hatch

Modern, extensible Python project management

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Quick Overview

Hatch is a modern, extensible Python project management tool. It aims to simplify and streamline the development workflow by providing a unified interface for project creation, dependency management, building, and publishing. Hatch is designed to be a comprehensive solution for Python developers, from beginners to experts.

Pros

All-in-one tool for Python project management, reducing the need for multiple tools
Extensible plugin system allowing for customization and additional functionality
Supports multiple Python versions and virtual environments
Provides a consistent project structure and configuration across different projects

Cons

Relatively new compared to established tools like setuptools or poetry
May have a steeper learning curve for developers accustomed to traditional tools
Some advanced features might be overkill for simple projects
Documentation could be more comprehensive for certain advanced use cases

Code Examples

Creating a new Python project:

hatch new my-project

This command creates a new Python project with a standardized structure and configuration.

Running tests with different Python versions:

hatch run +py=3.7,3.8,3.9 pytest

This command runs pytest for the project using Python versions 3.7, 3.8, and 3.9.

Building and publishing a package:

hatch build
hatch publish

These commands build the package and publish it to PyPI, respectively.

Getting Started

To get started with Hatch, follow these steps:

Install Hatch:

pip install hatch

Create a new project:

hatch new my-project
cd my-project

Add dependencies:

hatch add requests

Run your project:

hatch run python main.py

Build and publish your project:

hatch build
hatch publish

These steps will help you create, develop, and publish a Python project using Hatch.

Competitor Comparisons

pdm

7,771

A modern Python package and dependency manager supporting the latest PEP standards

Pros of PDM

Supports PEP 582 for local package installation, simplifying dependency management
Offers a lockfile for reproducible builds, enhancing project stability
Provides a plugin system for extending functionality

Cons of PDM

Smaller community and ecosystem compared to Hatch
Less integrated with other Python packaging tools
Steeper learning curve for users new to modern Python packaging

Code Comparison

PDM:

[project]
name = "my-project"
version = "0.1.0"
dependencies = [
    "requests>=2.0.0",
]

Hatch:

[project]
name = "my-project"
version = "0.1.0"
dependencies = [
    "requests>=2.0.0",
]

Both PDM and Hatch use similar pyproject.toml configurations, making it easy to switch between them. The main differences lie in their CLI commands and additional features rather than in the basic project setup.

PDM and Hatch are both modern Python project management tools, aiming to simplify dependency management and project workflows. While PDM focuses on PEP 582 compliance and offers a unique approach to virtual environments, Hatch provides a more integrated experience with existing Python tooling. The choice between them often depends on specific project requirements and personal preferences.

conda

6,398

A system-level, binary package and environment manager running on all major operating systems and platforms.

Pros of conda

Cross-platform package management for multiple programming languages
Robust environment management with isolated environments
Extensive ecosystem of pre-built scientific packages

Cons of conda

Slower package installation and environment creation
Larger installation footprint
More complex configuration and usage for beginners

Code comparison

conda

name: myenv
channels:
  - conda-forge
dependencies:
  - python=3.9
  - numpy
  - pandas

hatch

[project]
name = "myproject"
dependencies = [
    "numpy",
    "pandas",
]

Summary

Conda offers a comprehensive solution for managing packages and environments across multiple languages, making it particularly suitable for scientific computing. However, it can be slower and more complex compared to Hatch. Hatch, on the other hand, focuses on Python-specific project management with a simpler, more streamlined approach. It provides faster operations and a smaller footprint but lacks the cross-language capabilities of Conda. The choice between the two depends on the specific needs of the project and the developer's preferences.