Python

What are Python Virtual Environments?

Virtual environments are isolated Python environments that allow you to install packages for specific projects without affecting your system-wide Python installation.

Why Use Virtual Environments?

  • Dependency Isolation: Different projects can use different versions of the same package
  • Clean Development: Avoid polluting your global Python installation
  • Reproducibility: Easily share project dependencies with others
  • Version Control: Manage Python versions alongside package versions

Common Virtual Environment Tools

venv (Built-in)

Create a virtual environment

python -m venv myenv

Activate (Linux/macOS)

source myenv/bin/activate

Activate (Windows)

myenv\Scripts\activate

Deactivate

deactivate

virtualenv

Install

pip install virtualenv

Create a virtual environment

virtualenv myenv

Activate (Linux/macOS)

source myenv/bin/activate

Activate (Windows)

myenv\Scripts\activate

conda

Create environment

conda create --name myenv python=3.9

Activate

conda activate myenv

Deactivate

conda deactivate

Poetry

Install

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

Create a new project

poetry new project-name

Add dependencies

poetry add package-name

Activate

poetry shell

Managing Dependencies

pip

Install packages

pip install package-name

Save dependencies

pip freeze > requirements.txt

Install from requirements

pip install -r requirements.txt

Poetry

# Dependencies are managed in pyproject.toml
poetry add package-name
poetry add package-name==1.2.3  # specific version
poetry add "package-name>=1.2.3,<2.0.0"  # version constraints

Best Practices

  1. Always use virtual environments for new projects
  2. Include activation in your workflow scripts/documentation
  3. Version control your dependency files (requirements.txt, pyproject.toml)
  4. Don't commit the virtual environment directory (add to .gitignore)
  5. Use a consistent naming convention for your environments

Troubleshooting

  • Activation not working: Ensure you're using the correct activation command for your OS
  • Package not found: Verify the environment is activated and the package is installed
  • Multiple Python versions: Specify the Python version when creating the environment

Advanced Usage

Using different Python versions

With virtualenv

virtualenv -p python3.8 myenv

With conda

conda create --name myenv python=3.8

Project-specific configurations

# Set environment variables in .env file
DEBUG=True
API_KEY=your_secret_key

# Use python-dotenv to load them
pip install python-dotenv

Virtual environment wrappers

  • pyenv-virtualenv: Combines pyenv with virtualenv
  • virtualenvwrapper: Provides commands to manage multiple environments
  • pipenv: Combines pip and virtualenv functionality