Book Image

Interactive Data Visualization with Python - Second Edition

By : Abha Belorkar, Sharath Chandra Guntuku, Shubhangi Hora, Anshu Kumar
Book Image

Interactive Data Visualization with Python - Second Edition

By: Abha Belorkar, Sharath Chandra Guntuku, Shubhangi Hora, Anshu Kumar

Overview of this book

With so much data being continuously generated, developers, who can present data as impactful and interesting visualizations, are always in demand. Interactive Data Visualization with Python sharpens your data exploration skills, tells you everything there is to know about interactive data visualization in Python. You'll begin by learning how to draw various plots with Matplotlib and Seaborn, the non-interactive data visualization libraries. You'll study different types of visualizations, compare them, and find out how to select a particular type of visualization to suit your requirements. After you get a hang of the various non-interactive visualization libraries, you'll learn the principles of intuitive and persuasive data visualization, and use Bokeh and Plotly to transform your visuals into strong stories. You'll also gain insight into how interactive data and model visualization can optimize the performance of a regression model. By the end of the course, you'll have a new skill set that'll make you the go-to person for transforming data visualizations into engaging and interesting stories.
Table of Contents (9 chapters)

6. Interactive Visualizations of Data across Geographical Regions

Activity 6: Creating a Choropleth Map to Represent Total Renewable Energy Production and Consumption across the World

Solution

  1. Load the renewable energy production dataset:
    import pandas as pd
    renewable_energy_prod_url = "https://raw.githubusercontent.com/TrainingByPackt/Interactive-Data-Visualization-with-Python/master/datasets/share-of-electricity-production-from-renewable-sources.csv"
    renewable_energy_prod_df = pd.read_csv(renewable_energy_prod_url)
    renewable_energy_prod_df.head()

    The output is as follows:

    Figure 6.29: Renewable sources dataset
  2. Sort the production DataFrame based on the Year feature:
    renewable_energy_prod_df.sort_values(by=['Year'],inplace=True)
    renewable_energy_prod_df.head()

    The output is as follows:

    Figure 6.30: Renewable sources dataset after sorting by year
  3. Generate a choropleth map using the plotly express module animated based on Year:
    import plotly.express as...