Classic Computer Science Problems in Python

This book is great for semi-experienced python users. Every chapter introduces several new pythonic concepts and provides a very nice generic framework for trying out the algorithms described. It is the kind of book where you'd get the most out of it when you work through it.

If you've already done a decent amount of programming in at least two other languages (including at least one OO), this is a great book to learn about python from. It does not bother with the simple things that you can look up online in two seconds (flow control syntax, defining a simple function, etc.) or OOP concepts. Instead it is a very well chosen set of, as the title implies, classic algorithms with broad use value: K-means clustering, graph searching, constraint-satisfaction problems, even neural network basics. Each of these forms a chapter with a simple, comprehensible generic implementation, presented piecemeal in a narrative and then applied to a handful of different concrete problems. I'd seen most of these techniques before, but I wish my original introduction to them had been this succinct and balanced.Along the way of various features and conventions of python are introduced in a natural way. The author also uses the relatively new (not strictly enforced) typing annotations, which I appreciated as a fan of strong typing. Again, though, if you are out to learn programming with python, this probably is not the book for you. But if you already understand OOP well and want something interesting to survey a new language with, this is a lot of fun.

I've been doing CS / programming for 20 years now, and a professional for 13 years already. Yet I just got this book and for the small size of it, it's just awesome how good quality it is. After 20 years and still can learn new approaches to solving classical problems, new ways to express algorithms, make them simpler, more readable, or better explained etc. I've coded breadth-first tree traversals countless of times, but it's so well structured in this book that I can now visualise it easier. It also goes as far as machine learning.You don't have to be a Python guru for it. If you know Java or any other language, then a couple of days of getting acquainted with Python will be enough to understand this book well.

I am a season programer and really enjoyed this book. Found examples to be quite complete. It has helped me improve my Python skills.

It directly and straight starts with the examples and problems, and it avoids to mention too much the good old time of the 60s, 70s, ... This format helps a lot to keep the stuff clean and highlight the quintessential. For me, the book is a must-have, because it embraces the basic and essential standard algorithms such as searching, sorting, Genetic, neural network, and more ... and therefore it just a good friend