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

Understanding the CSV format

CSV, which stands for comma-separated value, is a file format used to store tabular data. As you may have guessed, a CSV file consists of text values that are separated by commas.

In a CSV file, each data entry is represented by a single line. (Another way of thinking about this is that each line is separated by a newline '\n' character, though newline characters are invisible in most text editors.)

By convention, the first row in a CSV file contains the columnheaders, or the names attributed to each column. In each subsequent row, the position of each value corresponds to the data variable to which that value belongs. In other words, the first value in a row corresponds to the first column header, the second value in a row corresponds to the second column header, and so on. The following example demonstrates the syntax of a CSV file:

<header1>, <header2>, <header3>, <header4>, <header5>
<value1>, <value2>, <value3&gt...