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import streamlit as st | |
import plotly.express as px | |
import pandas as pd | |
# 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 | |
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} | |
] | |
# Total the spending values | |
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") | |
# 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)") | |
# Display the graph in Streamlit | |
st.plotly_chart(fig) |