Pandas for Everyone: Python Data Analysis (Paperback)

This tutorial teaches students everything they 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.
Features Establishes a solid foundation for all the Pandas basics needed to be effective
Covers data frames, statistical calculations, data munging, modeling, machine learning, reproducible documents and much more

Teaches step-by-step through easy, incremental examples, with plenty of opportunities to "code along"

About the Author

Daniel Chen is a graduate student in the interdisciplinary PhD program in Genetics, Bioinformatics and Computational Biology (GBCB) at Virginia Tech. He is involved with Software Carpentry as an instructor and lesson maintainer. He completed his master’s degree in public health at Columbia University Mailman School of Public Health in Epidemiology and currently works at the Social and Decision Analytics Laboratory under the Bio complexity Institute of Virginia Tech where he is working with data to inform policy decision-making. He is the author of Pandas for Everyone and Pandas Data Analysis with Python Fundamentals Live Lessons.