Keeping Up with Python: the 2.2 Release
A new divisor operator ( // ) has been created that always truncates the fraction and rounds it to the next smallest whole number toward the left on the number line, regardless of the operands' numeric types. This operator works starting in 2.2 and does not require the __future__ directive above.
>>> 1 // 2 # floors result, returns integer 0 >>> 1.0 // 2.0 # floors result, returns float 0.0 >>> -1 // 2 # move left on number line -1
Without getting into the arguments of this change, the feeling is that perhaps Python's division operator has been flawed since the beginning, especially because Python is a strong choice as a first programming language for people who aren't used to floor division. One of the examples Guido uses in his “What's New in Python 2.2” ZPUG talk is:
def velocity(distance, totalTime): rate = distance / totalTimeThis is bad because this function is not numeric-type-independent. Your results with a pair of floats certainly differs from that of sending in a pair of integers. To bridge the dichotomy, you must resolve the following intransitivity in your head:
>>> 1 == 1.0 1 >>> 2 == 2.0 1 >>> 1 / 2 == 1.0 / 2.0 # classic division 0If you use Python's new model of division, the universe is at peace once again:
>>> from __future__ import division >>> 1 / 2 == 1.0 / 2.0 # true division 1 >>> 1 // 2 == 1.0 // 2.0 # floor division 1While this seems like the proper and right thing to do, one cannot help but be concerned with the code breakage it may lead to. Fortunately, the Python developers have kept this in mind, as this change will not be permanent until Python 3.0, which is still years away. Those who desire the new division can import it or start Python with the -Qnew command-line option. There are a few options to turn on warnings to prepare for the upcoming new division.
You can get more information from PEP 238, but dig through the comp.lang.python archives for the heated debates. Table 2 summarizes the division operators in the various releases of Python and the differences in operation when you import division (from __future__).
Merging Python types and classes has been on the want list for quite a while. Programmers are dismayed to discover that they cannot subclass existing data types, such as a list, to customize for their applications.
To learn more, it can't hurt to look through both the PEPs involved and a tutorial Guido wrote specifically for those who want to get up to speed quickly on the new style classes without having to wade through all the intricate details found in the PEPs (see Resources). We will also give you a teaser class that extends a Python list with enhanced stack features.
This example, stack2.py, is motivated by one of the iterator examples above (see also Example 6.2 at the Core Python Programming web site).
#!/bin/env python 'stack2.py -- subclasses and extends a list' class Stack(list): def __init__(self, *args): list.__init__(self, args) # call base class # constructor def push(self, *args): for eachItem in args: # can push multiple self.append(eachItem) # items def pop(self, n=1): if n == 1: # pop single item return list.pop(self) else: # pop multiple items return [ list.pop(self) for i in range(n) ]
Below is the output we get from flexing our newfound capabilities:
>>> from stack2 import Stack >>> m = Stack(123, 'xyz') >>> m [123, 'xyz'] >>> m.push(4.5) >>> m [123, 'xyz', 4.5] >>> m.push(1+2j, 'abc') >>> m [123, 'xyz', 4.5, (1+2j), 'abc'] >>> m.pop() 'abc' >>> m.pop(3) [(1+2j), 4.5, 'xyz'] >>> m In addition to being able to subclass built-in types, other highlights of the new style classes include:
“Cast” functions being factories.
New __class__, __dict__, and __bases__ attributes.
__getattribute__() Special Method (smarter than __getattr__()).
Superclass method calls.
New diamond diagram name resolution.
Fixed set of allowed class attributes with Slots.
For more information on the new style classes and the unification of types and classes, see both PEPs 252 and 253 as well as the aforementioned tutorial by Guido.
Getting Started with DevOps - Including New Data on IT Performance from Puppet Labs 2015 State of DevOps Report
August 27, 2015
12:00 PM CDT
DevOps represents a profound change from the way most IT departments have traditionally worked: from siloed teams and high-anxiety releases to everyone collaborating on uneventful and more frequent releases of higher-quality code. It doesn't matter how large or small an organization is, or even whether it's historically slow moving or risk averse — there are ways to adopt DevOps sanely, and get measurable results in just weeks.
Free to Linux Journal readers.Register Now!
- Hacking a Safe with Bash
- Django Models and Migrations
- Secure Server Deployments in Hostile Territory, Part II
- Huge Package Overhaul for Debian and Ubuntu
- Home Automation with Raspberry Pi
- The Controversy Behind Canonical's Intellectual Property Policy
- Shashlik - a Tasty New Android Simulator
- Embed Linux in Monitoring and Control Systems
- KDE Reveals Plasma Mobile
- diff -u: What's New in Kernel Development