I met this book in one of the PyConEs-2013 conferences. In that conference they talked about how to write code sustainable along time. The topic was very interesting to me because I was worried about a phenomenon every programmer know sooner or later: even in Python, when your code grows it gets harder to be maintenable. I had programmed applications that some months later where hard to understand when I had to make a revision over them. Many years before that I had switched to Python to avoid that same problem in Java and Perl, but then it was there again. In the conference they promised that principles explained in that book helped to prevent the problem. So I read the book and I have to admit that they were right.
Reading this book is shocking. There are so many practices that we think that are right that actually are terribly wrong that you first read some passages with a mixture of surprise and incredulity. Things like saying that code comments are a recognition of your failure to make your code readable sounds strange in the first read but afterwards you really get that author is really right.
Book examples are not in Python but in Java, nevertheless I think that no Python programmer would have any problem to grasp concepts explained there. Few of the concepts are too Java-ish but many others are useful to Python developers. Some of the main concepts are:
- Your function names should explain clearly what the function do. No abbreviations allowed in function names.
- Function should do one thing and one thing only. A function should have only one purpose. Of course, a function can have many steps but all of them should be focused to get function's goal, and every step should be actually implemented in it's own function. That lead to functions easier to test.
- Functions should be short: 2 lines is great, 5 lines is good, 10 lines average, 15 poor.
- Code comments should be restricted only to explain design decisions instead of what code does.
- Don't mix levels of abstraction in the same function, meaning that you should not call directly python API while other steps of your function call to your own custom functions. Instead of that wrap your call to API inside another custom function.
- Order your implementations so you can read your code from top to down.
- Reduce as far as possible the number of arguments you pass into functions. Functions with 1 argument are good, 2 are average and 3 is likely poor.
- Don't Repeat Yourself (well, at least this concept was known to me before reading this book).
- Classes should be small.
- Classes should have only one reason to change (Single Responsibility Principle). IMHO I think this principle is a logic extension of "single purpose" for functions.
- Class attributes should be ideally used for all class methods.If you find attributes just used by an small subset of methods you should ask yourself if those attributes and methods could go in a separate class.
- Classes should be open for extension but close to modifications. That means that we incorporate new features by subclassing existing classes not modifying them. That way we reduce the risk of breaking things when we include new features.
- TDD or condemn yourself to hell of include further modifications in your code fearing you are going to break the whole thing.
There are many more concepts, all fully explained with examples, but those are the ones I keep in my head when a write code.
To test if principles of this book were right, I developed an application called Geolocate following these concepts and TDD ones. In the beginning it was hard to change my behaviour about writing code but as my code was getting bigger I realized it was easier than in my previous projects to find errors and fix them. Besides, when my application got a respectable size I let it rest for five months to see how easy was to retake development after so much time without reading the code. I was amazed. Although with my previous projects I would have needed some days to understand a code so big, this time I had fully recovered control of how my code worked in just an hour.
My conclusion is that this book is a "must read" that will let you improve dramatically your code quality and your peace of mind to maintain that same code afterwards.