Update app.py

app.py

@@ -2,22 +2,22 @@ import streamlit as st
 import plotly.express as px
 import pandas as pd
 
-# Define the states and conditions of interest
-states = ["Minnesota", "Florida", "California"]
+# Define the states and conditions of interest with emojis
+states = ["Minnesota 🌲", "Florida 🌴", "California 🌞"]
 top_n = 10
 
-# Define the list dictionary of top 10 health conditions descending by cost
+# Define the list dictionary of top 10 health conditions descending by cost with emojis
 health_conditions = [
-    {"condition": "Heart disease", "spending": 214.3},
-    {"condition": "Trauma-related disorders", "spending": 198.6},
-    {"condition": "Cancer", "spending": 171.0},
-    {"condition": "Mental disorders", "spending": 150.8},
-    {"condition": "Osteoarthritis and joint disorders", "spending": 142.4},
-    {"condition": "Diabetes", "spending": 107.4},
-    {"condition": "Chronic obstructive pulmonary disease and asthma", "spending": 91.0},
-    {"condition": "Hypertension", "spending": 83.9},
-    {"condition": "Hyperlipidemia", "spending": 83.9},
-    {"condition": "Back problems", "spending": 67.0}
+    {"condition": "Heart disease ❤️", "spending": 214.3},
+    {"condition": "Trauma-related disorders 🤕", "spending": 198.6},
+    {"condition": "Cancer 🦀", "spending": 171.0},
+    {"condition": "Mental disorders 🧠", "spending": 150.8},
+    {"condition": "Osteoarthritis and joint disorders 🦴", "spending": 142.4},
+    {"condition": "Diabetes 🍬", "spending": 107.4},
+    {"condition": "Chronic obstructive pulmonary disease and asthma 🫁", "spending": 91.0},
+    {"condition": "Hypertension 🩺", "spending": 83.9},
+    {"condition": "Hyperlipidemia 🍔", "spending": 83.9},
+    {"condition": "Back problems 👨‍⚕️", "spending": 67.0}
 ]
 
 # Total the spending values
@@ -26,8 +26,17 @@ total_spending = sum([hc["spending"] for hc in health_conditions])
 # Create a DataFrame from the list dictionary
 df_top_conditions = pd.DataFrame(health_conditions)
 
+# Create a new field showing the state with the most cases per year for each condition
+df_top_conditions['most_cases_state'] = ''
+for i, row in df_top_conditions.iterrows():
+    condition = row['condition'].split(' ')[0]
+    condition_df = pd.read_csv(f'{condition}.csv')
+    condition_df = condition_df[condition_df['state'].isin(states)]
+    most_cases_state = condition_df.groupby('state')['cases'].sum().idxmax()
+    df_top_conditions.at[i, 'most_cases_state'] = f'{most_cases_state} 🏆'
+
 # Create the treemap graph using Plotly Express
-fig = px.treemap(df_top_conditions, path=["condition"], values="spending")
+fig = px.treemap(df_top_conditions, path=["condition"], values="spending", color='most_cases_state')
 
 # Set the title of the graph
 fig.update_layout(title=f"Top {top_n} Health Conditions in {', '.join(states)} by Spending (Total: ${total_spending}B)")