Python for Informatics: Exploring Data Using Python 3

The first 10 chapters are similar to the Think Python book but there have been some changes. Nearly all number-oriented exercises have been replaced with data-oriented exercises.

Topics are presented in the order to needed to build increasingly sophisticated data analysis solutions. Some topics like try and catch are pulled forward and presented as part of the chapter on conditionals while other concepts like functions are left until they are needed to handle program complexity rather introduced as an early lesson in abstraction. The word "recursion" does not appear in the book at all.

In chapters 11-14, nearly all of the material is brand new, focusing on real-world uses and simple examples of Python for data analysis including automating tasks on your computer, retrieving data across the network, scraping web pages for data, using web services, parsing XML data, and creating and using databases using Structured Query Language.

The ultimate goal of all of these changes is a shift from a Computer Science to an Informatics focus is to only include topics into a first technology class that can be applied even if one chooses not to become a professional programmer.

Students who find this book interesting and want to further explore should look at Allen B. Downey's Think Python book. Because there is a lot of overlap between the two books, students will quickly pick up skills in the additional areas of computing in general and computational thinking that are covered in Think Python. And given that the books have a similar writing style and at times have identical text and examples, you should be able to move quickly through Think Python with a minimum of effort.