import streamlit as st
import pandas as pd

# Dataset 1: List of Hospitals that are over 1000 bed count by city and state
hospitals = [
    {'City': 'New York', 'State': 'NY', 'Hospital Name': 'New York-Presbyterian Hospital', 'Bed Count': 2446},
    {'City': 'Houston', 'State': 'TX', 'Hospital Name': 'Memorial Hermann-Texas Medical Center', 'Bed Count': 2048},
    {'City': 'Philadelphia', 'State': 'PA', 'Hospital Name': 'Hospital of the University of Pennsylvania', 'Bed Count': 1875},
    {'City': 'Los Angeles', 'State': 'CA', 'Hospital Name': 'Cedars-Sinai Medical Center', 'Bed Count': 1434},
    {'City': 'Boston', 'State': 'MA', 'Hospital Name': 'Massachusetts General Hospital', 'Bed Count': 1051},
]

# Dataset 2: State population size and square miles
population = [
    {'State': 'CA', 'Population': 39538223, 'Square Miles': 163696},
    {'State': 'TX', 'Population': 29145505, 'Square Miles': 268596},
    {'State': 'NY', 'Population': 20215751, 'Square Miles': 54555},
    {'State': 'FL', 'Population': 21538187, 'Square Miles': 65755},
    {'State': 'PA', 'Population': 13002700, 'Square Miles': 46054},
]

# Convert the dictionaries into pandas dataframes
hospitals_df = pd.DataFrame(hospitals)
population_df = pd.DataFrame(population)

# Merge the two dataframes using 'State' as the key
merged_df = pd.merge(hospitals_df, population_df, on='State')

# Join the 'City' and 'State' columns into a single column
merged_df['City_State'] = merged_df['City'] + ', ' + merged_df['State']

# Calculate the number of hospital beds per 10,000 people in each city-state
merged_df['Beds per 10K People'] = (merged_df['Bed Count'] / merged_df['Population']) * 10000

# Display the final merged dataframe
st.write(merged_df)