Integration into Documentation with Sphinx Jupyter nbconvert -template "pythoncodeblocks.tpl" -to markdown README.ipynb The resulting template "pythoncodeblocks.tpl" was: The workaround for this, was to use nbconvert's support for custom templates.
#Ipynb viewer github code
This is required to highlight the code blocks in the README.md with language specifics. When first run, it was noticed that nbconvert wasn't marking the code blocks with the language (python). In Pandas_Alive, we clear the output output of the cells in README.ipynb with the flags: jupyter nbconvert -ClearMetadataPreprocessor.enabled=True -ClearOutput.enabled=True -to markdown README.ipynb. This will allow us to generate a new README.md whenever our README.ipynb changes. Nbconvert supports Markdown! Fantastic, we can add this step into our CI process (eg, GitHub Action). Now let's check the supported output types for nbconvert:
#Ipynb viewer github install
Nbconvert is a package built to convert Jupyter notebooks to other formats and can be installed similar to jupyter (eg, pip install jupyter, pip install nbconvert). This is not ideal whatsoever, this is nowhere near as attractive as the nicely rendered README.md. , "source" : () charts for () DataFrames, similar to the already ().\n", "\n", "With **Pandas_Alive**, creating stunning, animated visualisations is as easy as calling:\n", "\n", "`df.plot_animated()`\n", "\n", "!(examples/example-barh-chart.gif)" ]
For example would be hundreds of line like: Now if you only have the README.ipynb in the repository, GitHub will represent the file in it's raw form, JSON.
Fantastic, this is exactly where we want to go. Allowing the author, to run the entire notebook, generating the new examples & verifying the examples are working code. Similarly, the code used to generate examples or demonstrate usage can then be placed in code cells. Jupyter supports markdown & code cells, thus all the current documentation in the README.md can be copied within markdown cells. If we could tie the original README with live code that generates the examples, that would be ideal, enter README.ipynb. In the README, it's nice to have working examples on how a user may use the project. In projects, typically it's best practice to not have to repeat yourself in multiple places (this the DRY principle). Thus the workflow we go into in this post was adopted. This is very frustrating for new users to the package, as the examples simply don't work. If you can see where this is going, obviously whenever some new examples were made, the code to generate the examples was being forgotten to be copied over. Initially, this was contained in a generate_examples.py file and as the package evolved, the code to match the examples, was being copied over into code blocks in the README.md. The inspiration for this post also comes from Pandas_Alive, wherein there is working examples with output hosted on the README. Other useful sections when applicable are requirements, future plans and inspiration.
#Ipynb viewer github how to
Usage - A section on how the project is to be used (if applicable).ĭocumentation - Link to documentation for the project.Ĭontributing Guidelines - If this is an open source project, a note whether contributions are welcome & instructions how to get involved is well received.Ĭhangelog - Keeping a changelog of what is changing as the project evolves. Intro - A short description & output (if applicable) of the project. Ī good starting structure for a project's README is: Find a live example of this being implemented on. This post goes into how to set this workflow up. As to make sure documentation across a project is consistent as well, imagine if we could include this README that is the front page of our project, both on the repository, and in the documentation. README is typically the front page of a project, and should contain relevant information for current users & prospective users.
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