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...