c

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

@@ -1,4 +1,6 @@
 import polars as pl
+import pytest
+from polars.testing import assert_series_equal
 
 
 def test_nan_in_group_by_agg() -> None:
@@ -32,3 +34,47 @@ def test_nan_aggregations() -> None:
         str(df.group_by("b").agg(aggs).to_dict(as_series=False))
         == "{'b': [1], 'max': [3.0], 'min': [1.0], 'nan_max': [nan], 'nan_min': [nan]}"
     )
+
+
+@pytest.mark.parametrize(
+    ("s", "expect"),
+    (
+        (
+            pl.Series(
+                "x",
+                [
+                    -0.0,
+                    0.0,
+                    float("-nan"),
+                    float("nan"),
+                    1.0,
+                    None,
+                ],
+            ),
+            pl.Series("x", [None, 0.0, 1.0, float("nan")]),
+        ),
+        (
+            # No nulls
+            pl.Series(
+                "x",
+                [
+                    -0.0,
+                    0.0,
+                    float("-nan"),
+                    float("nan"),
+                    1.0,
+                ],
+            ),
+            pl.Series("x", [0.0, 1.0, float("nan")]),
+        ),
+    ),
+)
+def test_unique(s: pl.Series, expect: pl.Series) -> None:
+    out = s.unique()
+    assert_series_equal(expect, out)
+
+    out = s.n_unique()
+    assert expect.len() == out
+
+    out = s.gather(s.arg_unique()).sort()
+    assert_series_equal(expect, out)