Indexing, Slicing, and Matrixes

Because lists are sequences, indexing and slicing work the same way for lists as they do for strings. However, the result of indexing a list is whatever type of object lives at the offset you specify, while slicing a list always returns a new list:

>>> L = ['spam', 'Spam', 'SPAM!']
>>> L[2]                             # Offsets start at zero
'SPAM!'
>>> L[−2]                            # Negative: count from the right
'Spam'
>>> L[1:]                            # Slicing fetches sections
['Spam', 'SPAM!']

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One note here: because you can nest lists and other object types within lists, you will sometimes need to string together index operations to go deeper into a data structure. For example, one of the simplest ways to represent matrixes (multidimensional arrays) in Python is as lists with nested sublists. Here’s a basic 3 × 3 two-dimensional list-based array:

>>> matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]

With one index, you get an entire row (really, a nested sublist), and with two, you get an item within the row:

>>> matrix[1]
[4, 5, 6]
>>> matrix[1][1]
5
>>> matrix[2][0]
7
>>> matrix = [[1, 2, 3],
...           [4, 5, 6],
...           [7, 8, 9]]
>>> matrix[1][1]
5

Notice in the preceding interaction that lists can naturally span multiple lines if you want them to because they are contained by a pair of brackets (more on syntax in the next part of the book). Later in this chapter, you’ll also see a dictionary-based matrix representation. For high-powered numeric work, the NumPy extension mentioned in Chapter 5 provides other ways to handle matrixes.