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)
Preface

Introduction

In the previous chapter, we learned how to use the pandas, numpy, and matplotlib libraries while handling various datatypes. In this chapter, we will learn about several advanced operations involving pandas DataFrames and numpy arrays. We will be working with several powerful DataFrame operations, including subsetting, filtering grouping, checking uniqueness, and even dealing with missing data, among others. These techniques are extremely useful when working with data in any way. When we want to look at a portion of the data, we must subset, filter, or group the data. Pandas contains the functionality to create descriptive statistics of the dataset. These methods will allow us to start shaping our perception of the data. Ideally, when we have a dataset, we want it to be complete, but in reality, there is often missing or corrupt data. This can happen for a variety of reasons that we can't control, such as user error and sensor malfunction. Pandas has built-in functionalities...