Merge pull request #19 from raisadz/increase-coverage

Increase coverage for expressions

narwhals/expression.py

@@ -148,7 +148,13 @@ def is_null(self) -> Expr:
     def drop_nulls(self) -> Expr:
         return self.__class__(lambda plx: self._call(plx).drop_nulls())
 
-    def sample(self, n: int, fraction: float, *, with_replacement: bool) -> Expr:
+    def sample(
+        self,
+        n: int | None = None,
+        fraction: float | None = None,
+        *,
+        with_replacement: bool = False,
+    ) -> Expr:
         return self.__class__(
             lambda plx: self._call(plx).sample(
                 n, fraction=fraction, with_replacement=with_replacement

tests/test_common.py

@@ -257,7 +257,7 @@ def test_shape(df_raw: Any) -> None:
 
 
 @pytest.mark.parametrize("df_raw", [df_polars, df_pandas, df_mpd, df_lazy])
-def test_expr(df_raw: Any) -> None:
+def test_expr_binary(df_raw: Any) -> None:
     result = nw.LazyFrame(df_raw).with_columns(
         a=(1 + 3 * nw.col("a")) * (1 / nw.col("a")),
         b=nw.col("z") / (2 - nw.col("b")),
@@ -281,3 +281,42 @@ def test_expr(df_raw: Any) -> None:
         "f": [1, 1, 1],
     }
     compare_dicts(result_native, expected)
+
+
+@pytest.mark.parametrize("df_raw", [df_polars, df_pandas, df_lazy])
+def test_expr_unary(df_raw: Any) -> None:
+    result = (
+        nw.LazyFrame(df_raw)
+        .with_columns(
+            a_mean=nw.col("a").mean(),
+            a_sum=nw.col("a").sum(),
+            b_nunique=nw.col("b").n_unique(),
+            z_min=nw.col("z").min(),
+            z_max=nw.col("z").max(),
+        )
+        .select(nw.col("a_mean", "a_sum", "b_nunique", "z_min", "z_max").unique())
+    )
+    result_native = nw.to_native(result)
+    expected = {"a_mean": [2], "a_sum": [6], "b_nunique": [2], "z_min": [7], "z_max": [9]}
+    compare_dicts(result_native, expected)
+
+
+@pytest.mark.parametrize("df_raw", [df_polars, df_pandas, df_mpd, df_lazy])
+def test_expr_transform(df_raw: Any) -> None:
+    result = nw.LazyFrame(df_raw).with_columns(
+        a=nw.col("a").is_between(-1, 1), b=nw.col("b").is_in([4, 5])
+    )
+    result_native = nw.to_native(result)
+    expected = {"a": [True, False, False], "b": [True, True, False], "z": [7, 8, 9]}
+    compare_dicts(result_native, expected)
+
+
+@pytest.mark.parametrize("df_raw", [df_polars, df_pandas, df_lazy])
+def test_expr_min_max(df_raw: Any) -> None:
+    df = nw.LazyFrame(df_raw)
+    result_min = nw.to_native(df.select(nw.min("a", "b", "z")))
+    result_max = nw.to_native(df.select(nw.max("a", "b", "z")))
+    expected_min = {"a": [1], "b": [4], "z": [7]}
+    expected_max = {"a": [3], "b": [6], "z": [9]}
+    compare_dicts(result_min, expected_min)
+    compare_dicts(result_max, expected_max)