Dummy Dataset Generation

• $PRODUCT

  : The product or system name

• $DATASET_TYPE

  : Type of data (e.g., customer feedback, transactions, user profiles)

• $ROWS

  : Number of rows to generate (default: 100)

• $COLUMNS

  : Specific columns or fields to include

• $FORMAT

  : Output format (CSV, JSON, SQL, Python script)

• $CONSTRAINTS

  : Additional constraints or business rules

Step-by-Step Process

1. Identify dataset type - Understand the data domain
2. Define column specifications - Names, data types, and value ranges
3. Determine row count - How many sample records needed
4. Select output format - CSV, JSON, SQL INSERT, or Python script
5. Apply realistic patterns - Ensure data looks authentic and valid
6. Add business constraints - Respect business logic and relationships
7. Generate or script data - Create executable output
8. Validate output - Ensure data quality and completeness

Template: Python Script Output

import csv
import json
from datetime import datetime, timedelta
import random

# Configuration
ROWS = $ROWS
FILENAME = "$DATASET_TYPE.csv"

# Column definitions with realistic value generators
columns = {
    "id": "auto-increment",
    "name": "first_last_name",
    "email": "email",
    "created_at": "timestamp",
    # Add more columns...
}

def generate_dataset():
    """Generate realistic dummy dataset"""
    data = []
    for i in range(1, ROWS + 1):
        record = {
            "id": f"U{i:06d}",
            # Generate values based on column definitions
        }
        data.append(record)
    return data

def save_as_csv(data, filename):
    """Save dataset as CSV"""
    with open(filename, 'w', newline='') as f:
        writer = csv.DictWriter(f, fieldnames=data[0].keys())
        writer.writeheader()
        writer.writerows(data)

if __name__ == "__main__":
    dataset = generate_dataset()
    save_as_csv(dataset, FILENAME)
    print(f"Generated {len(dataset)} records in {FILENAME}")

Example Dataset Specification

• feedback_id (auto-increment, U001, U002...)
• customer_name (realistic names)
• email (valid email format)
• feedback_date (dates last 90 days)
• rating (1-5 stars)
• category (Bug, Feature Request, Complaint, Praise)
• text (realistic feedback)
• product (electronics, clothing, home)

• Ratings skewed: 40% 5-star, 30% 4-star, 20% 3-star, 10% 1-2 star
• Bug category only with ratings 1-3
• Feature requests only with ratings 3-5
• Email domains realistic (gmail, yahoo, company.com)

Output Deliverables

• Ready-to-execute Python script OR direct data file
• CSV file with proper headers and formatting
• JSON file with valid structure and types
• SQL INSERT statements for database population
• Data validation and constraint compliance
• Realistic, business-appropriate values
• Documentation of data generation logic
• Quick-start instructions for using the dataset

Output Formats