nbdump 0.0.1

Dump files to Jupyter notebook.

nbdump

Dump files to Jupyter notebook.

Installation

Currently nbdump is only a CLI, cannot be used as a library

# user
pip install -U nbdump

# dev: clone and use editable install
pip install -e .
pip install -U build twine

Usage

In this demo, we will use src_example/ as a fake repo that you want to import to notebook.

# see help
nbdump -h

# basic usage, this will dump entire `src_example/` to `nb1.ipynb`
nbdump src_example -o nb1.ipynb

# use shell expansion, this will come in handy later
nbdump src_example/**/*.py -o nb2.ipynb

# handle multiple files/dirs, will be deduplicated
nbdump src_example src_example/main.py requirements.txt -o nb3.ipynb

# append extra code cell, e.g. running the `src_example/main.py`
nbdump src_example -c '%run src_example/main.py' -o nb4.ipynb

# extra cells can be more than one
nbdump src_example \
    -c '%run src_example/main.py' \
    -c '!git status' \
    -o nb5.ipynb

# target notebook can be in a folder
nbdump src_example -o notebooks/nb6.ipynb

There is a catch, nbdump will not respect gitignore because the core functionality is just converting a bunch of files to notebook cells. This means, by using the first example on nb1.ipynb, nbdump will try to convert all files recursively, regardless of file format. The problem arises when src_example/ contains binary files such as pictures or even __pycache__/*.

Then shell expansion can be used to only select relevant files, such as the example on nb2.ipynb. Another solution is to use other tools like fd to list the files while respecting gitignore and skipping hidden files automatically.

# use fd to skip ignored files and hidden files
nbdump $(fd -t f . src_example) -o nb7.ipynb

Why?

• Because working in a standard environment (not just a notebook) is cleaner and more maintainable.
• Kaggle kernel with disabled internet connection can use nbdump instead of git clone inside the notebook.

TODO

• upload to test pypi and check on colab etc
• find out about the pypirc fiasco!!
• find out about ruff src = [src] on my lovely ml repo