update bar charts, much prettier

app.py

@@ -104,18 +104,39 @@ def full_report():
     
     country_df = df.groupby(['country', 'iso']).sum().reset_index()
     country_df = country_df.sort_values(by=['jumps'], ascending=False)
-
-    country_avg = df.groupby(['country', 'iso']).mean().reset_index()
-    country_avg = country_avg.sort_values(by=['jumps'], ascending=False)
-
-    total = px.bar(country_df, 
-                   x='country', y='jumps', 
-                   title='Total Jumps by Country', 
+    top_10_countries = country_df.iloc[:10]['country'].tolist()
+
+    country_df_to_plot = df.groupby(['country', 'iso', 'city']).sum().reset_index()
+    country_df_to_plot = country_df_to_plot[country_df_to_plot['country'].isin(top_10_countries)].reset_index(drop=True)
+    country_df_to_plot = country_df_to_plot.sort_values(by=['day', 'jumps'], ascending=True)
+    total = px.bar(country_df_to_plot, 
+                   y='country', x='jumps', 
+                   color='city',
+                   title='Total Jumps by Country/City', 
+                   orientation='h',
+                   category_orders={'country': top_10_countries},
                    template="plotly_dark")
-    avg = px.bar(country_avg, 
-                 x='country', y='jumps', 
-                 title='Average Jumps per Daily Session', 
+    total.update_layout(showlegend=False)
+
+    city_df = df.groupby(['city', 'iso']).sum().reset_index()
+    city_df = city_df.sort_values(by=['jumps'], ascending=False)
+    city_df['city'] = city_df.apply(lambda row: row['city'] + ', ' + row['iso'], axis=1)
+    top_10_cities = city_df.iloc[:10]['city'].tolist()
+
+    city_df = df.groupby(['city', 'iso', 'day']).sum().reset_index()
+    city_df = city_df[city_df['city'] != '(not set)']
+    city_df['city'] = city_df.apply(lambda row: row['city'] + ', ' + row['iso'], axis=1)
+    city_df = city_df[city_df['city'].isin(top_10_cities)].reset_index(drop=True)
+    city_df = city_df.sort_values(by=['jumps'], ascending=True)
+
+    avg = px.bar(city_df, 
+                 y='city', x='jumps', color='day',
+                 title='Total Jumps by City/Day', 
+                 orientation='h',
+                 category_orders={'city': top_10_cities},
                  template="plotly_dark")
+    
+    avg.update_layout(showlegend=False)
 
     country_df['rank'] = country_df['jumps'].rank(ascending=False)
     total_map = px.choropleth(country_df, locations="iso",