Update app.py

app.py

@@ -72,6 +72,30 @@ def create_severity_violation_chart(df, age_group=None):
     
     return fig
 
+def get_top_violations(df, age_group):
+    if age_group == 'All Ages':
+        violations = pd.concat([
+            df['Violation1_Drv1'].value_counts(),
+            df['Violation1_Drv2'].value_counts()
+        ]).groupby(level=0).sum()
+    else:
+        filtered_df = df[
+            (df['Age_Group_Drv1'] == age_group) | 
+            (df['Age_Group_Drv2'] == age_group)
+        ]
+        violations = pd.concat([
+            filtered_df['Violation1_Drv1'].value_counts(),
+            filtered_df['Violation1_Drv2'].value_counts()
+        ]).groupby(level=0).sum()
+    
+    # Convert to DataFrame and format
+    violations_df = violations.reset_index()
+    violations_df.columns = ['Violation Type', 'Count']
+    violations_df['Percentage'] = (violations_df['Count'] / violations_df['Count'].sum() * 100).round(2)
+    violations_df['Percentage'] = violations_df['Percentage'].map('{:.2f}%'.format)
+    
+    return violations_df.head()
+
 def main():
     st.title('Traffic Crash Analysis')
     
@@ -86,7 +110,7 @@ def main():
     fig = create_severity_violation_chart(df, selected_age)
     st.plotly_chart(fig, use_container_width=True)
     
-    # Display basic statistics
+    # Display statistics
     if selected_age == 'All Ages':
         total_incidents = len(df)
     else:
@@ -95,7 +119,153 @@ def main():
             (df['Age_Group_Drv2'] == selected_age)
         ])
     
-    st.write(f"Total incidents for {selected_age}: {total_incidents:,}")
+    # Create two columns for statistics
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        st.markdown(f"### Total Incidents")
+        st.markdown(f"**{total_incidents:,}** incidents for {selected_age}")
+    
+    # Display top violations table
+    with col2:
+        st.markdown("### Top Violations")
+        top_violations = get_top_violations(df, selected_age)
+        st.table(top_violations)
+
+if __name__ == "__main__":
+    main()import streamlit as st
+import pandas as pd
+import plotly.express as px
+
+def load_and_preprocess_data(file_path):
+    # Read the data
+    df = pd.read_csv(file_path)
+    
+    # Basic preprocessing
+    df = df.drop(['X', 'Y'], axis=1)
+    df.dropna(subset=['Incidentid', 'DateTime', 'Year', 'Latitude', 'Longitude'], inplace=True)
+    
+    # Fill missing values
+    numeric = ['Age_Drv1', 'Age_Drv2']
+    for col in numeric:
+        df[col].fillna(df[col].median(), inplace=True)
+        
+    categorical = ['Gender_Drv1', 'Violation1_Drv1', 'AlcoholUse_Drv1', 'DrugUse_Drv1',
+                  'Gender_Drv2', 'Violation1_Drv2', 'AlcoholUse_Drv2', 'DrugUse_Drv2',
+                  'Unittype_Two', 'Traveldirection_Two', 'Unitaction_Two', 'CrossStreet']
+    for col in categorical:
+        df[col].fillna('Unknown', inplace=True)
+    
+    # Remove invalid ages
+    df = df[
+        (df['Age_Drv1'] <= 90) & 
+        (df['Age_Drv2'] <= 90) & 
+        (df['Age_Drv1'] >= 16) & 
+        (df['Age_Drv2'] >= 16)
+    ]
+    
+    # Create age groups
+    bins = [15, 25, 35, 45, 55, 65, 90]
+    labels = ['16-25', '26-35', '36-45', '46-55', '56-65', '65+']
+    
+    df['Age_Group_Drv1'] = pd.cut(df['Age_Drv1'], bins=bins, labels=labels)
+    df['Age_Group_Drv2'] = pd.cut(df['Age_Drv2'], bins=bins, labels=labels)
+    
+    return df
+
+def create_severity_violation_chart(df, age_group=None):
+    # Apply age group filter if selected
+    if age_group != 'All Ages':
+        df = df[(df['Age_Group_Drv1'] == age_group) | (df['Age_Group_Drv2'] == age_group)]
+    
+    # Combine violations from both drivers
+    violations_1 = df.groupby(['Violation1_Drv1', 'Injuryseverity']).size().reset_index(name='count')
+    violations_2 = df.groupby(['Violation1_Drv2', 'Injuryseverity']).size().reset_index(name='count')
+    
+    violations_1.columns = ['Violation', 'Severity', 'count']
+    violations_2.columns = ['Violation', 'Severity', 'count']
+    
+    violations = pd.concat([violations_1, violations_2])
+    violations = violations.groupby(['Violation', 'Severity'])['count'].sum().reset_index()
+    
+    # Create visualization
+    fig = px.bar(
+        violations,
+        x='Violation',
+        y='count',
+        color='Severity',
+        title=f'Crash Severity Distribution by Violation Type - {age_group}',
+        labels={'count': 'Number of Incidents', 'Violation': 'Violation Type'},
+        height=600
+    )
+    
+    fig.update_layout(
+        xaxis_tickangle=-45,
+        legend_title='Severity Level',
+        barmode='stack'
+    )
+    
+    return fig
+
+def get_top_violations(df, age_group):
+    if age_group == 'All Ages':
+        violations = pd.concat([
+            df['Violation1_Drv1'].value_counts(),
+            df['Violation1_Drv2'].value_counts()
+        ]).groupby(level=0).sum()
+    else:
+        filtered_df = df[
+            (df['Age_Group_Drv1'] == age_group) | 
+            (df['Age_Group_Drv2'] == age_group)
+        ]
+        violations = pd.concat([
+            filtered_df['Violation1_Drv1'].value_counts(),
+            filtered_df['Violation1_Drv2'].value_counts()
+        ]).groupby(level=0).sum()
+    
+    # Convert to DataFrame and format
+    violations_df = violations.reset_index()
+    violations_df.columns = ['Violation Type', 'Count']
+    violations_df['Percentage'] = (violations_df['Count'] / violations_df['Count'].sum() * 100).round(2)
+    violations_df['Percentage'] = violations_df['Percentage'].map('{:.2f}%'.format)
+    
+    return violations_df.head()
+
+def main():
+    st.title('Traffic Crash Analysis')
+    
+    # Load data
+    df = load_and_preprocess_data('1.08_Crash_Data_Report_(detail).csv')
+    
+    # Create simple dropdown for age groups
+    age_groups = ['All Ages', '16-25', '26-35', '36-45', '46-55', '56-65', '65+']
+    selected_age = st.selectbox('Select Age Group:', age_groups)
+    
+    # Create and display chart
+    fig = create_severity_violation_chart(df, selected_age)
+    st.plotly_chart(fig, use_container_width=True)
+    
+    # Display statistics
+    if selected_age == 'All Ages':
+        total_incidents = len(df)
+    else:
+        total_incidents = len(df[
+            (df['Age_Group_Drv1'] == selected_age) | 
+            (df['Age_Group_Drv2'] == selected_age)
+        ])
+    
+    # Create two columns for statistics
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        st.markdown(f"### Total Incidents")
+        st.markdown(f"**{total_incidents:,}** incidents for {selected_age}")
+    
+    # Display top violations table
+    with col2:
+        st.markdown("### Top Violations")
+        top_violations = get_top_violations(df, selected_age)
+        st.table(top_violations)
 
 if __name__ == "__main__":
     main()