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)

NumPy Arrays

A NumPy array is similar to a list but differs in some ways. In the life of a data scientist, reading and manipulating an array is of prime importance, and it is also the most frequently encountered task. These arrays could be a one-dimensional list, a multi-dimensional table, or a matrix full of numbers and can be used for a variety of mathematical calculations.

An array could be filled with integers, floating-point numbers, Booleans, strings, or even mixed types. However, in the majority of cases, numeric data types are predominant. Some example scenarios where you will need to handle numeric arrays are as follows:

  • To read a list of phone numbers and postal codes and extract a certain pattern
  • To create a matrix with random numbers to run a Monte Carlo simulation on a statistical process
  • To scale and normalize a sales figure table, with lots of financial and transactional data
  • To create a smaller table of key descriptive statistics (for example...