Virtual Environment ​

Virtual environments in Python are tools for managing dependencies and isolating different projects from each other.

Use a virtual environment for each project: Creating a separate virtual environment for each of your Python projects ensures that the dependencies of one project do not conflict with those of another project. This helps in keeping your projects isolated and avoids potential dependency issues.

Use a virtual environment manager: There are several popular virtual environment managers available for Python, such as venv, virtualenv, and conda. These tools provide an easy and convenient way to create, manage, and activate virtual environments. You can choose the one that suits your workflow and learn how to use it effectively.

Activate the virtual environment: Once you've created a virtual environment, make sure to activate it before using it. Activating a virtual environment sets the appropriate Python interpreter and ensures that any packages you install or scripts you run are within the scope of the virtual environment. The activation command may vary depending on the virtual environment manager you are using. Some newer virtual environment managers activate automatically. 

Install dependencies within the virtual environment: After activating a virtual environment, use the appropriate package manager (such as pip or conda) to install the dependencies specific to your project within the virtual environment. This ensures that the dependencies are isolated to the virtual environment and won't affect your system-wide Python installation.

venv ​

Included by default no extra dependencies works with requirements.txt

pipenv ​

pipenv has the advantage of working with exiting requirements.txt files. This can be helpful for external projects.

Install pipenv

pip install pipenv --user

pip install pipenv --user

Install dependencies using pipenv:

cd project_folder
pipenv install moments

cd project_folder
pipenv install moments

At this point you can launch the virtualenv shell (pipenv shell), or run individual commands within the environment (pipenv run).

If you are using pipenv for virtual environment and package management, you can easily install dependencies from the requirements.txt file. Here's an example:

# Create a Pipfile and virtual environment
pipenv install --dev

# previous command automatically installs requirements.txt for me

# Install dependencies from requirements.txt
pipenv run pip install -r requirements.txt

# Create a Pipfile and virtual environment
pipenv install --dev

# previous command automatically installs requirements.txt for me

# Install dependencies from requirements.txt
pipenv run pip install -r requirements.txt

Poetry ​

Similar to npm, but for python. Does not require manually activating / deactivating the virtual environment.

https://python-poetry.org/ Poetry - Python dependency management and packaging made easy https://github.com/python-poetry/poetry GitHub - python-poetry/poetry: Python packaging and dependency management made easy

Install ​

curl -sSL https://install.python-poetry.org | python3 -

curl -sSL https://install.python-poetry.org | python3 -

https://python-poetry.org/docs/

Usage ​

Add poetry to existing project

poetry init

poetry init

Add module to current project

poetry add [module name]

poetry add [module name]

See what has already been added

poetry show --tree

poetry show --tree

https://python-poetry.org/docs/basic-usage/#specifying-dependencies Basic usage | Documentation | Poetry - Python dependency management and packaging made easy

Add packages from requirements.txt ​

cat requirements.txt | xargs -I % sh -c 'poetry add "%"'

cat requirements.txt | xargs -I % sh -c 'poetry add "%"'

https://duckduckgo.com/?t=ffab&q=poetry+install+requirements.txt&ia=web poetry install requirements.txt at DuckDuckGo https://stackoverflow.com/questions/62764148/how-to-import-requirements-txt-from-an-existing-project-using-poetry python - How to import requirements.txt from an existing project using Poetry - Stack Overflow

Links ​

https://duckduckgo.com/?t=ffab&q=python+pipenv+vs+poetry&ia=web python pipenv vs poetry at DuckDuckGo

https://dev.to/frostming/a-review-pipenv-vs-poetry-vs-pdm-39b4 A Review: Pipenv vs. Poetry vs. PDM - DEV Community https://github.com/pdm-project/pdm GitHub - pdm-project/pdm: A modern Python package and dependency manager supporting the latest PEP standards

virtualenv ​

make a new virtualenv:

https://docs.python-guide.org/dev/virtualenvs/

Comparison ​

Here's a comparison of some popular tools for managing Python virtual environments: venv, virtualenv, pipenv, conda, and poetry.

pipenv: pipenv is a popular tool that combines package management (using pip) and virtual environment management in one tool. It provides a Pipfile for managing dependencies and a Pipfile.lock for environment locking. It also has features like automatic environment activation, handling of dev dependencies, and improved security features. However, some users find its workflow and error messages to be complex or overwhelming.

poetry: poetry is a newer tool for managing Python dependencies and virtual environments. It aims to simplify the process of managing dependencies, building, and packaging Python projects. It provides a pyproject.toml file for managing dependencies and has features like automatic virtual environment creation, comprehensive dependency resolution, and build and packaging capabilities. However, it may have a steeper learning curve for users who are not familiar with its unique approach and file structure.

venv (built-in): venv is a built-in module in Python 3 that provides a simple way to create virtual environments. It's easy to use and comes with Python by default. However, it lacks some advanced features like dependency management and environment locking, and it may not be compatible with older versions of Python.

virtualenv: virtualenv is a widely used third-party tool for creating virtual environments. It supports both Python 2 and Python 3 and provides more advanced features like environment activation scripts and the ability to clone an existing environment. However, it does not include built-in dependency management.

conda: conda is a cross-platform package management and environment management tool provided by Anaconda for scientific computing and data science. It supports multiple programming languages, including Python, and provides robust dependency management, environment isolation, and reproducibility. It also has a large ecosystem of pre-built packages for scientific computing. However, it may introduce additional complexity if you're not using it for its intended use case, and it requires a separate installation if you're not already using Anaconda.