DOC: Fixing EX01 - added examples

ci/code_checks.sh

@@ -81,10 +81,8 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
     MSG='Partially validate docstrings (EX01)' ;  echo $MSG
     $BASE_DIR/scripts/validate_docstrings.py --format=actions --errors=EX01 --ignore_functions \
         pandas.Series.backfill \
-        pandas.Series.bfill \
         pandas.Series.ffill \
         pandas.Series.pad \
-        pandas.Series.argsort \
         pandas.Series.reorder_levels \
         pandas.Series.ravel \
         pandas.Series.first_valid_index \
@@ -424,7 +422,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         pandas.core.groupby.SeriesGroupBy.get_group \
         pandas.core.groupby.DataFrameGroupBy.all \
         pandas.core.groupby.DataFrameGroupBy.any \
-        pandas.core.groupby.DataFrameGroupBy.bfill \
         pandas.core.groupby.DataFrameGroupBy.count \
         pandas.core.groupby.DataFrameGroupBy.cummax \
         pandas.core.groupby.DataFrameGroupBy.cummin \
@@ -447,7 +444,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         pandas.core.groupby.DataFrameGroupBy.var \
         pandas.core.groupby.SeriesGroupBy.all \
         pandas.core.groupby.SeriesGroupBy.any \
-        pandas.core.groupby.SeriesGroupBy.bfill \
         pandas.core.groupby.SeriesGroupBy.count \
         pandas.core.groupby.SeriesGroupBy.cummax \
         pandas.core.groupby.SeriesGroupBy.cummin \
@@ -517,10 +513,7 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         pandas.api.extensions.ExtensionArray.shape \
         pandas.api.extensions.ExtensionArray.tolist \
         pandas.DataFrame.columns \
-        pandas.DataFrame.iterrows \
-        pandas.DataFrame.pipe \
         pandas.DataFrame.backfill \
-        pandas.DataFrame.bfill \
         pandas.DataFrame.ffill \
         pandas.DataFrame.pad \
         pandas.DataFrame.swapaxes \

pandas/core/frame.py

@@ -1371,18 +1371,7 @@ def iterrows(self) -> Iterable[tuple[Hashable, Series]]:
         -----
         1. Because ``iterrows`` returns a Series for each row,
            it does **not** preserve dtypes across the rows (dtypes are
-           preserved across columns for DataFrames). For example,
-
-           >>> df = pd.DataFrame([[1, 1.5]], columns=['int', 'float'])
-           >>> row = next(df.iterrows())[1]
-           >>> row
-           int      1.0
-           float    1.5
-           Name: 0, dtype: float64
-           >>> print(row['int'].dtype)
-           float64
-           >>> print(df['int'].dtype)
-           int64
+           preserved across columns for DataFrames).
 
            To preserve dtypes while iterating over the rows, it is better
            to use :meth:`itertuples` which returns namedtuples of the values
@@ -1392,6 +1381,20 @@ def iterrows(self) -> Iterable[tuple[Hashable, Series]]:
            This is not guaranteed to work in all cases. Depending on the
            data types, the iterator returns a copy and not a view, and writing
            to it will have no effect.
+
+        Examples
+        --------
+
+        >>> df = pd.DataFrame([[1, 1.5]], columns=['int', 'float'])
+        >>> row = next(df.iterrows())[1]
+        >>> row
+        int      1.0
+        float    1.5
+        Name: 0, dtype: float64
+        >>> print(row['int'].dtype)
+        float64
+        >>> print(df['int'].dtype)
+        int64
         """
         columns = self.columns
         klass = self._constructor_sliced

pandas/core/generic.py

@@ -7262,6 +7262,52 @@ def bfill(
         -------
         {klass} or None
             Object with missing values filled or None if ``inplace=True``.
+
+        Examples
+        --------
+        For Series:
+
+        >>> s = pd.Series([1, None, None, 2])
+        >>> s.bfill()
+        0    1.0
+        1    2.0
+        2    2.0
+        3    2.0
+        dtype: float64
+        >>> s.bfill(downcast='infer')
+        0    1
+        1    2
+        2    2
+        3    2
+        dtype: int64
+        >>> s.bfill(limit=1)
+        0    1.0
+        1    NaN
+        2    2.0
+        3    2.0
+        dtype: float64
+
+        With DataFrame:
+
+        >>> df = pd.DataFrame({{'A': [1, None, None, 4], 'B': [None, 5, None, 7]}})
+        >>> df
+              A     B
+        0   1.0	  NaN
+        1   NaN	  5.0
+        2   NaN   NaN
+        3   4.0   7.0
+        >>> df.bfill()
+              A     B
+        0   1.0   5.0
+        1   4.0   5.0
+        2   4.0   7.0
+        3   4.0   7.0
+        >>> df.bfill(downcast='infer', limit=1)
+              A	   B
+        0   1.0    5
+        1   NaN    5
+        2   4.0    7
+        3   4.0    7
         """
         return self.fillna(
             method="bfill", axis=axis, inplace=inplace, limit=limit, downcast=downcast

pandas/core/groupby/groupby.py

@@ -3019,6 +3019,61 @@ def bfill(self, limit: int | None = None):
         DataFrame.bfill:  Backward fill the missing values in the dataset.
         Series.fillna: Fill NaN values of a Series.
         DataFrame.fillna: Fill NaN values of a DataFrame.
+
+        Examples
+        --------
+
+        With Series:
+
+        >>> index = ['Falcon', 'Falcon', 'Parrot', 'Parrot', 'Parrot']
+        >>> s = pd.Series([None, 1, None, None, 3], index=index)
+        >>> s
+        Falcon    NaN
+        Falcon    1.0
+        Parrot    NaN
+        Parrot    NaN
+        Parrot    3.0
+        dtype: float64
+        >>> s.groupby(level=0).bfill()
+        Falcon    1.0
+        Falcon    1.0
+        Parrot    3.0
+        Parrot    3.0
+        Parrot    3.0
+        dtype: float64
+        >>> s.groupby(level=0).bfill(limit=1)
+        Falcon    1.0
+        Falcon    1.0
+        Parrot    NaN
+        Parrot    3.0
+        Parrot    3.0
+        dtype: float64
+
+        With DataFrame:
+
+        >>> df = pd.DataFrame({'A': [1, None, None, None, 4],
+        ...                    'B': [None, None, 5, None, 7]}, index=index)
+        >>> df
+                  A	    B
+        Falcon	1.0	  NaN
+        Falcon	NaN	  NaN
+        Parrot	NaN	  5.0
+        Parrot	NaN	  NaN
+        Parrot	4.0	  7.0
+        >>> df.groupby(level=0).bfill()
+                  A	    B
+        Falcon	1.0	  NaN
+        Falcon	NaN	  NaN
+        Parrot	4.0	  5.0
+        Parrot	4.0	  7.0
+        Parrot	4.0	  7.0
+        >>> df.groupby(level=0).bfill(limit=1)
+                  A	    B
+        Falcon	1.0	  NaN
+        Falcon	NaN	  NaN
+        Parrot	NaN	  5.0
+        Parrot	4.0	  7.0
+        Parrot	4.0	  7.0
         """
         return self._fill("bfill", limit=limit)
 

pandas/core/series.py

@@ -3811,6 +3811,15 @@ def argsort(
         See Also
         --------
         numpy.ndarray.argsort : Returns the indices that would sort this array.
+
+        Examples
+        --------
+        >>> s = pd.Series([3, 2, 1])
+        >>> s.argsort()
+        0    2
+        1    1
+        2    0
+        dtype: int64
         """
         values = self._values
         mask = isna(values)