Dummy Dataset Generation

Generate realistic dummy datasets for testing with customizable columns, constraints, and output formats (CSV, JSON, SQL, Python script). Creates executable scripts or direct data files for immediate use.

Use when:

Creating test data, generating sample datasets, building realistic mock data for development, or populating test environments.

Arguments:

$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 Identify dataset type - Understand the data domain Define column specifications - Names, data types, and value ranges Determine row count - How many sample records needed Select output format - CSV, JSON, SQL INSERT, or Python script Apply realistic patterns - Ensure data looks authentic and valid Add business constraints - Respect business logic and relationships Generate or script data - Create executable output 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 Dataset Type: Customer Feedback Columns: 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) Constraints: 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 CSV: Flat tabular format, easy to import into spreadsheets and databases JSON: Nested structure, ideal for APIs and NoSQL databases SQL: INSERT statements, directly executable on relational databases Python Script: Executable generator for custom or large datasets