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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()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() |