Book Image

Practical Data Wrangling

By : Allan Visochek
Book Image

Practical Data Wrangling

By: Allan Visochek

Overview of this book

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.
Table of Contents (16 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Summary


In summary, as datasets become larger and larger, it becomes important to consider how to manage memory while processing data, and how to store data to make it easy to work with and retrieve. Sufficiently large datasets should be processed entry by entry and not as a whole in order to preserve memory. For datasets that will need to be accessed frequently, it can be helpful to store and retrieve the data through a local database instance. This concludes the third and final section of Practical Data Wrangling! Congratulations!

In this book, I've made an effort to cover a wide range of approaches to data wrangling in order to give you the flexibility to tackle both standard and non-standard data wrangling challenges practically, efficiently, effectively, and with confidence. You should now have a broad and powerful set of tools that you can use to manipulate data and get the results that you need. 

Of course, not everything could fit in this book. As you approach various datasets in your...