DOC: RT03 fix for various DataFrameGroupBy and SeriesGroupBy methods (pandas-dev#57862)

* pandas.core.groupby.SeriesGroupBy.apply

* pandas.core.groupby.GroupBy.apply

* rm check

* add a bunch

* fix

* fix

* wip

* update pandas.core.groupby.DataFrameGroupBy.resample

* rm check from code_checls

* minor

Author: quangngd

ci/code_checks.sh

@@ -667,60 +667,40 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.core.groupby.DataFrameGroupBy.__iter__ RT03,SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.agg RT03" \
         -i "pandas.core.groupby.DataFrameGroupBy.aggregate RT03" \
-        -i "pandas.core.groupby.DataFrameGroupBy.apply RT03" \
         -i "pandas.core.groupby.DataFrameGroupBy.boxplot PR07,RT03,SA01" \
-        -i "pandas.core.groupby.DataFrameGroupBy.cummax RT03" \
-        -i "pandas.core.groupby.DataFrameGroupBy.cummin RT03" \
-        -i "pandas.core.groupby.DataFrameGroupBy.cumprod RT03" \
-        -i "pandas.core.groupby.DataFrameGroupBy.cumsum RT03" \
-        -i "pandas.core.groupby.DataFrameGroupBy.filter RT03,SA01" \
+        -i "pandas.core.groupby.DataFrameGroupBy.filter SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.get_group RT03,SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.groups SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.hist RT03" \
         -i "pandas.core.groupby.DataFrameGroupBy.indices SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.max SA01" \
-        -i "pandas.core.groupby.DataFrameGroupBy.mean RT03" \
         -i "pandas.core.groupby.DataFrameGroupBy.median SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.min SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.nth PR02" \
-        -i "pandas.core.groupby.DataFrameGroupBy.nunique RT03,SA01" \
+        -i "pandas.core.groupby.DataFrameGroupBy.nunique SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.ohlc SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.plot PR02,SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.prod SA01" \
-        -i "pandas.core.groupby.DataFrameGroupBy.rank RT03" \
-        -i "pandas.core.groupby.DataFrameGroupBy.resample RT03" \
         -i "pandas.core.groupby.DataFrameGroupBy.sem SA01" \
-        -i "pandas.core.groupby.DataFrameGroupBy.skew RT03" \
         -i "pandas.core.groupby.DataFrameGroupBy.sum SA01" \
-        -i "pandas.core.groupby.DataFrameGroupBy.transform RT03" \
         -i "pandas.core.groupby.SeriesGroupBy.__iter__ RT03,SA01" \
         -i "pandas.core.groupby.SeriesGroupBy.agg RT03" \
         -i "pandas.core.groupby.SeriesGroupBy.aggregate RT03" \
-        -i "pandas.core.groupby.SeriesGroupBy.apply RT03" \
-        -i "pandas.core.groupby.SeriesGroupBy.cummax RT03" \
-        -i "pandas.core.groupby.SeriesGroupBy.cummin RT03" \
-        -i "pandas.core.groupby.SeriesGroupBy.cumprod RT03" \
-        -i "pandas.core.groupby.SeriesGroupBy.cumsum RT03" \
-        -i "pandas.core.groupby.SeriesGroupBy.filter PR01,RT03,SA01" \
+        -i "pandas.core.groupby.SeriesGroupBy.filter PR01,SA01" \
         -i "pandas.core.groupby.SeriesGroupBy.get_group RT03,SA01" \
         -i "pandas.core.groupby.SeriesGroupBy.groups SA01" \
         -i "pandas.core.groupby.SeriesGroupBy.indices SA01" \
         -i "pandas.core.groupby.SeriesGroupBy.is_monotonic_decreasing SA01" \
         -i "pandas.core.groupby.SeriesGroupBy.is_monotonic_increasing SA01" \
         -i "pandas.core.groupby.SeriesGroupBy.max SA01" \
-        -i "pandas.core.groupby.SeriesGroupBy.mean RT03" \
         -i "pandas.core.groupby.SeriesGroupBy.median SA01" \
         -i "pandas.core.groupby.SeriesGroupBy.min SA01" \
         -i "pandas.core.groupby.SeriesGroupBy.nth PR02" \
         -i "pandas.core.groupby.SeriesGroupBy.ohlc SA01" \
         -i "pandas.core.groupby.SeriesGroupBy.plot PR02,SA01" \
         -i "pandas.core.groupby.SeriesGroupBy.prod SA01" \
-        -i "pandas.core.groupby.SeriesGroupBy.rank RT03" \
-        -i "pandas.core.groupby.SeriesGroupBy.resample RT03" \
         -i "pandas.core.groupby.SeriesGroupBy.sem SA01" \
-        -i "pandas.core.groupby.SeriesGroupBy.skew RT03" \
         -i "pandas.core.groupby.SeriesGroupBy.sum SA01" \
-        -i "pandas.core.groupby.SeriesGroupBy.transform RT03" \
         -i "pandas.core.resample.Resampler.__iter__ RT03,SA01" \
         -i "pandas.core.resample.Resampler.ffill RT03" \
         -i "pandas.core.resample.Resampler.get_group RT03,SA01" \

pandas/core/groupby/generic.py

@@ -240,6 +240,7 @@ def apply(self, func, *args, **kwargs) -> Series:
         Returns
         -------
         Series or DataFrame
+            A pandas object with the result of applying ``func`` to each group.
 
         See Also
         --------
@@ -600,6 +601,7 @@ def filter(self, func, dropna: bool = True, *args, **kwargs):
         Returns
         -------
         Series
+            The filtered subset of the original Series.
 
         Notes
         -----
@@ -1078,6 +1080,7 @@ def skew(
         Returns
         -------
         Series
+            Unbiased skew within groups.
 
         See Also
         --------
@@ -1941,6 +1944,7 @@ def filter(self, func, dropna: bool = True, *args, **kwargs) -> DataFrame:
         Returns
         -------
         DataFrame
+            The filtered subset of the original DataFrame.
 
         Notes
         -----
@@ -2108,6 +2112,7 @@ def nunique(self, dropna: bool = True) -> DataFrame:
         Returns
         -------
         nunique: DataFrame
+            Counts of unique elements in each position.
 
         Examples
         --------
@@ -2506,6 +2511,7 @@ def skew(
         Returns
         -------
         DataFrame
+            Unbiased skew within groups.
 
         See Also
         --------

pandas/core/groupby/groupby.py

@@ -333,6 +333,8 @@ class providing the base-class of operations.
 Returns
 -------
 %(klass)s
+    %(klass)s with the same indexes as the original object filled
+    with transformed values.
 
 See Also
 --------
@@ -1551,6 +1553,7 @@ def apply(self, func, *args, include_groups: bool = True, **kwargs) -> NDFrameT:
         Returns
         -------
         Series or DataFrame
+            A pandas object with the result of applying ``func`` to each group.
 
         See Also
         --------
@@ -2245,6 +2248,7 @@ def mean(
         Returns
         -------
         pandas.Series or pandas.DataFrame
+            Mean of values within each group. Same object type as the caller.
         %(see_also)s
         Examples
         --------
@@ -3512,11 +3516,8 @@ def resample(self, rule, *args, include_groups: bool = True, **kwargs) -> Resamp
 
         Returns
         -------
-        pandas.api.typing.DatetimeIndexResamplerGroupby,
-        pandas.api.typing.PeriodIndexResamplerGroupby, or
-        pandas.api.typing.TimedeltaIndexResamplerGroupby
-            Return a new groupby object, with type depending on the data
-            being resampled.
+        DatetimeIndexResampler, PeriodIndexResampler or TimdeltaResampler
+            Resampler object for the type of the index.
 
         See Also
         --------
@@ -4591,7 +4592,8 @@ def rank(
 
         Returns
         -------
-        DataFrame with ranking of values within each group
+        DataFrame
+            The ranking of values within each group.
         %(see_also)s
         Examples
         --------
@@ -4663,6 +4665,7 @@ def cumprod(self, *args, **kwargs) -> NDFrameT:
         Returns
         -------
         Series or DataFrame
+            Cumulative product for each group. Same object type as the caller.
         %(see_also)s
         Examples
         --------
@@ -4721,6 +4724,7 @@ def cumsum(self, *args, **kwargs) -> NDFrameT:
         Returns
         -------
         Series or DataFrame
+            Cumulative sum for each group. Same object type as the caller.
         %(see_also)s
         Examples
         --------
@@ -4783,6 +4787,7 @@ def cummin(
         Returns
         -------
         Series or DataFrame
+            Cumulative min for each group. Same object type as the caller.
         %(see_also)s
         Examples
         --------
@@ -4853,6 +4858,7 @@ def cummax(
         Returns
         -------
         Series or DataFrame
+            Cumulative max for each group. Same object type as the caller.
         %(see_also)s
         Examples
         --------