back to 100% cov

dataframe_api_compat/polars_standard/polars_standard.py

@@ -204,7 +204,7 @@ def __pow__(self, other: Column[DType] | Any) -> PolarsColumn[Any]:
                 ret = ret.cast(original_type)
         else:
             ret = self.column.pow(other)  # type: ignore[arg-type]
-            if _is_integer_dtype(original_type) and isinstance(other, (int, float)):
+            if _is_integer_dtype(original_type) and isinstance(other, int):
                 if other < 0:
                     raise ValueError("Cannot raise integer to negative power")
                 ret = ret.cast(original_type)
@@ -468,9 +468,7 @@ def __pow__(self, other: DataFrame | Any) -> PolarsDataFrame:
                 ]
             )
             for column in self.dataframe.columns:
-                if _is_integer_dtype(original_type[column]) and isinstance(
-                    other, (int, float)
-                ):
+                if _is_integer_dtype(original_type[column]) and isinstance(other, int):
                     if other < 0:
                         raise ValueError("Cannot raise integer to negative power")
                     ret = ret.with_columns(pl.col(column).cast(original_type[column]))

test_standard.py

@@ -532,6 +532,42 @@ def test_float_powers(library: str):
     pd.testing.assert_frame_equal(result_pd, expected)
 
 
+def test_float_scalar_powers(library: str):
+    df = integer_dataframe_1(library)
+    other = 1.0
+    result = df.__pow__(other)
+    result_pd = pd.api.interchange.from_dataframe(result.dataframe)
+    expected = pd.DataFrame({"a": [1.0, 2.0, 3.0], "b": [4.0, 5.0, 6.0]})
+    result_pd = convert_dataframe_to_pandas_numpy(result_pd)
+    pd.testing.assert_frame_equal(result_pd, expected)
+
+
+def test_float_powers_column(library: str):
+    ser = integer_series_1(library)
+    other = integer_series_1(library) * 1.0
+    result = ser.__pow__(other)
+    namespace = ser.__column_namespace__()
+    result_pd = pd.api.interchange.from_dataframe(
+        namespace.dataframe_from_dict({"result": result}).dataframe
+    )["result"]
+    expected = pd.Series([1.0, 4.0, 27.0], name="result")
+    result_pd = convert_series_to_pandas_numpy(result_pd)
+    pd.testing.assert_series_equal(result_pd, expected)
+
+
+def test_float_powers_scalar_column(library: str):
+    ser = integer_series_1(library)
+    other = 1.0
+    result = ser.__pow__(other)
+    namespace = ser.__column_namespace__()
+    result_pd = pd.api.interchange.from_dataframe(
+        namespace.dataframe_from_dict({"result": result}).dataframe
+    )["result"]
+    expected = pd.Series([1.0, 2.0, 3.0], name="result")
+    result_pd = convert_series_to_pandas_numpy(result_pd)
+    pd.testing.assert_series_equal(result_pd, expected)
+
+
 def test_negative_powers_column(library: str):
     ser = integer_series_1(library)
     other = integer_series_1(library) * -1