Jupyter: Virtual Environments

This page explains how to create and use virtual environments in Jupyter notebooks to manage dependencies and isolate project environments.

Syntax

To create a virtual environment using venv:

python -m venv myenv

To activate the virtual environment:

• On Windows:

  .\myenv\Scripts\activate
  

• On Unix or MacOS:

  source myenv/bin/activate
  

Example

1. Create a virtual environment named myenv:

   python -m venv myenv
   

2. Activate the virtual environment:

   • On Windows:

     .\myenv\Scripts\activate
     

   • On Unix or MacOS:

     source myenv/bin/activate
     

Output

You will see the virtual environment's name in the command prompt or terminal, indicating that the environment is active.

Explanation

Virtual environments provide a way to create isolated Python environments for projects. Activating a virtual environment ensures that the dependencies installed in that environment do not interfere with the global Python environment.

Use

• Dependency Isolation: Virtual environments allow you to isolate project dependencies, preventing conflicts with other projects.
• Environment Reproducibility: You can easily recreate the same environment on different machines or share it with collaborators using a requirements.txt file.

Important Points

• Use virtual environments to manage project-specific dependencies.
• Always activate the virtual environment before running Jupyter notebooks within it.
• Consider using tools like pip freeze to generate a requirements.txt file for reproducible environments.

Summary

Creating and using virtual environments in Jupyter notebooks is essential for managing dependencies and ensuring project reproducibility. By encapsulating project-specific dependencies in isolated environments, you can avoid conflicts and share your work with others in a way that is easy to reproduce. Incorporating virtual environments into your Jupyter workflow enhances the organization and maintainability of your projects.