@@ -15,24 +15,21 @@ Indexing
1515In pandas there are a few objects implemented which can serve as valid
1616containers for the axis labels:
1717
18- * `` Index ` `: the generic "ordered set" object, an ndarray of object dtype
18+ * :class: ` Index `: the generic "ordered set" object, an ndarray of object dtype
1919 assuming nothing about its contents. The labels must be hashable (and
2020 likely immutable) and unique. Populates a dict of label to location in
2121 Cython to do ``O(1) `` lookups.
22- * ``Int64Index ``: a version of ``Index `` highly optimized for 64-bit integer
23- data, such as time stamps
24- * ``Float64Index ``: a version of ``Index `` highly optimized for 64-bit float data
25- * ``MultiIndex ``: the standard hierarchical index object
26- * ``DatetimeIndex ``: An Index object with ``Timestamp `` boxed elements (impl are the int64 values)
27- * ``TimedeltaIndex ``: An Index object with ``Timedelta `` boxed elements (impl are the in64 values)
28- * ``PeriodIndex ``: An Index object with Period elements
22+ * :class: `MultiIndex `: the standard hierarchical index object
23+ * :class: `DatetimeIndex `: An Index object with :class: `Timestamp ` boxed elements (impl are the int64 values)
24+ * :class: `TimedeltaIndex `: An Index object with :class: `Timedelta ` boxed elements (impl are the in64 values)
25+ * :class: `PeriodIndex `: An Index object with Period elements
2926
3027There are functions that make the creation of a regular index easy:
3128
32- * `` date_range ` `: fixed frequency date range generated from a time rule or
29+ * :func: ` date_range `: fixed frequency date range generated from a time rule or
3330 DateOffset. An ndarray of Python datetime objects
34- * `` period_range ` `: fixed frequency date range generated from a time rule or
35- DateOffset. An ndarray of `` Period ` ` objects, representing timespans
31+ * :func: ` period_range `: fixed frequency date range generated from a time rule or
32+ DateOffset. An ndarray of :class: ` Period ` objects, representing timespans
3633
3734The motivation for having an ``Index `` class in the first place was to enable
3835different implementations of indexing. This means that it's possible for you,
@@ -43,28 +40,28 @@ From an internal implementation point of view, the relevant methods that an
4340``Index `` must define are one or more of the following (depending on how
4441incompatible the new object internals are with the ``Index `` functions):
4542
46- * `` get_loc ` `: returns an "indexer" (an integer, or in some cases a
43+ * :meth: ` ~Index. get_loc
4744 slice object) for a label
48- * `` slice_locs ` `: returns the "range" to slice between two labels
49- * `` get_indexer ` `: Computes the indexing vector for reindexing / data
45+ * :meth: ` ~Index. slice_locs
46+ * :meth: ` ~Index. get_indexer
5047 alignment purposes. See the source / docstrings for more on this
51- * `` get_indexer_non_unique ` `: Computes the indexing vector for reindexing / data
48+ * :meth: ` ~Index. get_indexer_non_unique
5249 alignment purposes when the index is non-unique. See the source / docstrings
5350 for more on this
54- * `` reindex ` `: Does any pre-conversion of the input index then calls
51+ * :meth: ` ~Index. reindex
5552 ``get_indexer ``
56- * `` union ``, `` intersection ` `: computes the union or intersection of two
53+ * :meth: ` ~Index. union, :meth: ` ~Index. intersection
5754 Index objects
58- * `` insert ` `: Inserts a new label into an Index, yielding a new object
59- * `` delete ` `: Delete a label, yielding a new object
60- * `` drop ` `: Deletes a set of labels
61- * `` take ` `: Analogous to ndarray.take
55+ * :meth: ` ~Index. insert
56+ * :meth: ` ~Index. delete
57+ * :meth: ` ~Index. drop
58+ * :meth: ` ~Index. take
6259
6360MultiIndex
6461~~~~~~~~~~
6562
66- Internally, the `` MultiIndex ` ` consists of a few things: the **levels **, the
67- integer **codes ** (until version 0.24 named * labels *) , and the level **names **:
63+ Internally, the :class: ` MultiIndex ` consists of a few things: the **levels **, the
64+ integer **codes **, and the level **names **:
6865
6966.. ipython :: python
7067
@@ -80,13 +77,13 @@ You can probably guess that the codes determine which unique element is
8077identified with that location at each layer of the index. It's important to
8178note that sortedness is determined **solely ** from the integer codes and does
8279not check (or care) whether the levels themselves are sorted. Fortunately, the
83- constructors `` from_tuples `` and `` from_arrays `` ensure that this is true, but
84- if you compute the levels and codes yourself, please be careful.
80+ constructors :meth: ` ~MultiIndex. from_tuples:meth: ` ~MultiIndex. from_arrays
81+ that this is true, but if you compute the levels and codes yourself, please be careful.
8582
8683Values
8784~~~~~~
8885
89- pandas extends NumPy's type system with custom types, like `` Categorical ` ` or
86+ pandas extends NumPy's type system with custom types, like :class: ` Categorical ` or
9087datetimes with a timezone, so we have multiple notions of "values". For 1-D
9188containers (``Index `` classes and ``Series ``) we have the following convention:
9289
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