DOC: Exchanged the contents of Indexing basics and Indexing ref docs

2 files changed, 735 insertions, 734 deletions

diff --git a/doc/source/reference/arrays.indexing.rst b/doc/source/reference/arrays.indexing.rst
index 58b87dd7f..cba028baa 100644
--- a/doc/source/reference/arrays.indexing.rst
+++ b/doc/source/reference/arrays.indexing.rst
@@ -3,8 +3,9 @@
 
 .. _arrays.indexing:
 
+********
 Indexing
-========
+********
 
 .. seealso::
 
@@ -12,711 +13,48 @@ Indexing
 
    :ref:`Indexing routines <routines.indexing>`
 
-.. sectionauthor:: adapted from "Guide to NumPy" by Travis E. Oliphant
+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`.
 
-.. currentmodule:: numpy
 
-.. index:: indexing, slicing
+Basic indexing
+==============
 
-:class:`ndarrays <ndarray>` can be indexed using the standard Python
-``x[obj]`` syntax, where *x* is the array and *obj* the selection.
-There are four kinds of indexing available depending on *obj*:
-single element indexing, basic slicing, advanced indexing and field access.
-
-Most of the following examples show the use of indexing when
-referencing data in an array. The examples work just as well
-when assigning to an array. See :ref:`assigning-values-to-indexed-arrays` for
-specific examples and explanations on how assignments work.
-
-Note that in Python, ``x[(exp1, exp2, ..., expN)]`` is equivalent to
-``x[exp1, exp2, ..., expN]``; the latter is just syntactic sugar
-for the former.
-
-.. _single-element-indexing:
-
-Single element 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. It is 0-based,
-and accepts negative indices for indexing from the end of the array. ::
-
-    >>> x = np.arange(10)
-    >>> x[2]
-    2
-    >>> x[-2]
-    8
-
-It is not necessary to
-separate each dimension's index into its own set of square brackets. ::
-
-    >>> x.shape = (2,5) # now x is 2-dimensional
-    >>> x[1,3]
-    8
-    >>> x[1,-1]
-    9
-
-Note that if one indexes a multidimensional array with fewer indices
-than dimensions, one gets a subdimensional array. For example: ::
-
-    >>> x[0]
-    array([0, 1, 2, 3, 4])
-
-That is, each index specified selects the array corresponding to the
-rest of the dimensions selected. In the above example, choosing 0
-means that the remaining dimension of length 5 is being left unspecified,
-and that what is returned is an array of that dimensionality and size.
-It must be noted that the returned array is not a copy of the original,
-but points to the same values in memory as does the original array.
-In  this case, the 1-D array at the first position (0) is returned.
-So using a single index on the returned array, results in a single
-element being returned. That is: ::
-
-    >>> x[0][2]
-    2
-
-So note that ``x[0,2] = x[0][2]`` though the second case is more
-inefficient as a new temporary array is created after the first index
-that is subsequently indexed by 2.
-
-.. note::
-
-    NumPy uses C-order indexing. That means that the last
-    index usually represents the most rapidly changing memory location,
-    unlike Fortran or IDL, where the first index represents the most
-    rapidly changing location in memory. This difference represents a
-    great potential for confusion.
-
-.. _basic-slicing-and-indexing:
-
-Basic slicing and indexing
---------------------------
-
-Basic slicing extends Python's basic concept of slicing to N
-dimensions. Basic slicing occurs when *obj* is a :class:`slice` object
-(constructed by ``start:stop:step`` notation inside of brackets), an
-integer, or a tuple of slice objects and integers. :py:data:`Ellipsis`
-and :const:`newaxis` objects can be interspersed with these as
-well.
-
-.. deprecated:: 1.15.0
-
-  In order to remain backward compatible with a common usage in
-  Numeric, basic slicing is also initiated if the selection object is
-  any non-ndarray and non-tuple sequence (such as a :class:`list`) containing
-  :class:`slice` objects, the :py:data:`Ellipsis` object, or the :const:`newaxis`
-  object, but not for integer arrays or other embedded sequences.
-
-.. index::
-   triple: ndarray; special methods; getitem
-   triple: ndarray; special methods; setitem
-   single: ellipsis
-   single: newaxis
-
-The simplest case of indexing with *N* integers returns an :ref:`array
-scalar <arrays.scalars>` representing the corresponding item.  As in
-Python, all indices are zero-based: for the *i*-th index :math:`n_i`,
-the valid range is :math:`0 \le n_i < d_i` where :math:`d_i` is the
-*i*-th element of the shape of the array.  Negative indices are
-interpreted as counting from the end of the array (*i.e.*, if
-:math:`n_i < 0`, it means :math:`n_i + d_i`).
-
-
-All arrays generated by basic slicing are always :term:`views <view>`
-of the original array.
-
-.. note::
-
-    NumPy slicing creates a :term:`view` instead of a copy as in the case of
-    built-in Python sequences such as string, tuple and list.
-    Care must be taken when extracting
-    a small portion from a large array which becomes useless after the
-    extraction, because the small portion extracted contains a reference
-    to the large original array whose memory will not be released until
-    all arrays derived from it are garbage-collected. In such cases an
-    explicit ``copy()`` is recommended.
-
-The standard rules of sequence slicing apply to basic slicing on a
-per-dimension basis (including using a step index). Some useful
-concepts to remember include:
-
-- The basic slice syntax is ``i:j:k`` where *i* is the starting index,
-  *j* is the stopping index, and *k* is the step (:math:`k\neq0`).
-  This selects the *m* elements (in the corresponding dimension) with
-  index values *i*, *i + k*, ..., *i + (m - 1) k* where
-  :math:`m = q + (r\neq0)` and *q* and *r* are the quotient and remainder
-  obtained by dividing *j - i* by *k*: *j - i = q k + r*, so that
-  *i + (m - 1) k < j*. 
-  For example::
-
-     >>> x = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
-     >>> x[1:7:2]
-     array([1, 3, 5])
-
-- Negative *i* and *j* are interpreted as *n + i* and *n + j* where
-  *n* is the number of elements in the corresponding dimension.
-  Negative *k* makes stepping go towards smaller indices.
-  From the above example::
-
-      >>> x[-2:10]
-      array([8, 9])
-      >>> x[-3:3:-1]
-      array([7, 6, 5, 4])
-
-- Assume *n* is the number of elements in the dimension being
-  sliced. Then, if *i* is not given it defaults to 0 for *k > 0* and
-  *n - 1* for *k < 0* . If *j* is not given it defaults to *n* for *k > 0*
-  and *-n-1* for *k < 0* . If *k* is not given it defaults to 1. Note that
-  ``::`` is the same as ``:`` and means select all indices along this
-  axis.
-  From the above example::
-
-      >>> x[5:]
-      array([5, 6, 7, 8, 9])
-
-- If the number of objects in the selection tuple is less than
-  *N*, then ``:`` is assumed for any subsequent dimensions.
-  For example::
-
-      >>> x = np.array([[[1],[2],[3]], [[4],[5],[6]]])
-      >>> x.shape
-      (2, 3, 1)
-      >>> x[1:2]
-      array([[[4],
-              [5],
-              [6]]])
-
-- An integer, *i*, returns the same values as ``i:i+1``
-  **except** the dimensionality of the returned object is reduced by
-  1. In particular, a selection tuple with the *p*-th
-  element an integer (and all other entries ``:``) returns the
-  corresponding sub-array with dimension *N - 1*. If *N = 1*
-  then the returned object is an array scalar. These objects are
-  explained in :ref:`arrays.scalars`.
+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`.
 
-- If the selection tuple has all entries ``:`` except the
-  *p*-th entry which is a slice object ``i:j:k``,
-  then the returned array has dimension *N* formed by
-  concatenating the sub-arrays returned by integer indexing of
-  elements *i*, *i+k*, ..., *i + (m - 1) k < j*,
+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.
 
-- Basic slicing with more than one non-``:`` entry in the slicing
-  tuple, acts like repeated application of slicing using a single
-  non-``:`` entry, where the non-``:`` entries are successively taken
-  (with all other non-``:`` entries replaced by ``:``). Thus,
-  ``x[ind1,...,ind2,:]`` acts like ``x[ind1][...,ind2,:]`` under basic
-  slicing.
-
-  .. warning:: The above is **not** true for advanced indexing.
-
-- You may use slicing to set values in the array, but (unlike lists) you
-  can never grow the array. The size of the value to be set in
-  ``x[obj] = value`` must be (broadcastable) to the same shape as
-  ``x[obj]``.
-
-- A slicing tuple can always be constructed as *obj*
-  and used in the ``x[obj]`` notation. Slice objects can be used in
-  the construction in place of the ``[start:stop:step]``
-  notation. For example, ``x[1:10:5,::-1]`` can also be implemented
-  as ``obj = (slice(1,10,5), slice(None,None,-1)); x[obj]`` . This
-  can be useful for constructing generic code that works on arrays
-  of arbitrary dimensions. See :ref:`dealing-with-variable-indices`
-  for more information.
-
-.. index::
-   pair: ndarray; view
-
-Structural indexing tools
-^^^^^^^^^^^^^^^^^^^^^^^^^
-There are some tools to facilitate the easy matching of array shapes with
-expressions and in assignments.
-
-:py:data:`Ellipsis` expands to the number of ``:`` objects needed for the
-selection tuple to index all dimensions. In most cases, this means that the
-length of the expanded selection tuple is ``x.ndim``. There may only be a
-single ellipsis present.
-From the above example::
-
-    >>> x[...,0]
-    array([[1, 2, 3],
-          [4, 5, 6]])
-
-This is equivalent to::
-
-    >>> x[:,:,0]
-    array([[1, 2, 3],
-          [4, 5, 6]])
-
-Each :const:`newaxis` object in the selection tuple serves to expand
-the dimensions of the resulting selection by one unit-length
-dimension.  The added dimension is the position of the :const:`newaxis`
-object in the selection tuple. :const:`newaxis` is an alias for
-'None', and 'None' can be used in place of this with the same result.
-From the above example::
-
-    >>> x[:,np.newaxis,:,:].shape
-    (2, 1, 3, 1)
-    >>> x[:,None,:,:].shape
-    (2, 1, 3, 1)
-
-This can be handy to combine two
-arrays in a way that otherwise would require explicitly reshaping
-operations. For example::
-
-    >>> x = np.arange(5)
-    >>> x[:,np.newaxis] + x[np.newaxis,:]
-    array([[0, 1, 2, 3, 4],
-          [1, 2, 3, 4, 5],
-          [2, 3, 4, 5, 6],
-          [3, 4, 5, 6, 7],
-          [4, 5, 6, 7, 8]])
-
-
-.. _advanced-indexing:
 
 Advanced indexing
------------------
-
-.. seealso:: :ref:`basics.broadcasting`
-
-Advanced indexing is triggered when the selection object, *obj*, is a
-non-tuple sequence object, an :class:`ndarray` (of data type integer or bool),
-or a tuple with at least one sequence object or ndarray (of data type
-integer or bool). There are two types of advanced indexing: integer
-and Boolean.
-
-Advanced indexing always returns a *copy* of the data (contrast with
-basic slicing that returns a :term:`view`).
-
-.. warning::
-
-   The definition of advanced indexing means that ``x[(1,2,3),]`` is
-   fundamentally different than ``x[(1,2,3)]``. The latter is
-   equivalent to ``x[1,2,3]`` which will trigger basic selection while
-   the former will trigger advanced indexing. Be sure to understand
-   why this occurs.
-
-   Also recognize that ``x[[1,2,3]]`` will trigger advanced indexing,
-   whereas due to the deprecated Numeric compatibility mentioned above,
-   ``x[[1,2,slice(None)]]`` will trigger basic slicing.
-
-Integer array indexing
-^^^^^^^^^^^^^^^^^^^^^^
-
-Integer array indexing allows selection of arbitrary items in the array
-based on their *N*-dimensional index. Each integer array represents a number
-of indices into that dimension.
-
-Negative values are permitted in the index arrays and work as they do with
-single indices or slices. If the index values are out of bounds then an
-``IndexError`` is thrown::
-
-    >>> x = np.array([[1, 2], [3, 4], [5, 6]])
-    >>> x[np.array([1, -1])]
-    array([[3, 4],
-          [5, 6]])
-    >>> x[np.array([3, 4])]
-    IndexError: index 3 is out of bounds for axis 0 with size 3
-
-
-When the index consists of as many integer arrays as dimensions of the array
-being indexed, the indexing is straightforward, but different from slicing.
-
-Advanced indices always are :ref:`broadcast<ufuncs.broadcasting>` and
-iterated as *one*::
-
-     result[i_1, ..., i_M] == x[ind_1[i_1, ..., i_M], ind_2[i_1, ..., i_M],
-                                ..., ind_N[i_1, ..., i_M]]
-
-Note that the resulting shape is identical to the (broadcast) indexing array
-shapes ``ind_1, ..., ind_N``. If the indices cannot be broadcast to the
-same shape, an exception ``IndexError: shape mismatch: indexing arrays could
-not be broadcast together with shapes...`` is raised.
-
-.. rubric:: Example
-
-From each row, a specific element should be selected. The row index is just
-``[0, 1, 2]`` and the column index specifies the element to choose for the
-corresponding row, here ``[0, 1, 0]``. Using both together the task
-can be solved using advanced indexing::
-
-    >>> x = np.array([[1, 2], [3, 4], [5, 6]])
-    >>> x[[0, 1, 2], [0, 1, 0]]
-    array([1, 4, 5])
-
-To achieve a behaviour similar to the basic slicing above, broadcasting can be
-used. The function :func:`ix_` can help with this broadcasting. This is best
-understood with an example.
-
-.. rubric:: Example
-
-From a 4x3 array the corner elements should be selected using advanced
-indexing. Thus all elements for which the column is one of ``[0, 2]`` and
-the row is one of ``[0, 3]`` need to be selected. To use advanced indexing
-one needs to select all elements *explicitly*. Using the method explained
-previously one could write::
-
-    >>> x = np.array([[ 0,  1,  2],
-    ...               [ 3,  4,  5],
-    ...               [ 6,  7,  8],
-    ...               [ 9, 10, 11]])
-    >>> rows = np.array([[0, 0],
-    ...                  [3, 3]], dtype=np.intp)
-    >>> columns = np.array([[0, 2],
-    ...                     [0, 2]], dtype=np.intp)
-    >>> x[rows, columns]
-    array([[ 0,  2],
-           [ 9, 11]])
-
-However, since the indexing arrays above just repeat themselves,
-broadcasting can be used (compare operations such as
-``rows[:, np.newaxis] + columns``) to simplify this::
-
-    >>> rows = np.array([0, 3], dtype=np.intp)
-    >>> columns = np.array([0, 2], dtype=np.intp)
-    >>> rows[:, np.newaxis]
-    array([[0],
-           [3]])
-    >>> x[rows[:, np.newaxis], columns]
-    array([[ 0,  2],
-           [ 9, 11]])
-
-This broadcasting can also be achieved using the function :func:`ix_`:
-
-    >>> x[np.ix_(rows, columns)]
-    array([[ 0,  2],
-           [ 9, 11]])
-
-Note that without the ``np.ix_`` call, only the diagonal elements would
-be selected, as was used in the previous example. This difference is the
-most important thing to remember about indexing with multiple advanced
-indices.
-
-.. rubric:: Example
-
-The broadcasting mechanism permits index arrays to be combined with
-scalars for other indices. The effect is that the scalar value is used
-for all the corresponding values of the index arrays::
-
-    >>> x = np.arange(35).reshape(5,7)
-    >>> x[np.array([0,2,4]), 1]
-    array([ 1, 15, 29])
-
-.. rubric:: Example
-
-A real-life example of where advanced indexing may be useful is for a color
-lookup table where we want to map the values of an image into RGB triples for
-display. The lookup table could have a shape (nlookup, 3). Indexing
-such an array with an image with shape (ny, nx) with dtype=np.uint8
-(or any integer type so long as values are with the bounds of the
-lookup table) will result in an array of shape (ny, nx, 3) where a
-triple of RGB values is associated with each pixel location.
-
-
-Boolean array indexing
-^^^^^^^^^^^^^^^^^^^^^^
-
-This advanced indexing occurs when *obj* is an array object of Boolean
-type, such as may be returned from comparison operators. A single
-boolean index array is practically identical to ``x[obj.nonzero()]`` where,
-as described above, :meth:`obj.nonzero() <ndarray.nonzero>` returns a
-tuple (of length :attr:`obj.ndim <ndarray.ndim>`) of integer index
-arrays showing the :py:data:`True` elements of *obj*. However, it is
-faster when ``obj.shape == x.shape``.
-
-If ``obj.ndim == x.ndim``, ``x[obj]`` returns a 1-dimensional array
-filled with the elements of *x* corresponding to the :py:data:`True`
-values of *obj*.  The search order will be :term:`row-major`,
-C-style. If *obj* has :py:data:`True` values at entries that are outside
-of the bounds of *x*, then an index error will be raised. If *obj* is
-smaller than *x* it is identical to filling it with :py:data:`False`.
-
-A common use case for this is filtering for desired element values.
-For example, one may wish to select all entries from an array which
-are not NaN::
-
-    >>> x = np.array([[1., 2.], [np.nan, 3.], [np.nan, np.nan]])
-    >>> x[~np.isnan(x)]
-    array([1., 2., 3.])
-
-Or wish to add a constant to all negative elements::
-
-    >>> x = np.array([1., -1., -2., 3])
-    >>> x[x < 0] += 20
-    >>> x
-    array([  1.,  19.,  18.,   3.])
-
-In general if an index includes a Boolean array, the result will be
-identical to inserting ``obj.nonzero()`` into the same position
-and using the integer array indexing mechanism described above.
-``x[ind_1, boolean_array, ind_2]`` is equivalent to
-``x[(ind_1,) + boolean_array.nonzero() + (ind_2,)]``.
-
-If there is only one Boolean array and no integer indexing array present,
-this is straight forward. Care must only be taken to make sure that the
-boolean index has *exactly* as many dimensions as it is supposed to work
-with.
-
-.. rubric:: Example
-
-From an array, select all rows which sum up to less or equal two::
-
-    >>> x = np.array([[0, 1], [1, 1], [2, 2]])
-    >>> rowsum = x.sum(-1)
-    >>> x[rowsum <= 2, :]
-    array([[0, 1],
-           [1, 1]])
-
-
-Combining multiple Boolean indexing arrays or a Boolean with an integer
-indexing array can best be understood with the
-:meth:`obj.nonzero() <ndarray.nonzero>` analogy. The function :func:`ix_`
-also supports boolean arrays and will work without any surprises.
-
-.. rubric:: Example
-
-Use boolean indexing to select all rows adding up to an even
-number. At the same time columns 0 and 2 should be selected with an
-advanced integer index. Using the :func:`ix_` function this can be done
-with::
-
-    >>> x = np.array([[ 0,  1,  2],
-    ...               [ 3,  4,  5],
-    ...               [ 6,  7,  8],
-    ...               [ 9, 10, 11]])
-    >>> rows = (x.sum(-1) % 2) == 0
-    >>> rows
-    array([False,  True, False,  True])
-    >>> columns = [0, 2]
-    >>> x[np.ix_(rows, columns)]
-    array([[ 3,  5],
-           [ 9, 11]])
-
-Without the ``np.ix_`` call, only the diagonal elements would be
-selected.
-
-Or without ``np.ix_`` (compare the integer array examples)::
-
-    >>> rows = rows.nonzero()[0]
-    >>> x[rows[:, np.newaxis], columns]
-    array([[ 3,  5],
-           [ 9, 11]])
-
-.. rubric:: Example
-
-If x has more dimensions than b then the result will be multi-dimensional::
-
-    >>> x = np.arange(35).reshape(5,7)
-    >>> b = x>20
-    >>> b[:,5]
-    array([False, False, False,  True,  True])
-    >>> x[b[:,5]]
-    array([[21, 22, 23, 24, 25, 26, 27],
-          [28, 29, 30, 31, 32, 33, 34]])
-
-Here the 4th and 5th rows are selected from the indexed array and
-combined to make a 2-D array.
-
-.. rubric:: Example
-
-Using a 2-D boolean array of shape (2,3)
-with four True elements to select rows from a 3-D array of shape
-(2,3,5) results in a 2-D result of shape (4,5)::
-
-    >>> x = np.arange(30).reshape(2,3,5)
-    >>> x
-    array([[[ 0,  1,  2,  3,  4],
-            [ 5,  6,  7,  8,  9],
-            [10, 11, 12, 13, 14]],
-          [[15, 16, 17, 18, 19],
-            [20, 21, 22, 23, 24],
-            [25, 26, 27, 28, 29]]])
-    >>> b = np.array([[True, True, False], [False, True, True]])
-    >>> x[b]
-    array([[ 0,  1,  2,  3,  4],
-          [ 5,  6,  7,  8,  9],
-          [20, 21, 22, 23, 24],
-          [25, 26, 27, 28, 29]])
-
-
-.. _combining-advanced-and-basic-indexing:
-
-Combining advanced and basic indexing
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-When there is at least one slice (``:``), ellipsis (``...``) or :const:`newaxis`
-in the index (or the array has more dimensions than there are advanced indices),
-then the behaviour can be more complicated. It is like concatenating the
-indexing result for each advanced index element.
-
-In the simplest case, there is only a *single* advanced index. A single
-advanced index can for example replace a slice and the result array will be
-the same, however, it is a copy and may have a different memory layout.
-A slice is preferable when it is possible.
-For example::
-
-    >>> x = np.array([[ 0,  1,  2],
-    ...               [ 3,  4,  5],
-    ...               [ 6,  7,  8],
-    ...               [ 9, 10, 11]])
-    >>> x[1:2, 1:3]
-    array([[4, 5]])
-    >>> x[1:2, [1, 2]]
-    array([[4, 5]])
-
-The easiest way to understand the situation may be to think in
-terms of the resulting shape. There are two parts to the indexing operation,
-the subspace defined by the basic indexing (excluding integers) and the
-subspace from the advanced indexing part. Two cases of index combination
-need to be distinguished:
-
-* The advanced indices are separated by a slice, :py:data:`Ellipsis` or
-  :const:`newaxis`. For example ``x[arr1, :, arr2]``.
-* The advanced indices are all next to each other.
-  For example ``x[..., arr1, arr2, :]`` but *not* ``x[arr1, :, 1]``
-  since ``1`` is an advanced index in this regard.
-
-In the first case, the dimensions resulting from the advanced indexing
-operation come first in the result array, and the subspace dimensions after
-that.
-In the second case, the dimensions from the advanced indexing operations
-are inserted into the result array at the same spot as they were in the
-initial array (the latter logic is what makes simple advanced indexing
-behave just like slicing). 
-
-.. rubric:: Example
-
-Suppose ``x.shape`` is (10,20,30) and ``ind`` is a (2,3,4)-shaped
-indexing :class:`intp` array, then ``result = x[...,ind,:]`` has
-shape (10,2,3,4,30) because the (20,)-shaped subspace has been
-replaced with a (2,3,4)-shaped broadcasted indexing subspace. If
-we let *i, j, k* loop over the (2,3,4)-shaped subspace then
-``result[...,i,j,k,:] = x[...,ind[i,j,k],:]``. This example
-produces the same result as :meth:`x.take(ind, axis=-2) <ndarray.take>`.
-
-.. rubric:: Example
-
-Let ``x.shape`` be (10,20,30,40,50) and suppose ``ind_1``
-and ``ind_2`` can be broadcast to the shape (2,3,4). Then
-``x[:,ind_1,ind_2]`` has shape (10,2,3,4,40,50) because the
-(20,30)-shaped subspace from X has been replaced with the
-(2,3,4) subspace from the indices.  However,
-``x[:,ind_1,:,ind_2]`` has shape (2,3,4,10,30,50) because there
-is no unambiguous place to drop in the indexing subspace, thus
-it is tacked-on to the beginning. It is always possible to use
-:meth:`.transpose() <ndarray.transpose>` to move the subspace
-anywhere desired. Note that this example cannot be replicated
-using :func:`take`.
-
-.. rubric:: Example
-
-Slicing can be combined with broadcasted boolean indices::
-
-    >>> b = y > 20
-    >>> b
-    array([[False, False, False, False, False, False, False],
-          [False, False, False, False, False, False, False],
-          [False, False, False, False, False, False, False],
-          [ True,  True,  True,  True,  True,  True,  True],
-          [ True,  True,  True,  True,  True,  True,  True]])
-    >>> y[b[:,5],1:3]
-    array([[22, 23],
-          [29, 30]])
-
-
-.. _arrays.indexing.fields:
-
-Field access
--------------
-
-.. seealso:: :ref:`arrays.dtypes`, :ref:`arrays.scalars`,
-            :ref:`structured_arrays`
-
-If the :class:`ndarray` object is a structured array the :term:`fields <field>`
-of the array can be accessed by indexing the array with strings,
-dictionary-like.
-
-Indexing ``x['field-name']`` returns a new :term:`view` to the array,
-which is of the same shape as *x* (except when the field is a
-sub-array) but of data type ``x.dtype['field-name']`` and contains
-only the part of the data in the specified field. Also,
-:ref:`record array <arrays.classes.rec>` scalars can be "indexed" this way.
-
-Indexing into a structured array can also be done with a list of field names,
-e.g. ``x[['field-name1','field-name2']]``. As of NumPy 1.16, this returns a
-view containing only those fields. In older versions of NumPy, it returned a
-copy. See the user guide section on :ref:`structured_arrays` for more
-information on multifield indexing.
-
-If the accessed field is a sub-array, the dimensions of the sub-array
-are appended to the shape of the result.
-For example::
-
-   >>> x = np.zeros((2,2), dtype=[('a', np.int32), ('b', np.float64, (3,3))])
-   >>> x['a'].shape
-   (2, 2)
-   >>> x['a'].dtype
-   dtype('int32')
-   >>> x['b'].shape
-   (2, 2, 3, 3)
-   >>> x['b'].dtype
-   dtype('float64')
-
-.. _flat-iterator-indexing:
-
-Flat Iterator indexing
-----------------------
-
-:attr:`x.flat <ndarray.flat>` returns an iterator that will iterate
-over the entire array (in C-contiguous style with the last index
-varying the fastest). This iterator object can also be indexed using
-basic slicing or advanced indexing as long as the selection object is
-not a tuple. This should be clear from the fact that :attr:`x.flat
-<ndarray.flat>` is a 1-dimensional view. It can be used for integer
-indexing with 1-dimensional C-style-flat indices. The shape of any
-returned array is therefore the shape of the integer indexing object.
+=================
 
-.. index::
-   single: indexing
-   single: ndarray
+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.
 
-Detailed notes
---------------
 
-These are some detailed notes, which are not of importance for day to day
-indexing (in no particular order):
+Other indexing options
+======================
 
-* The native NumPy indexing type is ``intp`` and may differ from the
-  default integer array type. ``intp`` is the smallest data type
-  sufficient to safely index any array; for advanced indexing it may be
-  faster than other types.
-* For advanced assignments, there is in general no guarantee for the
-  iteration order. This means that if an element is set more than once,
-  it is not possible to predict the final result.
-* An empty (tuple) index is a full scalar index into a zero-dimensional array.
-  ``x[()]`` returns a *scalar* if ``x`` is zero-dimensional and a view
-  otherwise. On the other hand, ``x[...]`` always returns a view.
-* If a zero-dimensional array is present in the index *and* it is a full
-  integer index the result will be a *scalar* and not a zero-dimensional array.
-  (Advanced indexing is not triggered.)
-* When an ellipsis (``...``) is present but has no size (i.e. replaces zero
-  ``:``) the result will still always be an array. A view if no advanced index
-  is present, otherwise a copy.
-* The ``nonzero`` equivalence for Boolean arrays does not hold for zero
-  dimensional boolean arrays.
-* When the result of an advanced indexing operation has no elements but an
-  individual index is out of bounds, whether or not an ``IndexError`` is
-  raised is undefined (e.g. ``x[[], [123]]`` with ``123`` being out of bounds).
-* When a *casting* error occurs during assignment (for example updating a
-  numerical array using a sequence of strings), the array being assigned
-  to may end up in an unpredictable partially updated state.
-  However, if any other error (such as an out of bounds index) occurs, the
-  array will remain unchanged.
-* The memory layout of an advanced indexing result is optimized for each
-  indexing operation and no particular memory order can be assumed.
-* When using a subclass (especially one which manipulates its shape), the
-  default ``ndarray.__setitem__`` behaviour will call ``__getitem__`` for
-  *basic* indexing but not for *advanced* indexing. For such a subclass it may
-  be preferable to call ``ndarray.__setitem__`` with a *base class* ndarray
-  view on the data. This *must* be done if the subclasses ``__getitem__`` does
-  not return views.
\ No newline at end of file
+:ref:`arrays.indexing.fields`, :ref:`flat-iterator-indexing` are a couple
+of other indexing options.
\ No newline at end of file
diff --git a/doc/source/user/basics.indexing.rst b/doc/source/user/basics.indexing.rst
index 66d35abee..78183d945 100644
--- a/doc/source/user/basics.indexing.rst
+++ b/doc/source/user/basics.indexing.rst
@@ -6,61 +6,684 @@ Indexing basics
 
 .. seealso::
 
-   :ref:`Indexing <arrays.indexing>`
+   :ref:`arrays.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:`arrays.indexing`.
+.. sectionauthor:: adapted from "Guide to NumPy" by Travis E. Oliphant
 
+.. currentmodule:: numpy
 
-Basic indexing
-==============
+.. index:: indexing, slicing
 
-Basic indexing will be triggered through single element indexing or
-slicing and striding.
+:class:`ndarrays <ndarray>` can be indexed using the standard Python
+``x[obj]`` syntax, where *x* is the array and *obj* the selection.
+There are four kinds of indexing available depending on *obj*:
+single element indexing, basic slicing, advanced indexing and field access.
 
-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.
+Most of the following examples show the use of indexing when
+referencing data in an array. The examples work just as well
+when assigning to an array. See :ref:`assigning-values-to-indexed-arrays` for
+specific examples and explanations on how assignments work.
 
+Note that in Python, ``x[(exp1, exp2, ..., expN)]`` is equivalent to
+``x[exp1, exp2, ..., expN]``; the latter is just syntactic sugar
+for the former.
 
-Advanced indexing
-=================
+.. _single-element-indexing:
 
-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.
+Single element indexing
+-----------------------
 
+Single element indexing works
+exactly like that for other standard Python sequences. It is 0-based,
+and accepts negative indices for indexing from the end of the array. ::
+
+    >>> x = np.arange(10)
+    >>> x[2]
+    2
+    >>> x[-2]
+    8
+
+It is not necessary to
+separate each dimension's index into its own set of square brackets. ::
+
+    >>> x.shape = (2,5) # now x is 2-dimensional
+    >>> x[1,3]
+    8
+    >>> x[1,-1]
+    9
+
+Note that if one indexes a multidimensional array with fewer indices
+than dimensions, one gets a subdimensional array. For example: ::
+
+    >>> x[0]
+    array([0, 1, 2, 3, 4])
+
+That is, each index specified selects the array corresponding to the
+rest of the dimensions selected. In the above example, choosing 0
+means that the remaining dimension of length 5 is being left unspecified,
+and that what is returned is an array of that dimensionality and size.
+It must be noted that the returned array is not a copy of the original,
+but points to the same values in memory as does the original array.
+In  this case, the 1-D array at the first position (0) is returned.
+So using a single index on the returned array, results in a single
+element being returned. That is: ::
+
+    >>> x[0][2]
+    2
+
+So note that ``x[0,2] = x[0][2]`` though the second case is more
+inefficient as a new temporary array is created after the first index
+that is subsequently indexed by 2.
+
+.. note::
+
+    NumPy uses C-order indexing. That means that the last
+    index usually represents the most rapidly changing memory location,
+    unlike Fortran or IDL, where the first index represents the most
+    rapidly changing location in memory. This difference represents a
+    great potential for confusion.
+
+.. _basic-slicing-and-indexing:
+
+Basic slicing and indexing
+--------------------------
+
+Basic slicing extends Python's basic concept of slicing to N
+dimensions. Basic slicing occurs when *obj* is a :class:`slice` object
+(constructed by ``start:stop:step`` notation inside of brackets), an
+integer, or a tuple of slice objects and integers. :py:data:`Ellipsis`
+and :const:`newaxis` objects can be interspersed with these as
+well.
+
+.. deprecated:: 1.15.0
+
+  In order to remain backward compatible with a common usage in
+  Numeric, basic slicing is also initiated if the selection object is
+  any non-ndarray and non-tuple sequence (such as a :class:`list`) containing
+  :class:`slice` objects, the :py:data:`Ellipsis` object, or the :const:`newaxis`
+  object, but not for integer arrays or other embedded sequences.
+
+.. index::
+   triple: ndarray; special methods; getitem
+   triple: ndarray; special methods; setitem
+   single: ellipsis
+   single: newaxis
+
+The simplest case of indexing with *N* integers returns an :ref:`array
+scalar <arrays.scalars>` representing the corresponding item.  As in
+Python, all indices are zero-based: for the *i*-th index :math:`n_i`,
+the valid range is :math:`0 \le n_i < d_i` where :math:`d_i` is the
+*i*-th element of the shape of the array.  Negative indices are
+interpreted as counting from the end of the array (*i.e.*, if
+:math:`n_i < 0`, it means :math:`n_i + d_i`).
+
+
+All arrays generated by basic slicing are always :term:`views <view>`
+of the original array.
+
+.. note::
+
+    NumPy slicing creates a :term:`view` instead of a copy as in the case of
+    built-in Python sequences such as string, tuple and list.
+    Care must be taken when extracting
+    a small portion from a large array which becomes useless after the
+    extraction, because the small portion extracted contains a reference
+    to the large original array whose memory will not be released until
+    all arrays derived from it are garbage-collected. In such cases an
+    explicit ``copy()`` is recommended.
+
+The standard rules of sequence slicing apply to basic slicing on a
+per-dimension basis (including using a step index). Some useful
+concepts to remember include:
+
+- The basic slice syntax is ``i:j:k`` where *i* is the starting index,
+  *j* is the stopping index, and *k* is the step (:math:`k\neq0`).
+  This selects the *m* elements (in the corresponding dimension) with
+  index values *i*, *i + k*, ..., *i + (m - 1) k* where
+  :math:`m = q + (r\neq0)` and *q* and *r* are the quotient and remainder
+  obtained by dividing *j - i* by *k*: *j - i = q k + r*, so that
+  *i + (m - 1) k < j*. 
+  For example::
+
+     >>> x = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
+     >>> x[1:7:2]
+     array([1, 3, 5])
+
+- Negative *i* and *j* are interpreted as *n + i* and *n + j* where
+  *n* is the number of elements in the corresponding dimension.
+  Negative *k* makes stepping go towards smaller indices.
+  From the above example::
+
+      >>> x[-2:10]
+      array([8, 9])
+      >>> x[-3:3:-1]
+      array([7, 6, 5, 4])
+
+- Assume *n* is the number of elements in the dimension being
+  sliced. Then, if *i* is not given it defaults to 0 for *k > 0* and
+  *n - 1* for *k < 0* . If *j* is not given it defaults to *n* for *k > 0*
+  and *-n-1* for *k < 0* . If *k* is not given it defaults to 1. Note that
+  ``::`` is the same as ``:`` and means select all indices along this
+  axis.
+  From the above example::
+
+      >>> x[5:]
+      array([5, 6, 7, 8, 9])
+
+- If the number of objects in the selection tuple is less than
+  *N*, then ``:`` is assumed for any subsequent dimensions.
+  For example::
+
+      >>> x = np.array([[[1],[2],[3]], [[4],[5],[6]]])
+      >>> x.shape
+      (2, 3, 1)
+      >>> x[1:2]
+      array([[[4],
+              [5],
+              [6]]])
+
+- An integer, *i*, returns the same values as ``i:i+1``
+  **except** the dimensionality of the returned object is reduced by
+  1. In particular, a selection tuple with the *p*-th
+  element an integer (and all other entries ``:``) returns the
+  corresponding sub-array with dimension *N - 1*. If *N = 1*
+  then the returned object is an array scalar. These objects are
+  explained in :ref:`arrays.scalars`.
+
+- If the selection tuple has all entries ``:`` except the
+  *p*-th entry which is a slice object ``i:j:k``,
+  then the returned array has dimension *N* formed by
+  concatenating the sub-arrays returned by integer indexing of
+  elements *i*, *i+k*, ..., *i + (m - 1) k < j*,
+
+- Basic slicing with more than one non-``:`` entry in the slicing
+  tuple, acts like repeated application of slicing using a single
+  non-``:`` entry, where the non-``:`` entries are successively taken
+  (with all other non-``:`` entries replaced by ``:``). Thus,
+  ``x[ind1,...,ind2,:]`` acts like ``x[ind1][...,ind2,:]`` under basic
+  slicing.
+
+  .. warning:: The above is **not** true for advanced indexing.
+
+- You may use slicing to set values in the array, but (unlike lists) you
+  can never grow the array. The size of the value to be set in
+  ``x[obj] = value`` must be (broadcastable) to the same shape as
+  ``x[obj]``.
+
+- A slicing tuple can always be constructed as *obj*
+  and used in the ``x[obj]`` notation. Slice objects can be used in
+  the construction in place of the ``[start:stop:step]``
+  notation. For example, ``x[1:10:5,::-1]`` can also be implemented
+  as ``obj = (slice(1,10,5), slice(None,None,-1)); x[obj]`` . This
+  can be useful for constructing generic code that works on arrays
+  of arbitrary dimensions. See :ref:`dealing-with-variable-indices`
+  for more information.
+
+.. index::
+   pair: ndarray; view
+
+Structural indexing tools
+^^^^^^^^^^^^^^^^^^^^^^^^^
+There are some tools to facilitate the easy matching of array shapes with
+expressions and in assignments.
+
+:py:data:`Ellipsis` expands to the number of ``:`` objects needed for the
+selection tuple to index all dimensions. In most cases, this means that the
+length of the expanded selection tuple is ``x.ndim``. There may only be a
+single ellipsis present.
+From the above example::
+
+    >>> x[...,0]
+    array([[1, 2, 3],
+          [4, 5, 6]])
+
+This is equivalent to::
+
+    >>> x[:,:,0]
+    array([[1, 2, 3],
+          [4, 5, 6]])
+
+Each :const:`newaxis` object in the selection tuple serves to expand
+the dimensions of the resulting selection by one unit-length
+dimension.  The added dimension is the position of the :const:`newaxis`
+object in the selection tuple. :const:`newaxis` is an alias for
+'None', and 'None' can be used in place of this with the same result.
+From the above example::
+
+    >>> x[:,np.newaxis,:,:].shape
+    (2, 1, 3, 1)
+    >>> x[:,None,:,:].shape
+    (2, 1, 3, 1)
+
+This can be handy to combine two
+arrays in a way that otherwise would require explicitly reshaping
+operations. For example::
+
+    >>> x = np.arange(5)
+    >>> x[:,np.newaxis] + x[np.newaxis,:]
+    array([[0, 1, 2, 3, 4],
+          [1, 2, 3, 4, 5],
+          [2, 3, 4, 5, 6],
+          [3, 4, 5, 6, 7],
+          [4, 5, 6, 7, 8]])
+
+
+.. _advanced-indexing:
 
-Other indexing options
-======================
+Advanced indexing
+-----------------
+
+.. seealso:: :ref:`basics.broadcasting`
+
+Advanced indexing is triggered when the selection object, *obj*, is a
+non-tuple sequence object, an :class:`ndarray` (of data type integer or bool),
+or a tuple with at least one sequence object or ndarray (of data type
+integer or bool). There are two types of advanced indexing: integer
+and Boolean.
+
+Advanced indexing always returns a *copy* of the data (contrast with
+basic slicing that returns a :term:`view`).
+
+.. warning::
+
+   The definition of advanced indexing means that ``x[(1,2,3),]`` is
+   fundamentally different than ``x[(1,2,3)]``. The latter is
+   equivalent to ``x[1,2,3]`` which will trigger basic selection while
+   the former will trigger advanced indexing. Be sure to understand
+   why this occurs.
+
+   Also recognize that ``x[[1,2,3]]`` will trigger advanced indexing,
+   whereas due to the deprecated Numeric compatibility mentioned above,
+   ``x[[1,2,slice(None)]]`` will trigger basic slicing.
+
+Integer array indexing
+^^^^^^^^^^^^^^^^^^^^^^
+
+Integer array indexing allows selection of arbitrary items in the array
+based on their *N*-dimensional index. Each integer array represents a number
+of indices into that dimension.
+
+Negative values are permitted in the index arrays and work as they do with
+single indices or slices. If the index values are out of bounds then an
+``IndexError`` is thrown::
+
+    >>> x = np.array([[1, 2], [3, 4], [5, 6]])
+    >>> x[np.array([1, -1])]
+    array([[3, 4],
+          [5, 6]])
+    >>> x[np.array([3, 4])]
+    IndexError: index 3 is out of bounds for axis 0 with size 3
 
-:ref:`arrays.indexing.fields`, :ref:`flat-iterator-indexing` are a couple
-of other indexing options.
+
+When the index consists of as many integer arrays as dimensions of the array
+being indexed, the indexing is straightforward, but different from slicing.
+
+Advanced indices always are :ref:`broadcast<ufuncs.broadcasting>` and
+iterated as *one*::
+
+     result[i_1, ..., i_M] == x[ind_1[i_1, ..., i_M], ind_2[i_1, ..., i_M],
+                                ..., ind_N[i_1, ..., i_M]]
+
+Note that the resulting shape is identical to the (broadcast) indexing array
+shapes ``ind_1, ..., ind_N``. If the indices cannot be broadcast to the
+same shape, an exception ``IndexError: shape mismatch: indexing arrays could
+not be broadcast together with shapes...`` is raised.
+
+.. rubric:: Example
+
+From each row, a specific element should be selected. The row index is just
+``[0, 1, 2]`` and the column index specifies the element to choose for the
+corresponding row, here ``[0, 1, 0]``. Using both together the task
+can be solved using advanced indexing::
+
+    >>> x = np.array([[1, 2], [3, 4], [5, 6]])
+    >>> x[[0, 1, 2], [0, 1, 0]]
+    array([1, 4, 5])
+
+To achieve a behaviour similar to the basic slicing above, broadcasting can be
+used. The function :func:`ix_` can help with this broadcasting. This is best
+understood with an example.
+
+.. rubric:: Example
+
+From a 4x3 array the corner elements should be selected using advanced
+indexing. Thus all elements for which the column is one of ``[0, 2]`` and
+the row is one of ``[0, 3]`` need to be selected. To use advanced indexing
+one needs to select all elements *explicitly*. Using the method explained
+previously one could write::
+
+    >>> x = np.array([[ 0,  1,  2],
+    ...               [ 3,  4,  5],
+    ...               [ 6,  7,  8],
+    ...               [ 9, 10, 11]])
+    >>> rows = np.array([[0, 0],
+    ...                  [3, 3]], dtype=np.intp)
+    >>> columns = np.array([[0, 2],
+    ...                     [0, 2]], dtype=np.intp)
+    >>> x[rows, columns]
+    array([[ 0,  2],
+           [ 9, 11]])
+
+However, since the indexing arrays above just repeat themselves,
+broadcasting can be used (compare operations such as
+``rows[:, np.newaxis] + columns``) to simplify this::
+
+    >>> rows = np.array([0, 3], dtype=np.intp)
+    >>> columns = np.array([0, 2], dtype=np.intp)
+    >>> rows[:, np.newaxis]
+    array([[0],
+           [3]])
+    >>> x[rows[:, np.newaxis], columns]
+    array([[ 0,  2],
+           [ 9, 11]])
+
+This broadcasting can also be achieved using the function :func:`ix_`:
+
+    >>> x[np.ix_(rows, columns)]
+    array([[ 0,  2],
+           [ 9, 11]])
+
+Note that without the ``np.ix_`` call, only the diagonal elements would
+be selected, as was used in the previous example. This difference is the
+most important thing to remember about indexing with multiple advanced
+indices.
+
+.. rubric:: Example
+
+The broadcasting mechanism permits index arrays to be combined with
+scalars for other indices. The effect is that the scalar value is used
+for all the corresponding values of the index arrays::
+
+    >>> x = np.arange(35).reshape(5,7)
+    >>> x[np.array([0,2,4]), 1]
+    array([ 1, 15, 29])
+
+.. rubric:: Example
+
+A real-life example of where advanced indexing may be useful is for a color
+lookup table where we want to map the values of an image into RGB triples for
+display. The lookup table could have a shape (nlookup, 3). Indexing
+such an array with an image with shape (ny, nx) with dtype=np.uint8
+(or any integer type so long as values are with the bounds of the
+lookup table) will result in an array of shape (ny, nx, 3) where a
+triple of RGB values is associated with each pixel location.
+
+
+Boolean array indexing
+^^^^^^^^^^^^^^^^^^^^^^
+
+This advanced indexing occurs when *obj* is an array object of Boolean
+type, such as may be returned from comparison operators. A single
+boolean index array is practically identical to ``x[obj.nonzero()]`` where,
+as described above, :meth:`obj.nonzero() <ndarray.nonzero>` returns a
+tuple (of length :attr:`obj.ndim <ndarray.ndim>`) of integer index
+arrays showing the :py:data:`True` elements of *obj*. However, it is
+faster when ``obj.shape == x.shape``.
+
+If ``obj.ndim == x.ndim``, ``x[obj]`` returns a 1-dimensional array
+filled with the elements of *x* corresponding to the :py:data:`True`
+values of *obj*.  The search order will be :term:`row-major`,
+C-style. If *obj* has :py:data:`True` values at entries that are outside
+of the bounds of *x*, then an index error will be raised. If *obj* is
+smaller than *x* it is identical to filling it with :py:data:`False`.
+
+A common use case for this is filtering for desired element values.
+For example, one may wish to select all entries from an array which
+are not NaN::
+
+    >>> x = np.array([[1., 2.], [np.nan, 3.], [np.nan, np.nan]])
+    >>> x[~np.isnan(x)]
+    array([1., 2., 3.])
+
+Or wish to add a constant to all negative elements::
+
+    >>> x = np.array([1., -1., -2., 3])
+    >>> x[x < 0] += 20
+    >>> x
+    array([  1.,  19.,  18.,   3.])
+
+In general if an index includes a Boolean array, the result will be
+identical to inserting ``obj.nonzero()`` into the same position
+and using the integer array indexing mechanism described above.
+``x[ind_1, boolean_array, ind_2]`` is equivalent to
+``x[(ind_1,) + boolean_array.nonzero() + (ind_2,)]``.
+
+If there is only one Boolean array and no integer indexing array present,
+this is straight forward. Care must only be taken to make sure that the
+boolean index has *exactly* as many dimensions as it is supposed to work
+with.
+
+.. rubric:: Example
+
+From an array, select all rows which sum up to less or equal two::
+
+    >>> x = np.array([[0, 1], [1, 1], [2, 2]])
+    >>> rowsum = x.sum(-1)
+    >>> x[rowsum <= 2, :]
+    array([[0, 1],
+           [1, 1]])
+
+
+Combining multiple Boolean indexing arrays or a Boolean with an integer
+indexing array can best be understood with the
+:meth:`obj.nonzero() <ndarray.nonzero>` analogy. The function :func:`ix_`
+also supports boolean arrays and will work without any surprises.
+
+.. rubric:: Example
+
+Use boolean indexing to select all rows adding up to an even
+number. At the same time columns 0 and 2 should be selected with an
+advanced integer index. Using the :func:`ix_` function this can be done
+with::
+
+    >>> x = np.array([[ 0,  1,  2],
+    ...               [ 3,  4,  5],
+    ...               [ 6,  7,  8],
+    ...               [ 9, 10, 11]])
+    >>> rows = (x.sum(-1) % 2) == 0
+    >>> rows
+    array([False,  True, False,  True])
+    >>> columns = [0, 2]
+    >>> x[np.ix_(rows, columns)]
+    array([[ 3,  5],
+           [ 9, 11]])
+
+Without the ``np.ix_`` call, only the diagonal elements would be
+selected.
+
+Or without ``np.ix_`` (compare the integer array examples)::
+
+    >>> rows = rows.nonzero()[0]
+    >>> x[rows[:, np.newaxis], columns]
+    array([[ 3,  5],
+           [ 9, 11]])
+
+.. rubric:: Example
+
+If x has more dimensions than b then the result will be multi-dimensional::
+
+    >>> x = np.arange(35).reshape(5,7)
+    >>> b = x>20
+    >>> b[:,5]
+    array([False, False, False,  True,  True])
+    >>> x[b[:,5]]
+    array([[21, 22, 23, 24, 25, 26, 27],
+          [28, 29, 30, 31, 32, 33, 34]])
+
+Here the 4th and 5th rows are selected from the indexed array and
+combined to make a 2-D array.
+
+.. rubric:: Example
+
+Using a 2-D boolean array of shape (2,3)
+with four True elements to select rows from a 3-D array of shape
+(2,3,5) results in a 2-D result of shape (4,5)::
+
+    >>> x = np.arange(30).reshape(2,3,5)
+    >>> x
+    array([[[ 0,  1,  2,  3,  4],
+            [ 5,  6,  7,  8,  9],
+            [10, 11, 12, 13, 14]],
+          [[15, 16, 17, 18, 19],
+            [20, 21, 22, 23, 24],
+            [25, 26, 27, 28, 29]]])
+    >>> b = np.array([[True, True, False], [False, True, True]])
+    >>> x[b]
+    array([[ 0,  1,  2,  3,  4],
+          [ 5,  6,  7,  8,  9],
+          [20, 21, 22, 23, 24],
+          [25, 26, 27, 28, 29]])
+
+
+.. _combining-advanced-and-basic-indexing:
+
+Combining advanced and basic indexing
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+When there is at least one slice (``:``), ellipsis (``...``) or :const:`newaxis`
+in the index (or the array has more dimensions than there are advanced indices),
+then the behaviour can be more complicated. It is like concatenating the
+indexing result for each advanced index element.
+
+In the simplest case, there is only a *single* advanced index. A single
+advanced index can for example replace a slice and the result array will be
+the same, however, it is a copy and may have a different memory layout.
+A slice is preferable when it is possible.
+For example::
+
+    >>> x = np.array([[ 0,  1,  2],
+    ...               [ 3,  4,  5],
+    ...               [ 6,  7,  8],
+    ...               [ 9, 10, 11]])
+    >>> x[1:2, 1:3]
+    array([[4, 5]])
+    >>> x[1:2, [1, 2]]
+    array([[4, 5]])
+
+The easiest way to understand the situation may be to think in
+terms of the resulting shape. There are two parts to the indexing operation,
+the subspace defined by the basic indexing (excluding integers) and the
+subspace from the advanced indexing part. Two cases of index combination
+need to be distinguished:
+
+* The advanced indices are separated by a slice, :py:data:`Ellipsis` or
+  :const:`newaxis`. For example ``x[arr1, :, arr2]``.
+* The advanced indices are all next to each other.
+  For example ``x[..., arr1, arr2, :]`` but *not* ``x[arr1, :, 1]``
+  since ``1`` is an advanced index in this regard.
+
+In the first case, the dimensions resulting from the advanced indexing
+operation come first in the result array, and the subspace dimensions after
+that.
+In the second case, the dimensions from the advanced indexing operations
+are inserted into the result array at the same spot as they were in the
+initial array (the latter logic is what makes simple advanced indexing
+behave just like slicing). 
+
+.. rubric:: Example
+
+Suppose ``x.shape`` is (10,20,30) and ``ind`` is a (2,3,4)-shaped
+indexing :class:`intp` array, then ``result = x[...,ind,:]`` has
+shape (10,2,3,4,30) because the (20,)-shaped subspace has been
+replaced with a (2,3,4)-shaped broadcasted indexing subspace. If
+we let *i, j, k* loop over the (2,3,4)-shaped subspace then
+``result[...,i,j,k,:] = x[...,ind[i,j,k],:]``. This example
+produces the same result as :meth:`x.take(ind, axis=-2) <ndarray.take>`.
+
+.. rubric:: Example
+
+Let ``x.shape`` be (10,20,30,40,50) and suppose ``ind_1``
+and ``ind_2`` can be broadcast to the shape (2,3,4). Then
+``x[:,ind_1,ind_2]`` has shape (10,2,3,4,40,50) because the
+(20,30)-shaped subspace from X has been replaced with the
+(2,3,4) subspace from the indices.  However,
+``x[:,ind_1,:,ind_2]`` has shape (2,3,4,10,30,50) because there
+is no unambiguous place to drop in the indexing subspace, thus
+it is tacked-on to the beginning. It is always possible to use
+:meth:`.transpose() <ndarray.transpose>` to move the subspace
+anywhere desired. Note that this example cannot be replicated
+using :func:`take`.
+
+.. rubric:: Example
+
+Slicing can be combined with broadcasted boolean indices::
+
+    >>> x = np.arange(35).reshape(5,7)
+    >>> b = x > 20
+    >>> b
+    array([[False, False, False, False, False, False, False],
+          [False, False, False, False, False, False, False],
+          [False, False, False, False, False, False, False],
+          [ True,  True,  True,  True,  True,  True,  True],
+          [ True,  True,  True,  True,  True,  True,  True]])
+    >>> x[b[:,5],1:3]
+    array([[22, 23],
+          [29, 30]])
+
+
+.. _arrays.indexing.fields:
+
+Field access
+-------------
+
+.. seealso:: :ref:`arrays.dtypes`, :ref:`arrays.scalars`,
+            :ref:`structured_arrays`
+
+If the :class:`ndarray` object is a structured array the :term:`fields <field>`
+of the array can be accessed by indexing the array with strings,
+dictionary-like.
+
+Indexing ``x['field-name']`` returns a new :term:`view` to the array,
+which is of the same shape as *x* (except when the field is a
+sub-array) but of data type ``x.dtype['field-name']`` and contains
+only the part of the data in the specified field. Also,
+:ref:`record array <arrays.classes.rec>` scalars can be "indexed" this way.
+
+Indexing into a structured array can also be done with a list of field names,
+e.g. ``x[['field-name1','field-name2']]``. As of NumPy 1.16, this returns a
+view containing only those fields. In older versions of NumPy, it returned a
+copy. See the user guide section on :ref:`structured_arrays` for more
+information on multifield indexing.
+
+If the accessed field is a sub-array, the dimensions of the sub-array
+are appended to the shape of the result.
+For example::
+
+   >>> x = np.zeros((2,2), dtype=[('a', np.int32), ('b', np.float64, (3,3))])
+   >>> x['a'].shape
+   (2, 2)
+   >>> x['a'].dtype
+   dtype('int32')
+   >>> x['b'].shape
+   (2, 2, 3, 3)
+   >>> x['b'].dtype
+   dtype('float64')
+
+.. _flat-iterator-indexing:
+
+Flat Iterator indexing
+----------------------
+
+:attr:`x.flat <ndarray.flat>` returns an iterator that will iterate
+over the entire array (in C-contiguous style with the last index
+varying the fastest). This iterator object can also be indexed using
+basic slicing or advanced indexing as long as the selection object is
+not a tuple. This should be clear from the fact that :attr:`x.flat
+<ndarray.flat>` is a 1-dimensional view. It can be used for integer
+indexing with 1-dimensional C-style-flat indices. The shape of any
+returned array is therefore the shape of the integer indexing object.
+
+.. index::
+   single: indexing
+   single: ndarray
 
 
 .. _assigning-values-to-indexed-arrays:
 
 Assigning values to indexed arrays
-==================================
+----------------------------------
 
 As mentioned, one can select a subset of an array to assign to using
 a single index, slices, and index and mask arrays. The value being
@@ -110,7 +733,7 @@ rather than being incremented 3 times.
 .. _dealing-with-variable-indices:
 
 Dealing with variable numbers of indices within programs
-========================================================
+--------------------------------------------------------
 
 The indexing syntax is very powerful but limiting when dealing with
 a variable number of indices. For example, if you want to write
@@ -158,3 +781,43 @@ converted to an array as a list would be. As an example: ::
  40
 
 
+Detailed notes
+--------------
+
+These are some detailed notes, which are not of importance for day to day
+indexing (in no particular order):
+
+* The native NumPy indexing type is ``intp`` and may differ from the
+  default integer array type. ``intp`` is the smallest data type
+  sufficient to safely index any array; for advanced indexing it may be
+  faster than other types.
+* For advanced assignments, there is in general no guarantee for the
+  iteration order. This means that if an element is set more than once,
+  it is not possible to predict the final result.
+* An empty (tuple) index is a full scalar index into a zero-dimensional array.
+  ``x[()]`` returns a *scalar* if ``x`` is zero-dimensional and a view
+  otherwise. On the other hand, ``x[...]`` always returns a view.
+* If a zero-dimensional array is present in the index *and* it is a full
+  integer index the result will be a *scalar* and not a zero-dimensional array.
+  (Advanced indexing is not triggered.)
+* When an ellipsis (``...``) is present but has no size (i.e. replaces zero
+  ``:``) the result will still always be an array. A view if no advanced index
+  is present, otherwise a copy.
+* The ``nonzero`` equivalence for Boolean arrays does not hold for zero
+  dimensional boolean arrays.
+* When the result of an advanced indexing operation has no elements but an
+  individual index is out of bounds, whether or not an ``IndexError`` is
+  raised is undefined (e.g. ``x[[], [123]]`` with ``123`` being out of bounds).
+* When a *casting* error occurs during assignment (for example updating a
+  numerical array using a sequence of strings), the array being assigned
+  to may end up in an unpredictable partially updated state.
+  However, if any other error (such as an out of bounds index) occurs, the
+  array will remain unchanged.
+* The memory layout of an advanced indexing result is optimized for each
+  indexing operation and no particular memory order can be assumed.
+* When using a subclass (especially one which manipulates its shape), the
+  default ``ndarray.__setitem__`` behaviour will call ``__getitem__`` for
+  *basic* indexing but not for *advanced* indexing. For such a subclass it may
+  be preferable to call ``ndarray.__setitem__`` with a *base class* ndarray
+  view on the data. This *must* be done if the subclasses ``__getitem__`` does
+  not return views.