Read more about Python for Everybody: Exploring Data Using Python 3

Python for Everybody: Exploring Data Using Python 3

(8 reviews)

Charles Severance, University of Michigan

Copyright Year: 2016

ISBN 13: 9781530051120

Publisher: Charles Severance

Language: English

Formats Available

Conditions of Use

Attribution-NonCommercial-ShareAlike Attribution-NonCommercial-ShareAlike


Learn more about reviews.

Reviewed by Mya T. Bowen, Assistant Professor, Roxbury Community College on 6/17/21

The text covers all areas and ideas of the subject appropriately and provides an effective glossary at the end of every chapter. read more

Reviewed by Shahab Hussain, Adjunct Professor, North Shore Community College on 6/2/21

This book presents majority of the Python Programming critical areas and explains with useful examples. read more

Reviewed by Syeda Ferdous Begum, Professor, Middlesex Community College on 4/14/21, updated 4/20/21

This book great for beginner programmer who does not know anything about programming. This text can be used in the computer science and information technology field. Even though it is targeted towards beginners, it contains advanced topics but... read more

Reviewed by Sankardas Roy, Assistant Professor, Bowling Green State University on 1/31/21

As the title suggests, the intended audience of this book is "everybody" who wants to explore Python for data analysis work. The book does cover the basics of Python programming, giving sufficient amount of subject details for a new programmer.... read more

Reviewed by Kuuipo Walsh, Director, Oregon State University on 8/28/20

Dr. Charles R. Severance's book introduces the fundamentals of Python programming in Chapters 1-10, without diving deeply into object-oriented programming. These chapters focus on code examples manipulating text and text files. Given the title, it... read more

Reviewed by Joe Paris, Faculty, Linn-Benton Community College on 7/2/20

This book is an approachable introduction to both Python the language and its application to information science -- namely retrieving, cleaning, and storing data for later analysis. Chapters two through ten are based heavily on Allen Downey and... read more

Reviewed by Matt Bailey, Associate Professor, Bucknell University on 2/25/19

The book is a comprehensive and approachable introduction to Python. The first nine chapters are terse, but comprehensive introduction to Python. Given the title, I had expected some discussion of the pandas Python package. It is more geared... read more

Reviewed by Giancarlo Schrementi, Instructor, Hollins University on 5/21/18

This book is a remix of the excellent Think Python book by Allen Downey. The book keeps the clarity of the original while including examples skewed towards data applications, particularly text processing. The remix adds chapters on regular... read more

Table of Contents

  • 1 Why should you learn to write programs?
  • 2 Variables, expressions, and statements
  • 3 Conditional execution
  • 4 Functions
  • 5 Iteration
  • 6 Strings
  • 7 Files
  • 8 Lists
  • 9 Dictionaries
  • 10 Tuples
  • 11 Regular expressions
  • 12 Networked programs
  • 13 Using Web Services
  • 14 Object-Oriented Programming
  • 15 Using databases and SQL
  • 16 Visualizing data
  • A Contributions
  • B Copyright Detail

Ancillary Material

  • Submit ancillary resource
  • About the Book

    I never seemed to find the perfect data-oriented Python book for my course, so I set out to write just such a book. Luckily at a faculty meeting three weeks before I was about to start my new book from scratch over the holiday break, Dr. Atul Prakash showed me the Think Python book which he had used to teach his Python course that semester. It is a well-written Computer Science text with a focus on short, direct explanations and ease of learning.The overall book structure has been changed to get to doing data analysis problems as quickly as possible and have a series of running examples and exercises about data analysis from the very beginning.

    Chapters 2–10 are similar to the Think Python book, but there have been major changes. Number-oriented examples and exercises have been replaced with data- oriented exercises. Topics are presented in the order needed to build increasingly sophisticated data analysis solutions. Some topics like try and except are pulled forward and presented as part of the chapter on conditionals. Functions are given very light treatment until they are needed to handle program complexity rather than introduced as an early lesson in abstraction. Nearly all user-defined functions have been removed from the example code and exercises outside of Chapter 4. The word “recursion”1 does not appear in the book at all.

    In chapters 1 and 11–16, all of the material is brand new, focusing on real-world uses and simple examples of Python for data analysis including regular expressions for searching and parsing, automating tasks on your computer, retrieving data across the network, scraping web pages for data, object-oriented programming, using web services, parsing XML and JSON data, creating and using databases using Structured Query Language, and visualizing data.

    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 useful even if one chooses not to become a professional programmer.

    About the Contributors


    Charles Severance is a Clinical Associate Professor at the University of Michigan School of Information.