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import matplotlib.pyplot as plt

# Data for Revenue and Net Earnings Comparison (2022 vs 2023)
years = ['2022', '2023']
revenues = [132070421, 172069985]
net_earnings = [16680433, 9885765]

# Data for Departmental Performance for Q1 2024
departments = [
    'Platforms & Product', 'Web Sales', 'Phone Sales', 'Loss Prevention',
    'Fraud Prevention', 'Customer Service', 'Infrastructure', 'Admin',
    'Executive', 'Customer Champion', 'Public Relations'
]
q1_2024_totals = [
    -272443, 6585809, 2324680, 1776065, -1012627, -3160019,
    -510256, -809025, -329874, -65686, -94244
]

# Data for Revenue and Net Revenue (2018-2024 YTD)
years_extended = ['2018', '2019', '2020', '2021', '2022', '2023', '2024 YTD']
revenues_extended = [
    227185200, 235181100, 85170820, 71442470, 140586800, 182899100, 42289452.40578
]
net_revenues = [
    222017400, 226897962, 80791675, 68192950, 134489173, 170415778, 39420265
]

# Plot 1: Revenue and Net Earnings Comparison (2022 vs 2023)
plt.figure(figsize=(10, 6))
bar_width = 0.35
index = range(len(years))
plt.bar(index, revenues, bar_width, label='Revenue')
plt.bar([i + bar_width for i in index], net_earnings, bar_width, label='Net Earnings')
plt.xlabel('Year')
plt.ylabel('Amount (in billions)')
plt.title('Revenue and Net Earnings Comparison (2022 vs 2023)')
plt.xticks([i + bar_width / 2 for i in index], years)
plt.legend()
plt.show()

# Plot 2: Departmental Performance for Q1 2024
plt.figure(figsize=(12, 6))
plt.bar(departments, q1_2024_totals, color='skyblue')
plt.xlabel('Department')
plt.ylabel('Total (Q1 2024)')
plt.title('Departmental Performance for Q1 2024')
plt.xticks(rotation=45, ha='right')
plt.tight_layout()
plt.show()

# Plot 3: Revenue and Net Revenue (2018-2024 YTD)
plt.figure(figsize=(10, 6))
plt.plot(years_extended, revenues_extended, marker='o', label='Revenue')
plt.plot(years_extended, net_revenues, marker='o', label='Net Revenue')
plt.xlabel('Year')
plt.ylabel('Amount (in billions)')
plt.title('Revenue and Net Revenue (2018-2024 YTD)')
plt.legend()
plt.show()
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