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

6. Making Things Interactive with Bokeh

Activity 6.01: Plotting Mean Car Prices of Manufacturers


  1. Create an Activity6.01.ipynb Jupyter notebook in the Chapter06/Activity6.01 folder.
  2. Import the necessary libraries:
    import pandas as pd
    from import output_notebook
  3. Load the automobiles.csv dataset from the Datasets folder:
    dataset = pd.read_csv('../../Datasets/automobiles.csv')
  4. Use the head method to print the first five rows of the dataset:

    The following figure shows the output of the preceding code:

Figure 6.36: Loading the top five rows of the automobile dataset

Plotting each car with its price

  1. Use the plotting interface of Bokeh to do some basic visualization first. Let's plot each car with its price. Import figure and show from the bokeh.plotting interface:
    from bokeh.plotting import figure, show
  2. First, use the index as our x-axis since we just want to plot each car with its price...