Foundational Python For Data Science 
Author: Kennedy Behrman This book sets out to be a simple introduction to Python, specifically how to use it to work with data. The book opens with an introduction to notebooks, with sections on Jupyter notebooks and Google Colab. The emphasis is much more on Colab, essentially you're told that Jupyter exists, and the author uses Colab for showing how to do things.
Python fundamentals are introduced next, with a couple of pages running through the main Python statements, then basic math operations, and how to use dot notation for classes and objects. This is very much along the lines of covering the absolute basics of what you need to know to use and do minor modifications to existing code. A chapter on sequences comes next, essentially introducing the way you work with data in Python. Other data structures are then introduced  dictionaries, sets and frozen sets. Behrman then looks at execution control  compound statements, ifs and loops, before introducing functions. The next part of the book concentrates on the main data science libraries, with chapters introducing and showing how to work with NumPy, SciPy and Pandas. Behrman then looks at other libraries for visualization, machine learning, and natural language work. The third part of the book goes back to Python, with chapters on functional programming, objectoriented programming, and a catchall 'other topics'. I thought this was a good book. It takes a very pragmatic view of what someone might need to know if they are mainly interested in getting at the data, and need a bit of Python to be able to make things work. It's not a book I'd recommend for learning to program, but there's a lot you can still do if you know how to write (or modify) a short bit of code so you can make use of NumPy or Pandas. Recommended. To be informed about new articles on I Programmer, sign up for our weekly newsletter, subscribe to the RSS feed and follow us on Twitter, Facebook or Linkedin.


Last Updated ( Saturday, 23 July 2022 ) 