Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+
+# Define the states and their populations and health concerns
+states = {
+    'Minnesota': {
+        'population': 5700000,
+        'health_concerns': ['obesity', 'diabetes', 'heart disease']
+    },
+    'Wisconsin': {
+        'population': 5850000,
+        'health_concerns': ['cancer', 'alcoholism', 'depression']
+    },
+    'Texas': {
+        'population': 29000000,
+        'health_concerns': ['obesity', 'diabetes', 'heart disease']
+    },
+    'Florida': {
+        'population': 21500000,
+        'health_concerns': ['cancer', 'alcoholism', 'depression']
+    },
+    'California': {
+        'population': 39500000,
+        'health_concerns': ['obesity', 'diabetes', 'heart disease']
+    },
+    'New York': {
+        'population': 19500000,
+        'health_concerns': ['cancer', 'alcoholism', 'depression']
+    }
+}
+
+# Augment the data by adding random noise and additional columns
+for state in states:
+    states[state]['population'] += int(np.random.normal(0, 500000))
+    states[state]['climate'] = np.random.choice(['cold', 'moderate', 'hot'])
+    states[state]['geography'] = np.random.choice(['coastal', 'inland', 'mountainous'])
+    states[state]['economy'] = np.random.choice(['agriculture', 'manufacturing', 'services'])
+
+# Create a pandas dataframe from the augmented data
+df = pd.DataFrame.from_dict(states, orient='index')
+df = df[['population', 'climate', 'geography', 'economy', 'health_concerns']]
+
+# Define the top 3 health concerns by state
+top_health_concerns = {
+    'Minnesota': ['obesity', 'diabetes', 'heart disease'],
+    'Wisconsin': ['cancer', 'alcoholism', 'depression'],
+    'Texas': ['obesity', 'diabetes', 'heart disease'],
+    'Florida': ['cancer', 'alcoholism', 'depression'],
+    'California': ['obesity', 'diabetes', 'heart disease'],
+    'New York': ['cancer', 'alcoholism', 'depression']
+}
+
+# Define the statistics for each health concern and cite references
+statistics = {
+    'obesity': {
+        'prevalence': '32.4%',
+        'source': 'https://www.cdc.gov/obesity/data/prevalence-maps.html'
+    },
+    'diabetes': {
+        'prevalence': '10.7%',
+        'source': 'https://www.cdc.gov/diabetes/data/statistics-report/index.html'
+    },
+    'heart disease': {
+        'prevalence': '12.1%',
+        'source': 'https://www.cdc.gov/heartdisease/facts.htm'
+    },
+    'cancer': {
+        'prevalence': '38.4%',
+        'source': 'https://www.cdc.gov/cancer/dcpc/data/types.htm'
+    },
+        'alcoholism': {
+        'prevalence': '14.5%',
+        'source': 'https://www.niaaa.nih.gov/publications/brochures-and-fact-sheets/alcohol-facts-and-statistics'
+    },
+        'depression': {
+        'prevalence': '7.6%',
+        'source': 'https://www.nimh.nih.gov/health/statistics/major-depression.shtml'
+    }
+}
+
+# Define the streamlit app
+def app():
+    st.title('Data Augmentation Example')
+    st.write('This app demonstrates data augmentation by adding random noise and additional columns to a short python dictionary list of the states.')
+
+# Display the augmented data
+st.header('Augmented Data')
+st.write(df)
+
+# Display the top 3 health concerns by state and their statistics
+st.header('Top 3 Health Concerns by State')
+for state in top_health_concerns:
+    st.subheader(state)
+    for health_concern in top_health_concerns[state]:
+        st.write(health_concern)
+        st.write('Prevalence:', statistics[health_concern]['prevalence'])
+        st.write('Source:', statistics[health_concern]['source'])
+    st.write('---')
+Run the streamlit app
+if name == 'main':
+app()