This website hosts my commonly referenced notes on data science and machine learning as well as some personal projects.
The data science cookbook is created using Jupyter Book.
There is an environment file to create a conda environment with all the necessary libraries:
conda env create --file environment.yml
Pages are build with this command:
jupyter-book build notebook_library/
Use the --all modifier when creating a new page so that the other pages reference it in the TOC.
jupyter-book build --all notebook_library/
Before learning about Jupyter Book, this was done using nbconvert. Here are the original steps:
To convert ipynb to html I'm using a custom nbconvert template. I created a python script to convert all notebook files:
- python convert_notebooks.py
Or manually run the line in terminal:
- jupyter nbconvert --to html Distributions.ipynb --template classic
Was using custom_classic but this is going to take some work to cleanup
- jupyter nbconvert --to html Distributions.ipynb --template custom_classic
Test locally with: 'live-server' or 'python -m http.server 8000'
- Bayesian inference using groundhog-day data.
- Integrate Plausible Analytics.