One of the most difficult parts of using Python after getting used to R is simply finding a Python set up that works for you. For now I’m documenting my process primarily for my own reference, but I’ll be able to add details and answer questions as I work on this Python from R series
At this point, I’ve tried several set ups
- Anaconda + Jupyter Notebooks
- VS Code
- Jupyter Lab
Anaconda is by far the easiest way to get started, but in this post I’m going to outline my current approach using Jupyter Lab.
Outside of Anaconda though, I started by downloading Python from the official site
The next step is to create a virtual environment. Outside of Anaconda, this requires using the command line. The commands I’m documenting are for Linux/MacOS. I haven’t done this on Windows, so can’t document how it works there.
# set the working directory cd ~/py # create a virtual environment python3 -m venv my_environment # activate the environment source my_environment/bin/activate # install jupyterlab and data science libraries pip install numpy, pandas, jupyterlab # make environment available in Jupyter python -m ipykernel install --user --name=my_environment # launch jupyter jupyter lab
At that point the environment will appear by name as a potential kernel to use for Jupyter in a drop down on the top right.