Create app.py

app.py

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+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)