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Choosing the right Python environment and package management tool in 2025

Β· 7 min read
Serhii Hrekov
Serhii Hrekov
software engineer, creator, artist, programmer, projects founder

Choosing the right Python environment and package management tool in 2025 depends on your project's complexity and your team's workflow. The tools available can be categorized into two main groups: those that manage Python versions (pyenv, asdf) and those that manage project dependencies (venv, virtualenv, Poetry). Combining these tools is often the most effective approach.

The Core Problem: Why Do We Need These Tools? πŸ€”β€‹

The primary reason for using these tools is to isolate your projects. Installing packages globally can lead to a phenomenon known as "dependency hell," where different projects require conflicting versions of the same library [2, 4]. A good environment management strategy ensures each project has its own dedicated Python interpreter and set of packages, preventing conflicts and making your work reproducible.

The Tools: What They Do and When to Use Them​

venv and virtualenv​

These are the foundational tools for creating virtual environments. They create a directory with a Python interpreter and a site-packages directory for project-specific libraries.

  • venv: Since Python 3.3, venv has been part of the standard library, making it the most accessible and recommended choice for simple projects [1]. It's a lightweight tool that comes with Python, so no installation is needed.
  • virtualenv: virtualenv is a more feature-rich, third-party alternative. It can be faster than venv and offers more advanced features, such as creating environments for older Python versions and having a richer programmatic API [1]. It remains a relevant tool, especially for complex or legacy projects where its advanced features are needed.

Verdict: Start with venv for simple projects. Use virtualenv if you need more features or are working with a codebase that uses it.

pyenv and asdf​

These tools solve a different problem: managing multiple Python versions on your system. They allow you to install and switch between various Python interpreters effortlessly.

  • pyenv: This is a Python-specific version manager. It allows you to install different versions of Python (e.g., 3.10, 3.11, 3.12) and set a global version or a local version for a specific project. This is crucial for working on multiple projects that require different Python interpreters [5].
  • asdf: Similar to pyenv, asdf is a version manager, but for multiple languages (Python, Node.js, Ruby, Go, etc.). It uses a plugin system, and its Python plugin actually uses pyenv's python-build behind the scenes. asdf is the best choice if you're a polyglot developer working with projects that use various programming languages [3].

Verdict: Use pyenv if you only work with Python. Use asdf if you need to manage multiple language versions in your development environment.

Poetry and Pipenv​

These are modern, all-in-one tools that combine a version manager, a dependency manager, and a virtual environment creator. They aim to simplify the entire workflow.

  • Poetry: Poetry is a highly popular tool for dependency management and packaging [2]. It uses a pyproject.toml file to declare dependencies and metadata, and it automatically creates and manages a virtual environment for you. Its dependency resolver is excellent, and it generates a poetry.lock file to ensure reproducible builds.
  • Pipenv: Pipenv was an earlier attempt to combine pip and virtualenv. While it's still a viable option, its adoption has been somewhat overtaken by Poetry [2]. Poetry is generally considered to be the more modern and feature-complete solution.

Verdict: Use Poetry for new projects in 2025. It simplifies dependency management, versioning, and virtual environments into a single, clean workflow.

The most effective workflow for most developers combines a version manager with a dependency manager.

  1. Use a Version Manager (pyenv or asdf): This is the first step. Use pyenv or asdf to install the specific Python interpreter version required by your project.
  2. Use Poetry for Everything Else: Let Poetry handle the rest. It will automatically create a virtual environment for your project using the correct Python version you set. Poetry then manages your dependencies, locking them for reproducibility, and can even build your package for distribution [2]. This approach provides the best of both worlds: a version-specific interpreter and a fully managed, reproducible environment.

This video provides a great overview of the different Python environment management tools and a practical guide on how to use them effectively.

Sources​

  1. virtualenv. "virtualenv vs venv". https://virtualenv.pypa.io/
  2. Hacker News. "Asking sincerely: With the various tools like pyenv, poetry, virtualenv, venv an...". https://news.ycombinator.com/item?id=35023704
  3. DEV Community. "Automagic Python virtual environments with asdf and direnv". https://yeray.dev/python/direnv-asdf-python-virtualenv
  4. Inedo Blog. "Python Environment Management Best Practices". https://blog.inedo.com/python/python-environment-management-best-practices/
  5. HyScaler. "Managing Python Versions with pyenv in 2025.: Unlock the Power of Seamless Development". https://hyscaler.com/insights/managing-python-versions-with-pyenv/