DOC: Replaced indexing stub doc with routines

1 files changed, 66 insertions, 56 deletions

diff --git a/doc/source/reference/arrays.indexing.rst b/doc/source/reference/arrays.indexing.rst
index cba028baa..100d22e02 100644
--- a/doc/source/reference/arrays.indexing.rst
+++ b/doc/source/reference/arrays.indexing.rst
@@ -1,60 +1,70 @@
-.. for doctests
-   >>> import numpy as np
-
+.. _routines.indexing:
 .. _arrays.indexing:
 
-********
-Indexing
-********
-
-.. seealso::
-
-   :ref:`basics.indexing`
-
-   :ref:`Indexing routines <routines.indexing>`
-
-Array indexing refers to any use of the square brackets ([]) to index
-array values. There are many options to indexing, which give NumPy
-indexing great power, but with power comes some complexity and the
-potential for confusion. This section is just an overview of the
-various options and issues related to indexing. Further details can be
-found in the relevant sections of :ref:`basics.indexing`.
-
-
-Basic indexing
-==============
-
-Basic indexing will be triggered through single element indexing or
-slicing and striding.
-
-Single element indexing works
-exactly like that for other standard Python sequences but has been expanded
-to support  multidimensional indexing
-for multidimensional NumPy arrays. For more details and examples, see
-:ref:`single-element-indexing`.
-
-It is possible to slice and stride arrays to extract arrays of the
-same number of dimensions, but of different sizes than the original.
-The slicing and striding work exactly the same way it does for lists
-and tuples except that they can be applied to multiple dimensions as
-well. See :ref:`basic-slicing-and-indexing` for more information and examples.
-
-
-Advanced indexing
+Indexing routines
 =================
 
-Arrays can be indexed with other arrays to
-select lists of values out of arrays into new arrays. There are
-two different ways of accomplishing this. One uses one or more arrays
-of index values. The other involves giving a boolean array of the proper
-shape to indicate the values to be selected. Index arrays are a very
-powerful tool that allows one to avoid looping over individual elements in
-arrays and thus greatly improve performance. See :ref:`advanced-indexing`
-for more information and examples.
-
-
-Other indexing options
-======================
-
-:ref:`arrays.indexing.fields`, :ref:`flat-iterator-indexing` are a couple
-of other indexing options.
\ No newline at end of file
+.. seealso:: :ref:`basics.indexing`
+
+.. currentmodule:: numpy
+
+Generating index arrays
+-----------------------
+.. autosummary::
+   :toctree: generated/
+
+   c_
+   r_
+   s_
+   nonzero
+   where
+   indices
+   ix_
+   ogrid
+   ravel_multi_index
+   unravel_index
+   diag_indices
+   diag_indices_from
+   mask_indices
+   tril_indices
+   tril_indices_from
+   triu_indices
+   triu_indices_from
+
+Indexing-like operations
+------------------------
+.. autosummary::
+   :toctree: generated/
+
+   take
+   take_along_axis
+   choose
+   compress
+   diag
+   diagonal
+   select
+   lib.stride_tricks.sliding_window_view
+   lib.stride_tricks.as_strided
+
+Inserting data into arrays
+--------------------------
+.. autosummary::
+   :toctree: generated/
+
+   place
+   put
+   put_along_axis
+   putmask
+   fill_diagonal
+
+Iterating over arrays
+---------------------
+.. autosummary::
+   :toctree: generated/
+
+   nditer
+   ndenumerate
+   ndindex
+   nested_iters
+   flatiter
+   lib.Arrayterator