Report Generator

# Report structurereport = {    'title': 'Monthly Sales Report',    'period': 'January 2024',    'sections': [        'executive_summary',        'kpi_dashboard',        'detailed_analysis',        'charts',        'recommendations'    ]}

import pandas as pdimport matplotlib.pyplot as pltfrom reportlab.lib.pagesizes import letterfrom reportlab.pdfgen import canvas
def generate_report(data, output_path):    # Load data    df = pd.read_csv(data)        # Calculate KPIs    total_revenue = df['revenue'].sum()    avg_order = df['revenue'].mean()    growth = df['revenue'].pct_change().mean()        # Create charts    fig, axes = plt.subplots(2, 2, figsize=(12, 10))    df.plot(kind='bar', ax=axes[0,0], title='Revenue by Month')    df.plot(kind='line', ax=axes[0,1], title='Trend')    plt.savefig('charts.png')        # Generate PDF    # ... PDF generation code        return output_path

def generate_html_report(data, title):    html = f'''    <!DOCTYPE html>    <html>    <head>        <title>{title}</title>        <style>            body {{ font-family: Arial; margin: 40px; }}            .kpi {{ display: flex; gap: 20px; }}            .kpi-card {{ background: #f5f5f5; padding: 20px; border-radius: 8px; }}            .metric {{ font-size: 2em; font-weight: bold; color: #2563eb; }}            table {{ border-collapse: collapse; width: 100%; }}            th, td {{ border: 1px solid #ddd; padding: 12px; text-align: left; }}        </style>    </head>    <body>        <h1>{title}</h1>        <div class="kpi">            <div class="kpi-card">                <div class="metric">${data['revenue']:,.0f}</div>                <div>Total Revenue</div>            </div>            <div class="kpi-card">                <div class="metric">{data['growth']:.1%}</div>                <div>Growth Rate</div>            </div>        </div>        <!-- More content -->    </body>    </html>    '''    return html

import pandas as pdimport matplotlib.pyplot as plt
def create_sales_report(csv_path, output_path):    # Read data    df = pd.read_csv(csv_path)        # Calculate metrics    metrics = {        'total_revenue': df['amount'].sum(),        'total_orders': len(df),        'avg_order': df['amount'].mean(),        'top_product': df.groupby('product')['amount'].sum().idxmax()    }        # Create visualizations    fig, axes = plt.subplots(2, 2, figsize=(14, 10))        # Revenue by product    df.groupby('product')['amount'].sum().plot(        kind='bar', ax=axes[0,0], title='Revenue by Product'    )        # Monthly trend    df.groupby('month')['amount'].sum().plot(        kind='line', ax=axes[0,1], title='Monthly Revenue'    )        plt.tight_layout()    plt.savefig(output_path.replace('.html', '_charts.png'))        # Generate HTML report    html = generate_html_report(metrics, 'Sales Report')        with open(output_path, 'w') as f:        f.write(html)        return output_path
create_sales_report('sales_data.csv', 'sales_report.html')