This tutorial teaches everything you need to get started with Python programming for the fast-growing field of data analysis. Daniel Chen tightly links each new concept with easy-to-apply, relevant examples from modern data analysis. Unlike other beginner's books, this guide helps today's newcomers learn both Python and its popular Pandas data science toolset in the context of tasks they'll really want to perform. Following the proven Software Carpentry approach to teaching programming, Chen introduces each concept with a simple motivating example, slowly offering deeper insights and expanding your ability to handle concrete tasks. Each chapter is illuminated with a concept map: an intuitive visual index of what you'll learn -- and an easy way to refer back to what you've already learned. An extensive set of easy-to-read appendices help you fill knowledge gaps wherever they may exist.
Coverage includes: * Setting up your Python and Pandas environment * Getting started with Pandas dataframes * Using dataframes to calculate and perform basic statistical tasks * Plotting in Matplotlib * Cleaning data, reshaping dataframes, handling missing values, working with dates, and more * Building basic data analytics models * Applying machine learning techniques: both supervised and unsupervised * Creating reproducible documents using literate programming techniques
