TempDataset

A lightweight Python library for generating realistic temporary datasets for testing and development. Generate 40+ different dataset types including business, financial, IoT, healthcare, and technology data!

✨ Features

• 40+ Dataset Types: Business, financial, IoT sensors, healthcare, social media, and technology datasets
• Zero Dependencies: Works with just Python standard library
• Multiple Formats: Generate CSV, JSON, or in-memory datasets
• Realistic Data: Built-in faker integration with realistic patterns
• Technology Focus: New datasets for DevOps, monitoring, web analytics, and system performance
• Memory Efficient: Optimized for large dataset generation
• Python 3.7+: Compatible with modern Python versions

🚀 Quick Start

Installation

pip install tempdataset

Basic Usage

import tempdataset

# Generate datasets
data = tempdataset.create_dataset('sales', 1000)
tech_data = tempdataset.create_dataset('web_analytics', 500)
server_metrics = tempdataset.create_dataset('server_metrics', 200)

# Save directly to files
tempdataset.create_dataset('sales_data.csv', 1000)
tempdataset.create_dataset('api_logs.json', 500)

# Get help and see all available datasets  
tempdataset.list_datasets() # Show all 40+ datasets
tempdataset.help()          # Quick help guide

## 📊 Available Datasets (40 Total)

TempDataset provides **40 comprehensive datasets** across 6 major categories:

### 🏢 Core Business Datasets (10)
- **`sales`** - Sales transactions and orders (27 columns)
- **`customers`** - Customer profiles and demographics (31 columns)  
- **`ecommerce`** - E-commerce transactions and reviews (35+ columns)
- **`employees`** - Employee records and HR data (30+ columns)
- **`marketing`** - Marketing campaigns and performance (32+ columns)
- **`retail`** - Retail store operations (28+ columns)
- **`suppliers`** - Supplier management data (22+ columns)
- **`crm`** - Customer relationship management (30+ columns)
- **`inventory`** - Inventory and warehouse data (25+ columns)
- **`reviews`** - Product and service reviews (15+ columns)

### 💰 Financial Datasets (8)
- **`stocks`** - Stock market trading data (20+ columns)
- **`banking`** - Banking transactions (20+ columns)
- **`cryptocurrency`** - Cryptocurrency trading (20+ columns)
- **`insurance`** - Insurance policies and claims (20+ columns)
- **`loans`** - Loan applications and management (20+ columns)
- **`investments`** - Investment portfolios (20+ columns)
- **`accounting`** - General ledger and accounting (20+ columns)
- **`payments`** - Digital payment processing (25+ columns)

### 🌐 Technology Datasets (8) ⭐ *NEW!*
- **`web_analytics`** - Website analytics and traffic (17 columns)
- **`app_usage`** - Mobile app usage analytics (15 columns)
- **`system_logs`** - System and application logs (11 columns)
- **`api_calls`** - API calls and performance (12 columns)
- **`server_metrics`** - Server performance monitoring (22 columns)
- **`user_sessions`** - User session tracking (20 columns)
- **`error_logs`** - Application error logs (16 columns)
- **`performance`** - Application performance monitoring (21 columns)

### 🏥 Healthcare Datasets (6)
- **`patients`** - Patient medical records (22 columns)
- **`appointments`** - Medical appointments (14 columns)
- **`lab_results`** - Laboratory test results (13 columns)
- **`prescriptions`** - Medication prescriptions (16 columns)
- **`medical_history`** - Patient medical history (11 columns)
- **`clinical_trials`** - Clinical trial data (14 columns)

### �️ IoT Sensor Datasets (6)  
- **`weather`** - Weather sensor monitoring (18 columns)
- **`energy`** - Smart meter energy data (14 columns)
- **`traffic`** - Traffic sensor monitoring (15 columns)
- **`environmental`** - Environmental monitoring (17 columns)
- **`industrial`** - Industrial sensor data (16 columns)
- **`smarthome`** - Smart home IoT devices (16 columns)

### 📱 Social Media Datasets (2)
- **`social_media`** - Social media posts and engagement (16 columns)
- **`user_profiles`** - Social media user profiles (17 columns)

### 🚀 Quick Examples

```python
# Generate different types of datasets
sales = tempdataset.create_dataset('sales', 1000)
tech_logs = tempdataset.create_dataset('system_logs', 500)  
health_data = tempdataset.create_dataset('patients', 200)
crypto = tempdataset.create_dataset('cryptocurrency', 300)

# Get help and list all datasets
tempdataset.list_datasets()  # Show all 40 datasets
tempdataset.help()          # Quick reference guide

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!