fix: Sampling for List columns with min_length > 32

dataframely/columns/list.py

@@ -131,9 +131,12 @@ def pyarrow_dtype(self) -> pa.DataType:
     def _sample_unchecked(self, generator: Generator, n: int) -> pl.Series:
         # First, sample the number of items per list element
         # NOTE: We default to 32 for the upper bound as we need some kind of reasonable
-        #  upper bound if none is set.
+        #  upper bound if none is set. If min_length is greater than 32, we use
+        #  min_length as the default upper bound instead.
+        min_len = self.min_length or 0
+        default_max = max(32, min_len)
         element_lengths = generator.sample_int(
-            n, min=self.min_length or 0, max=(self.max_length or 32) + 1
+            n, min=min_len, max=(self.max_length or default_max) + 1
         )
 
         # Then, we can sample the inner elements in a flat series

tests/column_types/test_list.py

@@ -1,6 +1,8 @@
 # Copyright (c) QuantCo 2025-2025
 # SPDX-License-Identifier: BSD-3-Clause
 
+from typing import cast
+
 import polars as pl
 import pytest
 
@@ -179,3 +181,14 @@ def test_inner_primary_key_struct(
     _, failure = schema.filter(df)
     assert failure.counts() == {"a|primary_key": failure_count}
     assert validation_mask(df, failure).to_list() == mask
+
+
+@pytest.mark.parametrize("min_length", [0, 10, 33, 100])
+def test_list_sampling_with_min_length(min_length: int) -> None:
+    """Test that sampling works correctly when min_length > 32."""
+    schema = create_schema("test", {"a": dy.List(dy.Int64(), min_length=min_length)})
+    df = schema.sample(num_rows=10)
+    assert len(df) == 10
+    # Verify all lists have at least min_length elements
+    min_list_len = cast(int, df["a"].list.len().min())
+    assert min_list_len >= min_length