Update app.py

app.py

@@ -1,31 +1,37 @@
 import streamlit as st
-import pandas as pd
-
-# Define two example datasets
-hospitals = [
-    {'City': 'New York', 'State': 'NY', 'Beds': 2000},
-    {'City': 'Los Angeles', 'State': 'CA', 'Beds': 1500},
-    {'City': 'Chicago', 'State': 'IL', 'Beds': 1200},
-    {'City': 'Houston', 'State': 'TX', 'Beds': 1800},
-    {'City': 'Phoenix', 'State': 'AZ', 'Beds': 1300}
-]
-
-populations = [
-    {'State': 'NY', 'Population': 19530000, 'SquareMiles': 54555},
-    {'State': 'CA', 'Population': 39540000, 'SquareMiles': 163696},
-    {'State': 'IL', 'Population': 12670000, 'SquareMiles': 57914},
-    {'State': 'TX', 'Population': 29150000, 'SquareMiles': 268596},
-    {'State': 'AZ', 'Population': 7279000, 'SquareMiles': 113990}
-]
-
-# Merge the datasets based on the state column
-df = pd.merge(pd.DataFrame(hospitals), pd.DataFrame(populations), on='State')
-
-# Filter the merged dataset to include only hospitals with over 1000 beds
-df = df[df['Beds'] > 1000]
-
-# Calculate the number of beds per square mile for each state
-df['BedsPerSquareMile'] = df['Beds'] / df['SquareMiles']
-
-# Display the resulting dataframe in Streamlit
-st.write(df)
+import pandas as pd 
+def generate_hospital_data():
+    # Generate hospital data
+    hospitals = {
+        "city": ["New York", "Los Angeles", "Chicago", "Houston", "Phoenix"],
+        "state": ["NY", "CA", "IL", "TX", "AZ"],
+        "bed_count": [1200, 1500, 1100, 1300, 1400],
+    }
+    df = pd.DataFrame(hospitals)
+    return df 
+def generate_state_data():
+    # Generate state data
+    states = {
+        "state": ["NY", "CA", "IL", "TX", "AZ"],
+        "population": [20000000, 40000000, 13000000, 29000000, 7000000],
+        "square_miles": [54556, 163696, 57914, 268596, 113990],
+    }
+    df = pd.DataFrame(states)
+    return df 
+def merge_datasets(hospitals_df, states_df):
+    # Merge hospital and state data
+    merged_df = pd.merge(hospitals_df, states_df, on="state")
+    return merged_df 
+def calculate_beds_per_capita(merged_df):
+    # Calculate beds per capita
+    merged_df["beds_per_capita"] = merged_df["bed_count"] / merged_df["population"]
+    return merged_df 
+def main():
+    # Generate data
+    hospitals_df = generate_hospital_data()
+    states_df = generate_state_data()     # Merge datasets
+    merged_df = merge_datasets(hospitals_df, states_df)     # Calculate beds per capita
+    merged_df = calculate_beds_per_capita(merged_df)     # Show merged and calculated data
+    st.write(merged_df) 
+if __name__ == "__main__":
+    main()