Chapter 7. Strings

The next major type on our built-in object tour is the Python string—an ordered collection of characters used to store and represent text-based information. We looked briefly at strings in Chapter 4. Here, we will revisit them in more depth, filling in some of the details we skipped then.

From a functional perspective, strings can be used to represent just about anything that can be encoded as text: symbols and words (e.g., your name), contents of text files loaded into memory, Internet addresses, Python programs, and so on. They can also be used to hold the absolute binary values of bytes, and multibyte Unicode text used in internationalized programs.

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You may have used strings in other languages, too. Python’s strings serve the same role as character arrays in languages such as C, but they are a somewhat higher-level tool than arrays. Unlike in C, in Python, strings come with a powerful set of processing tools. Also unlike languages such as C, Python has no distinct type for individual characters; instead, you just use one-character strings.

Strictly speaking, Python strings are categorized as immutable sequences, meaning that the characters they contain have a left-to-right positional order and that they cannot be changed in-place. In fact, strings are the first representative of the larger class of objects called sequences that we will study here. Pay special attention to the sequence operations introduced in this chapter, because they will work the same on other sequence types we’ll explore later, such as lists and tuples.

Table 7-1 previews common string literals and operations we will discuss in this chapter. Empty strings are written as a pair of quotation marks (single or double) with nothing in between, and there are a variety of ways to code strings. For processing, strings support expression operations such as concatenation (combining strings), slicing (extracting sections), indexing (fetching by offset), and so on. Besides expressions, Python also provides a set of string methods that implement common string-specific tasks, as well as modules for more advanced text-processing tasks such as pattern matching. We’ll explore all of these later in the chapter.

Table 7-1. Common string literals and operations

Operation

Interpretation

S = ''

Empty string

S = "spam's"

Double quotes, same as single

S = 's\np\ta\x00m'

Escape sequences

S = """..."""

Triple-quoted block strings

S = r'\temp\spam'

Raw strings

S = b'spam'

Byte strings in 3.0 (Chapter 36)

S = u'spam'

Unicode strings in 2.6 only (Chapter 36)

S1 + S2

S * 3

Concatenate, repeat

S[i]

S[i:j]

len(S)

Index, slice, length

"a %s parrot" % kind

String formatting expression

"a {0} parrot".format(kind)

String formatting method in 2.6 and 3.0

S.find('pa')

S.rstrip()

S.replace('pa', 'xx')

S.split(',')

S.isdigit()

S.lower()

S.endswith('spam')

'spam'.join(strlist)

S.encode('latin-1')

String method calls: search,

remove whitespace,

replacement,

split on delimiter,

content test,

case conversion,

end test,

delimiter join,

Unicode encoding, etc.

for x in S: print(x)

'spam' in S

[c * 2 for c in S]

map(ord, S)

Iteration, membership

Beyond the core set of string tools in Table 7-1, Python also supports more advanced pattern-based string processing with the standard library’s re (regular expression) module, introduced in Chapter 4, and even higher-level text processing tools such as XML parsers, discussed briefly in Chapter 36. This book’s scope, though, is focused on the fundamentals represented by Table 7-1.

To cover the basics, this chapter begins with an overview of string literal forms and string expressions, then moves on to look at more advanced tools such as string methods and formatting. Python comes with many string tools, and we won’t look at them all here; the complete story is chronicled in the Python library manual. Our goal here is to explore enough commonly used tools to give you a representative sample; methods we won’t see in action here, for example, are largely analogous to those we will.

Note

Content note: Technically speaking, this chapter tells only part of the string story in Python—the part most programmers need to know. It presents the basic str string type, which handles ASCII text and works the same regardless of which version of Python you use. That is, this chapter intentionally limits its scope to the string processing essentials that are used in most Python scripts.

From a more formal perspective, ASCII is a simple form of Unicode text. Python addresses the distinction between text and binary data by including distinct object types:

 

 
  • In Python 3.0 there are three string types: str is used for Unicode text (ASCII or otherwise), bytes is used for binary data (including encoded text), and bytearray is a mutable variant of bytes.
  • In Python 2.6, unicode strings represent wide Unicode text, and str strings handle both 8-bit text and binary data.

The bytearray type is also available as a back-port in 2.6, but not earlier, and it’s not as closely bound to binary data as it is in 3.0. Because most programmers don’t need to dig into the details of Unicode encodings or binary data formats, though, I’ve moved all such details to the Advanced Topics part of this book, in Chapter 36.

If you do need to deal with more advanced string concepts such as alternative character sets or packed binary data and files, see Chapter 36 after reading the material here. For now, we’ll focus on the basic string type and its operations. As you’ll find, the basics we’ll study here also apply directly to the more advanced string types in Python’s toolset.