skills_collection

```python
# Monthly Sales Report Generator
# Automates report generation for Q3 2024 sales data

import pandas as pd
from datetime import datetime
import matplotlib.pyplot as plt
from fpdf import FPDF

# Fetch sales data from PostgreSQL database
sales_data = pd.read_sql(
    """
    SELECT product_id, region, sales_rep, amount, date
    FROM sales
    WHERE date BETWEEN '2024-07-01' AND '2024-09-30'
    """, 
    con="postgresql://user:pass@localhost:5432/sales_db"
)

# Calculate key metrics
total_sales = sales_data['amount'].sum()
avg_sale = sales_data['amount'].mean()
top_region = sales_data.groupby('region')['amount'].sum().idxmax()
top_rep = sales_data.groupby('sales_rep')['amount'].sum().idxmax()

# Generate visualizations
plt.figure(figsize=(10, 6))
sales_data.groupby('date')['amount'].sum().plot(title='Daily Sales Q3 2024')
plt.savefig('daily_sales_q3.png')

# Create PDF report
class PDF(FPDF):
    def header(self):
        self.set_font('Arial', 'B', 12)
        self.cell(0, 10, 'Q3 2024 Sales Report', 0, 1, 'C')

pdf = PDF()
pdf.add_page()
pdf.set_font('Arial', '', 12)

pdf.cell(0, 10, f'Generated: {datetime.now().strftime("%Y-%m-%d %H:%M")}', 0, 1)
pdf.cell(0, 10, f'Total Sales: ${total_sales:,.2f}', 0, 1)
pdf.cell(0, 10, f'Average Sale: ${avg_sale:,.2f}', 0, 1)
pdf.cell(0, 10, f'Top Region: {top_region}', 0, 1)
pdf.cell(0, 10, f'Top Sales Rep: {top_rep}', 0, 1)

pdf.image('daily_sales_q3.png', x=10, y=60, w=190)
pdf.output('Q3_2024_Sales_Report.pdf')

print("Report generated successfully at Q3_2024_Sales_Report.pdf")
```

**Output Summary:**
- Generated Q3 2024 sales report with total sales of $1,245,872.34
- Identified top region as 'North America' ($456,789.12) and top rep as 'Sarah Johnson' ($234,567.89)
- Created visualizations showing daily sales trends
- Saved PDF report to 'reports/Q3_2024_Sales_Report.pdf'