# Example: Analyze sales data
import pandas as pd
import matplotlib.pyplot as plt

def analyze_sales_data(sales_data):
    # Load data into DataFrame
    df = pd.DataFrame(sales_data)

    # Calculate summary statistics
    summary = df.describe()

    # Create visualization
    plt.figure(figsize=(10, 6))
    df['sales'].plot(kind='line')
    plt.title('Sales Over Time')
    plt.xlabel('Date')
    plt.ylabel('Sales Amount')

    # Save plot
    plt.savefig('sales_analysis.png')

    return {
        'summary': summary.to_dict(),
        'chart': 'sales_analysis.png'
    }

# Example: Financial calculations
import numpy as np

def calculate_loan_payment(principal, annual_rate, years):
    """
    Calculate monthly loan payment using the standard formula
    """
    monthly_rate = annual_rate / 12
    num_payments = years * 12

    if monthly_rate == 0:
        return principal / num_payments

    payment = principal * (monthly_rate * (1 + monthly_rate)**num_payments) / \
              ((1 + monthly_rate)**num_payments - 1)

    return round(payment, 2)

def calculate_compound_interest(principal, rate, time, compound_frequency=12):
    """
    Calculate compound interest
    """
    amount = principal * (1 + rate/compound_frequency)**(compound_frequency * time)
    interest = amount - principal
    return {
        'final_amount': round(amount, 2),
        'interest_earned': round(interest, 2)
    }

# Example: Process CSV files
import csv
import json

def process_csv_file(file_path):
    """
    Process CSV file and convert to JSON
    """
    data = []

    with open(file_path, 'r') as file:
        reader = csv.DictReader(file)
        for row in reader:
            data.append(row)

    # Process data (example: calculate totals)
    total_sales = sum(float(row['sales']) for row in data if row['sales'])

    return {
        'records': data,
        'total_records': len(data),
        'total_sales': total_sales
    }

def generate_report(data, output_file):
    """
    Generate a formatted report
    """
    report = f"""
    Sales Report
    ===========

    Total Records: {len(data)}
    Total Sales: ${sum(float(row['sales']) for row in data if row['sales']):,.2f}

    Top Products:
    """

    # Sort by sales
    sorted_data = sorted(data, key=lambda x: float(x['sales']), reverse=True)

    for i, row in enumerate(sorted_data[:5]):
        report += f"{i+1}. {row['product']}: ${float(row['sales']):,.2f}\n"

    # Write to file
    with open(output_file, 'w') as file:
        file.write(report)

    return f"Report saved to {output_file}"

# Example: Process API data
import requests
import json

def fetch_and_process_api_data(api_url):
    """
    Fetch data from API and process it
    """
    try:
        response = requests.get(api_url)
        response.raise_for_status()

        data = response.json()

        # Process the data
        processed_data = {
            'total_items': len(data),
            'categories': list(set(item.get('category', 'Unknown') for item in data)),
            'price_range': {
                'min': min(item.get('price', 0) for item in data),
                'max': max(item.get('price', 0) for item in data),
                'average': sum(item.get('price', 0) for item in data) / len(data)
            }
        }

        return processed_data

    except requests.RequestException as e:
        return {'error': f'API request failed: {str(e)}'}

# Example: Create charts and graphs
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd

def create_sales_chart(sales_data):
    """
    Create a comprehensive sales visualization
    """
    df = pd.DataFrame(sales_data)

    # Set up the plotting style
    plt.style.use('seaborn-v0_8')
    fig, axes = plt.subplots(2, 2, figsize=(15, 10))

    # Sales over time
    axes[0, 0].plot(df['date'], df['sales'])
    axes[0, 0].set_title('Sales Over Time')
    axes[0, 0].set_xlabel('Date')
    axes[0, 0].set_ylabel('Sales')

    # Sales by category
    category_sales = df.groupby('category')['sales'].sum()
    axes[0, 1].pie(category_sales.values, labels=category_sales.index, autopct='%1.1f%%')
    axes[0, 1].set_title('Sales by Category')

    # Sales distribution
    axes[1, 0].hist(df['sales'], bins=20, alpha=0.7)
    axes[1, 0].set_title('Sales Distribution')
    axes[1, 0].set_xlabel('Sales Amount')
    axes[1, 0].set_ylabel('Frequency')

    # Top products
    top_products = df.groupby('product')['sales'].sum().nlargest(10)
    axes[1, 1].barh(range(len(top_products)), top_products.values)
    axes[1, 1].set_yticks(range(len(top_products)))
    axes[1, 1].set_yticklabels(top_products.index)
    axes[1, 1].set_title('Top 10 Products by Sales')
    axes[1, 1].set_xlabel('Total Sales')

    plt.tight_layout()
    plt.savefig('sales_analysis.png', dpi=300, bbox_inches='tight')

    return 'sales_analysis.png'

Error: Package not found

Error: Out of memory

Error: Execution timeout

Error: Permission denied