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There is no star system to these book reviews as opinions are not scalar(down with the reduce() fn!).

Favorite Python Books By Category

Absolute Beginner to Programming

Python Visual Quick Start Guide by Chris Fehily. Peachpit Press. 2002.

While this book, claims it's not for "the absolute beginner" -- it really assumes very little. Until I got this book, my favorite "beginner's book" was "Practical Python" (which I still think is quite excellent). This book, makes the top of my list, because of it's combination of succinctness, clarity of explanations, simple but elegant visual layout, and the "Tips" section. This book covers the "core" of Python, and focusses on providing a short programming examples in both interpreter and script mode. I ended up reading it from cover to cover in a few days -- and then, kept right on using it. It's an excellent reference, complementing the online Python tutorial's and "Python in a Nutshell". The "Nutshell" of course is much more wide ranging -- but the Visual Quickstart is the place I will go to for initial code examples; or to look at the "tips" section when I can't get something to work. The author has also written the "SQL Visual Quickstart Guide" which is equally excellent. This book is really the "lean beef" of python books -- it has just the information someone new to Python needs, anticipates the things they might "interpret wrong" as well as warnings and "gotcha's" in the "Tips" section, and has and no excess fat. It is quite modestly priced, relative to the other Python books. Finally, the Python Visual Quickstart is almost 100 % hyperbole free. Good for calorie reduced diets.

New to Python from another Programming Language

Dive Into Python by Mark Pilgrim. A! Press. 2004

Starts fast, and keeps going. Good extended examples. My favorite chapters, "Regular Expressions". "Unit Testing, Test First Programming, Refactoring" (3 chapters playing out a theme. In the Regular Expressions chapter I finally began (began I say) to grok what the heck regular expressions are, and how I would use them for something other than a cure for insomnia. Favorite line from that chapter is the final quote:

Some people, when confronted with a problem, think, "I know, I'll use regular expressions. Now they have two problems. -- Jamie Zawinski, in comp.emacs.xemacs

The tri-chapter on Unit Testing/ Test First Programming / Refactoring is probably one of the best intro's to the code-level practices of extreme programming methodology I've found, without the usual associated hyperbole. The exammples carry through a single extended example of converting to and from roman numerals. This allows one to really follow through the logic with a concrete easy to comprehend, but not trivial example. The following chapter, "Functional Programming" actually follows the example, and theme, by using functional programming techniques to build the regression tests for the Roman numeral conversion utilities.

Advanced Topics

Text Processing in Python by David Mertz. Addison Wesley. 2003. This is a phenomenal book for introducing the functional programming style of Python programming. While that style is usually associated with functional programming languages like LISP, esoterica like the Lambda calculus -- what it really is, is data centric programming. As such, it is a style of programming that will appeal to those with a strong data-analytic and statistical background. We already think in complex chains of functions, where the output of one function is the input of the next, leading to higher and higher data abstractions. .... But I digrress. Favorite chapters are "Parsers and State Machines" and the appendix "A Data Compression Primer". I'm still working through the Parsers and State Machines chapter, so more when I'm through. I always wondered, what are the in's and outs of all those data compression utilities, and now I know. David Mertz also has two excellent series of articles on the web Charming Python and XML Matters

Python Reference

Python in a Nutshell by Alex Martelli. )'Reilly. 2003. This book lives up to the ideals of the O'Reilly Nutshell series (some haven't). It's compact, pithy, and my first line of enquiry when I need more than the online docs. Particularly good are it's recommendations for 3rd party modules. Favorite chapter's are "Language", everything I can't remember in under 30 pgs. "Numeric Processing" is a nice intro to the "Numarray" package, which is useful if you're doing statistics, or any other number-crunching heavy tasks. I find the documentation here, provides a nicer intro than the docs that come with Nummarray (though of course, they're much more detailed. A good companion to this book, for illustrative code examples is "The Python Cookbook" which Martelli is a co-author on. And a good companion to that is the Active State Python Cookbook site that's an on-line version of the cookbook.

Computer Science

Python Programming: An Introduction to Computer Science by John Zelle. Franklin, Beedle and Assoc. 2004. This is a very nicely paced book. In this case, Python is not the object, but the medium of instruction. The object is to provide a first course level intro to computer science via Python. Now having tried to read the Purple Wizardly Lisp book, and finding myself equally fascinated and repelled, and having glanced through several Java based books, and finding myself bored, this one fits "just right". Many of my first experiments with Python began with a piece of code from this book, and modifying it and playing until I broke something. My favorite chapters were "Data Collections" which is a nice primer for doing statistical type programming, and "Algorithm Design and Recursion". I particularly like this chapter for starting very simply, but working itself up to covering the halting problem, in a little program aptly called, "turing.py". A lesson in proof by contradiction via programming.

More Python Books Reviewed

The ones above are my favorites. Below are reviews of other books. There's a whole slew of intro books, all of which are competant. And there are a few other Python books on advanced topics. More reviews to follow, in alphabetical order, by the same categories as above favorites. ....

Absolute Beginner to Programming

Practical Python by Magnus Lie Hetland. A! Press. 2002.

Starts slow, and keeps building at a good clip. Never talks down to the reader, or loses the reader. Particularly good chapters are "Batteries Included" covering the Python Standard Library and "Playful Programming", a humorour and practical introduction to some techniques more aggressively marketed as "Extreme Programming". What is also nice about the book are it's extended examples -- 10 chapters of extended programming projects. I'll have to admit -- I read the first half of the book carefully, and skimmed without doing this 2nd half (probably not what the author intended). Still, I found this part of the book very valuable, to give me an idea of what's possible in Python. The latter section is probably good for those who like well developed examples, and all the codes. Less conducive to those who just like to experiment and make mistakes.

New to Python from another Programming Language

The Quick Python Book by Daryl Harms and Kenneth McDonald. Manning. 2000. This book could be said to follow the "learn by typing approach", much of the book consists of small code fragments you can type into the python interpreter. This is a way of learning, that really plays to the strengths of Python. It also allows one to learn the language in small spurts of activity, and provides a built in mneumonic device -- typing the code snippets, makes it easier to remember syntax and idioms of the language. I used this book a lot when I started, and it makes a nice complement to "Dive Into Python" whose approach is to go through more detailed code examples. My favorite chapters are 14/15 combined "Using the File System/ Reading and Writing Files" and chapter 16, "Classes and Object Oriented Programming". This was one of the first Python books I used, and going in, I was quite intimidated by programming. Going through all those little code snippets, and playing with altering them helped build confidence. What it's missing, due to it's focus ... is really developing an understanding of how to write longer bodies of code ... i.e. very little material on code design principles. However, the book probably assumes the user already knows this (though I did not).

Advanced Topics

Python Reference

Computer Science

Started: -- MishtuBanerjee - 30 Aug 2004

Updated: -- MishtuBanerjee - 09 Nov 2004
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