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

Data Formatting

In this section, we will format a given dataset. The main motivations behind formatting data properly are as follows:

  • It helps all the downstream systems have a single and pre-agreed form of data for each data point, thus avoiding surprises and, in effect, there is no risk which might break the system.
  • To produce a human-readable report from lower-level data that is, most of the time, created for machine consumption.
  • To find errors in data.

There are a few ways to perform data formatting in Python. We will begin with the modulus % operator.

The % operator

Python gives us the modulus % operator to apply basic formatting on data. To demonstrate this, we will load the data by reading the combined_data.csv file, and then we will apply some basic formatting to it.

Note

The combined_data.csv file contains some sample medical data for four individuals. The file can be found here: https://packt.live/310179U.

We can load the data from the...