3. From Static to Interactive Visualization

Activity 3: Creating Different Interactive Visualizations Using Plotly Express

Solution

1. Open a new Jupyter notebook.
2. Import the necessary Python libraries and packages:

   import pandas as pd
   import plotly.express as px

3. Recreate the carbon dioxide emissions and GDP DataFrame from Exercise 22 in this notebook:

   url_co2 = 'https://raw.githubusercontent.com/TrainingByPackt/Interactive-Data-Visualization-with-Python/master/datasets/co2.csv'
   url_gm = 'https://raw.githubusercontent.com/TrainingByPackt/Interactive-Data-Visualization-with-Python/master/datasets/gapminder.csv'
   co2 = pd.read_csv(url_co2)
   gm = pd.read_csv(url_gm)
   df_gm = gm[['Country', 'region']].drop_duplicates()
   df_w_regions = pd.merge(co2, df_gm, left_on='country', right_on='Country', how='inner')
   df_w_regions = df_w_regions.drop('Country', axis='columns')
   new_co2 = pd.melt(df_w_regions...