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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.
| name | dummy-dataset |
| description | 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. |
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 rulesimport 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}")
Dataset Type: Customer Feedback
Columns:
Constraints:
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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