DOC Fix inconsistencies

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

@@ -2127,23 +2127,18 @@ 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

@@ -842,7 +842,7 @@ def itertuples(self, index=True, name="Pandas"):
             tuples.
 
         Yields
-        -------
+        ------
         collections.namedtuple
             Yields a namedtuple for each row in the DataFrame with the first
             field possibly being the index and following fields being the
@@ -7252,7 +7252,7 @@ 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

@@ -10291,11 +10291,11 @@ 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/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.
@@ -2629,7 +2629,7 @@ 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
@@ -4650,7 +4650,7 @@ 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'))
@@ -4848,7 +4848,7 @@ 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)

pandas/core/indexes/category.py

@@ -468,7 +468,7 @@ def get_loc(self, key, method=None):
         KeyError : if the key is not in the index
 
         Examples
-        ---------
+        --------
         >>> unique_index = pd.CategoricalIndex(list('abc'))
         >>> unique_index.get_loc('b')
         1

pandas/core/indexes/datetimes.py

@@ -213,7 +213,7 @@ class DatetimeIndex(DatetimeIndexOpsMixin, Int64Index, DatetimeDelegateMixin):
     been deprecated in favor of :func:`date_range`.
 
     See Also
-    ---------
+    --------
     Index : The base pandas Index type.
     TimedeltaIndex : Index of timedelta64 data.
     PeriodIndex : Index of Period data.

pandas/core/indexes/interval.py

@@ -729,7 +729,7 @@ def get_loc(self, key, method=None):
         loc : int if unique index, slice if monotonic index, else mask
 
         Examples
-        ---------
+        --------
         >>> i1, i2 = pd.Interval(0, 1), pd.Interval(1, 2)
         >>> index = pd.IntervalIndex([i1, i2])
         >>> index.get_loc(1)

pandas/core/indexes/multi.py

@@ -58,7 +58,7 @@ def _codes_to_ints(self, codes):
             Combinations of integers (one per row)
 
         Returns
-        ------
+        -------
         int_keys : scalar or 1-dimensional array, of dtype uint64
             Integer(s) representing one combination (each).
         """
@@ -98,7 +98,7 @@ def _codes_to_ints(self, codes):
             Combinations of integers (one per row)
 
         Returns
-        ------
+        -------
         int_keys : int, or 1-dimensional array of dtype object
             Integer(s) representing one combination (each).
         """
@@ -181,7 +181,7 @@ class MultiIndex(Index):
     Index : The base pandas Index type.
 
     Examples
-    ---------
+    --------
     A new ``MultiIndex`` is typically constructed using one of the helper
     methods :meth:`MultiIndex.from_arrays`, :meth:`MultiIndex.from_product`
     and :meth:`MultiIndex.from_tuples`. For example (using ``.from_arrays``):
@@ -1399,7 +1399,7 @@ def get_level_values(self, level):
             a single :class:`Index` (or subclass thereof).
 
         Examples
-        ---------
+        --------
 
         Create a MultiIndex:
 
@@ -2361,7 +2361,7 @@ def get_loc(self, key, method=None):
             boolean mask array, otherwise it is always a slice or int.
 
         Examples
-        ---------
+        --------
         >>> mi = pd.MultiIndex.from_arrays([list('abb'), list('def')])
 
         >>> mi.get_loc('b')
@@ -2371,7 +2371,7 @@ def get_loc(self, key, method=None):
         1
 
         Notes
-        ------
+        -----
         The key cannot be a slice, list of same-level labels, a boolean mask,
         or a sequence of such. If you want to use those, use
         :meth:`MultiIndex.get_locs` instead.
@@ -2479,7 +2479,7 @@ def get_loc_level(self, key, level=0, drop_level=True):
         (1, None)
 
         See Also
-        ---------
+        --------
         MultiIndex.get_loc  : Get location for a label or a tuple of labels.
         MultiIndex.get_locs : Get location for a label/slice/list/mask or a
                               sequence of such.
@@ -2691,7 +2691,7 @@ def get_locs(self, seq):
         locs : array of integers suitable for passing to iloc
 
         Examples
-        ---------
+        --------
         >>> mi = pd.MultiIndex.from_arrays([list('abb'), list('def')])
 
         >>> mi.get_locs('b')

pandas/core/indexes/period.py

@@ -154,7 +154,7 @@ class PeriodIndex(DatetimeIndexOpsMixin, Int64Index, PeriodDelegateMixin):
     >>> idx = pd.PeriodIndex(year=year_arr, quarter=q_arr)
 
     See Also
-    ---------
+    --------
     Index : The base pandas Index type.
     Period : Represents a period of time.
     DatetimeIndex : Index with datetime64 data.

pandas/core/indexes/timedeltas.py

@@ -131,7 +131,7 @@ class TimedeltaIndex(DatetimeIndexOpsMixin, dtl.TimelikeOps, Int64Index,
     to_frame
 
     See Also
-    ---------
+    --------
     Index : The base pandas Index type.
     Timedelta : Represents a duration between two dates or times.
     DatetimeIndex : Index of datetime64 data.

pandas/core/internals/blocks.py

@@ -2367,12 +2367,12 @@ def diff(self, n, axis=0):
         n : int, number of periods to diff
         axis : int, axis to diff upon. default 0
 
-        Return
-        ------
+        Returns
+        -------
         A list with a new TimeDeltaBlock.
 
-        Note
-        ----
+        Notes
+        -----
         The arguments here are mimicking shift so they are called correctly
         by apply.
         """

pandas/core/nanops.py

@@ -861,7 +861,7 @@ def nanargmax(values, axis=None, skipna=True, mask=None):
         nan-mask if known
 
     Returns
-    --------
+    -------
     result : int
         The index of max value in specified axis or -1 in the NA case
 
@@ -891,7 +891,7 @@ def nanargmin(values, axis=None, skipna=True, mask=None):
         nan-mask if known
 
     Returns
-    --------
+    -------
     result : int
         The index of min value in specified axis or -1 in the NA case
 
@@ -1099,7 +1099,7 @@ def nanprod(values, axis=None, skipna=True, min_count=0, mask=None):
     6.0
 
     Returns
-    --------
+    -------
     The product of all elements on a given axis. ( NaNs are treated as 1)
     """
     mask = _maybe_get_mask(values, skipna, mask)

pandas/core/ops.py

@@ -884,7 +884,7 @@ def _get_op_name(op, special):
     inequality elementwise.
 
 Notes
---------
+-----
 Mismatched indices will be unioned together.
 `NaN` values are considered different (i.e. `NaN` != `NaN`).
 

pandas/core/strings.py

@@ -1037,7 +1037,7 @@ def str_join(arr, sep):
         delimiter.
 
     Raises
-    -------
+    ------
     AttributeError
         If the supplied Series contains neither strings nor lists.
 

pandas/io/excel/_util.py

@@ -185,7 +185,7 @@ def _fill_mi_header(row, control_row):
         different indexes.
 
     Returns
-    ----------
+    -------
     Returns changed row and control_row
     """
     last = row[0]

pandas/io/formats/format.py

@@ -1611,7 +1611,7 @@ def get_level_lengths(levels, sentinel=''):
         Value which states that no new index starts on there.
 
     Returns
-    ----------
+    -------
     Returns list of maps. For each level returns map of indexes (key is index
     in row and value is length of index).
     """

pandas/io/json/table_schema.py

@@ -131,7 +131,7 @@ def convert_json_field_to_pandas_type(field):
     dtype
 
     Raises
-    -----
+    ------
     ValueError
         If the type of the provided field is unknown or currently unsupported
 

pandas/io/stata.py

@@ -597,7 +597,7 @@ class StataValueLabel:
     Parse a categorical column and prepare formatted output
 
     Parameters
-    -----------
+    ----------
     value : int8, int16, int32, float32 or float64
         The Stata missing value code
 
@@ -718,7 +718,7 @@ class StataMissingValue(StringMixin):
     An observation's missing value.
 
     Parameters
-    -----------
+    ----------
     value : int8, int16, int32, float32 or float64
         The Stata missing value code