tempdataset 0.1.1

A lightweight Python library for generating realistic temporary datasets

TempDataset

A lightweight Python library for generating realistic temporary datasets for testing and development. No heavy dependencies required - works with just the Python standard library!

Features

• Lightweight: Zero dependencies for core functionality
• Multiple Formats: Generate CSV, JSON, or in-memory datasets
• Realistic Data: Built-in datasets with realistic patterns
• Extensible: Easy to add custom dataset types
• Memory Efficient: Optimized for large dataset generation
• Python 3.7+: Compatible with modern Python versions

Quick Start

Installation

pip install tempdataset

pip install git+https://github.com/dot-css/TempDataset

Basic Usage

import tempdataset

# Generate 1000 rows of any dataset type
data = tempdataset.create_dataset('sales', 1000)
data.head()

# Save directly to CSV
tempdataset.create_dataset('sales.csv', 500)

# Save directly to JSON
tempdataset.create_dataset('customers.json', 500)

# Read data back
csv_data = tempdataset.read_csv('sales.csv')
json_data = tempdataset.read_json('customers.json')

# Get help and see all available datasets
tempdataset.help()          # Comprehensive help
tempdataset.list_datasets() # Quick dataset overview

Available Datasets

TempDataset provides 7 comprehensive datasets for various use cases:

🛒 Sales Dataset

Complete sales transaction data with 27 columns:

sales_data = tempdataset.create_dataset('sales', 1000)

Features: Order information, customer details, product data, financial calculations, geographic data, shipping details

Key Columns: order_id, customer_name, product_name, category, final_price, order_date, sales_rep, region, profit

👥 Customers Dataset

Comprehensive customer profiles with 31 columns:

customers_data = tempdataset.create_dataset('customers', 1000)

Features: Personal information, demographics, purchase history, loyalty data, account status, preferences

Key Columns: customer_id, full_name, email, age, annual_income, total_spent, loyalty_points, account_status

🛍️ E-commerce Dataset

Advanced e-commerce transaction data with 35+ columns:

ecommerce_data = tempdataset.create_dataset('ecommerce', 1000)

Features: Transaction details, customer behavior, product catalog, reviews, returns, digital metrics, seller information

Key Columns: transaction_id, customer_rating, seller_rating, return_status, device_type, conversion_rate

👨‍💼 Employees Dataset

Complete HR and employee management data with 30+ columns:

employees_data = tempdataset.create_dataset('employees', 1000)

Features: Personal info, job details, performance metrics, benefits, skills, department structure

Key Columns: employee_id, job_title, department, salary, performance_rating, benefits, skills

📢 Marketing Dataset

Marketing campaign performance data with 32+ columns:

marketing_data = tempdataset.create_dataset('marketing', 1000)

Features: Campaign metrics, channel performance, ROI analysis, audience data, conversion tracking

Key Columns: campaign_id, channel, impressions, clicks, conversions, roi, cost_per_click

🏪 Retail Dataset

In-store retail operations data with 28+ columns:

retail_data = tempdataset.create_dataset('retail', 1000)

Features: Point-of-sale transactions, inventory management, store operations, staff data, seasonal trends

Key Columns: receipt_id, store_id, product_sku, quantity_sold, staff_id, inventory_level

🏭 Suppliers Dataset

Supplier and vendor management data with 22+ columns:

suppliers_data = tempdataset.create_dataset('suppliers', 1000)

Features: Supplier profiles, performance metrics, contract management, quality ratings, delivery data

Key Columns: supplier_id, company_name, quality_rating, delivery_performance, contract_value

Quick Help

# Get comprehensive help and examples
tempdataset.help()

# List all datasets with descriptions  
tempdataset.list_datasets()

# See specific dataset schema
data = tempdataset.create_dataset('sales', 10)
print(data.columns)  # View all column names

Advanced Usage

Working with TempDataFrame

data = tempdataset.create_dataset('sales', 1000)

# Basic operations
data.head(10)          # First 10 rows
data.tail(5)           # Last 5 rows
data.describe()        # Statistical summary
data.info()            # Data info

# Filtering and selection
filtered = data.filter(lambda row: row['amount'] > 100)
selected = data.select(['customer_name', 'amount', 'date'])

# Export options
data.to_csv('output.csv')
data.to_json('output.json')
data.to_dict()                # Convert to dictionary

Performance Monitoring

import tempdataset

# Generate data
data = tempdataset.create_dataset('sales', 10000)

# Check performance stats
stats = tempdataset.get_performance_stats()
print(f"Generation time: {stats['generation_time']:.2f}s")
print(f"Memory usage: {stats['memory_usage']:.2f}MB")

# Reset stats for next operation
tempdataset.reset_performance_stats()

Development

Setting up Development Environment

# Clone the repository
git clone https://github.com/dot-css/TempDataset.git
cd TempDataset

# Install development dependencies
pip install -e .[dev]

# Run tests
pytest

# Run tests with coverage
pytest --cov=tempdataset

# Run performance benchmarks
pytest .benchmarks/

Running Tests

# Run all tests
pytest

# Run specific test categories
pytest -m "not slow"          # Skip slow tests
pytest -m integration         # Only integration tests
pytest -m performance         # Only performance tests

# Run with coverage report
pytest --cov=tempdataset --cov-report=html

Code Quality

# Format code
black tempdataset tests

# Lint code
flake8 tempdataset tests

# Type checking
mypy tempdataset

API Reference

Core Functions

create_dataset(dataset_type, rows=500)

Generate temporary datasets or save to files.

Parameters:

• dataset_type (str): Dataset type or filename
  • Available types: 'sales', 'customers', 'ecommerce', 'employees', 'marketing', 'retail', 'suppliers'
  • File formats: 'sales.csv', 'customers.json', etc.
• rows (int): Number of rows to generate (default: 500)

Returns:

• TempDataFrame containing the generated data (also saves to file if filename provided)

help()

Display comprehensive help information about all available datasets, including column descriptions, usage examples, and feature details.

list_datasets()

Get a quick overview of all available datasets with their key features and column counts.

read_csv(filename)

Read CSV file into TempDataFrame.

read_json(filename)

Read JSON file into TempDataFrame.

TempDataFrame Methods

• head(n=5): Get first n rows
• tail(n=5): Get last n rows
• describe(): Statistical summary
• info(): Dataset information
• filter(func): Filter rows by function
• select(columns): Select specific columns
• to_csv(filename): Export to CSV
• to_json(filename): Export to JSON
• to_dict(): Convert to dictionary

Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Workflow

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests for new functionality
5. Run the test suite
6. Submit a pull request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Changelog

See CHANGELOG.md for a detailed history of changes.

Support

• Documentation: https://tempdataset.readthedocs.io/
• Issue Tracker: https://github.com/dot-css/TempDataset/issues
• Discussions: https://github.com/dot-css/TempDataset/discussions

Acknowledgments

• Built with love for the Python testing community
• Inspired by the need for lightweight, dependency-free test data generation
• Thanks to all contributors who help make this project better!