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Learning Python, 2nd Edition
Python is a dynamic, interpreted, object oriented language used for both scripting and systems programming. Python is known for being easy to learn and use, while also being powerful enough to be used for such projects as Zope and the Chandler project. Its growing popularity is also based on its reputation for fostering programmer productivity and program maintainability. One drawback sometime cited is its relatively slow execution speed compared to compiled languages such as C.
For myself, I have probably read too many books about Python, but that is because I am an amateur hacker who learns programming slowly, and I find that reading several books about the same topic, covering the subject matter from different angles, allows me to better absorb the material. For me, this was a good review of the core language and a welcome refresher course on the newer aspects introduced in versions 2.2 and 2.3. For anyone who is new to Python and wants to learn from the ground up, this book would be a great place to start.
Mark Lutz is an authority on Python and one if its leading teachers, with both Learning and O'Reilly's Programming Python to his credit, as well as the courses and seminars he teaches professionally. In updating the original version, which was already very good, Mark has polished the chapters on the core language to a nearly perfect level, while his co-author David Ascher has done the same on the more advanced aspects of the book. In addition, Mr Lutz has benefited from extensive feedback from students and readers, and his explanations therefore anticipate common misunderstandings. Each chapter is accompanied by a problem and exercise section and answers are included at the back of the book.
A major addition to the new edition is a chapter on "Advanced Function Topics," including list comprehensions, generators and iterators. Python is sometimes used with a functional programing style almost similar to Lisp, although to List purists that may sound like heresy. The recent versions of the language have significantly upgraded Python's support for the functional style. Functions cover three chapters in the 2nd edition instead of just one.
Another major change since the first edition is extended coverage of Modules, which now occupies four chapter instead of just one. Python modules are a high level package structure for code and data, and they help facilitate code reuse. Yet another addition is coverage of Python's "new style classes." Coverage of classes and object oriented programming has been greatly expanded and now includes five whole chapters and almost 100 pages. Coverage of exceptions now is expanded to three chapters.
If you have been considering learning Python, now would be a great time since this new book is the perfect introductory text. If you already know Python and have read the first edition of Learning Python or another introductory text, then this book may not be essential since the new language features are covered pretty well on the web in various places, and you might be better advised to read one of the other fine books on non-introductory aspects of Python. But this book is about as good an introduction to the language as you are likely to find. The book does not cover all of the Python libraries nor many other topics, but it does briefly touch on the major libraries, frameworks, gui toolkits, and community resources.
If you want to learn the core Python language quickly, this may be your best bet. Learning Python only covers the basics, but it is deep in information on what it does cover. Well written, understandable, and in a very logical arrangement, this book is densely packed with info.
I have often found myself returning to the original book, and the new book will now fill this role. It is deep in information, well written, and a joy to read. For an experienced programmer who is just learning Python, it may be possible to thoroughly learn everything about the core language in one reading of this book. For relative newbies, it will be an often-used resource.
To read more reviews of books about Python, visit the Python Learning Foundation. You can purchase the Learning Python, 2nd Ed. from bn.com. Slashdot welcomes readers' book reviews -- to see your own review here, read the book review guidelines, then visit the submission page.
I would recommend "Python In A Nutshell" as it cuts to the chase and doesn't teach you programming so much as giving you what you need to know in Python. If you can already program, the "Nutshell" book is pretty good, plus the author can always be found around the Python Newsgroup(s).
Python is basically an attempt to merge Perl, TCL/TK and object orientated programming.
No way in hell. Python tries its best to avoid perlisms, and TCL/Tk doesn't even come close. Python has a strongly typed object system with one namespace.
I don't think that we really have to discuss the problems of Perl's "object system"
Perls object system is a hack. Python object system fits like a glove. ISTR that Larry kinda "copied" the objsystem from Python (and not vice versa), but it didn't really fit perl.
or the shortcomings of TCL/TK.
Shortcomings of TCL/Tk have really nothing to do with the topic. Don't try to sneak TCL/Tk into this. This has got to be the clumsiest strawman argument I've seen in a while. Chewbacca lives on Endor?
The result can be seen when you try to program a caller frame instance-preserving continuation in Python.
What do you mean? Closures (or "nested scopes" as they are referred to in the language docs - look them up before whining) work as expected. Can you give an example of the thing you are talking about in a language you know (assuming you know one). Are talking about what Stackless Python is trying to do?
But when the project advances they suddenly notice that python doesn't provide all necessary features and a whole rewrite is in order.
You don't really need "features", you can use libraries to add "features" and the core language is flexible enough for pretty much any tasks.
Save your wrists today - switch to Dvorak
Yeah, it's really sad how many large projects fail because they're implemented in a language that doesn't properly support continuations....
Wait a minute, I've been in the computer industry for decades, and other than myself, I could probably count on one hand the number of people I've met who even know what a continuation is. Other than as an amusing tool to utterly confuse any but the most advanced developers, continuations are probably only useful for coroutines, and coroutines are mostly useful for iterator generators, which recent versions of Python have generators nicely packaged in an easy-to-understand syntax (the yield statement).
Since few if any other popular languages give you even this much, it must be truly amazing that any software works at all.
One drawback sometime cited is its relatively slow execution speed compared to compiled languages such as C. this is the tradeoff between interpretation languages (python) and compiled languages (c, java). you get flexiablity in your code but you lose some speed in runtime. Well, most people use python to write scripts that are smaller than what they would write in C++ or java. you are not going to write half life 2 in python, right?...
One of the things I initially judge a language by is how elegant it is to code the quicksort algorithm (yes, I'm aware Python has a built-in sort function -- that is not the point). Here's quicksort in Python:
def quicksort(list):
if len(list) > 1:
pivot = list[0]
left = [x for x in list if x < pivot]
right = [x for x in list if x > pivot]
pivot = [x for x in list if x == pivot]
return quicksort(left) + pivot + quicksort(right)
return list
I'd say this speaks for itself. Enjoy.
I'm a long time perl programmer. Recently I did some research into Python. It really does look to be a more elegant language and enforce some structure that is needed in Perl.
That said I can't justify the switch. There are just too many good modules available in Perl (esp for the engineering work I do). When python has the bredth of packages that Perl does, and when they have a nicely organized way to access said modules, I'll be happy to switch.
One should not theorize before one has data. -Sherlock Holmes-
Excellent example:
1) I prefer the Python example. It's easier to read. And easier to write the first time. That's the reason Python is better than Ruby: when you write code, you get it right the first time more often. And that's such a huge advantage.
Of course, you screwed up. (I assume you wanted to print the value of k rather than the letter k)
2) You used a comprehension in Ruby but not in Python. And adict.items() would have been easier than adict.keys()
To be more fair:
adict = {'key':'val','key2':'val2','key3':3.14}
[sys.stdout.write(k+'-> '+str(v)+'\n') for k,v in adict.items()]
But if I was writing the code, I'd use a for loop rather than a comprehension.
But for penis length competitions, we'll use the latter.
Bryan