Satisfy linter

seaborn/_core/mappings.py

@@ -234,7 +234,9 @@ def setup(
                 # TODO should this happen upstream, or alternatively inside the norm?
                 data = scale.cast(data)
             data = mpl.dates.date2num(data.dropna())
-            prepare = lambda x: mpl.dates.date2num(pd.to_datetime(x))
+
+            def prepare(x):
+                return mpl.dates.date2num(pd.to_datetime(x))
 
             # TODO if norm is tuple, convert to datetime and then to numbers?
             # (Or handle that upstream within the DateTimeScale? Probably do this.)
@@ -301,7 +303,9 @@ def setup(
             # we could avoid this by defining a little scale_norm() wrapper that
             # removes nas more type-agnostically
             data = pd.Series(mpl.dates.date2num(data), index=data.index)
-            prepare = lambda x: mpl.dates.date2num(pd.to_datetime(x))
+
+            def prepare(x):
+                return mpl.dates.date2num(pd.to_datetime(x))
 
             # TODO if norm is tuple, convert to datetime and then to numbers?
 

seaborn/tests/_core/test_mappings.py

@@ -69,7 +69,6 @@ def test_categorical_default_palette_large(self):
 
         vector = pd.Series(list("abcdefghijklmnopqrstuvwxyz"))
         scale = ScaleWrapper.from_inferred_type(vector)
-        scale = ScaleWrapper.from_inferred_type(vector)
         n_colors = len(vector)
         expected = dict(zip(vector, color_palette("husl", n_colors)))
         m = ColorSemantic().setup(vector, scale)