nbtoolbelt 2017.11.dev1

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.

Installation

pip install nbtoolbelt

Documentation

Documentation is available on Read the Docs.

On the command line, you can use the options -h or --help.

Usage

On the command line:

nbtb [-h] [options] tool [options] nb.ipynb ...

As library: see documentation

Testing

pip install nbtoolbelt[test]

nbtoolbelt comes with a set of automatic test cases for pytest.

Developing

Some useful commands, and where to run them:

• In nbtoolbelt/docs/,

  • clean build directory: make clean

  • create html documentation tree: make html

  • create pdf documentation: make latexpdf

  • determine size of documentation: wc `find . -name '*.rst'`

• In nbtoolbelt/test/,

  • run all test cases: pytest .

• In nbtoolbelt/,

  • test package configuration: python setup.py check

  • create source distribution and wheel: python setup.py sdist bdist_wheel

• In nbtoolbelt/src/,

  • determine size of code: wc `find . -name '*.py'`

Licencse

Copyright (c) 2017 - Eindhoven University of Technology, The Netherlands

This software is made available under the terms of the MIT License.

Resources

• Python: Python 3

• Jupyter

• Jupyter notebook format

  • Documentation

  • Includes a format validator based on JSON schemas, such as nbformat.v4.schema.json

• JSON Schema

• ./jq: a lightweight and flexible command-line JSON processor

• Python 3 Patterns, Recipes and Idioms