Python project and dependency management tools, similar to Maven in Java and npm + webpack in Node, play an important role in developing and maintaining projects. Using proper dependency management tools can significantly improve development efficiency, reduce dependency conflicts, and ensure project stability, reliability, and security.
I. Functions required for common projects and dependency management tools
1. Dependency management
(1) Automated Dependency Installation
The Dependency Management tool automatically installs all the dependency packages required by the project without the need to manually install them one by one.
(2) Dependency version control
These tools allow developers to specify and lock down versions of dependent packages, ensuring consistency of dependencies as the project runs in different environments
2. Virtual environment management
Virtual environments allow multiple projects to run on the same machine without dependency conflicts. Dependency management tools typically create and manage virtual environments automatically, ensuring isolation of project dependencies.
3. Reliance on conflict resolution
Dependency management tools can automatically resolve dependency conflicts and ensure that the versions of installed dependency packages are compatible. For example.pipenv cap (a poem)poetry All have built-in dependency conflict resolution mechanisms.
4. Security checks
Some tools (e.g.pipenv) provides a built-in security check that scans dependency packages for known vulnerabilities and provides recommendations for fixing them.
# Use pipenv for security checking
pipenv check
5. Project initialization and templates
Some tools (e.g.poetry cap (a poem)hatch) provides project templates and scaffolding features to help developers quickly create new projects.
6. Publishing dependency packages
Some tools (e.g.poetry) has a built-in package publishing feature that makes it easy to publish your project to PyPI (Python Package Index).
# utilization poetry publishing kit
poetry publish --build
7. Generation of dependency profiles
Some dependency management tools can generate files describing project dependencies to facilitate team collaboration and deployment. For example.pip-tools It is possible to generate Documentation.
8. Versioning
This refers to how you manage and control your project versions. Each time you release a new version, you need to update the version number of your project (e.g. from 1.0.0 to 1.1.0) and make sure that changes to the version number follow certain rules (e.g. semanticized version control).
Versioning tools can help you automatically update version numbers, generate changelogs, create release tags, and more.
II. Comparison of common tools and their technologies
Common Python project and dependency management tools, includingPipenv、Poetry、Conda、Pip-tools 、Hatch(math.) andvenv. The following is a comparison of the tools in terms of introduction, features, functionality comparison and popularity.
1. Introduction to tools
（1）Pipenv
Pipenv At one time, it was officially recommended as a dependency management tool for Python projects, especially in scenarios where you need to automate the management of virtual environments and dependency locking. However, it has lost some of its popularity over time.
（2）Poetry
Poetry It has become increasingly popular in recent years, especially in projects that require modern dependency management and package distribution. It provides a better user experience and more powerful features.
（3）Conda
Conda It is very popular in the field of data science and machine learning because it supports not only Python, but also R and other languages, and its environment management features are very powerful.
（4）Pip-tools
Pip-tools This is popular in projects where precise control of dependency versioning is required, especially in projects that still use the of the project.
（5）Hatch
Hatch is a relatively new tool that, while powerful, is not yet up toPoetry maybeConda The popularity of the
（6）venv
venv is a module in the Python standard library for creating lightweight virtual environments that facilitate dependency isolation between projects.
2. Characterization
| artifact | Main usage scenarios | Main characteristics |
|---|---|---|
| Pipenv | General projects, automated management of virtual environments | Automated creation and management of virtual environments, dependency-locked files (), user-friendly CLI interface |
| Poetry | Modernization project, dependency management and package release | Comprehensive dependency management, built-in virtual environment management, project build and release, dependency resolution and locking |
| Conda | Data Science and Machine Learning | Cross-language support, powerful environment management, package management, support for multiple platforms (Windows, macOS, Linux) |
| Pip-tools | Precise control of version-dependent projects | Generating and updating requirements files, dependency locking, pip compatibility |
| Hatch | Modernizing project management and version control | Project templates, environment management, version control, dependency management, flexible plug-in system |
| venv | Basic virtual environment management | Python built-in modules for lightweight virtual environment management |
3. Functional comparison
| functionality | venv | Pipenv | Poetry | Conda | Pip-tools | Hatch |
|---|---|---|---|---|---|---|
| Creating a Virtual Environment | be | be | be | be | clogged | be |
| Automated management of virtual environments | clogged | be | be | be | clogged | be |
| Dependencies | clogged | be | be | be | be | be |
| version lock | clogged | be | be | be | be | be |
| Security check | clogged | be | clogged | clogged | clogged | clogged |
| Package Release | clogged | clogged | be | clogged | clogged | be |
| Dependence on conflict resolution | clogged | be | be | be | be | be |
| Project formwork and scaffolding | clogged | clogged | be | clogged | clogged | be |
| version management | clogged | clogged | be | clogged | clogged | be |
4. Popularity
The current (July 30, 2024) popularity of each tool, is as follows:
| artifact | GitHub Stars | PyPI downloads in the last six months (million) | Forks | Open Issues | Open PRs |
|---|---|---|---|---|---|
| Pipenv | 24k+ | 6466 | 1862 | 260 | 14 |
| Poetry | 30k+ | 20502 | 2236 | 602 | 74 |
| Conda | 6k+ | 97 | N/A | N/A | N/A |
| Pip-tools | 7k+ | 7254 | 608 | 160 | 27 |
| Hatch | 5k+ | 1163 | 285 | 239 | 26 |
| venv | Python Built-In | N/A | N/A | N/A | N/A |
III. Conclusion
From the technical comparison above, Poetry is currently the most suitable dependency management tool for new Python projects, both in terms of features and process. However, some of the other tools are also suitable for certain scenarios, such as Pip-tools for old projects with requirements and Conda for multi-language projects.
IV. VS pip
Using poetry as an example, we'll briefly describe the differences between the above project and package dependency management tools and pip.pip is a lightweight package management tool suitable for simple package installation and management tasks.poetry is a powerful project management tool for scenarios that require comprehensive management of project dependencies and configurations.
1. Functional comparison
| characterization | pip | poetry |
|---|---|---|
| Functions and uses |
pip is a package management tool for Python that installs and manages Python packages. It is mainly used for downloading and installing packages from the Python Package Index (PyPI) and does not involve project management. |
poetry is a comprehensive Python project management tool that not only installs and manages packages, but also creates and manages projects, handles dependencies, releases packages, and more. |
| configuration file |
pip utilization |
poetry utilization |
| Dependency management |
pip does not handle dependency conflicts by itself and needs to be resolved manually by the developer. It can be combined withpip-tools use to better manage dependencies. |
poetry Built-in dependency resolution and locking mechanism to automatically resolve dependency conflicts. It will generate a |
| Virtual Environment Management |
pip does not manage virtual environments per se, but is often used in conjunction withvirtualenv maybevenv Used together. Developers need to manually create and activate virtual environments. |
poetry Built-in virtual environment management feature to automatically create and manage virtual environments. Use thepoetry The creation and activation of virtual environments is handled automatically. |
2. Examples of use
（1）pip
# Install packages
pip install requests
# List installed packages
pip freeze >
# Install packages from
pip install -r
（2）poetry
# Create a new project
poetry new myproject
# Enter the project directory
cd myproject
# Install the package
poetry add requests
# Install all dependencies
poetry install
# Start the virtual environment
poetry shell
3. Recommendations
The example project uses Python's own pip, which is easy to use. Large-scale online production projects need to do a good job of dependency management, dependency conflict resolution, virtual environment management and other work to ensure that the project is delivered in a variety of environments, consistent and stable operation, poetry and other project and package management tools are more appropriate choice.