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Python Cookbook by David Ascher, Alex Martelli

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Chapter 4. Files

Introduction

Credit: Mark Lutz, author of Programming Python, co-author of Learning Python

Behold the file—one of the first things that any reasonably pragmatic programmer reaches for in a programming language’s toolbox. Because processing external files is a very real, tangible task, the quality of file-processing interfaces is a good way to assess the practicality of a programming tool.

As the examples in this chapter attest, Python shines here too. In fact, files in Python are supported in a variety of layers: from the built-in open function’s standard file object, to specialized tools in standard library modules such as os, to third-party utilities available on the Web. All told, Python’s arsenal of file tools provides several powerful ways to access files in your scripts.

File Basics

In Python, a file object is an instance of a built-in type. The built-in function open creates and returns a file object. The first argument, a string, specifies the file’s path (i.e., the filename preceded by an optional directory path). The second argument to open, also a string, specifies the mode in which to open the file. For example:

input = open('data', 'r')
output = open('/tmp/spam', 'w')

open accepts a file path in which directories and files are separated by slash characters (/), regardless of the proclivities of the underlying operating system. On systems that don’t use slashes, you can use a backslash character (\) instead, but there’s no real reason to do so. Backslashes are harder to fit nicely in string literals, since you have to double them up or use “raw” strings. If the file path argument does not include the file’s directory name, the file is assumed to reside in the current working directory (which is a disjoint concept from the Python module search path).

For the mode argument, use 'r' to read the file in text mode; this is the default value and is commonly omitted, so that open is called with just one argument. Other common modes are 'rb' to read the file in binary mode, 'w' to create and write to the file in text mode, and 'wb' to create and write to the file in binary mode.

The distinction between text mode and binary mode is important on non-Unix-like platforms, because of the line-termination characters used on these systems. When you open a file in binary mode, Python knows that it doesn’t need to worry about line-termination characters; it just moves bytes between the file and in-memory strings without any kind of translation. When you open a file in text mode on a non-Unix-like system, however, Python knows it must translate between the '\n' line-termination characters used in strings and whatever the current platform uses in the file itself. All of your Python code can always rely on '\n' as the line-termination character, as long as you properly indicate text or binary mode when you open the file.

Once you have a file object, you perform all file I/O by calling methods of this object, as we’ll discuss in a moment. When you’re done with the file, you should finish by calling the close method on the object, to close the connection to the file:

input.close(  )

In short scripts, people often omit this step, as Python automatically closes the file when a file object is reclaimed during garbage collection. However, it is good programming practice to close your files, and it is especially a good idea in larger programs. Note that try/finally is particularly well suited to ensuring that a file gets closed, even when the program terminates by raising an uncaught exception.

To write to a file, use the write method:

output.write(s)

where s is a string. Think of s as a string of characters if output is open for text-mode writing and as a string of bytes if output is open for binary-mode writing. There are other writing-related methods, such as flush, which sends any data that is being buffered, and writelines, which writes a list of strings in a single call. However, none of these other methods appear in the recipes in this chapter, as write is by far the most commonly used method.

Reading from a file is more common than writing to a file, and there are more issues involved, so file objects have more reading methods than writing ones. The readline method reads and returns the next line from a text file:

while 1:
    line = input.readline(  )
    if not line: break
    process(line)

This was idiomatic Python, but it is no longer the best way to read lines from a file. Another alternative is to use the readlines method, which reads the whole file and returns a list of lines:

for line in input.readlines(  ):
    process(line)

However, this is useful only for files that fit comfortably in physical memory. If the file is truly huge, readlines can fail or at least slow things down quite drastically as virtual memory fills up and the operating system has to start copying parts of physical memory to disk. Python 2.1 introduced the xreadlines method, which works just like readlines in a for loop but consumes a bounded amount of memory regardless of the size of the file:

for line in input.xreadlines(  ):
    process(line)

Python 2.2 introduced the ideal solution, whereby you can loop on the file object itself, implicitly getting a line at a time with the same memory and performance characteristics of xreadlines:

for line in input:
    process(line)

Of course, you don’t always want to read a file line by line. You may instead want to read some or all of the bytes in the file, particularly if you’ve opened the file for binary-mode reading, where lines are unlikely to be an applicable concept. In this case, you can use the read method. When called without arguments, read reads and returns all the remaining bytes from the file. When read is called with an integer argument N, it reads and returns the next N bytes (or all the remaining bytes, if less than N bytes remain). Other methods worth mentioning are seek and tell, which support random access to files. These are normally used with binary files made up of fixed-length records.

Portability and Flexibility

On the surface, Python’s file support is straightforward. However, there are two aspects of Python’s file support that I want to underscore up-front, before you peruse the code in this chapter: script portability and interface flexibility.

Keep in mind that most file interfaces in Python are fully portable across platform boundaries. It would be difficult to overstate the importance of this feature. A Python script that searches all files in a directory tree for a bit of text, for example, can be freely moved from platform to platform without source-code changes: just copy the script’s source file to the new target machine. I do it all the time—so much so that I can happily stay out of operating-system wars. With Python’s portability, the underlying platform is largely irrelevant.

Also, it has always struck me that Python’s file-processing interfaces are not restricted to real, physical files. In fact, most file tools work with any kind of object that exposes the same interface as a real file object. Thus, a file reader cares only about read methods, and a file writer cares only about write methods. As long as the target object implements the expected protocol, all goes well.

For example, suppose you have written a general file-processing function such as the following, intending to apply a passed-in function to each line in an input file:

def scanner(fileobject, linehandler):
    for line in fileobject.readlines(  ):
        linehandler(line)

If you code this function in a module file and drop that file in a directory listed on your Python search path, you can use it anytime you need to scan a text file, now or in the future. To illustrate, here is a client script that simply prints the first word of each line:

from myutils import scanner
def firstword(line): print line.split(  )[0]
file = open('data')
scanner(file, firstword)

So far, so good; we’ve just coded a reusable software component. But notice that there are no type declarations in the scanner function, only an interface constraint—any object with a readlines method will do. For instance, suppose you later want to provide canned test input from a string object, instead of from a real, physical file. The standard StringIO module, and the equivalent but faster cStringIO, provide the appropriate wrapping and interface forgery:

from cStringIO import StringIO
from myutils import scanner
def firstword(line): print line.split(  )[0]
string = StringIO('one\ntwo xxx\nthree\n')
scanner(string, firstword)

Here, StringIO objects are plug-and-play compatible with file objects, so scanner takes its three lines of text from an in-memory string object, rather than a true external file. You don’t need to change the scanner to make this work—just send it the right kind of object. For more generality, use a class to implement the expected interface instead:

class MyStream:
    def readlines(self):
        # Grab and return text from a source here
        return ['a\n', 'b c d\n']

from myutils import scanner
def firstword(line): print line.split(  )[0]
object = MyStream(  )
scanner(object, firstword)

This time, as scanner attempts to read the file, it really calls out to the readlines method you’ve coded in your class. In practice, such a method might use other Python standard tools to grab text from a variety of sources: an interactive user, a pop-up GUI input box, a shelve object, an SQL database, an XML or HTML page, a network socket, and so on. The point is that scanner doesn’t know or care what kind of object is implementing the interface it expects, or what that interface actually does.

Object-oriented programmers know this deliberate naiveté as polymorphism. The type of the object being processed determines what an operation, such as the readlines method call in scanner, actually does. Everywhere in Python, object interfaces, rather than specific data types, are the unit of coupling. The practical effect is that functions are often applicable to a much broader range of problems than you might expect. This is especially true if you have a background in strongly typed languages such as C or C++. It is almost as if we get C++ templates for free in Python. Code has an innate flexibility that is a byproduct of Python’s dynamic typing.

Of course, code portability and flexibility run rampant in Python development and are not really confined to file interfaces. Both are features of the language that are simply inherited by file-processing scripts. Other Python benefits, such as its easy scriptability and code readability, are also key assets when it comes time to change file-processing programs. But, rather than extolling all of Python’s virtues here, I’ll simply defer to the wonderful example programs in this chapter and this text at large for more details. Enjoy!

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