Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+
+# Define datasets
+hospital_data = [
+    {'city': 'New York', 'state': 'NY', 'bed_count': 1500},
+    {'city': 'Los Angeles', 'state': 'CA', 'bed_count': 2000},
+    {'city': 'Chicago', 'state': 'IL', 'bed_count': 1200},
+    {'city': 'Houston', 'state': 'TX', 'bed_count': 1300},
+    {'city': 'Philadelphia', 'state': 'PA', 'bed_count': 1100}
+]
+
+population_data = [
+    {'state': 'NY', 'population': 20000000, 'square_miles': 54555},
+    {'state': 'CA', 'population': 40000000, 'square_miles': 163696},
+    {'state': 'IL', 'population': 13000000, 'square_miles': 57914},
+    {'state': 'TX', 'population': 29000000, 'square_miles': 268596},
+    {'state': 'PA', 'population': 13000000, 'square_miles': 46055}
+]
+
+# Convert datasets to pandas dataframes
+hospital_df = pd.DataFrame(hospital_data)
+population_df = pd.DataFrame(population_data)
+
+# Merge datasets on 'state' column
+merged_df = pd.merge(hospital_df, population_df, on='state')
+
+# Filter merged dataset to only include hospitals with over 1000 beds
+filtered_df = merged_df[merged_df['bed_count'] > 1000]
+
+# Calculate hospital density as population per hospital bed
+filtered_df['hospital_density'] = filtered_df['population'] / filtered_df['bed_count']
+
+# Display merged and filtered dataset in Streamlit app
+st.write(filtered_df)