Add doc string to GroupBy methods

Author: topper-123

pandas/core/groupby/generic.py

@@ -87,28 +87,6 @@
 if TYPE_CHECKING:
     from pandas.core.internals import Block
 
-_agg_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
--------
-{return_type}
-    Computed {fname} of values within each group.
-
-See Also
---------
-{return_type}.groupby
-"""
-
 
 NamedAgg = namedtuple("NamedAgg", ["column", "aggfunc"])
 # TODO(typing) the return value on this callable should be any *scalar*.
@@ -827,30 +805,6 @@ def count(self) -> Series:
         )
         return self._reindex_output(result, fill_value=0)
 
-    @doc(_agg_template, fname="sum", no=True, mc=0, return_type="Series")
-    def sum(self, numeric_only: bool = True, min_count: int = 0) -> Series:
-        return super().sum(numeric_only=numeric_only, min_count=min_count)
-
-    @doc(_agg_template, fname="prod", no=True, mc=0, return_type="Series")
-    def prod(self, numeric_only: bool = True, min_count: int = 0) -> Series:
-        return super().prod(numeric_only=numeric_only, min_count=min_count)
-
-    @doc(_agg_template, fname="min", no=False, mc=-1, return_type="Series")
-    def min(self, numeric_only: bool = False, min_count: int = -1) -> Series:
-        return super().min(numeric_only=numeric_only, min_count=min_count)
-
-    @doc(_agg_template, fname="max", no=False, mc=-1, return_type="Series")
-    def max(self, numeric_only: bool = False, min_count: int = -1) -> Series:
-        return super().max(numeric_only=numeric_only, min_count=min_count)
-
-    @doc(_agg_template, fname="first", no=False, mc=-1, return_type="Series")
-    def first(self, numeric_only: bool = False, min_count: int = -1) -> Series:
-        return super().first(numeric_only=numeric_only, min_count=min_count)
-
-    @doc(_agg_template, fname="last", no=False, mc=-1, return_type="Series")
-    def last(self, numeric_only: bool = False, min_count: int = -1) -> Series:
-        return super().last(numeric_only=numeric_only, min_count=min_count)
-
     def _apply_to_column_groupbys(self, func):
         """ return a pass thru """
         return func(self)
@@ -1930,30 +1884,6 @@ def groupby_series(obj, col=None):
             results.index = ibase.default_index(len(results))
         return results
 
-    @doc(_agg_template, fname="sum", no=True, mc=0, return_type="DataFrame")
-    def sum(self, numeric_only: bool = True, min_count: int = 0) -> DataFrame:
-        return super().sum(numeric_only=numeric_only, min_count=min_count)
-
-    @doc(_agg_template, fname="prod", no=True, mc=0, return_type="DataFrame")
-    def prod(self, numeric_only: bool = True, min_count: int = 0) -> DataFrame:
-        return super().prod(numeric_only=numeric_only, min_count=min_count)
-
-    @doc(_agg_template, fname="min", no=False, mc=-1, return_type="DataFrame")
-    def min(self, numeric_only: bool = False, min_count: int = -1) -> DataFrame:
-        return super().min(numeric_only=numeric_only, min_count=min_count)
-
-    @doc(_agg_template, fname="max", no=False, mc=-1, return_type="DataFrame")
-    def max(self, numeric_only: bool = False, min_count: int = -1) -> DataFrame:
-        return super().max(numeric_only=numeric_only, min_count=min_count)
-
-    @doc(_agg_template, fname="first", no=False, mc=-1, return_type="DataFrame")
-    def first(self, numeric_only: bool = False, min_count: int = -1) -> DataFrame:
-        return super().first(numeric_only=numeric_only, min_count=min_count)
-
-    @doc(_agg_template, fname="last", no=False, mc=-1, return_type="DataFrame")
-    def last(self, numeric_only: bool = False, min_count: int = -1) -> DataFrame:
-        return super().last(numeric_only=numeric_only, min_count=min_count)
-
     boxplot = boxplot_frame_groupby
 
 

pandas/core/groupby/groupby.py

@@ -191,6 +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.
@@ -1454,21 +1472,25 @@ def size(self):
             result.name = self.obj.name
         return self._reindex_output(result, fill_value=0)
 
+    @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
         )
 
+    @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
         )
 
+    @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
@@ -1491,6 +1513,7 @@ def get_loc_notna(x, loc: int):
         else:
             return get_loc_notna(x, loc=loc)
 
+    @doc(_groupby_agg_method_template, fname="first", no=False, mc=-1)
     def first(self, numeric_only: bool = False, min_count: int = -1):
         first_compat = partial(self._get_loc, loc=0)
 
@@ -1501,6 +1524,7 @@ def first(self, numeric_only: bool = False, min_count: int = -1):
             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):
         last_compat = partial(self._get_loc, loc=-1)
 
@@ -2618,9 +2642,6 @@ def _reindex_output(
         return output.reset_index(drop=True)
 
 
-GroupBy._add_numeric_operations()
-
-
 @doc(GroupBy)
 def get_groupby(
     obj: NDFrame,