tempdataset 0.1.0

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

For additional features with Faker support:

pip install tempdataset[faker]

Basic Usage

import tempdataset

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

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

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

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

Available Datasets

Sales Dataset

Generates realistic sales transaction data with:

• Transaction IDs
• Customer information
• Product details
• Sales amounts and quantities
• Timestamps
• Geographic data

# Generate sales data
sales_data = tempdataset.create_dataset('sales', 1000)

# Access data
print(f"Generated {len(sales_data)} rows")
print(f"Columns: {sales_data.columns}")
print(f"Memory usage: {sales_data.memory_usage()}")

Advanced Usage

Working with TempDataFrame

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

# Basic operations
print(data.head(10))          # First 10 rows
print(data.tail(5))           # Last 5 rows
print(data.describe())        # Statistical summary
print(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 ('sales') or filename ('sales.csv', 'sales.json')
• rows (int): Number of rows to generate (default: 500)

Returns:

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

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!