What is the difference between venv, pyvenv, pyenv, virtualenv, virtualenvwrapper, pipenv, etc?

 

There are several tools and libraries in the Python ecosystem that are used for creating and managing virtual environments, which allow you to isolate Python environments for different projects. Each tool has its own purpose and features. Let's explore the differences between them:

1. venv: venv is a module that comes with Python 3.3 and later versions. It's a built-in tool for creating lightweight virtual environments. It's simple and recommended for creating isolated environments.

   Example:

   bash

python3 -m venv myenv
source myenv/bin/activate

pyvenv: pyvenv was an older version of venv provided with Python 3.3, but it's not recommended to use anymore. Instead, use venv.

pyenv: pyenv is a tool for managing multiple versions of Python on your system. It helps you switch between different Python versions easily.

Example:

bash

pyenv install 3.9.6
pyenv global 3.9.6

virtualenv: virtualenv is a third-party tool that creates isolated Python environments. It works with both Python 2 and Python 3.

Example:

bash

pip install virtualenv
virtualenv myenv
source myenv/bin/activate

virtualenvwrapper: virtualenvwrapper is a set of shell scripts that enhance the experience of working with virtualenv. It provides additional commands and shortcuts to manage virtual environments.

Example:

bash

pip install virtualenvwrapper
mkvirtualenv myenv
workon myenv

pipenv: pipenv is a higher-level tool that combines virtualenv and pip. It simplifies dependency management and provides tools for creating and managing virtual environments along with project-specific dependencies.

Example:

bash

6. pip install pipenv
   pipenv install requests
   

Each tool has its own strengths and use cases. For simple isolation of environments, venv or virtualenv are good choices. If you need more advanced features or dependency management, you might prefer pipenv. pyenv is useful for managing different Python versions, and virtualenvwrapper provides additional convenience when working with virtual environments.

Remember that the Python ecosystem evolves, so it's a good idea to check for the latest recommendations and best practices for your specific use case.