Conda Environments

Conda provides an extensive interface to manage python virtual environments that provides additional functionality over venv

Creating an environment

In Windows, open the Anaconda prompt, in WSL2 / Linux any shell prompt once you have installed miniconda or Anaconda.

• create the environment

  conda create --name myenv

• activate the environment

  conda activate myenv

Configuring the environment for running Jupyter notebooks

• install packages (example packages for data science experiments)

  conda install pandas scikit-learn matplotlib jupyter jupyterlab sqlalchemy seaborn pip git nbconvert

• you might want to install tools from a different update channel:

  conda install -c conda-forge jupyter_contrib_nbextensions
  conda update conda
  conda update --all

Alternatively, you can install packages (and optionally pin versions) at the point you create the environment:

conda create --name myenv python=3.11 pandas scikit-learn matplotlib jupyter jupyterlab sqlalchemy seaborn pip git nbconvert openpyxl

Adding virtual python kernel

In most cases you will also want to add a virtual python kernel to run notebooks.

See also

• Managing Environments (Conda Docs)
• The definitive guide to Python virtual environments with conda
• Conda Cheatsheet (PDF)