Python Cookbook

Author: Alex Martelli, Anna Ravenscroft, David Ascher
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About This Book

Portable, powerful, and a breeze to use, Python is the popular open source object-oriented programming language used for both standalone programs and scripting applications. It is now being used by an increasing number of major organizations, including NASA and Google.Updated for Python 2.4, The Python Cookbook, 2nd Edition offers a wealth of useful code for all Python programmers, not just advanced practitioners. Like its predecessor, the new edition provides solutions to problems that Python programmers face everyday.It now includes over 200 recipes that range from simple tasks, such as working with dictionaries and list comprehensions, to complex tasks, such as monitoring a network and building a templating system. This revised version also includes new chapters on topics such as time, money, and metaprogramming.


Here's a list of additional topics covered:

  • Manipulating text
  • Searching and sorting
  • Working with files and the filesystem
  • Object-oriented programming
  • Dealing with threads and processes
  • System administration
  • Interacting with databases
  • Creating user interfaces
  • Network and web programming
  • Processing XML
  • Distributed programming
  • Debugging and testing

Another advantage of The Python Cookbook, 2nd Edition is its trio of authors--three well-known Python programming experts, who are highly visible on email lists and in newsgroups, and speak often at Python conferences.With scores of practical examples and pertinent background information, The Python Cookbook, 2nd Edition is the one source you need if you're looking to build efficient, flexible, scalable, and well-integrated systems.

Python Cookbook


Review Date:


by anonymous   2017-08-20

Have you read the Python Cookbook? It's a pretty good source for Pythonic.

Plus you'll find much more from Alex Martelli on Stack Overflow.

by technomalogical   2017-08-20

+1 for Dive into Python and Python in a Nutshell. I also highly recommend effbot's Guide to the Standard Library. You'll probably also want to check out the Python Cookbook for some good examples of idiomatic Python code. Check out Foundations of Python Networking to pick up where the SysAdmin book leaves off in terms of network protocols (fyi: all APress books are available as PDFs, which I love)

by anonymous   2017-08-20

What you are looking for is Denis Otkidach's excellent CachedAttribute:

class CachedAttribute(object):    
    '''Computes attribute value and caches it in the instance.
    From the Python Cookbook (Denis Otkidach)
    This decorator allows you to create a property which can be computed once and
    accessed many times. Sort of like memoization.
    def __init__(self, method, name=None):
        # record the unbound-method and the name
        self.method = method = name or method.__name__
        self.__doc__ = method.__doc__
    def __get__(self, inst, cls):
        # self: <__main__.cache object at 0xb781340c>
        # inst: <__main__.Foo object at 0xb781348c>
        # cls: <class '__main__.Foo'>       
        if inst is None:
            # instance attribute accessed on class, return self
            # You get here if you write ``
            return self
        # compute, cache and return the instance's attribute value
        result = self.method(inst)
        # setattr redefines the instance's attribute so this doesn't get called again
        setattr(inst,, result)
        return result

It can be used like this:

def demo_cache():
    class Foo(object):
        def bar(self):
            print 'Calculating'  
            return 42
    # Calculating
    # 42

Notice that accessing subsequent times does not call the getter function. (Calculating is not printed.)

    # 42
    # 1

Deleting from foo.__dict__ re-exposes the property defined in Foo. Thus, calling again recalculates the value again.

    # Calculating
    # 42


The decorator was published in the Python Cookbook and can also be found on ActiveState.

This is efficient because although the property exists in the class's __dict__, after computation, an attribute of the same name is created in the instance's __dict__. Python's attribute lookup rules gives precedence to the attribute in the instance's __dict__, so the property in class becomes effectively overridden.