awesome-jupyter

There are a number of ways to run tests within notebooks and to run notebooks as tests. ipytest: https://github.com/chmp/ipytest nbval: https://github.com/computationalmodelling/nbval papermill: https://github.com/nteract/papermill awesome-jupyter > Testing: https://github.com/markusschanta/awesome-jupyter "Is there a cell tag convention to skip execution?" https://discourse.jupyter.org/t/is-there-a-cell-tag-conventi... Methods for importing notebooks like regular Python modules: ipynb, import_ipnb, nbimporter, nbdev nbimporter README on why not to import notebooks like modules: https://github.com/grst/nbimporter#update-2019-06-i-do-not-r... nbdev parses jupyter notebook cell comments to determine whether to export a module to a .py file: https://nbdev1.fast.ai/export.html There's a Jupyter kernel for running ansible playbooks as noteb

What are some Software Development methods for reducing errors: 1. AUTOMATED TESTS; test assertions To write spreadsheet tests: A. Write your own test assertion library for their macro language; write assertEqual() in VBscript and Apps Script. B. Use another language with a test library and a test runner; e.g. Python and the `assert` keyword, unittest.TestCase().assertEqual() or pytest. C. Test the spreadsheet GUI with something like AutoHotKey. From https://news.ycombinator.com/item?id=35896192 : > The Scientific Method is testing, so testing (tests, assertions, fixtures) should be core to any scientific workflow system. > awesome-jupyter#testing: https://github.com/markusschanta/awesome-jupyter#testing > ml-tooling/best-of-jupyter lists papermill/papermill under "Interactive Widgets/Visualization" https://github.com/ml-tooling/best-of-

The Scientific Method is testing, so testing (tests, assertions, fixtures) should be core to any scientific workflow system. - [ ] (It's not possible to run `!pytest` in a Jupyter notebook without installing an extension with JupyterLite in WASM onnly where there's not yet a terminal or even yet a slow-but-usable [cheerpx] webvm bridged to jupyter kernel WASM ~process-space.) awesome-jupyter#testing: https://github.com/markusschanta/awesome-jupyter#testing ml-tooling/best-of-jupyter lists papermill/papermill under "Interactive Widgets/Visualization" https://github.com/ml-tooling/best-of-jupyter#interactive-wi... "Markdown based notebooks" would store files next to the .ipynb.md, which implies a need for an MHTML/ZIP-like archive (for report notebook artifacts produced by scientific workflow systems with provenance metadata); but W3C Web

Here is a resource: https://github.com/markusschanta/awesome-jupyter#articlesgui...

Surprised I hadn't heard of mplfinance. Similar code for econometric charts with bands and an annotation table with dated events would be useful and probably already exists somewhere. Maybe Predictive Forecasting libraries for time series already have something that could be factored out into a maintained package for such common charts? E.g. seaborn has real nice confidence intervals with matplotlib, too; though the matplotlib native color schemes are colorblind-friendly: "Perceptually Uniform Sequential colormaps" https://matplotlib.org/3.5.0/tutorials/colors/colormaps.html awesome-jupyter > Rendering/Publishing/Conversion https://github.com/markusschanta/awesome-jupyter#renderingpu... : > ContainDS Dashboards - JupyterHub extension to host authenticated scripts or notebooks in any framework (Voilà, Streamlit, Plotly Dash etc) IIRC, Cont

There are many ways to share reproducible Jupyter notebooks. Google Colab now supports ipywidgets (js) in notebooks. While you can install additional packages in Colab, additional packages must be installed by each user (e.g. with `! pip install sympy` in an initial input cell) for each new kernel. repo2docker builds a docker image from software dependency versions specified in e.g. requirements.txt, environment.yml, and/or a postInstall script and then installs a current version of JupyterLab in the container. Zero-to-BinderHub describes how to get BinderHub (which builds and launches containers) running on a hosting provider w/ k8s. awesome-python-in-education/blob/master/README.md#jupyter Google AI Platform Notebooks is hosted JupyterLab. awesome-jupyter > Hosted Notebook Solutions lists a number of services: https://github.com/marku