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)

Applying Your Knowledge to a Data Wrangling Task

Suppose you are asked the following question:

In India, did the enrollment in primary/secondary/tertiary education increase with the improvement of per capita GDP in the past 15 years? To provide an accurate and analyzed result, machine learning and data visualization techniques will be used by an expert data scientist. The actual modeling and analysis will be done by a senior data scientist, who will use machine learning and data visualization for analysis. As a data wrangling expert, your job will be to acquire and provide a clean dataset that contains educational enrollment and GDP data side by side.

Suppose you have a link for a dataset from the United Nations and you can download the dataset of education (for all the nations around the world). But this dataset has some missing values and, moreover, it does not have any Gross Domestic Product (GDP) information. Someone has also given you another separate CSV file (downloaded...