Tools to work with Jupyter notebooks
validate: validate notebooks
head: show head or tail of notebooks
dump: dump notebook info and source on terminal
stats: summarize notebooks with statistics
view: view notebook, including all embedded images, LaTeX, and HTML in a browser
cat: catenate multiple notebooks
clean: clean notebooks by removing specified elements
run: execute notebooks, with pre/post cleaning
split: split notebooks into MarkDown, code, and raw
punch: punch holes into notebooks and fill them (for creating exercises)
Available as library functions and as configurable command-line scripts.
pip install nbtoolbelt
Documentation is available on Read the Docs.
On the command line, you can use the options -h or --help.
On the command line:
nbtb [-h] [options] tool [options] nb.ipynb ...
As library: see documentation
pip install nbtoolbelt[test]
nbtoolbelt comes with a set of automatic test cases for pytest.
Some useful commands, and where to run them:
clean build directory: make clean
create html documentation tree: make html
create pdf documentation: make latexpdf
determine size of documentation: wc `find . -name '*.rst'`
run all test cases: pytest .
test package configuration: python setup.py check -r -s
create source distribution and wheel: python setup.py sdist bdist_wheel
create digital signature: gpg --detach-sign -a ...
upload to PyPI: twine upload ...
determine size of code: wc `find . -name '*.py'`
Copyright (c) 2017 - Eindhoven University of Technology, The Netherlands
This software is made available under the terms of the MIT License.
Python: Python 3
Includes a format validator based on JSON schemas, such as nbformat.v4.schema.json
./jq: a lightweight and flexible command-line JSON processor
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