diff --git a/ci/code_checks.sh b/ci/code_checks.sh
index f4a0488581606..8155d398d4d88 100755
--- a/ci/code_checks.sh
+++ b/ci/code_checks.sh
@@ -106,8 +106,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         pandas.test \
         pandas.NaT \
         pandas.io.formats.style.Styler.to_html \
-        pandas.HDFStore.groups \
-        pandas.HDFStore.walk \
         pandas.read_feather \
         pandas.DataFrame.to_feather \
         pandas.read_parquet \
@@ -120,11 +118,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         pandas.io.stata.StataReader.value_labels \
         pandas.io.stata.StataReader.variable_labels \
         pandas.io.stata.StataWriter.write_file \
-        pandas.core.resample.Resampler.__iter__ \
-        pandas.core.resample.Resampler.groups \
-        pandas.core.resample.Resampler.indices \
-        pandas.core.resample.Resampler.get_group \
-        pandas.core.resample.Resampler.ffill \
         pandas.core.resample.Resampler.asfreq \
         pandas.core.resample.Resampler.count \
         pandas.core.resample.Resampler.nunique \
diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index 1a17fef071a2f..30f5f21f03658 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -753,6 +753,19 @@ def groups(self) -> dict[Hashable, np.ndarray]:
         2  7  8  9
         >>> df.groupby(by=["a"]).groups
         {1: [0, 1], 7: [2]}
+
+        For Resampler:
+
+        >>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
+        ...                 ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
+        >>> ser
+        2023-01-01    1
+        2023-01-15    2
+        2023-02-01    3
+        2023-02-15    4
+        dtype: int64
+        >>> ser.resample('M').groups
+        {Timestamp('2023-01-31 00:00:00'): 2, Timestamp('2023-02-28 00:00:00'): 4}
         """
         return self.grouper.groups
 
@@ -794,6 +807,20 @@ def indices(self) -> dict[Hashable, npt.NDArray[np.intp]]:
         eagle   7  8  9
         >>> df.groupby(by=["a"]).indices
         {1: array([0, 1]), 7: array([2])}
+
+        For Resampler:
+
+        >>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
+        ...                 ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
+        >>> ser
+        2023-01-01    1
+        2023-01-15    2
+        2023-02-01    3
+        2023-02-15    4
+        dtype: int64
+        >>> ser.resample('M').indices
+        defaultdict(<class 'list'>, {Timestamp('2023-01-31 00:00:00'): [0, 1],
+        Timestamp('2023-02-28 00:00:00'): [2, 3]})
         """
         return self.grouper.indices
 
@@ -965,6 +992,21 @@ def get_group(self, name, obj=None) -> DataFrame | Series:
                 a  b  c
         owl     1  2  3
         toucan  1  5  6
+
+        For Resampler:
+
+        >>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
+        ...                 ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
+        >>> ser
+        2023-01-01    1
+        2023-01-15    2
+        2023-02-01    3
+        2023-02-15    4
+        dtype: int64
+        >>> ser.resample('M').get_group('2023-01-31')
+        2023-01-01    1
+        2023-01-15    2
+        dtype: int64
         """
         inds = self._get_index(name)
         if not len(inds):
@@ -1032,6 +1074,27 @@ def __iter__(self) -> Iterator[tuple[Hashable, NDFrameT]]:
         (7,)
            a  b  c
         2  7  8  9
+
+        For Resampler:
+
+        >>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
+        ...                 ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
+        >>> ser
+        2023-01-01    1
+        2023-01-15    2
+        2023-02-01    3
+        2023-02-15    4
+        dtype: int64
+        >>> for x, y in ser.resample('M'):
+        ...     print(f'{x}\\n{y}\\n')
+        2023-01-31 00:00:00
+        2023-01-01    1
+        2023-01-15    2
+        dtype: int64
+        2023-02-28 00:00:00
+        2023-02-01    3
+        2023-02-15    4
+        dtype: int64
         """
         keys = self.keys
         level = self.level
diff --git a/pandas/core/resample.py b/pandas/core/resample.py
index 9566a2f113b36..9391696d1925a 100644
--- a/pandas/core/resample.py
+++ b/pandas/core/resample.py
@@ -503,6 +503,54 @@ def ffill(self, limit: int | None = None):
         --------
         Series.fillna: Fill NA/NaN values using the specified method.
         DataFrame.fillna: Fill NA/NaN values using the specified method.
+
+        Examples
+        --------
+        Here we only create a ``Series``.
+
+        >>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
+        ...                 ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
+        >>> ser
+        2023-01-01    1
+        2023-01-15    2
+        2023-02-01    3
+        2023-02-15    4
+        dtype: int64
+
+        Example for ``ffill`` with downsampling (we have fewer dates after resampling):
+
+        >>> ser.resample('M').ffill()
+        2023-01-31    2
+        2023-02-28    4
+        Freq: M, dtype: int64
+
+        Example for ``ffill`` with upsampling (fill the new dates with
+        the previous value):
+
+        >>> ser.resample('W').ffill()
+        2023-01-01    1
+        2023-01-08    1
+        2023-01-15    2
+        2023-01-22    2
+        2023-01-29    2
+        2023-02-05    3
+        2023-02-12    3
+        2023-02-19    4
+        Freq: W-SUN, dtype: int64
+
+        With upsampling and limiting (only fill the first new date with the
+        previous value):
+
+        >>> ser.resample('W').ffill(limit=1)
+        2023-01-01    1.0
+        2023-01-08    1.0
+        2023-01-15    2.0
+        2023-01-22    2.0
+        2023-01-29    NaN
+        2023-02-05    3.0
+        2023-02-12    NaN
+        2023-02-19    4.0
+        Freq: W-SUN, dtype: float64
         """
         return self._upsample("ffill", limit=limit)