Practical Data Wrangling

Key Features

• This easy-to-follow guide takes you through every step of the data wrangling process in the best possible way
• Work with different types of datasets, and reshape the layout of your data to make it easier for analysis
• Get simple examples and real-life data wrangling solutions for data pre-processing

Book Description

Around 80% of time in data analysis is spent on cleaning and preparing data for analysis. This is, however, an important task, and is a prerequisite to the rest of the data analysis workflow, including visualization, analysis and reporting. Python and R are considered a popular choice of tool for data analysis, and have packages that can be best used to manipulate different kinds of data, as per your requirements. This book will show you the different data wrangling techniques, and how you can leverage the power of Python and R packages to implement them.

You’ll start by understanding the data wrangling process and get a solid foundation to work with different types of data. You’ll work with different data structures and acquire and parse data from various locations. You’ll also see how to reshape the layout of data and manipulate, summarize, and join data sets. Finally, we conclude with a quick primer on accessing and processing data from databases, conducting data exploration, and storing and retrieving data quickly using databases.

The book includes practical examples on each of these points using simple and real-world data sets to give you an easier understanding. By the end of the book, you’ll have a thorough understanding of all the data wrangling concepts and how to implement them in the best possible way.

What you will learn

• Read a csv file into python and R, and print out some statistics on the data
• Gain knowledge of the data formats and programming structures involved in retrieving API data
• Make effective use of regular expressions in the data wrangling process
• Explore the tools and packages available to prepare numerical data for analysis
• Find out how to have better control over manipulating the structure of the data
• Create a dexterity to programmatically read, audit, correct, and shape data
• Write and complete programs to take in, format, and output data sets

About the Author

Allan Visochek is a freelance web developer and data analyst in New Haven, Connecticut. Outside of work, Allan has a deep interest in machine learning and artificial intelligence.

Allan thoroughly enjoys teaching and sharing knowledge. After graduating from the Udacity Data Analyst Nanodegree program, he was contracted to Udacity for several months as a forum mentor and project reviewer, offering guidance to students working on data analysis projects. He has also written technical content for LearnToProgram.

Table of Contents

1. Programming with Data
2. An Introduction to Programming in Python
3. Reading, Writing and Modifying Data in Python I
4. Reading, Writing and Modifying Data in Python II
5. Text Data and Regular expressions
6. Cleaning Numerical Data: An Introduction To R and Rstudio
7. Data Munging in R using Dplyr
8. Getting data from the web
9. Working with really large datasets

Allan Visochek is a freelance web developer and data analyst in New Haven, Connecticut. Outside of work, Allan has a deep interest in machine learning and artificial intelligence.Allan thoroughly enjoys teaching and sharing knowledge. After graduating from the Udacity Data Analyst Nanodegree program, he was contracted to Udacity for several months as a forum mentor and project reviewer, offering guidance to students working on data analysis projects. He has also written technical content for LearnToProgram.