtype=pl.Categorical)
+        cat_series = pl.Series(str_series, dtype=pl.Categorical)
+
+    indices, categories = _extract_codes_and_categories(cat_series)
+    np.testing.assert_array_equal(str_series, categories[indices].tolist())