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Book Review: Effective Python: 59 Specific Ways To Write Better Python
MassDosage writes: If you are familiar with the "Effective" style of books then you probably already know how this book is structured. If not here's a quick primer: the book consists of a number of small sections each of which focus on a specific problem, issue or idea and these are discussed in a "here's the best way to do X" manner. These sections are grouped into related chapters but can be read in pretty much any order and generally don't depend on each other (and when they do this will be called out in the text). The idea is that you can read the book from cover to cover if you want but you can also just dip in and out and read only the sections that are of interest to you. This also means that you can use the book as a reference in future when you inevitably forget the details or want to double check something. Read below for the rest of Mass Dosage's review.
Effective Python: 59 Specific Ways To Write Better Python
author
Brett Slatkin
pages
227
publisher
Addison-Wesley
rating
9/10
reviewer
Mass Dosage
ISBN
978-0-13-403428-7
summary
helps you harness the full power of Python to write exceptionally robust, efficient, maintainable, and well-performing code.
Effective Python stays true to this ethos and delivers 59 (not 60, nope, not 55) but 59 specific ways to write better Python. These are logically grouped into chapters covering broader conceptual topics like "Pythonic thinking", general technical features like "Concurrency and parallelism" as well as nitty gritty language details like "Meta classes and attributes". The range of topics is excellent and cover relevant aspects of the language that I'd imagine pretty much any developer will encounter at some point while developing Python programs. Even though there is no required order to reading the various sections if you want to read the book from cover to cover it's organized in such a way that you can do this. It starts off with getting your head around coding in Python before moving on to specifics of the language and then ending with advice on collaboration and setting up and running Python programs in production environments.
I really enjoyed the author's approach to each of the topics covered. He explains each item in a very thorough and considered manner with plenty of detail but manages to do this while still being clear and concise. Where relevant he describes multiple ways of achieving a goal while contrasting the pros and cons of various alternative solutions, ending off with what he considers the preferred approach. The reader can then make up their own mind based on the various options which applies best in a given situation instead of just being given one solution. The author clearly understand the internals of the Python language and the philosophy behind some of the design decisions that have resulted in certain features. This means that instead of just offering a solution he also gives you the context and reasoning behind things which I found made it a lot easier to understand. The discussions and reasoning feel balanced and informed by the experience of a developer who has been doing this "in the trenches" for years as opposed to someone in an ivory tower issuing dictates which sound good in theory but don't actually work in the real world. The vast majority of the topics are illustrated through code samples which are built on and modified at each stage along the way to a final solution. This gives the reader something practical they can take away and use and experiment with and clearly shows how something is done. The code samples are easily comprehensible with just enough code to demonstrate a point but not so much that you get distracted by unnecessary additions.
While most of the topics are Python specific plenty of the best practices and advice apply equally well to other programming languages. For example in one section the author recommends resisting some of the brevity offered by the Python where this can lead to unreadable code that is hard to understand but the same could be said of writing code in many other languages (I'm looking at you, Perl). This also applies to a section related to choosing the best data structure for the problem at hand — if you end up nesting Maps within Maps in your code then you're probably doing something wrong regardless of the language. Still, the main focus here is Python and the author does not shy away from going deep into technical details so you'll definitely need some knowledge of the language and ideally some experience using it in order to get the most out of it.
Effective Python is not a book for complete newbies to Python and I think it's suited more to intermediate users of the language wanting to take their skills to the next level or advanced programmers who might need some fresh takes on the way they do things. The subjects and opinions in this book could either convince you to do something differently or reassure you of the reasons why you're already doing things a certain way (external affirmation that you're right is also useful at times!) I'm no Python expert but I found the book drew me in and kept my attention and I certainly learnt a lot which will come in handy the next time I put on my Pythonista hat and do some Python coding. Highly recommended.
You can purchase Effective Python: 59 Specific Ways to Write Better Python from amazon.com. Slashdot welcomes readers' book reviews (sci-fi included) -- to see your own review here, read the book review guidelines, then visit the submission page. If you'd like to see what books we have available from our review library please let us know.
I really enjoyed the author's approach to each of the topics covered. He explains each item in a very thorough and considered manner with plenty of detail but manages to do this while still being clear and concise. Where relevant he describes multiple ways of achieving a goal while contrasting the pros and cons of various alternative solutions, ending off with what he considers the preferred approach. The reader can then make up their own mind based on the various options which applies best in a given situation instead of just being given one solution. The author clearly understand the internals of the Python language and the philosophy behind some of the design decisions that have resulted in certain features. This means that instead of just offering a solution he also gives you the context and reasoning behind things which I found made it a lot easier to understand. The discussions and reasoning feel balanced and informed by the experience of a developer who has been doing this "in the trenches" for years as opposed to someone in an ivory tower issuing dictates which sound good in theory but don't actually work in the real world. The vast majority of the topics are illustrated through code samples which are built on and modified at each stage along the way to a final solution. This gives the reader something practical they can take away and use and experiment with and clearly shows how something is done. The code samples are easily comprehensible with just enough code to demonstrate a point but not so much that you get distracted by unnecessary additions.
While most of the topics are Python specific plenty of the best practices and advice apply equally well to other programming languages. For example in one section the author recommends resisting some of the brevity offered by the Python where this can lead to unreadable code that is hard to understand but the same could be said of writing code in many other languages (I'm looking at you, Perl). This also applies to a section related to choosing the best data structure for the problem at hand — if you end up nesting Maps within Maps in your code then you're probably doing something wrong regardless of the language. Still, the main focus here is Python and the author does not shy away from going deep into technical details so you'll definitely need some knowledge of the language and ideally some experience using it in order to get the most out of it.
Effective Python is not a book for complete newbies to Python and I think it's suited more to intermediate users of the language wanting to take their skills to the next level or advanced programmers who might need some fresh takes on the way they do things. The subjects and opinions in this book could either convince you to do something differently or reassure you of the reasons why you're already doing things a certain way (external affirmation that you're right is also useful at times!) I'm no Python expert but I found the book drew me in and kept my attention and I certainly learnt a lot which will come in handy the next time I put on my Pythonista hat and do some Python coding. Highly recommended.
You can purchase Effective Python: 59 Specific Ways to Write Better Python from amazon.com. Slashdot welcomes readers' book reviews (sci-fi included) -- to see your own review here, read the book review guidelines, then visit the submission page. If you'd like to see what books we have available from our review library please let us know.
The best way to write Python is not to write it at all. Everywhere I've worked, any large project written in a dynamically typed language ballooned into a maintenance nightmare, sometimes to the point of people being afraid to touch the code to avoid breaking a rare and poorly-tested code path.
Seriously, it boggles the mind that, after all academic research and technical progress in compiler development, we still use dynamic languages. A compiler works like a mathematical proof machine, doubly so when a type-inferenced language is used. This is a tremendous advantage for error-checking and program correctness, yet it's "cool and hip" to take the hard way. Dynamic languages, python included, make easy things trivial, and hard things impossible. It's a tradeoff that should never be made.
I write python for a living, and I'm with you on dynamically typed languages, for big projects.
Having said that, small to medium projects and prototyping python is fantastic. Python lets me get a project up and running, without all the boilerplate and unnecessary overhead, which allows me to concentrate on getting the algorithm correct.
Then, if need be, I port to a language like Java (or C++ or C or w/ever).
STFU, Python shill. Python has no place. Compiled languages are the ONLY way to go if you want to get any big boy work done.
BTW, this Python shill actually HATES Python for a number of structural reasons and I avoid it for most tasks. It doesn't seem to be the proper tool for the kind of work I generally do, but that doesn't mean it doesn't have its use. A good programmer has many tools and understands which ones to use for what task.
"File to fit, pound to insert, paint to match" - Aircraft Maintenance 101