Book Excerpt: The Python Standard Library by Example
3.1.2 Comparison
Under Python 2, classes can define a __cmp__() method that returns -1, 0, or 1 based on whether the object is less than, equal to, or greater than the item being compared. Python 2.1 introduces the rich comparison methods API (__lt__(), __le__(), __eq__(), __ne__(), __gt__(), and __ge__()), which perform a single comparison operation and return a Boolean value. Python 3 deprecated __cmp__() in favor of these new methods, so functools provides tools to make it easier to write Python 2 classes that comply with the new comparison requirements in Python 3.
Rich Comparison
The rich comparison API is designed to allow classes with complex comparisons to implement each test in the most efficient way possible. However, for classes where comparison is relatively simple, there is no point in manually creating each of the rich comparison methods. The total_ordering() class decorator takes a class that provides some of the methods and adds the rest of them.
import functools
import inspect
from pprint import pprint
@functools.total_ordering
class MyObject(object):
def __init__(self, val):
self.val = val
def __eq__(self, other):
print ’ testing __eq__(%s, %s)’ % (self.val, other.val)
return self.val == other.val
def __gt__(self, other):
print ’ testing __gt__(%s, %s)’ % (self.val, other.val)
return self.val > other.val
print ’Methods:\n’
pprint(inspect.getmembers(MyObject, inspect.ismethod))
a = MyObject(1)
b = MyObject(2)
print ’\nComparisons:’
for expr in [ ’a < b’, ’a <= b’, ’a == b’, ’a >= b’, ’a > b’ ]:
print ’\n%-6s:’ % expr
result = eval(expr)
print ’ result of %s: <%s’ % (expr, result)
The class must provide implementation of __eq__() and one other rich comparison method. The decorator adds implementations of the rest of the methods that work by using the comparisons provided.
$ python functools_total_ordering.py Methods: [(’__eq__’, <unbound method MyObject.__eq__>), (’__ge__’, <unbound method MyObject.__ge__>), (’__gt__’, <unbound method MyObject.__gt__>), (’__init__’, <unbound method MyObject.__init__>), (’__le__’, <unbound method MyObject.__le__>), (’__lt__’, <unbound method MyObject.__lt__>)] Comparisons: a < b: testing __gt__(2, 1) result of a < b: True a <= b: testing __gt__(1, 2) result of a <= b: True a == b: testing __eq__(1, 2) result of a == b: False a >= b: testing __gt__(2, 1) result of a >= b: False a > b: testing __gt__(1, 2) result of a > b: False
Collation Order
Since old-style comparison functions are deprecated in Python 3, the cmp argument to functions like sort() is also no longer supported. Python 2 programs that use comparison functions can use cmp_to_key() to convert them to a function that returns a collation key, which is used to determine the position in the final sequence.
import functools
class MyObject(object):
def __init__(self, val):
self.val = val
def __str__(self):
return ’MyObject(%s)’ % self.val
def compare_obj(a, b):
"""Old-style comparison function.
"""
print ’comparing %s and %s’ % (a, b)
return cmp(a.val, b.val)
# Make a key function using cmp_to_key()
get_key = functools.cmp_to_key(compare_obj)
def get_key_wrapper(o):
"""Wrapper function for get_key to allow for print statements.
"""
new_key = get_key(o)
print ’key_wrapper(%s) -> %s’ % (o, new_key)
return new_key
objs = [ MyObject(x) for x in xrange(5, 0, -1) ]
for o in sorted(objs, key=get_key_wrapper):
print o Normally, cmp_to_key() would be used directly, but in this example, an extra wrapper function is introduced to print out more information as the key function is being called.
The output shows that sorted() starts by calling get_key_wrapper() for each item in the sequence to produce a key. The keys returned by cmp_to_key() are instances of a class defined in functools that implements the rich comparison API using the old-style comparison function passed in. After all keys are created, the sequence is sorted by comparing the keys.
$ python functools_cmp_to_key.py key_wrapper(MyObject(5)) -> <functools.K object at 0x100da2a50> key_wrapper(MyObject(4)) -> <functools.K object at 0x100da2a90> key_wrapper(MyObject(3)) -> <functools.K object at 0x100da2ad0> key_wrapper(MyObject(2)) -> <functools.K object at 0x100da2b10> key_wrapper(MyObject(1)) -> <functools.K object at 0x100da2b50> comparing MyObject(4) and MyObject(5) comparing MyObject(3) and MyObject(4) comparing MyObject(2) and MyObject(3) comparing MyObject(1) and MyObject(2) MyObject(1) MyObject(2) MyObject(3) MyObject(4) MyObject(5)
See Also:
functools (http://docs.python.org/library/functools.html) The standard library documentation for this module.
Rich comparison methods (http://docs.python.org/reference/datamodel.html# object.__lt__) Description of the rich comparison methods from the Python Reference Guide.
inspect (page 1200) Introspection API for live objects.
© Copyright Pearson Education. All rights reserved.
Excerpt from Python Standard Library by Example, The. |
|
| By Doug Hellmann Published by Addison-Wesley Professional ISBN-10: 0-321-76734-9 ISBN-13: 978-0-321-76734-9 |
|
This excerpt is from the book, ‘The Python Standard Library by Example’ by Doug Hellmann, published by Pearson/Addison-Wesley Professional, June 2011, ISBN 0321767349, Copyright 2011 Pearson Education, Inc. For more info please visit www.informit.com/title/0321767349
- « first
- ‹ previous
- 1
- 2
- 3
- 4
Realizing the promise of Apache® Hadoop® requires the effective deployment of compute, memory, storage and networking to achieve optimal results. With its flexibility and multitude of options, it is easy to over or under provision the server infrastructure, resulting in poor performance and high TCO. Join us for an in depth, technical discussion with industry experts from leading Hadoop and server companies who will provide insights into the key considerations for designing and deploying an optimal Hadoop cluster.
Sponsored by AMD
Built-in forensics, incident response, and security with Red Hat Enterprise Linux 6
Every security policy provides guidance and requirements for ensuring adequate protection of information and data, as well as high-level technical and administrative security requirements for a system in a given environment. Traditionally, providing security for a system focuses on the confidentiality of the information on it. However, protecting the data integrity and system and data availability is just as important. For example, when processing United States intelligence information, there are three attributes that require protection: confidentiality, integrity, and availability.
Learn more about catching the bad guy in this free white paper.
Sponsored by DLT Solutions
| Designing Electronics with Linux | May 22, 2013 |
| Dynamic DNS—an Object Lesson in Problem Solving | May 21, 2013 |
| Using Salt Stack and Vagrant for Drupal Development | May 20, 2013 |
| Making Linux and Android Get Along (It's Not as Hard as It Sounds) | May 16, 2013 |
| Drupal Is a Framework: Why Everyone Needs to Understand This | May 15, 2013 |
| Home, My Backup Data Center | May 13, 2013 |
- Designing Electronics with Linux
- Making Linux and Android Get Along (It's Not as Hard as It Sounds)
- Dynamic DNS—an Object Lesson in Problem Solving
- Using Salt Stack and Vagrant for Drupal Development
- New Products
- A Topic for Discussion - Open Source Feature-Richness?
- What's the tweeting protocol?
- Validate an E-Mail Address with PHP, the Right Way
- Drupal Is a Framework: Why Everyone Needs to Understand This
- Tech Tip: Really Simple HTTP Server with Python
Enter to Win an Adafruit Pi Cobbler Breakout Kit for Raspberry Pi

It's Raspberry Pi month at Linux Journal. Each week in May, Adafruit will be giving away a Pi-related prize to a lucky, randomly drawn LJ reader. Winners will be announced weekly.
Fill out the fields below to enter to win this week's prize-- a Pi Cobbler Breakout Kit for Raspberry Pi.
Congratulations to our winners so far:
- 5-8-13, Pi Starter Pack: Jack Davis
- 5-15-13, Pi Model B 512MB RAM: Patrick Dunn
- 5-21-13, Prototyping Pi Plate Kit: Philip Kirby
- Next winner announced on 5-27-13!
Free Webinar: Hadoop
How to Build an Optimal Hadoop Cluster to Store and Maintain Unlimited Amounts of Data Using Microservers
Realizing the promise of Apache® Hadoop® requires the effective deployment of compute, memory, storage and networking to achieve optimal results. With its flexibility and multitude of options, it is easy to over or under provision the server infrastructure, resulting in poor performance and high TCO. Join us for an in depth, technical discussion with industry experts from leading Hadoop and server companies who will provide insights into the key considerations for designing and deploying an optimal Hadoop cluster.
Some of key questions to be discussed are:
- What is the “typical” Hadoop cluster and what should be installed on the different machine types?
- Why should you consider the typical workload patterns when making your hardware decisions?
- Are all microservers created equal for Hadoop deployments?
- How do I plan for expansion if I require more compute, memory, storage or networking?



2 hours 7 min ago
12 hours 10 min ago
16 hours 37 min ago
20 hours 13 min ago
20 hours 45 min ago
23 hours 9 min ago
23 hours 12 min ago
23 hours 14 min ago
1 day 3 hours ago
1 day 5 hours ago