CLN/TYP: Groupby agg methods

pandas/core/groupby/groupby.py

@@ -36,7 +36,6 @@ class providing the base-class of operations.
 from pandas._libs import Timestamp
 import pandas._libs.groupby as libgroupby
 from pandas._typing import FrameOrSeries, Scalar
-from pandas.compat import set_function_name
 from pandas.compat.numpy import function as nv
 from pandas.errors import AbstractMethodError
 from pandas.util._decorators import Appender, Substitution, cache_readonly, doc
@@ -192,6 +191,24 @@ class providing the base-class of operations.
     """,
 )
 
+_groupby_agg_method_template = """
+Compute {fname} of group values.
+
+Parameters
+----------
+numeric_only : bool, default {no}
+    Include only float, int, boolean columns. If None, will attempt to use
+    everything, then use only numeric data.
+min_count : int, default {mc}
+    The required number of valid values to perform the operation. If fewer
+    than ``min_count`` non-NA values are present the result will be NA.
+
+Returns
+-------
+Series or DataFrame
+    Computed {fname} of values within each group.
+"""
+
 _pipe_template = """
 Apply a function `func` with arguments to this %(klass)s object and return
 the function's result.
@@ -945,6 +962,37 @@ def _wrap_transformed_output(self, output: Mapping[base.OutputKey, np.ndarray]):
     def _wrap_applied_output(self, keys, values, not_indexed_same: bool = False):
         raise AbstractMethodError(self)
 
+    def _agg_general(
+        self,
+        numeric_only: bool = True,
+        min_count: int = -1,
+        *,
+        alias: str,
+        npfunc: Callable,
+    ):
+        self._set_group_selection()
+
+        # try a cython aggregation if we can
+        try:
+            return self._cython_agg_general(
+                how=alias, alt=npfunc, numeric_only=numeric_only, min_count=min_count,
+            )
+        except DataError:
+            pass
+        except NotImplementedError as err:
+            if "function is not implemented for this dtype" in str(
+                err
+            ) or "category dtype not supported" in str(err):
+                # raised in _get_cython_function, in some cases can
+                #  be trimmed by implementing cython funcs for more dtypes
+                pass
+            else:
+                raise
+
+        # apply a non-cython aggregation
+        result = self.aggregate(lambda x: npfunc(x, axis=self.axis))
+        return result
+
     def _cython_agg_general(
         self, how: str, alt=None, numeric_only: bool = True, min_count: int = -1
     ):
@@ -1438,74 +1486,36 @@ def size(self):
             result = self._obj_1d_constructor(result)
         return self._reindex_output(result, fill_value=0)
 
-    @classmethod
-    def _add_numeric_operations(cls):
-        """
-        Add numeric operations to the GroupBy generically.
-        """
+    @doc(_groupby_agg_method_template, fname="sum", no=True, mc=0)
+    def sum(self, numeric_only: bool = True, min_count: int = 0):
+        return self._agg_general(
+            numeric_only=numeric_only, min_count=min_count, alias="add", npfunc=np.sum
+        )
 
-        def groupby_function(
-            name: str,
-            alias: str,
-            npfunc,
-            numeric_only: bool = True,
-            min_count: int = -1,
-        ):
+    @doc(_groupby_agg_method_template, fname="prod", no=True, mc=0)
+    def prod(self, numeric_only: bool = True, min_count: int = 0):
+        return self._agg_general(
+            numeric_only=numeric_only, min_count=min_count, alias="prod", npfunc=np.prod
+        )
 
-            _local_template = """
-            Compute %(f)s of group values.
-
-            Parameters
-            ----------
-            numeric_only : bool, default %(no)s
-                Include only float, int, boolean columns. If None, will attempt to use
-                everything, then use only numeric data.
-            min_count : int, default %(mc)s
-                The required number of valid values to perform the operation. If fewer
-                than ``min_count`` non-NA values are present the result will be NA.
-
-            Returns
-            -------
-            Series or DataFrame
-                Computed %(f)s of values within each group.
-            """
-
-            @Substitution(name="groupby", f=name, no=numeric_only, mc=min_count)
-            @Appender(_common_see_also)
-            @Appender(_local_template)
-            def func(self, numeric_only=numeric_only, min_count=min_count):
-                self._set_group_selection()
-
-                # try a cython aggregation if we can
-                try:
-                    return self._cython_agg_general(
-                        how=alias,
-                        alt=npfunc,
-                        numeric_only=numeric_only,
-                        min_count=min_count,
-                    )
-                except DataError:
-                    pass
-                except NotImplementedError as err:
-                    if "function is not implemented for this dtype" in str(
-                        err
-                    ) or "category dtype not supported" in str(err):
-                        # raised in _get_cython_function, in some cases can
-                        #  be trimmed by implementing cython funcs for more dtypes
-                        pass
-                    else:
-                        raise
-
-                # apply a non-cython aggregation
-                result = self.aggregate(lambda x: npfunc(x, axis=self.axis))
-                return result
-
-            set_function_name(func, name, cls)
-
-            return func
+    @doc(_groupby_agg_method_template, fname="min", no=False, mc=-1)
+    def min(self, numeric_only: bool = False, min_count: int = -1):
+        return self._agg_general(
+            numeric_only=numeric_only, min_count=min_count, alias="min", npfunc=np.min
+        )
 
+    @doc(_groupby_agg_method_template, fname="max", no=False, mc=-1)
+    def max(self, numeric_only: bool = False, min_count: int = -1):
+        return self._agg_general(
+            numeric_only=numeric_only, min_count=min_count, alias="max", npfunc=np.max
+        )
+
+    @doc(_groupby_agg_method_template, fname="first", no=False, mc=-1)
+    def first(self, numeric_only: bool = False, min_count: int = -1):
         def first_compat(obj: FrameOrSeries, axis: int = 0):
             def first(x: Series):
+                """Helper function for first item that isn't NA.
+                """
                 x = x.array[notna(x.array)]
                 if len(x) == 0:
                     return np.nan
@@ -1518,8 +1528,19 @@ def first(x: Series):
             else:
                 raise TypeError(type(obj))
 
+        return self._agg_general(
+            numeric_only=numeric_only,
+            min_count=min_count,
+            alias="first",
+            npfunc=first_compat,
+        )
+
+    @doc(_groupby_agg_method_template, fname="last", no=False, mc=-1)
+    def last(self, numeric_only: bool = False, min_count: int = -1):
         def last_compat(obj: FrameOrSeries, axis: int = 0):
             def last(x: Series):
+                """Helper function for last item that isn't NA.
+                """
                 x = x.array[notna(x.array)]
                 if len(x) == 0:
                     return np.nan
@@ -1532,12 +1553,12 @@ def last(x: Series):
             else:
                 raise TypeError(type(obj))
 
-        cls.sum = groupby_function("sum", "add", np.sum, min_count=0)
-        cls.prod = groupby_function("prod", "prod", np.prod, min_count=0)
-        cls.min = groupby_function("min", "min", np.min, numeric_only=False)
-        cls.max = groupby_function("max", "max", np.max, numeric_only=False)
-        cls.first = groupby_function("first", "first", first_compat, numeric_only=False)
-        cls.last = groupby_function("last", "last", last_compat, numeric_only=False)
+        return self._agg_general(
+            numeric_only=numeric_only,
+            min_count=min_count,
+            alias="last",
+            npfunc=last_compat,
+        )
 
     @Substitution(name="groupby")
     @Appender(_common_see_also)
@@ -2637,9 +2658,6 @@ def _reindex_output(
         return output.reset_index(drop=True)
 
 
-GroupBy._add_numeric_operations()
-
-
 @doc(GroupBy)
 def get_groupby(
     obj: NDFrame,

pandas/util/_decorators.py

@@ -329,7 +329,7 @@ def wrapper(*args, **kwargs) -> Callable[..., Any]:
     return decorate
 
 
-def doc(*args: Union[str, Callable], **kwargs: str) -> Callable[[F], F]:
+def doc(*args: Union[str, Callable], **kwargs) -> Callable[[F], F]:
     """
     A decorator take docstring templates, concatenate them and perform string
     substitution on it.
@@ -345,8 +345,8 @@ def doc(*args: Union[str, Callable], **kwargs: str) -> Callable[[F], F]:
     *args : str or callable
         The string / docstring / docstring template to be appended in order
         after default docstring under function.
-    **kwargs : str
-        The string which would be used to format docstring template.
+    **kwargs
+        The objects which would be used to format docstring template.
     """
 
     def decorator(func: F) -> F: