01_basic_usage.v

// Example 1: Basic Usage - Data Loading and Exploration
// This example demonstrates the fundamental operations in vframes:
// - Initializing a context
// - Loading data from various sources
// - Exploring the DataFrame structure
// - Basic column operations

import vframes
import x.json2

fn print_header(s string) {
	println('\n=== ${s} ===')
}

fn main() {
	// Initialize an in-memory context
	// You can also use a persisted database: vframes.init(location: 'data.db')
	mut ctx := vframes.init()!
	defer { ctx.close() }

	print_header('Creating DataFrame from records')

	// Create sample data - a dataset of employees
	// Note: Use json2.Any for all values
	employees := [
		{'id': json2.Any(1), 'name': json2.Any('Alice Johnson'), 'department': json2.Any('Engineering'), 'salary': json2.Any(85000.0), 'years': json2.Any(5)},
		{'id': json2.Any(2), 'name': json2.Any('Bob Smith'), 'department': json2.Any('Sales'), 'salary': json2.Any(65000.0), 'years': json2.Any(3)},
		{'id': json2.Any(3), 'name': json2.Any('Carol White'), 'department': json2.Any('Engineering'), 'salary': json2.Any(92000.0), 'years': json2.Any(7)},
		{'id': json2.Any(4), 'name': json2.Any('David Brown'), 'department': json2.Any('Marketing'), 'salary': json2.Any(58000.0), 'years': json2.Any(2)},
		{'id': json2.Any(5), 'name': json2.Any('Eve Davis'), 'department': json2.Any('Sales'), 'salary': json2.Any(72000.0), 'years': json2.Any(4)},
		{'id': json2.Any(6), 'name': json2.Any('Frank Miller'), 'department': json2.Any('Engineering'), 'salary': json2.Any(105000.0), 'years': json2.Any(10)},
		{'id': json2.Any(7), 'name': json2.Any('Grace Wilson'), 'department': json2.Any('Marketing'), 'salary': json2.Any(61000.0), 'years': json2.Any(3)},
		{'id': json2.Any(8), 'name': json2.Any('Henry Taylor'), 'department': json2.Any('Sales'), 'salary': json2.Any(68000.0), 'years': json2.Any(2)},
	]

	// Load the data into a DataFrame
	df := ctx.read_records(employees)!
	s0 := df.shape()!
	println('Loaded ${s0[0]} rows and ${s0[1]} columns')

	print_header('DataFrame Shape and Structure')

	// Get basic information about the DataFrame
	shape := df.shape()!
	println('Shape: ${shape[0]} rows, ${shape[1]} columns')
	println('Columns: ${df.columns()!}')
	println('Data Types: ${df.dtypes()!}')

	print_header('Viewing Data (First 5 rows)')

	// Display first 5 rows
	df.head(5)!

	print_header('Viewing Data (Last 3 rows)')

	// Display last 3 rows
	df.tail(3)!

	print_header('DataFrame Info and Statistics')

	// Show column information
	println('Column info:')
	df.info()!

	// Show summary statistics
	print_header('Summary Statistics')
	df.describe()!

	print_header('Column Operations')

	// Select a subset of columns
	println('\nSelecting subset of columns (name, department, salary):')
	df_subset := df.subset(['name', 'department', 'salary'])!
	df_subset.head(3)!

	// Add a new calculated column
	println('\nAdding a calculated column (salary_per_year):')
	df_with_calc := df.add_column('salary_per_year', 'salary / years')!
	df_with_calc.head(5)!

	// Add prefix to all columns
	println('\nAdding prefix "emp_" to all columns:')
	df_prefixed := df.add_prefix('emp_')!
	println('Columns after prefix: ${df_prefixed.columns()!}')
	df_prefixed.head(3)!

	// Delete a column
	println('\nDeleting the "years" column:')
	df_deleted := df.delete_column('years')!
	println('Columns after deletion: ${df_deleted.columns()!}')
	df_deleted.head(3)!

	print_header('Slicing Data')

	// Get rows 3-5 (inclusive)
	println('\nSlicing rows 3-5:')
	df_sliced := df.slice(3, 5)!
	df_sliced.head(10)!

	print_header('Basic Usage Complete')
	println('This example demonstrated:')
	println('- Initializing a vframes context')
	println('- Loading data from records')
	println('- Exploring DataFrame structure (shape, columns, dtypes)')
	println('- Viewing data (head, tail)')
	println('- Getting info and statistics')
	println('- Selecting columns (subset)')
	println('- Adding calculated columns')
	println('- Adding prefixes/suffixes to columns')
	println('- Deleting columns')
	println('- Slicing rows')
}