Book Image

The Data Wrangling Workshop - Second Edition

By : Brian Lipp, Shubhadeep Roychowdhury, Dr. Tirthajyoti Sarkar
Book Image

The Data Wrangling Workshop - Second Edition

By: Brian Lipp, Shubhadeep Roychowdhury, Dr. Tirthajyoti Sarkar

Overview of this book

While a huge amount of data is readily available to us, it is not useful in its raw form. For data to be meaningful, it must be curated and refined. If you’re a beginner, then The Data Wrangling Workshop will help to break down the process for you. You’ll start with the basics and build your knowledge, progressing from the core aspects behind data wrangling, to using the most popular tools and techniques. This book starts by showing you how to work with data structures using Python. Through examples and activities, you’ll understand why you should stay away from traditional methods of data cleaning used in other languages and take advantage of the specialized pre-built routines in Python. Later, you’ll learn how to use the same Python backend to extract and transform data from an array of sources, including the internet, large database vaults, and Excel financial tables. To help you prepare for more challenging scenarios, the book teaches you how to handle missing or incorrect data, and reformat it based on the requirements from your downstream analytics tool. By the end of this book, you will have developed a solid understanding of how to perform data wrangling with Python, and learned several techniques and best practices to extract, clean, transform, and format your data efficiently, from a diverse array of sources.
Table of Contents (11 chapters)

Data Wrangling in Statistics and Visualization

A good data wrangling professional is expected to encounter a dizzying array of diverse data sources each day. As we explained previously, due to a multitude of complex sub-processes and mutual interactions that give rise to such data, they all fall into the category of discrete or continuous random variables.

It would be extremely difficult and confusing for a data wrangler or a data science team if all of this data continued to be treated as completely random without any shape or pattern. A formal statistical basis must be given to such random data streams, and one of the simplest ways to start that process is to measure their descriptive statistics.

Assigning a stream of data to a particular distribution function (or a combination of many distributions) is actually part of inferential statistics. However, inferential statistics starts only when descriptive statistics is done alongside measuring all the important parameters of...