The simplest and clearest explanations out of nearly a dozen introductory python texts I have collected. Artfully crafted instructional progression with by far the most relevant and amusing analogies and example cases. It is interesting to note that this is basically the third formal edition of a constantly improving open-source resource that was initially restructured and reworked by a very gifted teacher to teach data analysis and data mining; if you have tried to work through Think Python you should be able to recognize the refinement resulting from the writer's having used previous editions of this text to teach tens of thousands of students. I keep going back to this book as a reference as well. It really does contain the concisest explanations, and I am recognizing more and more that the code blocks and larger processing methods he describes are paragons of industry approaches. Starting here will make your life so much simpler and your learning immediately so much more productive: you won't get lost in all the technical asides and theoretical maelstroms so many datascience Python texts seem to love to spin into and through. (I have also found the text to be a very helpful foundation for integrating all the open source videos and resources the author put together while teaching his classes through Coursera [and the University of Michigan]). - A data analyst from a non-datascience background learning Python for the first time.