Multidimensional Indices

This section of the indexing guide deals with indices that only operate on NumPy arrays. Unlike integers and slices, which also work on built-in Python sequence types such as list, tuple, and str, the remaining index types do not work at all on built-in sequence types. For example, if you try to use a tuple index on a list, you will get an IndexError The semantics of these indices are defined by the NumPy library, not the Python language.

To begin, we should be sure we understand what an array is:

• What is an Array?

Basic Multidimensional Indices

There are two types of multidimensional indices, basic and advanced indices. Basic indices are so-called because they are simpler and the most common. They also are notable because they always return a view (see Views vs. Copies).

We’ve already learned about two types of basic indices in previous sections:

• Integer Indices

• Slices

There are three others:

• Tuple Indices

• Ellipses

• newaxis

Advanced Indices

Lastly are the so-called advanced indices. These are “advanced” in the sense that they are more complex. They are also distinct from “basic” indices in that they always return a copy (see Views vs. Copies). Advanced indices allow selecting arbitrary parts of an array, in ways that are impossible with the basic index types. Advanced indexing is also sometimes called “fancy indexing” or indexing by arrays, as the indices themselves are arrays.

Using an array that does not have an integer or boolean dtype as an index results in an error.

• Integer Array Indices

• Boolean Array Indices