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
About the Author
About the Reviewer
Customer Feedback


In summary this chapter was an introduction to the XML and CSV data formats. In Python, CSV data can be processed using the Python csv module or using the pandas module depending on personal preference and the nature of the task. The csv module can also be used to write output CSV data. (While it was not covered here, it is also possible to use the pandas module to output data in CSV and JSON formats.) Finally, XML data can be parsed using the Python xml.etree.ElementTree module.

In the next chapter, you will have the chance to work on a much more applied project--extracting street names from addresses. In the next chapter, I will introduce regular expressions, a tool for matching and extracting patterns in text data.