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

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.

routines.indexing

Indexing basics

:ref:`arrays.indexing`