CLN/ASV: Remove NumPy/built-in aliases in ASVs

asv_bench/benchmarks/frame_methods.py

@@ -511,8 +511,8 @@ def time_apply_axis_1(self):
     def time_apply_lambda_mean(self):
         self.df.apply(lambda x: x.mean())
 
-    def time_apply_np_mean(self):
-        self.df.apply(np.mean)
+    def time_apply_str_mean(self):
+        self.df.apply("mean")
 
     def time_apply_pass_thru(self):
         self.df.apply(lambda x: x)

asv_bench/benchmarks/groupby.py

@@ -329,16 +329,11 @@ def time_different_str_functions(self, df):
             {"value1": "mean", "value2": "var", "value3": "sum"}
         )
 
-    def time_different_numpy_functions(self, df):
-        df.groupby(["key1", "key2"]).agg(
-            {"value1": np.mean, "value2": np.var, "value3": np.sum}
-        )
-
-    def time_different_python_functions_multicol(self, df):
-        df.groupby(["key1", "key2"]).agg([sum, min, max])
+    def time_different_str_functions_multicol(self, df):
+        df.groupby(["key1", "key2"]).agg(["sum", "min", "max"])
 
-    def time_different_python_functions_singlecol(self, df):
-        df.groupby("key1")[["value1", "value2", "value3"]].agg([sum, min, max])
+    def time_different_str_functions_singlecol(self, df):
+        df.groupby("key1").agg({"value1": "mean", "value2": "var", "value3": "sum"})
 
 
 class GroupStrings:
@@ -381,8 +376,8 @@ def time_cython_sum(self, df):
     def time_col_select_lambda_sum(self, df):
         df.groupby(["key1", "key2"])["data1"].agg(lambda x: x.values.sum())
 
-    def time_col_select_numpy_sum(self, df):
-        df.groupby(["key1", "key2"])["data1"].agg(np.sum)
+    def time_col_select_str_sum(self, df):
+        df.groupby(["key1", "key2"])["data1"].agg("sum")
 
 
 class Size:
@@ -894,8 +889,8 @@ def setup(self):
     def time_transform_lambda_max(self):
         self.df.groupby(level="lev1").transform(lambda x: max(x))
 
-    def time_transform_ufunc_max(self):
-        self.df.groupby(level="lev1").transform(np.max)
+    def time_transform_str_max(self):
+        self.df.groupby(level="lev1").transform("max")
 
     def time_transform_lambda_max_tall(self):
         self.df_tall.groupby(level=0).transform(lambda x: np.max(x, axis=0))
@@ -926,7 +921,7 @@ def setup(self):
         self.df = DataFrame({"signal": np.random.rand(N)})
 
     def time_transform_mean(self):
-        self.df["signal"].groupby(self.g).transform(np.mean)
+        self.df["signal"].groupby(self.g).transform("mean")
 
 
 class TransformNaN:

asv_bench/benchmarks/reshape.py

@@ -218,15 +218,15 @@ def time_pivot_table_margins(self):
 
     def time_pivot_table_categorical(self):
         self.df2.pivot_table(
-            index="col1", values="col3", columns="col2", aggfunc=np.sum, fill_value=0
+            index="col1", values="col3", columns="col2", aggfunc="sum", fill_value=0
         )
 
     def time_pivot_table_categorical_observed(self):
         self.df2.pivot_table(
             index="col1",
             values="col3",
             columns="col2",
-            aggfunc=np.sum,
+            aggfunc="sum",
             fill_value=0,
             observed=True,
         )