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)

Identifying and Cleaning Outliers

When confronted with real-world data, we often see a specific thing in a set of records: there are some data points that do not fit with the rest of the records. They have some values that are too big, too small, or that are completely missing. These kinds of records are called outliers.

Statistically, there is a proper definition and idea about what an outlier means. And often, you need deep domain expertise to understand when to call a particular record an outlier. However, in this exercise, we will look into some basic techniques that are commonplace for flagging and filtering outliers in real-world data for day-to-day work.

Exercise 6.07: Outliers in Numerical Data

In this exercise, we will construct a notion of an outlier based on numerical data. Imagine a cosine curve. If you remember the math for this from high school, then a cosine curve is a very smooth curve within the limit of [1, -1]. We will plot this cosine curve using the plot...