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)
7. Combining What We Have Learned

Pyplot Basics

pyplot contains a simpler interface for creating visualizations that allow the users to plot the data without explicitly configuring the Figure and Axes themselves. They are automatically configured to achieve the desired output. It is handy to use the alias plt to reference the imported submodule, as follows:

import matplotlib.pyplot as plt

The following sections describe some of the common operations that are performed when using pyplot.

Creating Figures

You can use plt.figure() to create a new Figure. This function returns a Figure instance, but it is also passed to the backend. Every Figure-related command that follows is applied to the current Figure and does not need to know the Figure instance.

By default, the Figure has a width of 6.4 inches and a height of 4.8 inches with a dpi (dots per inch) of 100. To change the default values of the Figure, we can use the parameters figsize and dpi.

The following code snippet shows how we can manipulate...