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Public Council Teleconference: Application Rationalization — Hidden Costs and Smart Decisions
November 17 at 11:00 am US/Eastern (GMT-5)
Join Honorio Padrón, of The Hackett Group, who will share the drivers for companies to tackle application rationalization and the results of research that define the hidden cost of complexity. Additionally, we will discuss key decision milestones—to start or not, holding the course steady and fulfilling expectations.
Virtual Desktop Cost-Benefit Analysis — Michael Jacobs, Catlin Group
The analysis contained in this presentation measures the cost of everything from the machines and licenses to the infrastructure for virtual vs. traditional desktop environments.
Honor your best senior team members - Apply for the CIO Ones to Watch Award
Get well-earned public recognition for your top up-and-coming team members, your IT organization and your enterprise. Award winners will be announced, publicized and feted in May 2010, great timing to help attract new IT recruits to your company.
Learn more about the CIO Executive Council »February 19, 2008 — CIO —
Programming language decisions often come down to personal preference and experience. Most modern languages are capable of performing the majority of programming tasks and include the necessary libraries to be useful day to day. Sometimes, interoperability concerns can dictate a particular platform, but nowadays, interoperability is commonly best achieved through XML interchange, shared SQL databases or Web services.
Therefore, when choosing a language for a particular purpose, it is often more important to look at how a language is designed, what it makes particularly easy, and what it makes more difficult. If features or performance do not detract, intangibles such as "feel," "elegance" and a sense of programmer productivity should be given serious weight.
Python is a powerful, opinionated and idiosyncratic scripting language, loved (and hated) by programmers the world over for its style, syntax and attention to whitespace. It excels as a "glue" language for putting together applications quickly, and many Python developers feel more productive in Python than in other languages. This article shows you why, and also points out situations when Python is perhaps not such a good choice.
First, let's take a quick look at the way Python works: a very short technical overview (suitable even for nontechnical managers).
To give you a feel for how Python looks, here is a short code snippet:
def say_hello(name):
""" Issue a familiar greeting
"""
print "Hello %s" % name
say_hello("Guido")
You may not know much about Python, but you can probably guess what's going on. This is Python's single best feature: Things generally work the way you expect. This obviousness in syntax makes the language relatively easy to learn for new programmers and easy to remember for occasional ones. However, the fact that it differs substantially from most other languages can be a barrier.
Python was created by Guido van Rossum, its "Benevolent Dictator for Life." The language and its standard library are developed by a thriving open-source community, but under Guido's watchful eye, Python's consistency and spirit remain intact. First released in the 1990s, Python is still evolving today.
Python is fully object oriented and includes a few functional programming constructs. It also has built-in support for commonly used data structures such as lists, dictionaries and sets. Its creators emphasize readability, consistency and simplicity; they believe that programming languages should be concise, but not too clever for their own good.
The main implementation of Python is written in C and runs on virtually any modern platform. There are also implementations that run inside a Java Virtual Machine (Jython, JPype), on the .Net platform (IronPython) and even one written in Python itself, called PyPy.
The C implementation is highly optimized, and is usually more than fast enough for normal programming tasks. However, if raw speed is your primary priority, look to a compiled language such as C. For embedded systems with limited memory, Python's runtime overhead may also be a problem.
Python is the default choice of scripting language for many developers. In the words of one Pythonista, it is rare to start a project with Python and discover that it was an entirely inappropriate choice as it grows, because Python scales both in project size and performance. That said, the degree of freedom that the language grants developers means they sometimes have to be a little more disciplined in how they structure their code.
It takes almost no effort to get started with Python. At its simplest, you can just launch the python interpreter and type away in interactive mode. The results of your statements are printed to the console immediately:
$ python
>>> price = 30.0
>>> quantity = 2
>>> print "Total: %f" % round(price * quantity)
The total cost is 60.0
Of course, this is useful only for very simple tasks, but save those statements to a file with a .py file, run that file through the interpreter and the script is executed.
As programs grow more complex, developers may define functions and classes and split code across multiple modules, or source files that make up the same program. Modules can be organized into packages, which can be turned into distributable, self-contained bundles (known as eggs).
You can find thousands of free Python packages at the Python Package Index. For day-to-day tasks, Python's standard library includes everything from shell interaction to file management, XML and CSV manipulation, and much more.
Python has a strong role in business computing, particularly in Web and enterprise development. Let's take a look at when it's the best (and not-so-best) choice.