Add test module and new test

py-polars/tests/unit/datatypes/test_struct.py

@@ -161,48 +161,6 @@ def test_struct_function_expansion() -> None:
     assert pl.Struct(struct_schema) == s.to_frame().schema["a"]
 
 
-def test_value_counts_expr() -> None:
-    df = pl.DataFrame(
-        {
-            "id": ["a", "b", "b", "c", "c", "c", "d", "d"],
-        }
-    )
-    out = (
-        df.select(
-            [
-                pl.col("id").value_counts(sort=True),
-            ]
-        )
-        .to_series()
-        .to_list()
-    )
-    assert out == [
-        {"id": "c", "counts": 3},
-        {"id": "b", "counts": 2},
-        {"id": "d", "counts": 2},
-        {"id": "a", "counts": 1},
-    ]
-
-    # nested value counts. Then the series needs the name
-    # 6200
-
-    df = pl.DataFrame({"session": [1, 1, 1], "id": [2, 2, 3]})
-
-    assert df.group_by("session").agg(
-        [pl.col("id").value_counts(sort=True).first()]
-    ).to_dict(False) == {"session": [1], "id": [{"id": 2, "counts": 2}]}
-
-
-def test_value_counts_logical_type() -> None:
-    # test logical type
-    df = pl.DataFrame({"a": ["b", "c"]}).with_columns(
-        pl.col("a").cast(pl.Categorical).alias("ac")
-    )
-    out = df.select([pl.all().value_counts()])
-    assert out["ac"].struct.field("ac").dtype == pl.Categorical
-    assert out["a"].struct.field("a").dtype == pl.Utf8
-
-
 def test_nested_struct() -> None:
     df = pl.DataFrame({"d": [1, 2, 3], "e": ["foo", "bar", "biz"]})
     # Nest the dataframe

py-polars/tests/unit/operations/test_value_counts.py

@@ -0,0 +1,57 @@
+from __future__ import annotations
+
+import pytest
+
+import polars as pl
+from polars.testing import assert_frame_equal
+
+
+def test_value_counts() -> None:
+    s = pl.Series("a", [1, 2, 2, 3])
+    result = s.value_counts()
+    expected = pl.DataFrame(
+        {"a": [1, 2, 3], "counts": [1, 2, 1]}, schema_overrides={"counts": pl.UInt32}
+    )
+    result_sorted = result.sort("a")
+    assert_frame_equal(result_sorted, expected)
+
+
+def test_value_counts_logical_type() -> None:
+    # test logical type
+    df = pl.DataFrame({"a": ["b", "c"]}).with_columns(
+        pl.col("a").cast(pl.Categorical).alias("ac")
+    )
+    out = df.select(pl.all().value_counts())
+    assert out["ac"].struct.field("ac").dtype == pl.Categorical
+    assert out["a"].struct.field("a").dtype == pl.Utf8
+
+
+def test_value_counts_expr() -> None:
+    df = pl.DataFrame(
+        {
+            "id": ["a", "b", "b", "c", "c", "c", "d", "d"],
+        }
+    )
+    out = df.select(pl.col("id").value_counts(sort=True)).to_series().to_list()
+    assert out == [
+        {"id": "c", "counts": 3},
+        {"id": "b", "counts": 2},
+        {"id": "d", "counts": 2},
+        {"id": "a", "counts": 1},
+    ]
+
+    # nested value counts. Then the series needs the name
+    # 6200
+
+    df = pl.DataFrame({"session": [1, 1, 1], "id": [2, 2, 3]})
+
+    assert df.group_by("session").agg(
+        pl.col("id").value_counts(sort=True).first()
+    ).to_dict(False) == {"session": [1], "id": [{"id": 2, "counts": 2}]}
+
+
+def test_value_counts_duplicate_name() -> None:
+    s = pl.Series("counts", [1])
+
+    with pytest.raises(pl.DuplicateError, match="counts"):
+        s.value_counts()

py-polars/tests/unit/series/test_series.py

@@ -1695,16 +1695,6 @@ def test_to_dummies() -> None:
     assert_frame_equal(result, expected)
 
 
-def test_value_counts() -> None:
-    s = pl.Series("a", [1, 2, 2, 3])
-    result = s.value_counts()
-    expected = pl.DataFrame(
-        {"a": [1, 2, 3], "counts": [1, 2, 1]}, schema_overrides={"counts": pl.UInt32}
-    )
-    result_sorted = result.sort("a")
-    assert_frame_equal(result_sorted, expected)
-
-
 def test_chunk_lengths() -> None:
     s = pl.Series("a", [1, 2, 2, 3])
     # this is a Series with one chunk, of length 4