Fundamental Python for Data Science
Author: Kennedy Behrman
Audience: Data Scientists
Reviewer: Kay Ewbank
This book is intended as a simple introduction to Python, in particular 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 focus is much more on Colab, basically you’re being told that Jupyter exists, and the author uses Colab to show how to do things.
Python fundamentals are then introduced, with a few pages going through the main Python instructions, then basic mathematical operations and how to use dot notation for classes and objects. This goes the way of covering the absolute basics of what you need to know to use and making minor changes to existing code.
A chapter on sequences comes next, essentially showing how you work with data in Python. Other data structures are then introduced – dictionaries, sets and frozen sets. Behrman then turns to execution control – compound statements, ifs and loops, before introducing functions.
The next part of the book focuses 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 returns to Python, with chapters on functional programming, object-oriented programming, and a catch-all “other topics”.
I thought it was a good book. It takes a very pragmatic view of what someone might need to know if they’re primarily interested in data access and need a bit of Python to get things working.
It’s not a book I would recommend for learning to program, but there’s still a lot you can do if you know how to write (or modify) a little bit of code so you can use NumPy or Pandas. Recommended.
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