Credit: Fred L. Drake, Jr., PythonLabs
Text-processing applications form a substantial part of the application space for any scripting language, if only because everyone can agree that text processing is useful. Everyone has bits of text that need to be reformatted or transformed in various ways. The catch, of course, is that every application is just a little bit different from every other application, so it can be difficult to find just the right reusable code to work with different file formats, no matter how similar they are.
Sounds like an easy question, doesn't it? After all, we know it when we see it, don't we? Text is a sequence of characters, and it is distinguished from binary data by that very fact. Binary data, after all, is a sequence of bytes.
Unfortunately, all data enters our applications as a sequence of bytes. There's no library function we can call that will tell us whether a particular sequence of bytes represents text, although we can create some useful heuristics that tell us whether data can safely (not necessarily correctly) be handled as text. Recipe 1.11 shows just such a heuristic.
Python strings are immutable sequences of bytes or characters. Most of the ways we create and process strings treat them as sequences of characters, but many are just as applicable to sequences of bytes. Unicode strings are immutable sequences of Unicode characters: transformations of Unicode strings into and from plain strings use codecs (coder-decoders) objects that embody knowledge about the many standard ways in which sequences of characters can be represented by sequences of bytes (also known as encodings and character sets). Note that Unicode strings do not serve double duty as sequences of bytes. Recipe 1.20, Recipe 1.21, and Recipe 1.22 illustrate the fundamentals of Unicode in Python.
Okay, let's assume that our application knows from the context that it's looking at text. That's usually the best approach because that's where external input comes into play. We're looking at a file either because it has a well-known name and defined format (common in the "Unix" world) or because it has a well-known filename extension that indicates the format of the contents (common on Windows). But now we have a problem: we had to use the word format to make the previous paragraph meaningful. Wasn't text supposed to be simple?
Let's face it: there's no such thing as "pure" text, and if there were, we probably wouldn't care about it (with the possible exception of applications in the field of computational linguistics, where pure text may indeed sometimes be studied for its own sake). What we want to deal with in our applications is information contained in text. The text we care about may contain configuration data, commands to control or define processes, documents for human consumption, or even tabular data. Text that contains configuration data or a series of commands usually can be expected to conform to a fairly strict syntax that can be checked before relying on the information in the text. Informing the user of an error in the input text is typically sufficient to deal with things that aren't what we were expecting.
Documents intended for humans tend to be simple, but they vary widely in detail. Since they are usually written in a natural language, their syntax and grammar can be difficult to check, at best. Different texts may use different character sets or encodings, and it can be difficult or even impossible to tell which character set or encoding was used to create a text if that information is not available in addition to the text itself. It is, however, necessary to support proper representation of natural-language documents. Natural-language text has structure as well, but the structures are often less explicit in the text and require at least some understanding of the language in which the text was written. Characters make up words, which make up sentences, which make up paragraphs, and still larger structures may be present as well. Paragraphs alone can be particularly difficult to locate unless you know what typographical conventions were used for a document: is each line a paragraph, or can multiple lines make up a paragraph? If the latter, how do we tell which lines are grouped together to make a paragraph? Paragraphs may be separated by blank lines, indentation, or some other special mark. See Recipe 19.10 for an example of reading a text file as a sequence of paragraphs separated by blank lines.
Tabular data has many issues that are similar to the problems associated with natural-language text, but it adds a second dimension to the input format: the text is no longer linear—it is no longer a sequence of characters, but rather a matrix of characters from which individual blocks of text must be identified and organized.
Parsing the data into a structure internal to our application
Transforming the input into something similar in some way, but with changes of some kind
Generating completely new data
Parsing can be performed in a variety of ways, and many
formats can be suitably handled by ad hoc parsers that deal
effectively with a very constrained format. Examples of this approach
include parsers for RFC 2822-style email headers (see the
rfc822 module in Python's standard library)
and the configuration files handled by the
ConfigParser module. The
netrc module offers another example of a
parser for an application-specific file format, this one based on the
shlex offers a fairly typical tokenizer for
basic languages, useful in creating readable configuration files or
allowing users to enter commands to an interactive prompt. These sorts
of ad hoc parsers are abundant in Python's standard library, and
recipes using them can be found in Chapter 2 and Chapter 13. More formal parsing
tools are also available for Python; they depend on larger add-on
packages and are surveyed in the introduction to Chapter 16.
Transforming text from one format to another is more interesting when viewed as text processing, which is what we usually think of first when we talk about text. In this chapter, we'll take a look at some ways to approach transformations that can be applied for different purposes. Sometimes we'll work with text stored in external files, and other times we'll simply work with it as strings in memory.
The generation of textual data from application-specific data
structures is most easily performed using Python's
write method of a file or file-like object.
This is often done using a method of the application object or a
function, which takes the output file as a parameter. The function can
then use statements such as these:
print >>thefile, sometext thefile.write(sometext)
which generate output to the appropriate file. However, this
isn't generally thought of as text processing, as here there is no
input text to be processed. Examples of using both
write can of course be found throughout this
Working with text stored as a string in memory can be easy when the text is not too large. Operations that search the text can operate over multiple lines very easily and quickly, and there's no need to worry about searching for something that might cross a buffer boundary. Being able to keep the text in memory as a simple string makes it very easy to take advantage of the built-in string operations available as methods of the string object.
File-based transformations deserve special treatment, because there can be substantial overhead related to I/O performance and the amount of data that must actually be stored in memory. When working with data stored on disk, we often want to avoid loading entire files into memory, due to the size of the data: loading an 80 MB file into memory should not be done too casually! When our application needs only part of the data at a time, working on smaller segments of the data can yield substantial performance improvements, simply because we've allowed enough space for our program to run. If we are careful about buffer management, we can still maintain the performance advantage of using a small number of relatively large disk read and write operations by working on large chunks of data at a time. File-related recipes are found in Chapter 12 .
Another interesting source for textual data comes to
light when we consider the network. Text is often retrieved from the
network using a socket. While we can always view a socket as a file
makefile method of the
socket object), the data that is retrieved over a socket may come in
chunks, or we may have to wait for more data to arrive. The textual
data may not consist of all data until the end of the data stream, so
a file object created with
may not be entirely appropriate to pass to text-processing code. When
working with text from a network connection, we often need to read the
data from the connection before passing it along for further
processing. If the data is large, it can be handled by saving it to a
file as it arrives and then using that file when performing
text-processing operations. More elaborate solutions can be built when
the text processing needs to be started before all the data is
available. Examples of parsers that are useful in such situations may
be found in the
HTMLParser modules in the standard
The main tool Python gives us to process text is strings—immutable sequences of characters. There are actually two kinds of strings: plain strings, which contain 8-bit (ASCII) characters; and Unicode strings, which contain Unicode characters. We won't deal much with Unicode strings here: their functionality is similar to that of plain strings, except each character takes up 2 (or 4) bytes, so that the number of different characters is in the tens of thousands (or even billions), as opposed to the 256 different characters that make up plain strings. Unicode strings are important if you must deal with text in many different alphabets, particularly Asian ideographs. Plain strings are sufficient to deal with English or any of a limited set of non-Asian languages. For example, all western European alphabets can be encoded in plain strings, typically using the international standard encoding known as ISO-8859-1 (or ISO-8859-15, if you need the Euro currency symbol as well).
In Python, you express a literal string (curiously more often known as a string literal) as:
'this is a literal string' "this is another string"
String values can be enclosed in either single or double quotes. The two different kinds of quotes work the same way, but having both allows you to include one kind of quotes inside of a string specified with the other kind of quotes, without needing to escape them with the backslash character:
'isn\'t that grand' "isn't that grand"
To have a string literal span multiple lines, you can use a backslash as the last character on the line, which indicates that the next line is a continuation:
big = "This is a long string\ that spans two lines."
You must embed newlines in the string if you want the string to output on two lines:
big = "This is a long string\n\ that prints on two lines."
Another approach is to enclose the string in a pair of matching triple quotes (either single or double):
bigger = """ This is an even bigger string that spans three lines. """
Using triple quotes, you don't need to use the continuation
character, and line breaks in the string literal are preserved as
newline characters in the resulting Python string object. You can also
make a string literal "raw" string by preceding
it with an
big = r"This is a long string\ with a backslash and a newline in it"
With a raw string, backslash escape sequences are left alone,
rather than being interpreted. Finally, you can precede a string
literal with a
U to make it a Unicode string:
hello = u'Hello\u0020World'
Strings are immutable, which means that no matter what operation you do on a string, you will always produce a new string object, rather than mutating the existing string. A string is a sequence of characters, which means that you can access a single character by indexing:
mystr = "my string" mystr # 'm' mystr[-2] # 'n'
You can also access a portion of the string with a slice:
mystr[1:4] # 'y s' mystr[3:] # 'string' mystr[-3:] # 'ing'
Slices can be extended, that is, include a third parameter that is known as the stride or step of the slice:
mystr[:3:-1] # 'gnirt' mystr[1::2] # 'ysrn'
You can loop on a string's characters:
for c in mystr:
c to each of the
mystr in turn. You can form another
list(mystr) # returns ['m','y',' ','s','t','r','i','n','g']
You can concatenate strings by addition:
mystr+'oid' # 'my stringoid'
You can also repeat strings by multiplication:
'xo'*3 # 'xoxoxo'
In general, you can do anything to a string that you can do to any other sequence, as long as it doesn't require changing the sequence, since strings are immutable.
String objects have many useful methods. For example,
you can test a string's contents with
s.isdigit( ), which returns
s is not
empty and all of the characters in
digits (otherwise, it returns
False). You can produce a new modified
string with a method call such as
), which returns a new string that is like
s, but with every letter changed into its
uppercase equivalent. You can search for a string inside another with
returns the number of times the substring '
needle' appears in the string
haystack. When you have a large string that
spans multiple lines, you can split it into a list of single-line
list_of_lines = one_large_string.splitlines( )
one_large_string = '\n'.join(list_of_lines)
The recipes in this chapter show off many methods of the string object. You can find complete documentation in Python's Library Reference and Python in a Nutshell.
Strings in Python can also be manipulated with regular
expressions, via the
Regular expressions are a powerful (but complicated) set of tools that
you may already be familiar with from another language (such as Perl),
or from the use of tools such as the vi editor and text-mode commands such as
grep. You'll find a number of uses
of regular expressions in recipes in the second half of this chapter.
For complete documentation, see the Library
Reference and Python in a Nutshell.
J.E.F. Friedl, Mastering Regular Expressions
(O'Reilly) is also recommended if you need to master this
subject—Python's regular expressions are basically the same as Perl's,
which Friedl covers thoroughly.
Python's standard module
string offers much of the same functionality
that is available from string methods, packaged up as functions
instead of methods. The
module also offers a few additional functions, such as the useful
string.maketrans function that is
demonstrated in a few recipes in this chapter; several helpful string
example, is '
0123456789') and, in
Python 2.4, the new class
for simple yet flexible formatting of strings with embedded variables,
which as you'll see features in one of this chapter's recipes. The
provides a handy way to put strings together and to obtain precisely
formatted strings from such objects as floating-point numbers. Again,
you'll find recipes in this chapter that show how to use
% for your purposes. Python also has lots of
standard and extension modules that perform special processing on
strings of many kinds. This chapter doesn't cover such specialized
resources, but Chapter 12
is, for example, entirely devoted to the important specialized subject
of processing XML.