kill it with the 3D scatter plot

 # Create a killer 3d scatter plot of company revenue. The axis points are the top 10 restaurants, average order and rating # Calculate average order value for each restaurant avg_order_value = df.groupby('restaurant_name')['cost_of_the_order'].mean() # Calculate average rating for each restaurant avg_rating = df_rated.groupby('restaurant_name')['rating'].mean() # Get top 10 restaurants by revenue top_10_restaurants = revenue_by_restaurant.head(10).index # Filter data for top 10 restaurants df_top10 = df[df['restaurant_name'].isin(top_10_restaurants)] # Create a new DataFrame for plotting plot_df = pd.DataFrame({ 'Restaurant': top_10_restaurants, 'Revenue': revenue_by_restaurant.loc[top_10_restaurants], 'Average Order Value': avg_order_value.loc[top_10_restaurants], 'Average Rating': avg_rating.loc[top_10_restaurants] }) # Create 3D scatter plot fig = px.scatter_3d(plot_df, x='Average Order Value', y='Average Rating', z='Revenue', color='Restaurant', size_max=10, opacity=0.7) fig.update_layout(title='Company Revenue vs. Average Order Value and Rating (Top 10 Restaurants)', scene=dict(xaxis_title='Average Order Value', yaxis_title='Average Rating', zaxis_title='Revenue'))