Create app2.py

app2.py

@@ -0,0 +1,199 @@
+import streamlit as st
+import pandas as pd
+import plotly.express as px
+import altair as alt
+
+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)
+
+    # Convert Year to int 
+    df['Year'] = df['Year'].astype(int)
+
+    # 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 create_injuries_fatalities_chart(crash_data):
+    # Filter rows where we have valid data for all necessary columns
+    crash_data = crash_data[['DateTime', 'Totalinjuries', 'Totalfatalities', 'Unittype_One', 'Unittype_Two']].dropna()
+
+    # Convert "DateTime" to datetime type
+    crash_data['DateTime'] = pd.to_datetime(crash_data['DateTime'], errors='coerce')
+    crash_data['Month'] = crash_data['DateTime'].dt.month_name()
+
+    # sort months in order
+    month_order = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']
+    crash_data['Month'] = pd.Categorical(crash_data['Month'], categories=month_order, ordered=True)
+
+    # Dropdown for Unit Type selection
+    unit_type_pairs = set()
+    for _, row in crash_data[['Unittype_One', 'Unittype_Two']].dropna().iterrows():
+        if row['Unittype_One'] != 'Driverless' or row['Unittype_Two'] != 'Driverless':
+            pair = ' vs '.join(sorted([row['Unittype_One'], row['Unittype_Two']]))
+            unit_type_pairs.add(pair)
+    unit_type_pairs = sorted(list(unit_type_pairs))
+    unit_type = st.selectbox("Select Unit Type Pair", options=['Total'] + unit_type_pairs)
+
+    # Filter data based on the selected unit type
+    if unit_type == 'Total':
+        filtered_data = crash_data
+    else:
+        unit_one, unit_two = unit_type.split(' vs ')
+        filtered_data = crash_data[((crash_data['Unittype_One'] == unit_one) & (crash_data['Unittype_Two'] == unit_two)) |
+                                   ((crash_data['Unittype_One'] == unit_two) & (crash_data['Unittype_Two'] == unit_one))]
+
+    # Group data by month and calculate total injuries and fatalities
+    monthly_sum = filtered_data.groupby('Month').agg({'Totalinjuries': 'sum', 'Totalfatalities': 'sum'}).reset_index()
+
+    # Reshape the data for easier plotting
+    injuries = monthly_sum[['Month', 'Totalinjuries']].rename(columns={'Totalinjuries': 'Value'})
+    injuries['Measure'] = 'Total Injuries'
+
+    fatalities = monthly_sum[['Month', 'Totalfatalities']].rename(columns={'Totalfatalities': 'Value'})
+    fatalities['Measure'] = 'Total Fatalities'
+
+    combined_data = pd.concat([injuries, fatalities])
+
+    # Plot line chart
+    line_chart = alt.Chart(combined_data).mark_line(point=True).encode(
+        x=alt.X('Month:N', sort=month_order, title='Month'),
+        y=alt.Y('Value:Q', title='Total Injuries & Fatalities'),
+        color=alt.Color('Measure:N', title='', scale=alt.Scale(domain=['Total Injuries', 'Total Fatalities'], range=['blue', 'red'])),
+        tooltip=['Month', 'Measure:N', 'Value:Q']
+    ).properties(
+        title=f'Total Injuries and Fatalities by Month for Unit Type Pair: {unit_type}',
+        width=600,
+        height=400
+    )
+
+    return line_chart
+
+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 crash severity vs violation type chart
+    fig = create_severity_violation_chart(df, selected_age)
+    st.plotly_chart(fig, use_container_width=True)
+
+    # Create and display injuries and fatalities chart
+    injuries_fatalities_chart = create_injuries_fatalities_chart(df)
+    st.altair_chart(injuries_fatalities_chart, 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()