Update app.py

app.py

@@ -1,23 +1,24 @@
 import streamlit as st
-import plotly.express as px
 import pandas as pd
+import plotly.graph_objects as go
+import plotly.express as px
 
 # Define the states and conditions of interest
 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", "emoji": "💗", "spending": 214.3},
+    {"condition": "🤕 Trauma-related disorders", "emoji": "🚑", "spending": 198.6},
+    {"condition": "🦀 Cancer", "emoji": "🎗️", "spending": 171.0},
+    {"condition": "🧠 Mental disorders", "emoji": "🧘", "spending": 150.8},
+    {"condition": "🦴 Osteoarthritis and joint disorders", "emoji": "🏥", "spending": 142.4},
+    {"condition": "💉 Diabetes", "emoji": "🩸", "spending": 107.4},
+    {"condition": "🫁 Chronic obstructive pulmonary disease and asthma", "emoji": "🫀", "spending": 91.0},
+    {"condition": "🩺 Hypertension", "emoji": "💉", "spending": 83.9},
+    {"condition": "🔬 Hyperlipidemia", "emoji": "🔬", "spending": 83.9},
+    {"condition": "🦴 Back problems", "emoji": "🧍", "spending": 67.0}
 ]
 
 # Total the spending values
@@ -26,11 +27,18 @@ 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 the treemap graph using Plotly Express
-fig = px.treemap(df_top_conditions, path=["condition"], values="spending")
+# Create the map graph using Plotly Express
+fig_map = px.choropleth(locations=["CA", "FL", "MN"], locationmode="USA-states", color=[1, 2, 3], scope="usa")
+fig_map.update_layout(geo=dict(bgcolor= "rgba(0, 0, 0, 0)", lakecolor="rgb(255, 255, 255)"))
 
-# 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)")
+# Create the bar chart using Plotly Graph Objects
+fig_bar = go.Figure(go.Bar(x=df_top_conditions["emoji"] + " " + df_top_conditions["condition"], y=df_top_conditions["spending"], marker_color="#1f77b4"))
+fig_bar.update_layout(title=f"Top {top_n} Health Conditions in {', '.join(states)} by Spending (Total: ${total_spending}B)",
+                      yaxis_title="Spending ($B)", plot_bgcolor='rgba(0,0,0,0)', xaxis=dict(showgrid=False), yaxis=dict(showgrid=False))
 
-# Display the graph in Streamlit
-st.plotly_chart(fig)
+# Display the map and the bar chart in Streamlit
+col1, col2 = st.beta_columns([2, 1])
+with col1:
+    st.plotly_chart(fig_map, use_container_width=True)
+with col2:
+    st.plotly_chart(fig_bar, use_container_width=True)