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

Basic Plots

In this section, we are going to go through the different types of simple plots. This includes bar charts, pie charts, stacked bar, and area charts, histograms, box plots, scatter plots and bubble plots. Please refer to the previous chapter to get more details about these plots. More sophisticated plots, such as violin plots, will be covered in the next chapter, using Seaborn instead of Matplotlib.

Bar Chart

The, height, [width]) creates a vertical bar plot. For horizontal bars, use the plt.barh() function.

Important parameters:

  • x: Specifies the x coordinates of the bars
  • height: Specifies the height of the bars
  • width (optional): Specifies the width of all bars; the default is 0.8

Example:['A', 'B', 'C', 'D'], [20, 25, 40, 10])

The preceding code creates a bar plot, as shown in the following diagram:

Figure 3.16: A simple bar chart

If you want to...