black

pandas/tests/groupby/aggregate/test_aggregate.py

@@ -250,10 +250,10 @@ def test_agg_str_with_kwarg_axis_1_raises(df, reduction_func):
     "func, expected, dtype, result_dtype_dict",
     [
         ("sum", [5, 7, 9], "int64", {}),
-        ("std", [4.5**0.5] * 3, int, {"i": float, "j": float, "k": float}),
+        ("std", [4.5 ** 0.5] * 3, int, {"i": float, "j": float, "k": float}),
         ("var", [4.5] * 3, int, {"i": float, "j": float, "k": float}),
         ("sum", [5, 7, 9], "Int64", {"j": "int64"}),
-        ("std", [4.5**0.5] * 3, "Int64", {"i": float, "j": float, "k": float}),
+        ("std", [4.5 ** 0.5] * 3, "Int64", {"i": float, "j": float, "k": float}),
         ("var", [4.5] * 3, "Int64", {"i": "float64", "j": "float64", "k": "float64"}),
     ],
 )
@@ -280,7 +280,7 @@ def test_multiindex_groupby_mixed_cols_axis1(func, expected, dtype, result_dtype
     [
         ("sum", [[2, 4], [10, 12], [18, 20]], {10: "int64", 20: "int64"}),
         # std should ideally return Int64 / Float64 #43330
-        ("std", [[2**0.5] * 2] * 3, "float64"),
+        ("std", [[2 ** 0.5] * 2] * 3, "float64"),
         ("var", [[2] * 2] * 3, {10: "float64", 20: "float64"}),
     ],
 )
@@ -524,8 +524,7 @@ def test_groupby_agg_dict_with_getitem():
 def test_groupby_agg_dict_dup_columns():
     # GH#55006
     df = DataFrame(
-        [[1, 2, 3, 4], [1, 3, 4, 5], [2, 4, 5, 6]],
-        columns=["a", "b", "c", "c"],
+        [[1, 2, 3, 4], [1, 3, 4, 5], [2, 4, 5, 6]], columns=["a", "b", "c", "c"],
     )
     gb = df.groupby("a")
     result = gb.agg({"b": "sum"})
@@ -889,11 +888,12 @@ def f(x):
                 raise ValueError("length must not be 0")
             return len(x)
 
-        df = DataFrame({"A": pd.Categorical(['a', 'a'],
-                        categories=['a', 'b', 'c']), "B": [1, 1]})
-        msg = 'length must not be 0'
+        df = DataFrame(
+            {"A": pd.Categorical(["a", "a"], categories=["a", "b", "c"]), "B": [1, 1]}
+        )
+        msg = "length must not be 0"
         with pytest.raises(ValueError, match=msg):
-            df.groupby('A').agg(f)
+            df.groupby("A").agg(f)
 
     def test_agg_namedtuple(self):
         df = DataFrame({"A": [0, 1], "B": [1, 2]})
@@ -1198,7 +1198,7 @@ def test_agg_with_one_lambda(self):
         # check pd.NameAgg case
         result1 = df.groupby(by="kind").agg(
             height_sqr_min=pd.NamedAgg(
-                column="height", aggfunc=lambda x: np.min(x**2)
+                column="height", aggfunc=lambda x: np.min(x ** 2)
             ),
             height_max=pd.NamedAgg(column="height", aggfunc="max"),
             weight_max=pd.NamedAgg(column="weight", aggfunc="max"),
@@ -1207,7 +1207,7 @@ def test_agg_with_one_lambda(self):
 
         # check agg(key=(col, aggfunc)) case
         result2 = df.groupby(by="kind").agg(
-            height_sqr_min=("height", lambda x: np.min(x**2)),
+            height_sqr_min=("height", lambda x: np.min(x ** 2)),
             height_max=("height", "max"),
             weight_max=("weight", "max"),
         )
@@ -1244,7 +1244,7 @@ def test_agg_multiple_lambda(self):
 
         # check agg(key=(col, aggfunc)) case
         result1 = df.groupby(by="kind").agg(
-            height_sqr_min=("height", lambda x: np.min(x**2)),
+            height_sqr_min=("height", lambda x: np.min(x ** 2)),
             height_max=("height", "max"),
             weight_max=("weight", "max"),
             height_max_2=("height", lambda x: np.max(x)),
@@ -1255,7 +1255,7 @@ def test_agg_multiple_lambda(self):
         # check pd.NamedAgg case
         result2 = df.groupby(by="kind").agg(
             height_sqr_min=pd.NamedAgg(
-                column="height", aggfunc=lambda x: np.min(x**2)
+                column="height", aggfunc=lambda x: np.min(x ** 2)
             ),
             height_max=pd.NamedAgg(column="height", aggfunc="max"),
             weight_max=pd.NamedAgg(column="weight", aggfunc="max"),