Update app.py

app.py

@@ -1,30 +1,23 @@
 import streamlit as st
 import pandas as pd
 import plotly.express as px
-import plotly.graph_objects as go
-import numpy as np
 
 def load_and_preprocess_data(file_path):
     # Read the data
     df = pd.read_csv(file_path)
     
-    # Drop redundant columns
+    # Basic preprocessing
     df = df.drop(['X', 'Y'], axis=1)
-    
-    # Handle missing values
     df.dropna(subset=['Incidentid', 'DateTime', 'Year', 'Latitude', 'Longitude'], inplace=True)
     
-    # Fill numeric values
+    # Fill missing values
     numeric = ['Age_Drv1', 'Age_Drv2']
     for col in numeric:
         df[col].fillna(df[col].median(), inplace=True)
-    
-    # Fill categorical values
-    categorical = [
-        'Gender_Drv1', 'Violation1_Drv1', 'AlcoholUse_Drv1', 'DrugUse_Drv1',
-        'Gender_Drv2', 'Violation1_Drv2', 'AlcoholUse_Drv2', 'DrugUse_Drv2',
-        'Unittype_Two', 'Traveldirection_Two', 'Unitaction_Two', 'CrossStreet'
-    ]
+        
+    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)
     
@@ -36,113 +29,73 @@ def load_and_preprocess_data(file_path):
         (df['Age_Drv2'] >= 16)
     ]
     
-    # Create age groups for both drivers
-    df['Age_Group_Drv1'] = pd.cut(
-        df['Age_Drv1'],
-        bins=[15, 25, 35, 45, 55, 65, 90],
-        labels=['16-25', '26-35', '36-45', '46-55', '56-65', '65+']
-    )
+    # 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_Drv2'] = pd.cut(
-        df['Age_Drv2'],
-        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, selected_age_group=None):
-    # Filter by age group if selected
-    if selected_age_group:
-        df = df[
-            (df['Age_Group_Drv1'] == selected_age_group) | 
-            (df['Age_Group_Drv2'] == selected_age_group)
-        ]
-    
-    # Create violation categories for both drivers
-    violations_drv1 = df.groupby(['Violation1_Drv1', 'Injuryseverity']).size().reset_index(name='count')
-    violations_drv2 = df.groupby(['Violation1_Drv2', 'Injuryseverity']).size().reset_index(name='count')
+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_drv1.columns = ['Violation', 'Severity', 'count']
-    violations_drv2.columns = ['Violation', 'Severity', 'count']
-    violations_combined = pd.concat([violations_drv1, violations_drv2])
+    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']
     
-    # Aggregate the combined violations
-    violations_agg = violations_combined.groupby(['Violation', 'Severity'])['count'].sum().reset_index()
+    violations = pd.concat([violations_1, violations_2])
+    violations = violations.groupby(['Violation', 'Severity'])['count'].sum().reset_index()
     
-    # Create the stacked bar chart
+    # Create visualization
     fig = px.bar(
-        violations_agg,
+        violations,
         x='Violation',
         y='count',
         color='Severity',
-        title=f'Distribution of Crash Severity by Violation Type {selected_age_group if selected_age_group else ""}',
+        title=f'Crash Severity Distribution by Violation Type - {age_group}',
         labels={'count': 'Number of Incidents', 'Violation': 'Violation Type'},
         height=600
     )
     
-    # Customize the layout
     fig.update_layout(
         xaxis_tickangle=-45,
-        legend_title='Severity',
-        barmode='stack',
-        showlegend=True
+        legend_title='Severity Level',
+        barmode='stack'
     )
     
     return fig
 
 def main():
-    st.title('Traffic Crash Analysis Dashboard')
+    st.title('Traffic Crash Analysis')
     
     # Load data
     df = load_and_preprocess_data('1.08_Crash_Data_Report_(detail).csv')
     
-    # Create age group selector
-    st.sidebar.header('Filters')
-    age_groups = ['All'] + list(df['Age_Group_Drv1'].unique())
-    selected_age_group = st.sidebar.selectbox('Select Age Group', age_groups)
-    
-    # Create and display the chart
-    if selected_age_group == 'All':
-        fig = create_severity_violation_chart(df)
-    else:
-        fig = create_severity_violation_chart(df, selected_age_group)
+    # 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)
     
-    # Add additional insights
-    st.subheader('Analysis Insights')
-    
-    # Calculate and display some statistics
-    if selected_age_group == 'All':
-        total_crashes = len(df)
+    # Display basic statistics
+    if selected_age == 'All Ages':
+        total_incidents = len(df)
     else:
-        total_crashes = len(df[
-            (df['Age_Group_Drv1'] == selected_age_group) | 
-            (df['Age_Group_Drv2'] == selected_age_group)
+        total_incidents = len(df[
+            (df['Age_Group_Drv1'] == selected_age) | 
+            (df['Age_Group_Drv2'] == selected_age)
         ])
     
-    st.write(f"Total number of crashes: {total_crashes:,}")
-    
-    # Show top violations
-    st.subheader('Top Violations')
-    if selected_age_group == 'All':
-        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'] == selected_age_group) | 
-            (df['Age_Group_Drv2'] == selected_age_group)
-        ]
-        violations = pd.concat([
-            filtered_df['Violation1_Drv1'].value_counts(),
-            filtered_df['Violation1_Drv2'].value_counts()
-        ]).groupby(level=0).sum()
-    
-    st.write(violations.head())
+    st.write(f"Total incidents for {selected_age}: {total_incidents:,}")
 
 if __name__ == "__main__":
     main()