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

Reading the contents of a file


A data file, like Python code, is simply a collection of text that follows a set of syntactical rules. In order for a Python program to make use of the data in the data file, it has to be converted to a data structure that can be processed programmatically.

Reading a file typically refers to the process a program uses to collect raw data, while converting data from a raw text format to a data structure is called parsing the data. The term reading a file can also refer to both collecting and parsing data. Usually, there are tools that take care of reading and parsing the data for you. In order to parse the JSON data in this chapter, I will be using Python's built-in json module. As mentioned previously, the structure of Python dictionaries and arrays correspond to the structures of JSON data, so representing JSON data in Python is quite intuitive.

Modules in Python

A Python module is a set of already made code that can be included in a program to add on extra functionality...