Page Comparison

...

We have included all of the basics, including NumPy compiled with MKL, and a number of other popular data science tools. Users can install their own environments and use them in Jupyter by building a custom kernel.

Anchor
python-environments python-environments

Python environments

Interactive computing depends strongly on controlling virtual environments. If you want to build your own, you should review our environments page first, and then return to the custom kernels in Jupyter section below to connect either a standard python virtual environment (from python -m venv …) or a conda environment (from conda env update …), to Jupyter.

Anchor
custom-kernels-in-jupyter custom-kernels-in-jupyter

Custom kernels in Jupyter

# create a python virtual environment or conda environment
python -m venv ./venv
# access the environment
source ./venv/bin/activate
# install the kernel software with pip
pip install ipykernel
# register your kernel so Jupyter can find it
python -m ipykernel install --user --name project-alpha --display-name "Project Alpha, 2025-Feb"

...