Book Image

The Data Visualization Workshop

By : Mario Döbler, Tim Großmann
Book Image

The Data Visualization Workshop

By: Mario Döbler, Tim Großmann

Overview of this book

Do you want to transform data into captivating images? Do you want to make it easy for your audience to process and understand the patterns, trends, and relationships hidden within your data? The Data Visualization Workshop will guide you through the world of data visualization and help you to unlock simple secrets for transforming data into meaningful visuals with the help of exciting exercises and activities. Starting with an introduction to data visualization, this book shows you how to first prepare raw data for visualization using NumPy and pandas operations. As you progress, you’ll use plotting techniques, such as comparison and distribution, to identify relationships and similarities between datasets. You’ll then work through practical exercises to simplify the process of creating visualizations using Python plotting libraries such as Matplotlib and Seaborn. If you’ve ever wondered how popular companies like Uber and Airbnb use geoplotlib for geographical visualizations, this book has got you covered, helping you analyze and understand the process effectively. Finally, you’ll use the Bokeh library to create dynamic visualizations that can be integrated into any web page. By the end of this workshop, you’ll have learned how to present engaging mission-critical insights by creating impactful visualizations with real-world data.
Table of Contents (9 chapters)
Preface
7
7. Combining What We Have Learned

Introduction

In the previous chapter, we focused on various visualizations and identified which visualization is best suited to show certain information for a given dataset. We learned about the features, uses, and best practices for following various plots such as comparison plots, relation plots, composition plots, distribution plots, and geoplots.

Matplotlib is probably the most popular plotting library for Python. It is used for data science and machine learning visualizations all around the world. John Hunter was an American neurobiologist who began developing Matplotlib in 2003. It aimed to emulate the commands of the MATLAB software, which was the scientific standard back then. Several features, such as the global style of MATLAB, were introduced into Matplotlib to make the transition to Matplotlib easier for MATLAB users. This chapter teaches you how to best utilize the various functions and methods of Matplotlib to create insightful visualizations.

Before we start...