Notes

import pandas as pd
import numpy as np
import plotly.express as px

# Sample data for demonstration purposes
np.random.seed(42)
weeks = list(range(1, 53))
departments = ['Department A', 'Department B', 'Department C']
status = ['Approved', 'Processing', 'Canceled']
violation_types = ['Speeding', 'Parking', 'Parking Reverse', 'Failing to Stop', 'Wrong Turn']
categories = ['Warning', 'Violation', 'Government Violation']
area = ['Dammam', 'Jeddah', 'Riyadh', 'Jubail Highway', 'DM Airport', 'Inside Camp']
sources = ['Security Man', 'Camera']
security_men = ['John Doe', 'Abdulwahid Alhemod', 'Jane Smith', 'Sam Brown', 'Faisal Alhemeli']
security_departments = ['Security Department 1', 'Security Department 2']

# Generate random data for each chart
data = {
'Week': np.random.choice(weeks, 500),
'Department': np.random.choice(departments, 500),
'Status': np.random.choice(status, 500),
'Violation Type': np.random.choice(violation_types, 500),
'Source': np.random.choice(sources, 500),
'Security Man': np.random.choice(security_men, 500),
'Security Department': np.random.choice(security_departments, 500),
'categories': np.random.choice(categories, 500),
'area': np.random.choice(area, 500),
}

# Create the DataFrame
df = pd.DataFrame(data)

# Map security departments to security men
department_mapping = {
'Faisal Alhemeli': 'Security Department 1',
'Abdulwahid Alhemod': 'Security Department 1',
}
df['Security Department'] = df['Security Man'].map(department_mapping).fillna('Security Department 2')

# Chart 1: Line chart for 2023 and 2024 traffic violations based on week numbers
df['Year'] = np.random.choice([2023, 2024], df.shape[0])
line_chart_data = df.groupby(['Year', 'Week']).size().reset_index(name='Count')

# Chart 2: Bar chart for total violations on each department
bar_chart_total_dept = df.groupby('Department').size().reset_index(name='Total Violations')

# Chart 3: Bar chart for each department's total violations
bar_chart_dept_total = df.groupby(['Department', 'Violation Type']).size().reset_index(name='Count')

# Chart 4: Bar chart for status of traffic violations
bar_chart_status = df.groupby('Status').size().reset_index(name='Count')

# Chart 5: Bar chart for each department's violations weekly
bar_chart_dept_weekly = df.groupby(['Department', 'Week']).size().reset_index(name='Count')

# Chart 6: Pie chart for violation categories
pie_chart_categories = df.groupby('categories').size().reset_index(name='Count')

# Chart 7: Bar chart for area of traffic violations
bar_chart_area = df.groupby('area').size().reset_index(name='Count')

# Chart 8: Pie chart for responsible of traffic violations
pie_chart_responsible = df.groupby('Source').size().reset_index(name='Count')

# Chart 9: Bar chart for type of violation
bar_chart_violation_type = df.groupby('Violation Type').size().reset_index(name='Count')

# Chart 10: Bar chart for source of traffic violation
bar_chart_source = df.groupby('Source').size().reset_index(name='Count')

# Chart 11: Bar chart for security men initiating traffic violations
bar_chart_security_men = df.groupby('Security Man').size().reset_index(name='Count')

# Chart 12: Bar chart for security man departments
bar_chart_security_departments = df.groupby(['Security Man', 'Security Department']).size().reset_index(name='Count')

# Now you can use Plotly to create the charts
# For example, to create a line chart:
# fig1 = px.line(line_chart_data, x='Week', y='Count', color='Year', title='Traffic Violations by Week')
# fig2 = px.bar(bar_chart_total_dept, x='Department', y='Total Violations', color='Year', title='Traffic Violations by Week')
# fig3 = px.bar(bar_chart_dept_total, x='Department', y='Count', color='Violation Type', title='Traffic Violations by Week')
# fig3.update_layout(barmode='group'),
# fig4 = px.bar(bar_chart_status, x='Status', y='Count', title='Traffic Violations by Week')
# fig5 = px.bar(bar_chart_dept_weekly, x='Week', y='Count', color='Department', title='Traffic Violations by Week')
# fig6 = px.pie(pie_chart_categories, values='Count', names='categories', title='Traffic Violations by Week')
# fig7 = px.bar(bar_chart_area, x='area', y='Count', color='area', title='Traffic Violations by Week')
# fig8 = px.pie(pie_chart_responsible, values='Count', names='Source', title='Traffic Violations by Week')
# fig9 = px.bar(bar_chart_violation_type, x='Violation Type', y='Count', color='Violation Type', title='Traffic Violations by Week')
# fig10 = px.bar(bar_chart_source, x='Source', y='Count', color='Source', title='Traffic Violations by Week')
fig11 = px.bar(bar_chart_security_men, x='Security Man', y='Count', color='Security Man', title='Traffic Violations by Week')
fig12 = px.line(line_chart_data, x='Week', y='Count', color='Year', title='Traffic Violations by Week')

# Similarly, you can create other charts using the respective dataframes