diff --git a/crates/polars-io/src/parquet/read_impl.rs b/crates/polars-io/src/parquet/read_impl.rs
index 66a84fcb114a..5f99e770b343 100644
--- a/crates/polars-io/src/parquet/read_impl.rs
+++ b/crates/polars-io/src/parquet/read_impl.rs
@@ -95,13 +95,10 @@ pub(super) fn array_iter_to_series(
 }
 
 /// Materializes hive partitions.
-/// We have a special num_rows arg, as df can be empty.
-fn materialize_hive_partitions(
-    df: &mut DataFrame,
-    hive_partition_columns: Option<&[Series]>,
-    num_rows: usize,
-) {
+fn materialize_hive_partitions(df: &mut DataFrame, hive_partition_columns: Option<&[Series]>) {
     if let Some(hive_columns) = hive_partition_columns {
+        let num_rows = df.height();
+
         for s in hive_columns {
             unsafe { df.with_column_unchecked(s.new_from_index(0, num_rows)) };
         }
@@ -223,7 +220,7 @@ fn rg_to_dfs_optionally_par_over_columns(
         if let Some(rc) = &row_count {
             df.with_row_count_mut(&rc.name, Some(*previous_row_count + rc.offset));
         }
-        materialize_hive_partitions(&mut df, hive_partition_columns, md.num_rows());
+        materialize_hive_partitions(&mut df, hive_partition_columns);
 
         apply_predicate(&mut df, predicate.as_deref(), true)?;
 
@@ -299,7 +296,7 @@ fn rg_to_dfs_par_over_rg(
             if let Some(rc) = &row_count {
                 df.with_row_count_mut(&rc.name, Some(row_count_start as IdxSize + rc.offset));
             }
-            materialize_hive_partitions(&mut df, hive_partition_columns, md.num_rows());
+            materialize_hive_partitions(&mut df, hive_partition_columns);
 
             apply_predicate(&mut df, predicate.as_deref(), false)?;
 
diff --git a/py-polars/tests/unit/io/test_hive.py b/py-polars/tests/unit/io/test_hive.py
index 9dc016120619..83fce2da1194 100644
--- a/py-polars/tests/unit/io/test_hive.py
+++ b/py-polars/tests/unit/io/test_hive.py
@@ -55,6 +55,28 @@ def test_hive_partitioned_predicate_pushdown(
     assert q.filter(pl.col("sugars_g") == 25).collect().shape == (1, 4)
 
 
+@pytest.mark.write_disk()
+def test_hive_partitioned_slice_pushdown(io_files_path: Path, tmp_path: Path) -> None:
+    df = pl.read_ipc(io_files_path / "*.ipc")
+
+    root = tmp_path / "partitioned_data"
+
+    # Ignore the pyarrow legacy warning until we can write properly with new settings.
+    warnings.filterwarnings("ignore")
+    pq.write_to_dataset(
+        df.to_arrow(),
+        root_path=root,
+        partition_cols=["category", "fats_g"],
+        use_legacy_dataset=True,
+    )
+
+    q = pl.scan_parquet(root / "**/*.parquet", hive_partitioning=True)
+
+    # tests: 11682
+    assert q.head(1).collect().select(pl.all_horizontal(pl.all().count() == 1)).item()
+    assert q.head(0).collect().columns == ["calories", "sugars_g", "category", "fats_g"]
+
+
 @pytest.mark.write_disk()
 def test_hive_partitioned_projection_pushdown(
     io_files_path: Path, tmp_path: Path