Sequence Operations

As sequences, strings support operations that assume a positional ordering among items. For example, if we have a four-character string, we can verify its length with the built-in len function and fetch its components with indexing expressions:

>>> S = 'Spam'
>>> len(S)               # Length
4
>>> S[0]                 # The first item in S, indexing by zero-based position
'S'
>>> S[1]                 # The second item from the left
'p'

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In Python, indexes are coded as offsets from the front, and so start from 0: the first item is at index 0, the second is at index 1, and so on.

Notice how we assign the string to a variable named S here. We’ll go into detail on how this works later (especially in Chapter 6), but Python variables never need to be declared ahead of time. A variable is created when you assign it a value, may be assigned any type of object, and is replaced with its value when it shows up in an expression. It must also have been previously assigned by the time you use its value. For the purposes of this chapter, it’s enough to know that we need to assign an object to a variable in order to save it for later use.

In Python, we can also index backward, from the end—positive indexes count from the left, and negative indexes count back from the right:

>>> S[-1]                # The last item from the end in S
'm'
>>> S[-2]                # The second to last item from the end
'a'

Formally, a negative index is simply added to the string’s size, so the following two operations are equivalent (though the first is easier to code and less easy to get wrong):

>>> S[-1]                # The last item in S
'm'
>>> S[len(S)-1]          # Negative indexing, the hard way
'm'

Notice that we can use an arbitrary expression in the square brackets, not just a hardcoded number literal—anywhere that Python expects a value, we can use a literal, a variable, or any expression. Python’s syntax is completely general this way.

In addition to simple positional indexing, sequences also support a more general form of indexing known as slicing, which is a way to extract an entire section (slice) in a single step. For example:

>>> S                     # A 4-character string
'Spam'
>>> S[1:3]                # Slice of S from offsets 1 through 2 (not 3)
'pa'

Probably the easiest way to think of slices is that they are a way to extract an entire column from a string in a single step. Their general form, X[I:J], means “give me everything in X from offset I up to but not including offset J.” The result is returned in a new object. The second of the preceding operations, for instance, gives us all the characters in string S from offsets 1 through 2 (that is, 3 – 1) as a new string. The effect is to slice or “parse out” the two characters in the middle.

In a slice, the left bound defaults to zero, and the right bound defaults to the length of the sequence being sliced. This leads to some common usage variations:

>>> S[1:]                 # Everything past the first (1:len(S))
'pam'
>>> S                     # S itself hasn't changed
'Spam'
>>> S[0:3]                # Everything but the last
'Spa'
>>> S[:3]                 # Same as S[0:3]
'Spa'
>>> S[:-1]                # Everything but the last again, but simpler (0:-1)
'Spa'
>>> S[:]                  # All of S as a top-level copy (0:len(S))
'Spam'

Note how negative offsets can be used to give bounds for slices, too, and how the last operation effectively copies the entire string. As you’ll learn later, there is no reason to copy a string, but this form can be useful for sequences like lists.

Finally, as sequences, strings also support concatenation with the plus sign (joining two strings into a new string) and repetition (making a new string by repeating another):

>>> S
'Spam'
>>> S + 'xyz'             # Concatenation
'Spamxyz'
>>> S                     # S is unchanged
'Spam'
>>> S * 8                 # Repetition
'SpamSpamSpamSpamSpamSpamSpamSpam'

Notice that the plus sign (+) means different things for different objects: addition for numbers, and concatenation for strings. This is a general property of Python that we’ll call polymorphism later in the book—in sum, the meaning of an operation depends on the objects being operated on. As you’ll see when we study dynamic typing, this polymorphism property accounts for much of the conciseness and flexibility of Python code. Because types aren’t constrained, a Python-coded operation can normally work on many different types of objects automatically, as long as they support a compatible interface (like the + operation here). This turns out to be a huge idea in Python; you’ll learn more about it later on our tour.