import pandas as pd

# Sample analysis results
sales_report = pd.DataFrame({
    'product': ['Laptop', 'Mouse', 'Keyboard', 'Monitor'],
    'q1_sales': [25, 150, 75, 40],
    'q2_sales': [30, 180, 85, 45],
    'total_revenue': [55000, 8250, 12000, 17000]
})

print("Sales report to export:")
print(sales_report)
print()

# Export to CSV
sales_report.to_csv('sales_report.csv', index=False)
print("✅ Exported to CSV: sales_report.csv")

# Export to Excel
sales_report.to_excel('sales_report.xlsx', index=False)
print("✅ Exported to Excel: sales_report.xlsx")

# Export to JSON
sales_report.to_json('sales_report.json', orient='records', indent=2)
print("✅ Exported to JSON: sales_report.json")

import pandas as pd

# Customer data
customers = pd.DataFrame({
    'customer_id': [1001, 1002, 1003, 1004],
    'name': ['Alice Johnson', 'Bob Smith', 'Charlie Brown', 'Diana Wilson'],
    'total_orders': [8, 12, 5, 15],
    'total_spent': [2450.75, 3890.25, 1205.50, 4560.00]
})

print("Customer data:")
print(customers)
print()

# Basic CSV export
customers.to_csv('customers.csv', index=False)
print("✅ Saved basic CSV")

# CSV with custom options
customers.to_csv('customers_formatted.csv', 
                index=False,
                float_format='%.2f',  # Format numbers to 2 decimal places
                encoding='utf-8')     # Handle special characters
print("✅ Saved formatted CSV")

# Verify the export
loaded_data = pd.read_csv('customers.csv')
print("\nVerification - loaded CSV:")
print(loaded_data)

import pandas as pd
import numpy as np

# Data with missing values and special characters
messy_data = pd.DataFrame({
    'product_name': ['Laptop Pro', 'Mouse™', 'Keyboard & Stand', 'Monitor"'],
    'price': [1299.99, np.nan, 89.50, 345.00],
    'description': ['High-end laptop', None, 'Wireless,bluetooth', 'Large screen']
})

print("Data with missing values and special characters:")
print(messy_data)
print()

# Export with custom handling
messy_data.to_csv('messy_data.csv',
                 index=False,
                 na_rep='Missing',      # Replace NaN with 'Missing'
                 quoting=1,             # Quote fields with special characters
                 encoding='utf-8')      # Handle unicode characters

print("✅ Exported messy data with proper handling")

# Load and verify
loaded_messy = pd.read_csv('messy_data.csv')
print("\nLoaded messy data:")
print(loaded_messy)

import pandas as pd

# Department performance data
performance = pd.DataFrame({
    'department': ['Sales', 'Marketing', 'IT', 'HR'],
    'employees': [25, 15, 20, 8],
    'budget': [250000, 180000, 300000, 120000],
    'actual_spend': [245000, 175000, 285000, 115000]
})

# Calculate variance
performance['variance'] = performance['actual_spend'] - performance['budget']
performance['variance_pct'] = (performance['variance'] / performance['budget'] * 100).round(1)

print("Department performance:")
print(performance)
print()

# Export to Excel with custom sheet name
performance.to_excel('department_performance.xlsx', 
                    sheet_name='Q1_Performance',
                    index=False)
print("✅ Exported to Excel with custom sheet name")

# Verify the export
loaded_excel = pd.read_excel('department_performance.xlsx', sheet_name='Q1_Performance')
print("\nVerification - loaded Excel:")
print(loaded_excel)

import pandas as pd

# Create related datasets
summary = pd.DataFrame({
    'metric': ['Total Revenue', 'Total Orders', 'Average Order Value'],
    'value': [285000, 1150, 247.83]
})

monthly_sales = pd.DataFrame({
    'month': ['January', 'February', 'March'],
    'revenue': [95000, 85000, 105000],
    'orders': [380, 350, 420]
})

top_products = pd.DataFrame({
    'product': ['Laptop', 'Monitor', 'Keyboard', 'Mouse'],
    'revenue': [125000, 85000, 45000, 30000]
})

print("Summary data:")
print(summary)
print("\nMonthly sales:")
print(monthly_sales)
print("\nTop products:")
print(top_products)

# Export multiple sheets to one Excel file
with pd.ExcelWriter('quarterly_report.xlsx') as writer:
    summary.to_excel(writer, sheet_name='Summary', index=False)
    monthly_sales.to_excel(writer, sheet_name='Monthly_Sales', index=False)
    top_products.to_excel(writer, sheet_name='Top_Products', index=False)

print("\n✅ Created Excel file with 3 sheets: quarterly_report.xlsx")

import pandas as pd

# Product catalog
products = pd.DataFrame({
    'id': [1, 2, 3, 4],
    'name': ['Laptop Pro', 'Wireless Mouse', 'Mechanical Keyboard', '4K Monitor'],
    'category': ['Computers', 'Accessories', 'Accessories', 'Monitors'],
    'price': [1299.99, 49.99, 129.99, 399.99],
    'in_stock': [True, True, False, True]
})

print("Product catalog:")
print(products)
print()

# Export to JSON (records format - most common)
products.to_json('products.json', orient='records', indent=2)
print("✅ Exported to JSON (records format)")

# Export to JSON (index format)
products.to_json('products_index.json', orient='index', indent=2)
print("✅ Exported to JSON (index format)")

# Read and display the JSON content
with open('products.json', 'r') as f:
    json_content = f.read()
    print("\nJSON content (first 200 characters):")
    print(json_content[:200] + "...")

import pandas as pd

# Sales data with dates
sales_data = pd.DataFrame({
    'sale_date': pd.to_datetime(['2023-01-15', '2023-01-20', '2023-01-25']),
    'customer': ['Alice', 'Bob', 'Charlie'],
    'amount': [299.99, 149.50, 89.99],
    'product': ['Laptop', 'Monitor', 'Keyboard']
})

print("Sales data with dates:")
print(sales_data)
print()

# Export with proper date formatting
sales_data.to_json('sales_with_dates.json',
                  orient='records',
                  date_format='iso',  # ISO date format
                  indent=2)
print("✅ Exported JSON with proper date formatting")

# Verify by loading back
loaded_json = pd.read_json('sales_with_dates.json', orient='records')
print("\nLoaded from JSON:")
print(loaded_json)
print("Date column type:", loaded_json['sale_date'].dtype)

import pandas as pd

# Comprehensive business analysis
regional_performance = pd.DataFrame({
    'region': ['North', 'South', 'East', 'West'],
    'revenue': [180000, 165000, 195000, 170000],
    'customers': [720, 658, 780, 680],
    'avg_order_value': [250.00, 250.76, 250.00, 250.00]
})

customer_segments = pd.DataFrame({
    'segment': ['Premium', 'Standard', 'Basic'],
    'count': [425, 1230, 1183],
    'avg_lifetime_value': [2850, 1245, 485],
    'retention_rate': [0.92, 0.78, 0.65]
})

# Calculate additional metrics
regional_performance['revenue_per_customer'] = (
    regional_performance['revenue'] / regional_performance['customers']
).round(2)

customer_segments['total_value'] = (
    customer_segments['count'] * customer_segments['avg_lifetime_value']
)

print("Regional performance:")
print(regional_performance)
print("\nCustomer segments:")
print(customer_segments)

# Export to multiple formats for different stakeholders

# 1. CSV for data team
regional_performance.to_csv('regional_performance.csv', index=False)
customer_segments.to_csv('customer_segments.csv', index=False)

# 2. Excel for management
with pd.ExcelWriter('business_report.xlsx') as writer:
    regional_performance.to_excel(writer, sheet_name='Regional_Performance', index=False)
    customer_segments.to_excel(writer, sheet_name='Customer_Segments', index=False)

# 3. JSON for web dashboard
regional_performance.to_json('regional_data.json', orient='records', indent=2)

print("\n✅ Exported complete business report:")
print("  📄 CSV files for data team")
print("  📊 Excel file for management")
print("  🌐 JSON file for web dashboard")