Python Cookbook by David Beazley
My rating: 5 of 5 stars
The third edition of Python Cookbook is part of what seems to be a general trend of issuing new editions focusing on Python 3. For me, whose focus is on data analysis and technical computing, this is the time to be thinking about the change from Python 2 to Python 3 as the base libraries of Numpy, Scipy, Matplotlib, and iPython have been ported, and the various other libraries I use that depend on these are being ported as well. But this edition is not just porting the old cookbook, it is a complete rewrite to go with the big shift that was Python 2 to 3. Because a lot of what I used the old cookbook and many of the recipes at the ActiveState website was for handling issues related to crossing versions (I had some projects with Jython, which was several versions behind CPython) and ways of getting around issues that are purportedly solved in Python 3. For that the third edition of Python Cookbook fills its purpose of showing idiomatic ways of performing some programming tasks, and being a reference for how to do thing well and elegantly taking advantage of the language and libraries, not fighting it.
I focus on using Python as a data analysis platform. So I generally only learned as much of the language I needed to in order to use the scientific stack of Numpy, Scipy, Matplotlib, Pandas, and the libraries that were built around them. But that means that I have not gotten to know large portions of the standard library. And introductory books don't cover this either, they focus on using the language itself. There are also a large number of books and references that focus on Python as a web development tool or a system administration tool, so those parts of the standard library get a lot of coverage in teaching materials. But the rest you almost have to stumble upon. In ideal conditions, the way you would learn about much of the standard library is to have someone who was more experienced nearby show you what you needed to know, as she demonstrated methods in her code that did the things that you never learned in class. But sometimes there is no such person. The Python Cookbook plays this role, of demonstrating how to do things in Python 3 that are practical and you probably would not learn while learning the language itself.
Some areas that I found useful are the heapq, generators, and the I/O. heapq is a module and data structure I just never got around to learning. Usually discussions about Python data structures made their way to deque and heapq was discussed by reference. But after looking at the priority queue discussion, I fired up an iPython notebook and went through every recipe that used heapq and I've started thinking about how to rewrite a model I recently coded up. Generators and File I/O are areas that I knew in passing through my use with them in data analysis, but Python Cookbook opened up new ways of understanding (I am starting to get why JSON is so useful). Now, there is nothing special about these, but seeing these parts of the standard library in use in an elegant way is something beyond what you would get from a module documentation or a standard tutorial.
I do miss the introductions to each chapter that was in the 1st and 2nd editions of Python Cookbook. But what Python Cookbook does give is an idiomatic feel of using Python3, when there are not all that many mentors out there to go around. So this is something very useful for others who are starting to use Python 3. It is not for learning the language, but it is for using the language well.
Disclaimer: I received a free electronic copy of this book as part of the Oreilly Bloggers program
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