DOC: Fix section headers in docstrings (#26301)

Author: Scowley4

pandas/core/arrays/base.py

@@ -1059,8 +1059,8 @@ def _create_method(cls, op, coerce_to_dtype=True):
             `op` cannot be stored in the ExtensionArray. The dtype of the
             ndarray uses NumPy's normal inference rules.
 
-        Example
-        -------
+        Examples
+        --------
         Given an ExtensionArray subclass called MyExtensionArray, use
 
         >>> __add__ = cls._create_method(operator.add)

pandas/core/arrays/categorical.py

@@ -2140,23 +2140,18 @@ def _reverse_indexer(self):
         -------
         dict of categories -> indexers
 
-        Example
-        -------
-        In [1]: c = pd.Categorical(list('aabca'))
-
-        In [2]: c
-        Out[2]:
+        Examples
+        --------
+        >>> c = pd.Categorical(list('aabca'))
+        >>> c
         [a, a, b, c, a]
         Categories (3, object): [a, b, c]
-
-        In [3]: c.categories
-        Out[3]: Index(['a', 'b', 'c'], dtype='object')
-
-        In [4]: c.codes
-        Out[4]: array([0, 0, 1, 2, 0], dtype=int8)
-
-        In [5]: c._reverse_indexer()
-        Out[5]: {'a': array([0, 1, 4]), 'b': array([2]), 'c': array([3])}
+        >>> c.categories
+        Index(['a', 'b', 'c'], dtype='object')
+        >>> c.codes
+        array([0, 0, 1, 2, 0], dtype=int8)
+        >>> c._reverse_indexer()
+        {'a': array([0, 1, 4]), 'b': array([2]), 'c': array([3])}
 
         """
         categories = self.categories

pandas/core/arrays/datetimes.py

@@ -586,7 +586,7 @@ def __iter__(self):
         Return an iterator over the boxed values
 
         Yields
-        -------
+        ------
         tstamp : Timestamp
         """
 

pandas/core/arrays/interval.py

@@ -93,7 +93,7 @@
 qcut : Bin values into equal-sized Intervals based on rank or sample quantiles.
 
 Notes
-------
+-----
 See the `user guide
 <http://pandas.pydata.org/pandas-docs/stable/advanced.html#intervalindex>`_
 for more.

pandas/core/arrays/sparse.py

@@ -1955,7 +1955,7 @@ def from_coo(cls, A, dense_index=False):
         s : SparseSeries
 
         Examples
-        ---------
+        --------
         >>> from scipy import sparse
         >>> A = sparse.coo_matrix(([3.0, 1.0, 2.0], ([1, 0, 0], [0, 2, 3])),
                                shape=(3, 4))

pandas/core/dtypes/common.py

@@ -763,7 +763,7 @@ def is_dtype_equal(source, target):
     target : The second dtype to compare
 
     Returns
-    ----------
+    -------
     boolean
         Whether or not the two dtypes are equal.
 
@@ -804,7 +804,7 @@ def is_dtype_union_equal(source, target):
     target : The second dtype to compare
 
     Returns
-    ----------
+    -------
     boolean
         Whether or not the two dtypes are equal.
 

pandas/core/frame.py

@@ -7250,7 +7250,7 @@ def corrwith(self, other, axis=0, drop=False, method='pearson'):
             Pairwise correlations.
 
         See Also
-        -------
+        --------
         DataFrame.corr
         """
         axis = self._get_axis_number(axis)

pandas/core/generic.py

@@ -10327,11 +10327,11 @@ def transform(self, func, *args, **kwargs):
         Return index for %(position)s non-NA/null value.
 
         Returns
-        --------
+        -------
         scalar : type of index
 
         Notes
-        --------
+        -----
         If all elements are non-NA/null, returns None.
         Also returns None for empty %(klass)s.
         """

pandas/core/groupby/groupby.py

@@ -799,13 +799,13 @@ def _try_cast(self, result, obj, numeric_only=False):
 
     def _transform_should_cast(self, func_nm):
         """
-        Parameters:
-        -----------
+        Parameters
+        ----------
         func_nm: str
             The name of the aggregation function being performed
 
-        Returns:
-        --------
+        Returns
+        -------
         bool
             Whether transform should attempt to cast the result of aggregation
         """

pandas/core/indexes/base.py

@@ -183,7 +183,7 @@ class Index(IndexOpsMixin, PandasObject):
         When True, attempt to create a MultiIndex if possible
 
     See Also
-    ---------
+    --------
     RangeIndex : Index implementing a monotonic integer range.
     CategoricalIndex : Index of :class:`Categorical` s.
     MultiIndex : A multi-level, or hierarchical, Index.
@@ -2649,7 +2649,7 @@ def _convert_can_do_setop(self, other):
         loc : int if unique index, slice if monotonic index, else mask
 
         Examples
-        ---------
+        --------
         >>> unique_index = pd.Index(list('abc'))
         >>> unique_index.get_loc('b')
         1
@@ -4699,7 +4699,7 @@ def slice_indexer(self, start=None, end=None, step=None, kind=None):
         This function assumes that the data is sorted, so use at your own peril
 
         Examples
-        ---------
+        --------
         This is a method on all index types. For example you can do:
 
         >>> idx = pd.Index(list('abcd'))
@@ -4902,7 +4902,7 @@ def slice_locs(self, start=None, end=None, step=None, kind=None):
         This method only works if the index is monotonic or unique.
 
         Examples
-        ---------
+        --------
         >>> idx = pd.Index(list('abcd'))
         >>> idx.slice_locs(start='b', end='c')
         (1, 3)