clean-ups

Author: topper-123

doc/source/user_guide/advanced.rst

@@ -7,7 +7,7 @@ MultiIndex / advanced indexing
 ******************************
 
 This section covers :ref:`indexing with a MultiIndex <advanced.hierarchical>`
-and :ref:`other advanced indexing features <indexing.index_types>`.
+and :ref:`other advanced indexing features <advanced.index_types>`.
 
 See the :ref:`Indexing and Selecting Data <indexing>` for general indexing documentation.
 
@@ -738,7 +738,7 @@ faster than fancy indexing.
    %timeit ser.iloc[indexer]
    %timeit ser.take(indexer)
 
-.. _indexing.index_types:
+.. _advanced.index_types:
 
 Index types
 -----------
@@ -749,7 +749,7 @@ and documentation about ``TimedeltaIndex`` is found :ref:`here <timedeltas.index
 
 In the following sub-sections we will highlight some other index types.
 
-.. _indexing.categoricalindex:
+.. _advanced.categoricalindex:
 
 CategoricalIndex
 ~~~~~~~~~~~~~~~~
@@ -846,7 +846,7 @@ values **not** in the categories, similarly to how you can reindex **any** panda
       In [1]: pd.concat([df4, df5])
       TypeError: categories must match existing categories when appending
 
-.. _indexing.rangeindex:
+.. _advanced.rangeindex:
 
 Int64Index and RangeIndex
 ~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -855,7 +855,7 @@ Int64Index and RangeIndex
 
     In pandas 2.0, :class:`NumericIndex` will become the default index type for numeric types
     instead of ``Int64Index``, ``Float64Index`` and ``UInt64Index`` and those index types
-    will be removed. See :ref:`here <indexing.numericindex>` for more.
+    will be removed. See :ref:`here <advanced.numericindex>` for more.
     ``RangeIndex`` however, will not be removed, as it represents an optimized version of an integer index.
 
 :class:`Int64Index` is a fundamental basic index in pandas. This is an immutable array
@@ -864,7 +864,7 @@ implementing an ordered, sliceable set.
 :class:`RangeIndex` is a sub-class of ``Int64Index``  that provides the default index for all ``NDFrame`` objects.
 ``RangeIndex`` is an optimized version of ``Int64Index`` that can represent a monotonic ordered set. These are analogous to Python `range types <https://docs.python.org/3/library/stdtypes.html#typesseq-range>`__.
 
-.. _indexing.float64index:
+.. _advanced.float64index:
 
 Float64Index
 ~~~~~~~~~~~~
@@ -873,7 +873,7 @@ Float64Index
 
     In pandas 2.0, :class:`NumericIndex` will become the default index type for numeric types
     instead of ``Int64Index``, ``Float64Index`` and ``UInt64Index`` and those index types
-    will be removed. See :ref:`here <indexing.numericindex>` for more.
+    will be removed. See :ref:`here <advanced.numericindex>` for more.
     ``RangeIndex`` however, will not be removed, as it represents an optimized version of an integer index.
 
 By default a :class:`Float64Index` will be automatically created when passing floating, or mixed-integer-floating values in index creation.
@@ -971,7 +971,7 @@ If you need integer based selection, you should use ``iloc``:
    dfir.iloc[0:5]
 
 
-.. _indexing.numericindex:
+.. _advanced.numericindex:
 
 NumericIndex
 ~~~~~~~~~~~~
@@ -982,16 +982,16 @@ NumericIndex
 
     In pandas 2.0, :class:`NumericIndex` will become the default index type for numeric types
     instead of ``Int64Index``, ``Float64Index`` and ``UInt64Index`` and those index types
-    will be removed. See :ref:`here <indexing.numericindex>` for more.
+    will be removed.
     ``RangeIndex`` however, will not be removed, as it represents an optimized version of an integer index.
 
 :class:`NumericIndex` is an index type that can hold data of any numpy int/uint/float dtype. For example:
 
 .. ipython:: python
 
-   index = pd.NumericIndex([1, 2, 4, 5], dtype="int8")
-   index
-   ser = pd.Series(range(4), index=index)
+   idx = pd.NumericIndex([1, 2, 4, 5], dtype="int8")
+   idx
+   ser = pd.Series(range(4), index=idx)
    ser
 
 ``NumericIndex`` works the same way as the existing ``Int64Index``, ``Float64Index`` and

doc/source/user_guide/categorical.rst

@@ -1141,7 +1141,7 @@ Categorical index
 ``CategoricalIndex`` is a type of index that is useful for supporting
 indexing with duplicates. This is a container around a ``Categorical``
 and allows efficient indexing and storage of an index with a large number of duplicated elements.
-See the :ref:`advanced indexing docs <indexing.categoricalindex>` for a more detailed
+See the :ref:`advanced indexing docs <advanced.categoricalindex>` for a more detailed
 explanation.
 
 Setting the index will create a ``CategoricalIndex``:

doc/source/whatsnew/v0.13.0.rst

@@ -310,7 +310,7 @@ Float64Index API change
 
 - Added a new index type, ``Float64Index``. This will be automatically created when passing floating values in index creation.
   This enables a pure label-based slicing paradigm that makes ``[],ix,loc`` for scalar indexing and slicing work exactly the
-  same. See :ref:`the docs<indexing.float64index>`, (:issue:`263`)
+  same. See :ref:`the docs<advanced.float64index>`, (:issue:`263`)
 
   Construction is by default for floating type values.
 

doc/source/whatsnew/v0.16.1.rst

@@ -168,7 +168,7 @@ values NOT in the categories, similarly to how you can reindex ANY pandas index.
                                  ordered=False, name='B',
                                  dtype='category')
 
-See the :ref:`documentation <indexing.categoricalindex>` for more. (:issue:`7629`, :issue:`10038`, :issue:`10039`)
+See the :ref:`documentation <advanced.categoricalindex>` for more. (:issue:`7629`, :issue:`10038`, :issue:`10039`)
 
 .. _whatsnew_0161.enhancements.sample:
 

doc/source/whatsnew/v1.4.0.rst

@@ -20,9 +20,8 @@ Enhancements
 More flexible numeric dtypes for indexes
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-Until now, it has only been possible to create numeric indexes with int64/float64/uint64 dtypes,
-but not with lower bit sizes (int32, int8, uint32, uint8 etc). It is now possible to create
-an index of any numpy dtype using the new :class:`NumericIndex` (:issue:`41153`):
+Until now, it has only been possible to create numeric indexes with int64/float64/uint64 dtypes.
+It is now possible to create an index of any numpy int/uint/float dtype using the new :class:`NumericIndex` index type (:issue:`41153`):
 
 .. ipython:: python
 
@@ -32,7 +31,7 @@ an index of any numpy dtype using the new :class:`NumericIndex` (:issue:`41153`)
 
 In order to maintain backwards compatibility, calls to the base :class:`Index` will in
 pandas 1.x. return :class:`Int64Index`, :class:`UInt64Index` and :class:`Float64Index`.
-For example, notice that the code below returns an ``Int64Index`` with dtype ``int64``:
+For example, the code below returns an ``Int64Index`` with dtype ``int64``:
 
 .. code-block:: ipython
 
@@ -42,7 +41,8 @@ For example, notice that the code below returns an ``Int64Index`` with dtype ``i
 For the duration of Pandas 1.x, in order to maintain backwards compatibility, all
 operations that until now have returned :class:`Int64Index`, :class:`UInt64Index` and
 :class:`Float64Index` will continue to so. This means, that in order to use
-``NumericIndex``, you will have to call it explicitly. For example:
+``NumericIndex``, you will have to call ``NumericIndex`` explicitly. For example the below series
+will have an ``Int64Index``:
 
 .. code-block:: ipython
 
@@ -52,16 +52,16 @@ operations that until now have returned :class:`Int64Index`, :class:`UInt64Index
 
 Instead if you want to use a ``NumericIndex``, you should do:
 
-.. code-block:: ipython
+.. ipython:: python
 
-    In [2]: ser = pd.Series([1, 2, 3], index=pd.NumericIndex([1, 2, 3], dtype="int8"))
-    In [3]: ser.index
-     NumericIndex([1, 2, 3], dtype='int8')
+    idx = pd.NumericIndex([1, 2, 3], dtype="int8")
+    ser = pd.Series([1, 2, 3], index=idx)
+    ser.index
 
 In Pandas 2.0, :class:`NumericIndex` will become the default numeric index type and
 ``Int64Index``, ``UInt64Index`` and ``Float64Index`` will be removed.
 
-See :ref:`here <indexing.numericindex>` for more.
+See :ref:`here <advanced.numericindex>` for more.
 
 .. _whatsnew_140.enhancements.enhancement2: