A Comprehensive Overview of Python Version, Environment, and Package Management Tools

Background

When I started using Python and creating my first package, I was confused. Managing packages and environments turned out to be more difficult than expected, and many tools exist without a clear overview. This article aims to give a structured evaluation of those tools.

Tool Classification

The tools are divided into five important categories for environment and package management:

Python version management

Package management

Environment management (mainly virtual environments)

Package building

Package publishing

Many tools overlap categories; some are single‑purpose, others are multi‑purpose.

Python Version Management

A tool for Python version management should allow easy installation of Python versions and switching between them.

Motivation

Different projects may require different Python versions, or you may want to test a library against multiple versions. You also might want to try the latest features or pre‑release versions.

Tools

The Venn diagram shows four main tools: pyenv , conda , rye , and PyFlow . Below is a brief look at pyenv :

<code># Install a specific Python version
pyenv install 3.10.4

# Switch between versions (global, local, shell)
pyenv shell <version>
pyenv local <version>
pyenv global <version>
</code>

Environment Management (Virtual Environments)

Definition

Environment management tools let you create and manage (virtual) environments.

Motivation

Projects often need different versions of the same dependency, leading to conflicts. Virtual environments isolate dependencies per project.

Tools

Common tools include venv , virtualenv , pipenv , conda , pdm , poetry , hatch , rye , and PyFlow . The two built‑in single‑purpose tools are:

venv

Built‑in Python module for creating virtual environments. Important commands:

<code># Create a new environment
python3 -m venv <env_name>

# Activate the environment
. <env_name>/bin/activate

# Deactivate
deactivate
</code>

virtualenv

Improved version of venv with more features and speed.

<code># Create a new environment
virtualenv <env_name>

# Activate
. <env_name>/bin/activate

# Deactivate
deactivate
</code>

Package Management

Definition

Package managers download and install libraries and their dependencies.

Motivation

Packages provide a hierarchical module structure and a convenient import syntax, and they enable sharing code with other developers.

Tools

Popular tools include pip , pipx , pipenv , conda , pdm , poetry , rye , and PyFlow . The most well‑known is pip :

<code># Install a package
pip install <package_name>
</code>

Multi‑Purpose Tools

pipenv

Combines pip and virtualenv , handling both dependency and environment management.

<code># Install a package
pipenv install <package_name>

# Run a script inside the virtual environment
pipenv run <script_name.py>

# Activate the virtual environment
pipenv shell
</code>

Conda

A general‑purpose package manager that works for Python and non‑Python packages. It also supports building and publishing packages, though its workflow differs from the pure‑Python tools.

Flit

Focused on building and publishing pure‑Python packages. It does not manage versions, dependencies, or environments.

<code># Initialize a new pyproject.toml
flit init

# Build and publish
flit publish
</code>

Poetry

Handles package management, environment management, building, and publishing (but not Python version management).

<code># Create a new project structure
poetry new <project_name>

# Interactive pyproject.toml creation
poetry init

# Install dependencies
poetry install

# Add a dependency
poetry add <package_name>

# Show dependency tree
poetry show --tree

# Build package
poetry build

# Publish to PyPI
poetry publish
</code>

pdm

Inspired by Poetry and PyFlow, supports package management, environment management, building, and publishing. It implements PEP 582 (local packages).

<code># Interactive pyproject.toml creation
pdm init

# Install packages
pdm install

# Add a dependency
pdm add <package_name>

# List dependencies as a graph
pdm list --graph

# Run a script (no dedicated shell command)
pdm run python <script_name.py>

# Build and publish
pdm build
pdm publish
</code>

Hatch

Manages environments declaratively via pyproject.toml , supports building and publishing, and allows plugins.

<code># Create a new project
hatch new <project_name>

# Initialize an existing project
hatch new --init

# Show dependencies in a table
hatch dep show table

# Open a shell (virtual environment)
hatch shell

# Run a script inside the environment
hatch run python <script_name.py>

# Build and publish
hatch build
hatch publish
</code>

Rye

Developed by the creator of Flask, written in Rust, and supports all five categories (version, package, environment, build, publish).

<code># Initialize a new project
rye init <project_name>

# Pin a Python version
rye pin 3.10

# Add a dependency (not installed yet)
rye add <package_name>

# Sync virtual environment and lock file (installs dependencies)
rye sync

# Open a shell
rye shell

# Run a script inside the environment
rye run python <script_name.py>

# Build and publish
rye build
rye publish
</code>

Summary Table

Flit

Poetry

pdm

Hatch

Rye

Manages dependencies

❌

✅

✅

❌

✅

Resolves/locks dependencies

❌

✅

✅

❌

✅

Clean build/publish flow

✅

✅

✅

✅

✅

Supports plugins

❌

✅

✅

✅

❌

Supports PEP 660

✅

✅

✅

✅

✅

Supports PEP 621

✅

❌

✅

✅

✅

For further reading and resources, see the linked articles at the end of the original post.