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Python Virtual Environments

Learning Objectives

  • Understand why virtual environments are crucial for reproducible research
  • Learn how to create and manage Python virtual environments
  • Master dependency management with requirements.txt
  • Develop best practices for environment management in scientific projects

Why Virtual Environments?

Virtual environments solve several critical problems:

  1. Isolation: Keep project dependencies separate
  2. Reproducibility: Ensure consistent package versions across different machines
  3. Conflict Prevention: Avoid package version conflicts between projects
  4. Clean Management: Easy to create, delete, and recreate environments

venv

Virtual Environment Tools

venv (Built-in)

Python's built-in solution, simple and lightweight: 

# Create virtual environment
python -m venv myenv

# Activate virtual environment
# On Unix/macOS:
source myenv/bin/activate
# On Windows:
myenv\Scripts\activate

# Deactivate
deactivate

There are other tools for python virtual environment management, such as conda, pyenv, pipenv, poetry, uv, etc. You can choose one of them to manage your dependencies. Here we will focus on venv and requirements.txt.

Managing Dependencies

pip Requirements

  1. Generate requirements.txt: 

    # Manual: List all installed packages
pip freeze > requirements.txt

  2. Install from requirements.txt: 

    pip install -r requirements.txt

  1. Project Setup: 

    # Create virtual environment
python -m venv venv

  2. Development Workflow: 

    # 1. Activate environment
source venv/bin/activate

# 2. Install/update dependencies
pip install package_name
pip freeze > requirements.txt

# 3. Code and test

# 4. Deactivate when done
deactivate

  3. Collaboration Workflow: 

    # 1. Clone repository
git clone project_url

# 2. Create environment
make env

# 3. Activate environment
source venv/bin/activate

# 4. Work on project

# 5. Update dependencies if needed
make requirements

  4. Deployment Workflow: 

    # 1. Create fresh environment
python -m venv prod_env

# 2. Install only production dependencies
pip install -r requirements.txt --no-dev

# 3. Run tests
pytest

# 4. Deploy

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