Run linter

Author: fpunny

pandas/core/base.py

@@ -342,16 +342,16 @@ def _aggregate(self, arg, *args, **kwargs):
                         raise SpecificationError("nested renamer is not supported")
                     elif isinstance(obj, ABCDataFrame):
                         # GH 29268
-                        if (k not in obj.columns):
+                        if k not in obj.columns:
                             # Check if list thingy
                             try:
-                                keys = np.frombuffer(k, dtype=np.dtype('<U1'))
+                                keys = np.frombuffer(k, dtype=np.dtype("<U1"))
                             except (AttributeError, TypeError):
                                 raise KeyError(f"Column '{k}' does not exist!")
 
                             # Check keys
                             for key in keys:
-                                if (key not in obj.columns):
+                                if key not in obj.columns:
                                     raise KeyError(f"Column '{key}' does not exist!")
 
                                 # Memorize operation
@@ -388,10 +388,11 @@ def _agg_2dim(how):
                 """
                 colg = self._gotitem(self._selection, ndim=2, subset=obj)
                 return colg.aggregate(how)
-            
+
             # GH 29268
             def _agg_multi_dim(name, how, keys):
                 from pandas.core.frame import DataFrame
+
                 _obj = {k: self._gotitem(k, ndim=1, subset=None) for k in keys}
                 result = {com.get_callable_name(agg): agg(_obj) for agg in how}
                 return DataFrame(result, columns=result.keys())

pandas/core/groupby/generic.py

@@ -921,6 +921,7 @@ def aggregate(self, func=None, *args, **kwargs):
         if relabeling:
             # GH 29268
             from types import LambdaType
+
             for k, v in list(kwargs.items()):
                 if isinstance(v[0], list) & isinstance(v[1], LambdaType):
                     serialized_key = np.sort(np.array(v[0]))

pandas/tests/groupby/aggregate/test_aggregate.py

@@ -640,7 +640,7 @@ def test_agg_multiple_columns(self):
         df = pd.DataFrame({"A": [0, 0, 1, 1], "B": [1, 2, 3, 4], "C": [3, 4, 5, 6]})
         result = df.groupby("A").agg(
             add=(["B", "C"], lambda x: x["B"].max() + x["C"].min()),
-            minus=(["C", "B"], lambda x: x["B"].max() - x["C"].min())
+            minus=(["C", "B"], lambda x: x["B"].max() - x["C"].min()),
         )
         expected = pd.DataFrame(
             {"add": [5, 9], "minus": [-1, -1]}, index=pd.Index([0, 1], name="A")