almn-uhc commited on
Commit
ac9f7ab
1 Parent(s): b124a68

Update app.py

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Files changed (1) hide show
  1. app.py +46 -29
app.py CHANGED
@@ -1,39 +1,56 @@
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  import streamlit as st
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  import pandas as pd
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  def generate_hospital_data():
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-     # Generate hospital data
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-     hospitals = {
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-         "city": ["New York", "Los Angeles", "Chicago", "Houston", "Phoenix"],
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-         "state": ["NY", "CA", "IL", "TX", "AZ"],
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-         "bed_count": [1200, 1500, 1100, 1300, 1400],
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-     }
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-     df = pd.DataFrame(hospitals)
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-     return df 
 
 
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  def generate_state_data():
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-     # Generate state data
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-     states = {
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-         "state": ["NY", "CA", "IL", "TX", "AZ"],
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-         "population": [20000000, 40000000, 13000000, 29000000, 7000000],
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-         "square_miles": [54556, 163696, 57914, 268596, 113990],
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-     }
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-     df = pd.DataFrame(states)
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-     return df 
 
 
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  def merge_datasets(hospitals_df, states_df):
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-     # Merge hospital and state data
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-     merged_df = pd.merge(hospitals_df, states_df, on="state")
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-     return merged_df 
 
 
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  def calculate_beds_per_capita(merged_df):
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-     # Calculate beds per capita
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-     merged_df["beds_per_capita"] = merged_df["bed_count"] / merged_df["population"]
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-     return merged_df 
 
 
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  def main():
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-     # Generate data
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-     hospitals_df = generate_hospital_data()
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-     states_df = generate_state_data()     # Merge datasets
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-     merged_df = merge_datasets(hospitals_df, states_df)     # Calculate beds per capita
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-     merged_df = calculate_beds_per_capita(merged_df)     # Show merged and calculated data
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-     st.write(merged_df) 
 
 
 
 
 
 
 
 
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  if __name__ == "__main__":
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-     main()
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  import streamlit as st
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  import pandas as pd
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+
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  def generate_hospital_data():
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+ # Generate hospital data
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+ hospitals = {
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+ "city": ["New York", "Los Angeles", "Chicago", "Houston", "Phoenix"],
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+ "state": ["NY", "CA", "IL", "TX", "AZ"],
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+ "bed_count": [1200, 1500, 1100, 1300, 1400],
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+ }
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+ df = pd.DataFrame(hospitals)
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+ return df
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+
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+
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  def generate_state_data():
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+ # Generate state data
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+ states = {
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+ "state": ["NY", "CA", "IL", "TX", "AZ"],
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+ "population": [20000000, 40000000, 13000000, 29000000, 7000000],
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+ "square_miles": [54556, 163696, 57914, 268596, 113990],
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+ }
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+ df = pd.DataFrame(states)
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+ return df
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+
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+
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  def merge_datasets(hospitals_df, states_df):
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+ # Merge hospital and state data
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+ merged_df = pd.merge(hospitals_df, states_df, on="state")
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+ return merged_df
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+
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+
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  def calculate_beds_per_capita(merged_df):
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+ # Calculate beds per capita
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+ merged_df["beds_per_capita"] = merged_df["bed_count"] / merged_df["population"]
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+ return merged_df
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+
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+
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  def main():
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+ # Generate data
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+ hospitals_df = generate_hospital_data()
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+ states_df = generate_state_data()
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+
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+ # Merge datasets
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+ merged_df = merge_datasets(hospitals_df, states_df)
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+
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+ # Calculate beds per capita
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+ merged_df = calculate_beds_per_capita(merged_df)
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+
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+ # Show merged and calculated data
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+ st.write(merged_df)
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+
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+
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  if __name__ == "__main__":
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+ main()
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