ruchy 4.1.1

// DataFrame Complete Examples - Ruchy v3.64.0
// Demonstrates all implemented DataFrame features

println("=== DataFrame Examples v3.64.0 ===\n");

// ==============================================
// 1. CONSTRUCTION
// ==============================================
println("1. Construction Methods\n");

// Builder pattern
let df1 = DataFrame::new()
    .column("name", ["Alice", "Bob", "Charlie"])
    .column("age", [25, 30, 35])
    .build();

println("Builder pattern:");
println("Rows:", df1.rows());
println("Columns:", df1.columns());
println("Names:", df1.column_names());

// Empty DataFrame
let empty = DataFrame::new().build();
println("\nEmpty DataFrame columns:", empty.columns());

// ==============================================
// 2. CSV IMPORT
// ==============================================
println("\n2. CSV Import with Type Inference\n");

let csv = "product,quantity,price
Widget,10,99.99
Gadget,5,149.99
Tool,15,79.99";

let sales = DataFrame::from_csv_string(csv);
println("CSV imported:");
println("Products:", sales.rows(), "rows");

// ==============================================
// 3. JSON IMPORT
// ==============================================
println("\n3. JSON Import\n");

let json = '[
    {"city": "NYC", "temp": 72},
    {"city": "SF", "temp": 65},
    {"city": "LA", "temp": 80}
]';

let weather = DataFrame::from_json(json);
println("JSON imported:");
println("Cities:", weather.rows(), "rows");

// ==============================================
// 4. TRANSFORMATIONS
// ==============================================
println("\n4. Transform Operations\n");

// Add computed column (row-based)
let with_revenue = sales.with_column("revenue", row =>
    row["quantity"] * row["price"]
);
println("Added revenue column");

// Transform existing column
let with_discount = with_revenue.transform("price", p => p * 0.9);
println("Applied 10% discount to prices");

// ==============================================
// 5. SORTING
// ==============================================
println("\n5. Sorting\n");

// Sort ascending
let by_price = sales.sort_by("price");
println("Sorted by price (ascending)");

// Sort descending
let by_qty_desc = sales.sort_by("quantity", true);
println("Sorted by quantity (descending)");

// ==============================================
// 6. METHOD CHAINING
// ==============================================
println("\n6. Method Chaining Pipeline\n");

let analysis = DataFrame::from_csv_string(csv)
    .with_column("revenue", row => row["quantity"] * row["price"])
    .with_column("high_value", row => row["revenue"] > 1000)
    .sort_by("revenue", true);

println("Chained operations complete");
println("Final columns:", analysis.column_names());

// ==============================================
// 7. REAL-WORLD: SALES ANALYSIS
// ==============================================
println("\n7. Real-World Example: Sales Analysis\n");

let daily_sales = "date,region,units,price
2024-01-15,North,10,99
2024-01-15,South,5,99
2024-01-16,North,8,99
2024-01-16,South,12,99";

let report = DataFrame::from_csv_string(daily_sales)
    .with_column("revenue", row => row["units"] * row["price"])
    .sort_by("revenue", true);

println("Sales report generated");
println("Total records:", report.rows());

// ==============================================
// 8. REAL-WORLD: CUSTOMER SEGMENTATION
// ==============================================
println("\n8. Real-World Example: Customer Segmentation\n");

let customers = DataFrame::new()
    .column("customer", ["Alice", "Bob", "Charlie", "Diana"])
    .column("purchases", [15, 3, 8, 25])
    .column("total_spent", [1500, 300, 800, 2500])
    .build();

let segments = customers
    .with_column("avg_purchase", row => row["total_spent"] / row["purchases"])
    .with_column("segment", row => {
        if row["purchases"] > 10 then "Premium" else "Regular"
    })
    .sort_by("total_spent", true);

println("Customer segmentation complete");
println("Segments:", segments.rows(), "customers");

// ==============================================
// 9. COMBINING OPERATIONS
// ==============================================
println("\n9. Complex Data Pipeline\n");

let raw_data = "item,qty,cost,category
A,10,5.99,Food
B,20,3.50,Food
C,5,12.99,Electronics
D,15,8.50,Food";

let processed = DataFrame::from_csv_string(raw_data)
    .with_column("subtotal", row => row["qty"] * row["cost"])
    .with_column("tax", row => row["subtotal"] * 0.08)
    .with_column("total", row => row["subtotal"] + row["tax"])
    .transform("total", t => t * 1.0)  // Round to 2 decimals (would be: round(t, 2))
    .sort_by("total", true);

println("Data pipeline complete");
println("Processed:", processed.rows(), "items");

// ==============================================
// 10. ACCESSOR METHODS
// ==============================================
println("\n10. DataFrame Inspection\n");

let sample = DataFrame::new()
    .column("x", [1, 2, 3])
    .column("y", [4, 5, 6])
    .column("z", [7, 8, 9])
    .build();

println("Sample DataFrame:");
println("- Rows:", sample.rows());
println("- Columns:", sample.columns());
println("- Column names:", sample.column_names());

println("\n=== All DataFrame Features Demonstrated! ===");
println("Version: 3.64.0");
println("Features: 60% complete (production-ready core)");