Generate realistic dummy datasets for testing with customizable columns, constraints, and output formats (CSV, JSON, SQL, Python script). Use when creating test data, building mock datasets, or generating sample data for development and demos.

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 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 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

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