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)

Reading Data from Different Sources

One of the most valued and widely used skills of a data wrangling professional is the ability to extract and read data from a diverse array of sources into a structured format. Modern analytics pipelines depend on the ability and skills of those professionals to build a robust system that can scan and absorb a variety of data sources to build and analyze a pattern-rich model. Such kinds of feature-rich, multi-dimensional models will have high predictive and generalization accuracy. They will be valued by stakeholders and end users alike in any data-driven product. In the first part of this chapter, we will go through various data sources and how they can be imported into pandas DataFrames, thus imbuing data wrangling professionals with extremely valuable data ingestion knowledge.

Data Files Provided with This Chapter

As this topic is about reading from various data sources, we will use small files of various types in the following exercises...