[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

narwhals/_pandas_like/namespace.py

@@ -258,13 +258,21 @@ def concat(
             )
         raise NotImplementedError
 
-    def when(self, *predicates: IntoPandasLikeExpr, **constraints: Any) -> PandasWhen: # noqa: ARG002
+    def when(self, *predicates: IntoPandasLikeExpr, **constraints: Any) -> PandasWhen:  # noqa: ARG002
         plx = self.__class__(self._implementation, self._backend_version)
         condition = plx.all_horizontal(*predicates)
         return PandasWhen(condition, self._implementation, self._backend_version)
 
+
 class PandasWhen:
-    def __init__(self, condition: PandasLikeExpr, implementation: Implementation, backend_version: tuple[int, ...], then_value: Any = None, otherise_value: Any = None) -> None:
+    def __init__(
+        self,
+        condition: PandasLikeExpr,
+        implementation: Implementation,
+        backend_version: tuple[int, ...],
+        then_value: Any = None,
+        otherise_value: Any = None,
+    ) -> None:
         self._implementation = implementation
         self._backend_version = backend_version
         self._condition = condition
@@ -274,18 +282,21 @@ def __init__(self, condition: PandasLikeExpr, implementation: Implementation, ba
     def __call__(self, df: PandasLikeDataFrame) -> list[PandasLikeSeries]:
         from narwhals._pandas_like.namespace import PandasLikeNamespace
 
-        plx = PandasLikeNamespace(implementation=self._implementation, backend_version=self._backend_version)
+        plx = PandasLikeNamespace(
+            implementation=self._implementation, backend_version=self._backend_version
+        )
 
         condition = self._condition._call(df)[0]
 
-        value_series = plx._create_broadcast_series_from_scalar(self._then_value, condition)
-        otherwise_series = plx._create_broadcast_series_from_scalar(self._otherwise_value, condition)
-        return [
-            value_series.zip_with(condition, otherwise_series)
-        ]
+        value_series = plx._create_broadcast_series_from_scalar(
+            self._then_value, condition
+        )
+        otherwise_series = plx._create_broadcast_series_from_scalar(
+            self._otherwise_value, condition
+        )
+        return [value_series.zip_with(condition, otherwise_series)]
 
     def then(self, value: Any) -> PandasThen:
-
         self._then_value = value
 
         return PandasThen(
@@ -298,8 +309,8 @@ def then(self, value: Any) -> PandasThen:
             backend_version=self._condition._backend_version,
         )
 
-class PandasThen(PandasLikeExpr):
 
+class PandasThen(PandasLikeExpr):
     def __init__(
         self,
         call: PandasWhen,

narwhals/expr.py

@@ -3605,6 +3605,7 @@ def sum_horizontal(*exprs: IntoExpr | Iterable[IntoExpr]) -> Expr:
         )
     )
 
+
 class When:
     def __init__(self, condition: Expr) -> None:
         self._condition = condition
@@ -3614,14 +3615,16 @@ def __init__(self, condition: Expr) -> None:
     def then(self, value: Any) -> Then:
         return Then(lambda plx: plx.when(self._condition._call(plx)).then(value))
 
+
 class Then(Expr):
-    def __init__(self, call) -> None: # noqa: ANN001
+    def __init__(self, call) -> None:  # noqa: ANN001
         self._call = call
 
     def otherwise(self, value: Any) -> Expr:
         return Expr(lambda plx: self._call(plx).otherwise(value))
 
-def when(*predicates: IntoExpr | Iterable[IntoExpr], **constraints: Any) -> When: # noqa: ARG001
+
+def when(*predicates: IntoExpr | Iterable[IntoExpr], **constraints: Any) -> When:  # noqa: ARG001
     """
     Start a `when-then-otherwise` expression.
     Expression similar to an `if-else` statement in Python. Always initiated by a `pl.when(<condition>).then(<value if condition>)`., and optionally followed by chaining one or more `.when(<condition>).then(<value>)` statements.
@@ -3646,7 +3649,10 @@ def when(*predicates: IntoExpr | Iterable[IntoExpr], **constraints: Any) -> When
         >>> @nw.narwhalify
         ... def func(df_any):
         ...     from narwhals.expr import when
-        ...     return df_any.with_columns(when(nw.col("a") < 3).then(5).otherwise(6).alias("a_when"))
+        ...
+        ...     return df_any.with_columns(
+        ...         when(nw.col("a") < 3).then(5).otherwise(6).alias("a_when")
+        ...     )
 
         We can then pass either pandas or polars to `func`:
 

tests/test_when.py

@@ -31,6 +31,7 @@ def test_when(request: Any, constructor: Any) -> None:
     }
     compare_dicts(result, expected)
 
+
 def test_when_otherwise(request: Any, constructor: Any) -> None:
     if "pyarrow_table" in str(constructor):
         request.applymarker(pytest.mark.xfail)