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Using Python and R in Polyglot Notebooks

Polyglot notebooks is now offering Jupyter support, meaning you can use Python and R in your polyglot notebook along with previously supported languages like C#, JavaScript, and SQL.

This feature is currently in preview.

Setup

Before you begin, make sure you have the following installed:

  1. The Anaconda distribution. Comes with Python and Jupyter.
  2. OR Install Python and add to your PATH. You would need to install Jupyter
  3. If you are working with R - Install R

Connecting to a Python kernel

Run the following command in a notebook cell:

If working with Jupyter using Anaconda:

#!connect jupyter --kernel-name pythonkernel --conda-env base --kernel-spec python3

If working with Python and Jupyter directly without Anaconda:

#!connect jupyter --kernel-name pythonkernel --kernel-spec python3

Once connected, create a new cell and select your Python kernel from the kernel picker in the bottom right hand corner of the cell.

Connecting to an R kernel

First, ensure that R is added to Jupyter. If not, switch to the Anaconda Prompt, and run this command:

conda install -c r r-irkernel

Check to see if your R kernel appears in the Jupyter kernel spec list. If not, add your R kernel to Jupyter by running these commands in the R console:

install.packages('IRkernel')
IRkernel::installspec()

If you installed a new kernelspec or added new environment variables, you will need to restart VSCode.

If working with Jupyter using Anaconda, run the following command in a notebook cell:

#!connect jupyter --kernel-name Rkernel --conda-env base --kernel-spec ir

If working with Jupyter directly without Anaconda:

#!connect jupyter --kernel-name Rkernel --kernel-spec ir

Once connected, create a new cell and select your R kernel from the kernel picker.

Connecting to a remote Jupyter server.

To connect to a remote Jupyter server, run this command in a notebook cell:

#!connect jupyter --url <url_for_jupyter> --token <token_you_used_for_jupyter> --kernel-name pythonkernel --kernel-spec python3

For R, run the same command but replace python3 with ir under --kernel-spec and give a new name for kernel-name.

Using Virtual environments

Both with Python venv and Conda environments, you can create the environments and add them to Jupyter as a kernel spec.

For Python venv, run the following commands in the terminal:

python3 -m venv myenv
myenv\Scripts\activate

pip install ipykernel
python -m ipykernel install --user --name=myenv

For Conda, run the following commands in the terminal or Anaconda Bash Prompt (Windows):

conda create -n myenv 
conda activate myenv

conda install ipykernel
python -m ipykernel install --user --name=myenv

These environments can then be accessible as a kernel-spec in #connect command.

Additionally, for Conda environments, you can use the --conda-env option in the #connect command to use the environment.

For example, if you create a conda environment condaenvpython3.9 to use the python==3.9 version:

conda create -n condaenvpython3.9 python==3.9
conda activate condaenvpython3.9

conda install ipykernel
python -m ipykernel install --user --name=condaenvpython3.9

You can target it using the following command and be able to use python==3.9 in your notebook.

#!connect jupyter --kernel-name pythonkernel --conda-env condaenvpython3.9 --kernel-spec python3

Or, you can get similar experience by adding condaenvpython3.9 as a kernel spec to Jupyter and then using the --kernel-spec option to target it.

#!connect jupyter --kernel-name pythonkernel --conda-env base --kernel-spec condaenvpython3.9

Using DevContainers

The default Python3 configuration does not include the kernel, so you need to modify the devcontainer.json file to use a Dockerfile:

FROM mcr.microsoft.com/devcontainers/dotnet:1-8.0

RUN apt update
RUN apt install -y python3 python3-pip python3-ipykernel

After loading the DevContainer, run the following command in a cell:

#!connect jupyter --kernel-name pythonkernel --conda-env base --kernel-spec python3