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)

Advanced Mathematical Operations

Generating numerical arrays is a fairly common task. So far, we have been doing this by creating a Python list object and then converting that into a NumPy array. However, we can bypass that and work directly with native NumPy methods. The arange function creates a series of numbers based on the minimum and maximum bounds you give and the step size you specify. Another function, linspace, creates a series of fixed numbers of the intermediate points between two extremes.

In the next exercise, we are going to create a list and then convert that into a NumPy array. We will then show you how to perform some advanced mathematical operations on that array.

Exercise 3.04: Advanced Mathematical Operations on NumPy Arrays

In this exercise, we'll practice using all the built-in mathematical functions of the NumPy library. Here, we are going to be creating a list and converting it into a NumPy array. Then, we will perform some advanced mathematical...