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)


In this chapter, we started with the basics of NumPy arrays, including how to create them and their essential properties. We discussed and showed how a NumPy array is optimized for vectorized element-wise operations and differs from a regular Python list. Then, we moved on to practicing various operations on NumPy arrays such as indexing, slicing, filtering, and reshaping. We also covered special one-dimensional and two-dimensional arrays, such as zeros, ones, identity matrices, and random arrays.

In the second major topic of this chapter, we started with pandas series objects and quickly moved on to a critically important object – pandas DataFrames. They are analogous to Excel or Matlab or a database tab, but with many useful properties for data wrangling. We demonstrated some basic operations on DataFrames, such as indexing, sub-setting, row and column addition, and deletion.

Next, we covered the basics of plotting with matplotlib, the most widely used and...