Python Virtual Environment
The virtual environment ecosystem is composed by 3 different parts:
- Python version Depending on the system you're using, a particular Python version can be installed via that system package manager or via
pyenv. - Virtual Environment A virtual environment is a Python environment such that the Python interpreter, libraries and scripts installed into it are isolated from those installed in other virtual environments, and (by default) any libraries installed in a “system” Python, i.e., one which is installed as part of your operating system. docs.python.org
- Dependency management A list of all the Python dependencies for a given project.Typically called
requirements.txt, but can also take a more complex form where there's a distinction between which dependencies belong to production and which belong to development/testing.
Python version
The most popular option is pyenv, even when a particular version is available in the OS package manager
pyenv
Check the documentation at https://github.com/pyenv/pyenv .
Allows to specify a particular Python version to be used in a virtual environment.
List all Python versions available to pyenv
pyenv install -l
Choose a version and install it
pyenv install 3.10.4
Versions are available under are available under ~/.pyenv/versions/
Global
The global command sets the global Python version, which is useful for ensuring a particular Python version by default. If you wanted to use 3.7.10 by default, then you could run this:
pyenv global 3.7.10
This command sets the ~/.pyenv/version to 3.7.10.
Local
The local command is used to set an application-specific Python version
pyenv local 3.10.4
This command creates a .python-version file in your current directory. If you have pyenv active in your environment, this file will automatically activate this version for you.
Virtual Environment
venv
The most simple is venv using the Python system version
python -m venv simple-venv
| activate | deactivate |
|---|---|
source simple-venv/bin/activate |
deactivate |
Pyenv + Virtualenv
Pyenv supports the creation of a virtualenv tied to a particular Python version.
pyenv virtualenv 3.10.4 sample-virtual-env
| activate | deactivate |
|---|---|
source ~/.pyenv/versions/sample-virtual-env/bin/activate |
deactivate |
pyenv local sample-virtual-env |
deactivate |
Dependency Management
| pip | pipenv | Poetry | |
|---|---|---|---|
| search | pip search |
pipenv search |
poetry search |
| install | pip install |
pipenv install |
poetry add |
| install dev | pipenv install |
poetry add |
|
| uninstall | pip uninstall |
pipenv uninstall |
poetry remove |
| uninstall dev | pipenv uninstall |
poetry remove |
|
| list packages | pip list | pip list | poetry show |
| build | poetry build | ||
| publish | poetry publish |
Pip
https://pip.pypa.io/en/stable/
Pip is the package installer for Python. You can use it to install packages from the Python Package Index and other indexes.
Pip is available by default
Requirements.txt
Even though this is the most common way of sharing project dependencies, it is not the greatest, since there is no distinction between production and development packages.
Can be generated by running
pip freeze > requirements.txt
pipenv
https://pipenv.pypa.io/en/latest/
Pipenv is a tool that aims to bring the best of all packaging worlds (bundler, composer, npm, cargo, yarn, etc.) to the Python world. Windows is a first-class citizen, in our world. It automatically creates and manages a virtualenv for your projects, as well as adds/removes packages from your Pipfile as you install/uninstall packages. It also generates the ever-important Pipfile.lock, which is used to produce deterministic builds. Pipenv uses Pipfile and Pipfile.lock to separate abstract dependency declarations from the last tested combination.
Install
Before proceed with the installation, please make sure the virtual environment of your choice is active
pip install pipenv
Pipfile and Pipfile.lock
Pipfile holds the settings used in the project, below there's a list most common ones:
- source: python package repository, by default is set to pypi
- dev-packages: list of development packages and macthing version defined by the user, which is * if not specified
- packages: list of production packages and matching version defined by the user, which is * if not specified
- requires: python version of the project
Even though pipfile allows for a package to be installed without a particular version being set, this is not advisable, since it will take longer to determine the best version to be installed. This is mostly noticed when the project has a lot of packages.
Pipfile.lock holds the exact version that was installed upon the time of the version lock. As an example, lets say
we're using Django but haven't set a particular version, this means everytime we run pipenv install the latest
version of Django would be installed.
Poetry
This is a packaging and dependency management all in one, which means:
- it supports builds
- you can publish your package to both public and private repositories
- includes a virtualenv https://python-poetry.org/docs/basic-usage/#using-your-virtual-environment.
Check the documentation available at https://python-poetry.org/docs/, the installation should be system-wide, even though it can be installed on a particular environment.
poetry new poetry-demo
This will create the poetry-demo directory with the following content.
poetry-demo
├── pyproject.toml
├── README.rst
├── poetry_demo
│ └── __init__.py
└── tests
├── __init__.py
└── test_poetry_demo.py
Poetry + pyenv
To use poetry with a particular Python version, that version only needs to be available in the system. In this case,
we're using a Python version provided by pyenv. For further documentation please
check https://python-poetry.org/docs/managing-environments/#switching-between-environments
cd poetry-demo
poetry env use ~/.pyenv/versions/3.10.4/bin/python
Dependency management
https://python-poetry.org/docs/dependency-specification/
Similarly to Pipenv, Poetry also allows a version lock and a distinction between production and development packages.
- tool.poetry.dependencies: list of production packages and matching version defined by the user, which is * if not specified. Python version is also specified here
- tool.poetry.dev-dependencies: list of development packages and matching version defined by the user, which is * if not specified
pipx
Pipx allows the installation and execution of Python applications in isolated environments. It's not recommended to use within a project, but very suitable for running standalone python applications.