DOC: add more links to the API in advanced.rst

doc/source/advanced.rst

@@ -15,7 +15,8 @@
 MultiIndex / Advanced Indexing
 ******************************
 
-This section covers indexing with a ``MultiIndex`` and :ref:`more advanced indexing features <indexing.index_types>`.
+This section covers :ref:`indexing with a MultiIndex <advanced.hierarchical>`
+and :ref:`other advanced indexing features <indexing.index_types>`.
 
 See the :ref:`Indexing and Selecting Data <indexing>` for general indexing documentation.
 
@@ -37,7 +38,7 @@ Hierarchical / Multi-level indexing is very exciting as it opens the door to som
 quite sophisticated data analysis and manipulation, especially for working with
 higher dimensional data. In essence, it enables you to store and manipulate
 data with an arbitrary number of dimensions in lower dimensional data
-structures like Series (1d) and DataFrame (2d).
+structures like ``Series`` (1d) and ``DataFrame`` (2d).
 
 In this section, we will show what exactly we mean by "hierarchical" indexing
 and how it integrates with all of the pandas indexing functionality
@@ -83,8 +84,8 @@ to use the :meth:`MultiIndex.from_product` method:
    iterables = [['bar', 'baz', 'foo', 'qux'], ['one', 'two']]
    pd.MultiIndex.from_product(iterables, names=['first', 'second'])
 
-As a convenience, you can pass a list of arrays directly into Series or
-DataFrame to construct a ``MultiIndex`` automatically:
+As a convenience, you can pass a list of arrays directly into ``Series`` or
+``DataFrame`` to construct a ``MultiIndex`` automatically:
 
 .. ipython:: python
 
@@ -213,8 +214,8 @@ tuples:
    s + s[:-2]
    s + s[::2]
 
-``reindex`` can be called with another ``MultiIndex``, or even a list or array
-of tuples:
+The :meth:`~DataFrame.reindex` method of ``Series``/``DataFrames`` can be
+called with another ``MultiIndex``, or even a list or array of tuples:
 
 .. ipython:: python
 
@@ -413,7 +414,7 @@ selecting data at a particular level of a ``MultiIndex`` easier.
    # using the slicers
    df.loc[(slice(None),'one'),:]
 
-You can also select on the columns with :meth:`~pandas.MultiIndex.xs`, by
+You can also select on the columns with ``xs``, by
 providing the axis argument.
 
 .. ipython:: python
@@ -426,7 +427,7 @@ providing the axis argument.
    # using the slicers
    df.loc[:,(slice(None),'one')]
 
-:meth:`~pandas.MultiIndex.xs` also allows selection with multiple keys.
+``xs`` also allows selection with multiple keys.
 
 .. ipython:: python
 
@@ -437,7 +438,7 @@ providing the axis argument.
    # using the slicers
    df.loc[:,('bar','one')]
 
-You can pass ``drop_level=False`` to :meth:`~pandas.MultiIndex.xs` to retain
+You can pass ``drop_level=False`` to ``xs`` to retain
 the level that was selected.
 
 .. ipython:: python
@@ -460,9 +461,9 @@ Compare the above with the result using ``drop_level=True`` (the default value).
 Advanced reindexing and alignment
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-The parameter ``level`` has been added to the ``reindex`` and ``align`` methods
-of pandas objects. This is useful to broadcast values across a level. For
-instance:
+Using the parameter ``level`` in the :meth:`~DataFrame.reindex` and
+:meth:`~DataFrame.align` methods of pandas objects is useful to broadcast
+values across a level. For instance:
 
 .. ipython:: python
 
@@ -480,10 +481,10 @@ instance:
    df2_aligned
 
 
-Swapping levels with :meth:`~pandas.MultiIndex.swaplevel`
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Swapping levels with ``swaplevel``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-The ``swaplevel`` function can switch the order of two levels:
+The :meth:`~MultiIndex.swaplevel` method can switch the order of two levels:
 
 .. ipython:: python
 
@@ -492,21 +493,21 @@ The ``swaplevel`` function can switch the order of two levels:
 
 .. _advanced.reorderlevels:
 
-Reordering levels with :meth:`~pandas.MultiIndex.reorder_levels`
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Reordering levels with ``reorder_levels``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-The ``reorder_levels`` function generalizes the ``swaplevel`` function,
-allowing you to permute the hierarchical index levels in one step:
+The :meth:`~MultiIndex.reorder_levels` method generalizes the ``swaplevel``
+method, allowing you to permute the hierarchical index levels in one step:
 
 .. ipython:: python
 
    df[:5].reorder_levels([1,0], axis=0)
 
-Sorting a :class:`~pandas.MultiIndex`
--------------------------------------
+Sorting a ``MultiIndex``
+------------------------
 
-For MultiIndex-ed objects to be indexed and sliced effectively, they need
-to be sorted. As with any index, you can use ``sort_index``.
+For :class:`MultiIndex`-ed objects to be indexed and sliced effectively,
+they need to be sorted. As with any index, you can use :meth:`~DataFrame.sort_index`.
 
 .. ipython:: python
 
@@ -658,9 +659,9 @@ faster than fancy indexing.
 Index Types
 -----------
 
-We have discussed ``MultiIndex`` in the previous sections pretty extensively. ``DatetimeIndex`` and ``PeriodIndex``
-are shown :ref:`here <timeseries.overview>`, and information about
-``TimedeltaIndex`` is found :ref:`here <timedeltas.timedeltas>`.
+We have discussed ``MultiIndex`` in the previous sections pretty extensively.
+Documentation about ``DatetimeIndex`` and ``PeriodIndex`` are shown :ref:`here <timeseries.overview>`,
+and documentation about ``TimedeltaIndex`` is found :ref:`here <timedeltas.timedeltaindex>`.
 
 In the following sub-sections we will highlight some other index types.
 
@@ -1004,8 +1005,8 @@ Non-monotonic indexes require exact matches
 
 If the index of a ``Series`` or ``DataFrame`` is monotonically increasing or decreasing, then the bounds
 of a label-based slice can be outside the range of the index, much like slice indexing a
-normal Python ``list``. Monotonicity of an index can be tested with the ``is_monotonic_increasing`` and
-``is_monotonic_decreasing`` attributes.
+normal Python ``list``. Monotonicity of an index can be tested with the :meth:`~Index.is_monotonic_increasing` and
+:meth:`~Index.is_monotonic_decreasing` attributes.
 
 .. ipython:: python
 
@@ -1039,9 +1040,9 @@ On the other hand, if the index is not monotonic, then both slice bounds must be
     In [11]: df.loc[2:3, :]
     KeyError: 'Cannot get right slice bound for non-unique label: 3'
 
-:meth:`Index.is_monotonic_increasing` and :meth:`Index.is_monotonic_decreasing` only check that
+``Index.is_monotonic_increasing`` and ``Index.is_monotonic_decreasing`` only check that
 an index is weakly monotonic. To check for strict monotonicity, you can combine one of those with
-:meth:`Index.is_unique`
+the :meth:`~Index.is_unique` attribute.
 
 .. ipython:: python
 
@@ -1057,7 +1058,7 @@ Compared with standard Python sequence slicing in which the slice endpoint is
 not inclusive, label-based slicing in pandas **is inclusive**. The primary
 reason for this is that it is often not possible to easily determine the
 "successor" or next element after a particular label in an index. For example,
-consider the following Series:
+consider the following ``Series``:
 
 .. ipython:: python