DOC: fix bunch of doc build errors

doc/source/api.rst

@@ -819,7 +819,6 @@ Reshaping, sorting, transposing
 .. autosummary::
    :toctree: generated/
 
-   DataFrame.delevel
    DataFrame.pivot
    DataFrame.reorder_levels
    DataFrame.sort

doc/source/index.rst.template

@@ -135,7 +135,6 @@ See the package overview for more detail about what's in the library.
     timedeltas
     categorical
     visualization
-    rplot
     io
     remote_data
     enhancingperf

doc/source/indexing.rst

@@ -106,9 +106,8 @@ of multi-axis indexing.
   label based access and not positional access is supported.
   Thus, in such cases, it's usually better to be explicit and use ``.iloc`` or ``.loc``.
 
-  See more at :ref:`Advanced Indexing <advanced>`, :ref:`Advanced
-  Hierarchical <advanced.advanced_hierarchical>` and :ref:`Fallback Indexing
-  <advanced.fallback>`
+  See more at :ref:`Advanced Indexing <advanced>` and :ref:`Advanced
+  Hierarchical <advanced.advanced_hierarchical>`.
 
 Getting values from an object with multi-axes selection uses the following
 notation (using ``.loc`` as an example, but applies to ``.iloc`` and ``.ix`` as

doc/source/missing_data.rst

@@ -422,7 +422,7 @@ at the new values.
    ser = Series(np.sort(np.random.uniform(size=100)))
 
    # interpolate at new_index
-   new_index = ser.index + Index([49.25, 49.5, 49.75, 50.25, 50.5, 50.75])
+   new_index = ser.index | Index([49.25, 49.5, 49.75, 50.25, 50.5, 50.75])
    interp_s = ser.reindex(new_index).interpolate(method='pchip')
    interp_s[49:51]
 

doc/source/visualization.rst

@@ -689,7 +689,7 @@ See the `matplotlib pie documenation <http://matplotlib.org/api/pyplot_api.html#
 
     plt.close('all')
 
-.. _visualization.missing_data
+.. _visualization.missing_data:
 
 Plotting with Missing Data
 --------------------------

pandas/core/groupby.py

@@ -918,8 +918,8 @@ def cumcount(self, **kwargs):
         ascending : bool, default True
             If False, number in reverse, from length of group - 1 to 0.
 
-        Example
-        -------
+        Examples
+        --------
 
         >>> df = pd.DataFrame([['a'], ['a'], ['a'], ['b'], ['b'], ['a']],
         ...                   columns=['A'])
@@ -963,8 +963,8 @@ def head(self, n=5):
         Essentially equivalent to ``.apply(lambda x: x.head(n))``,
         except ignores as_index flag.
 
-        Example
-        -------
+        Examples
+        --------
 
         >>> df = DataFrame([[1, 2], [1, 4], [5, 6]],
                             columns=['A', 'B'])
@@ -990,8 +990,8 @@ def tail(self, n=5):
         Essentially equivalent to ``.apply(lambda x: x.tail(n))``,
         except ignores as_index flag.
 
-        Example
-        -------
+        Examples
+        --------
 
         >>> df = DataFrame([[1, 2], [1, 4], [5, 6]],
                             columns=['A', 'B'])
@@ -2452,8 +2452,8 @@ def filter(self, func, dropna=True, *args, **kwargs):
         dropna : Drop groups that do not pass the filter. True by default;
             if False, groups that evaluate False are filled with NaNs.
 
-        Example
-        -------
+        Examples
+        --------
         >>> grouped.filter(lambda x: x.mean() > 0)
 
         Returns
@@ -3083,7 +3083,7 @@ def filter(self, func, dropna=True, *args, **kwargs):
         Each subframe is endowed the attribute 'name' in case you need to know
         which group you are working on.
 
-        Example
+        Examples
         --------
         >>> grouped = df.groupby(lambda x: mapping[x])
         >>> grouped.filter(lambda x: x['A'].sum() + x['B'].sum() > 0)

pandas/io/json.py

@@ -573,7 +573,9 @@ def nested_to_record(ds, prefix="", level=0):
     -------
     d - dict or list of dicts, matching `ds`
 
-    Example:
+    Examples
+    --------
+
     IN[52]: nested_to_record(dict(flat1=1,dict1=dict(c=1,d=2),
                                   nested=dict(e=dict(c=1,d=2),d=2)))
     Out[52]:

pandas/tools/plotting.py

@@ -2328,10 +2328,10 @@ def _plot(data, x=None, y=None, subplots=False,
 series_ax = """ax : matplotlib axes object
         If not passed, uses gca()"""
 
-df_note = """- If `kind`='bar' or 'barh', you can specify relative alignments
+df_note = """- If `kind` = 'bar' or 'barh', you can specify relative alignments
       for bar plot layout by `position` keyword.
       From 0 (left/bottom-end) to 1 (right/top-end). Default is 0.5 (center)
-    - If `kind`='hexbin', you can control the size of the bins with the
+    - If `kind` = 'hexbin', you can control the size of the bins with the
       `gridsize` argument. By default, a histogram of the counts around each
       `(x, y)` point is computed. You can specify alternative aggregations
       by passing values to the `C` and `reduce_C_function` arguments.
@@ -2425,18 +2425,19 @@ def _plot(data, x=None, y=None, subplots=False,
     Notes
     -----
 
-    If `kind`='hexbin', you can control the size of the bins with the
+    If `kind` = 'hexbin', you can control the size of the bins with the
     `gridsize` argument. By default, a histogram of the counts around each
     `(x, y)` point is computed. You can specify alternative aggregations
     by passing values to the `C` and `reduce_C_function` arguments.
     `C` specifies the value at each `(x, y)` point and `reduce_C_function`
     is a function of one argument that reduces all the values in a bin to
     a single number (e.g. `mean`, `max`, `sum`, `std`).
 
-    If `kind`='scatter' and the argument `c` is the name of a dataframe column,
+    If `kind` = 'scatter' and the argument `c` is the name of a dataframe column,
     the values of that column are used to color each point.
     - See matplotlib documentation online for more on this subject
     %(klass_note)s
+
     """
 
 @Appender(_shared_docs['plot'] % _shared_doc_df_kwargs)