Nbconvert Webpdf

[Giorgio Aguilar]

Gettingerror as "nbconvert failed: Pyppeteer is not installed to support Web PDF conversion. Please install nbconvert[webpdf] to enable." while trying to download jupyter notebook file (.ipynb) as a PDF file

I've encountered the problem of nbconvert failed: Playwright is not installed to support Web PDF conversion. Please install nbconvert[webpdf] to enable., when I was planning to convert jupyter notebook into webpdf on my mac, after that I've download the pyppeteer and nbconvert by using pip install command, I still couldn't convert jupyter notebook file into webpdf, why's that? Do I need to brew install that or something? (besides, I can already convert notebook to pdf by installing Tex related packages on my computer)

After I type in the command line:jupyter nbconvert C3_W2_Collaborative_RecSys_Assignment.ipynb --to webpdf --debug --allow-chromium-download, I found that there's one python package missing: ModuleNotFoundError: No module named 'playwright', then I installed that by typing pip install playwright and type in the converting command again, pop!, problem solved.

The nbconvert tool, jupyter nbconvert, converts notebooks to various otherformats via Jinja templates. The nbconvert tool allows you to convert an.ipynb notebook file into various static formats including:

Jupyter uses a shared copyright model. Each contributor maintains copyrightover their contributions to Jupyter. But, it is important to note that thesecontributions are typically only changes to the repositories. Thus, the Jupytersource code, in its entirety is not the copyright of any single person orinstitution. Instead, it is the collective copyright of the entire JupyterDevelopment Team. If individual contributors want to maintain a record of whatchanges/contributions they have specific copyright on, they should indicatetheir copyright in the commit message of the change, when they commit thechange to one of the Jupyter repositories.

Have you ever wanted to export your Jupyter Notebook to PDF file? The PDF is an abbreviation for Portable Document Format. It can be displayed on any operating system. That makes it format of choice for many who wants to share their results. In this post I will show you 4 different ways how to export Jupyter Notebook as PDF file.

The big advantage of this approach is that we don't need to install additional libraries to make it works! However, it is manual approach - hard to automate. What is more, we can't control the process of export (for example, we can't hide code in the exported file).

This approach requires you to install some additional packages. For me, the option that exports with LaTeX is difficult. It requires you to install much more packages than option of export via HTML. If you don't have required packages you will get the error message:

The flag should be added only one time. It is not necessary after Chromium installation. The nbconvert has many optional arguments that control the export. For example, you can easily hide the code with --no-input flag:

There is an open-source framework Mercury that makes Python notebooks sharing painless. It converts notebooks to interactive documents (web apps, reports, slides, dashboards). You can share a notebook with interactive widgets. Your end-users can tweak widgets values and execute the notebook with new values (without changing the code). The Mercury allows to export the executed notebook into standalone HTML or PDF file. The end-user just need to clik the Download button.

Saving notebooks to PDF is a great way to persist results in a shareble format. PDFs can be easily published online or send in the email. There are several ways to convert Jupyter Notebook as PDF. The automatic conversion can be easily achieved with nbconvert tool. Notebooks shared with Mercury framework can be easily converted to PDF. The PDF notebook can be manually downloaded from the website.

I am trying to figure out how to change the font size for code cells when exporting a notebook with webpdf using nbconvert 7.16. I have found several suggestions, but they all seem to fit only for old-style templates. I have tried to create my own version of the webpdf template, but have not yet found the right right way to influence font size. An option for automatically folding lines in code cells upon webpdf-output would also do.

This means that if you include your slides on a webpage, they should work asexpected. However, some features (specifically, speaker notes & timers) will notwork on website because they require access to a local copy of reveal.js.

This will create file your_talk.slides.html, which you should be able toaccess with open your_talk.slides.html. To access the speaker notes, presss after the slides load and they should open in a new window.

Note: This does not enable slides that run completely offline. While you have alocal copy of reveal.js, by default, the slides need to access mathjax, require,and jquery via a public CDN. Addressing this use case is an open issue and PRs are always encouraged.

Convert a notebook to an executable script.This is the simplest way to get a Python (or other language, depending onthe kernel) script out of a notebook. If there were any magics in anJupyter notebook, this may only be executable from a Jupyter session.

This will open the notebook, execute it, capture new output, and save theresult in mynotebook.nbconvert.ipynb. Specifying --inplace willoverwrite the input file instead of writing a new file. By default,nbconvert will abort conversion if any exceptions occur duringexecution of a cell. If you specify --allow-errors (in addition to the--execute flag) then conversion will continue and the output from anyexception will be included in the cell output.

The output file created by nbconvert will have the same base name asthe notebook and will be placed in the current working directory. Anysupporting files (graphics, etc) will be placed in a new directory with thesame base name as the notebook, suffixed with _files:

Bokeh plots are actually rendered by a companion JavaScrip library, BokehJS. However, GitHub strips all JavaScript from pages and notebooks that it renders. Bokeh will will never be able to display in notebooks rendered on GitHub as long as this is true (and we have no control whatsoever over this). To see Bokeh plots in rendered notebooks, you will have to

Sorry, I just saw GH in the URL and made an assumption, since that is the usual issue. I didn't notice you mentioned downloading the notebook. I'm not sure offhand, since I don't really use nbconvert. For this I'd suggest a GH issue with detailed version information, then we can try to ping one of the nbconvert devs for more assistance.

I understand but it seems there is an embedding feature which puts plots inside an HTML and the plots will be rendered inside them. Isn't this an option for notebooks? I'm not going to host notebooks on GitHub but I need them pre-rendered. I saw there is also an option to include JS inside IPYNB file but still the plots are not there unless I open the notebook on the browser and run the cell manually.

first, however for the notebook rendering on anaconda.org (which I

have been involved with developing), I am fairly sure we are simply

using jupyter nbconvert from the command line to create that

content. And as you can see from anaconda.org examples the Bokeh

plots display properly and are interactive, e.g.

library, BokehJS. However, GitHub strips all JavaScript from

pages and notebooks that it renders. Bokeh will will never

be able to display in notebooks rendered on GitHub as long

as this is true (and we have no control whatsoever over

this). To see Bokeh plots in rendered notebooks, you will

have to

Also to clarify, you are trying to generate statically rendered (HTML) notebook output? Or you are trying to execute notebooks programmatically? The former is fairly well-known ground I think, with "jupiter nbconvert" as Ian mentions. I am not aware of anyone using nbconvrt for programmatic notebook evaluation, though, so I could easily believe there are issues there, if you are the first person to try it.

Bryan is more expert on Bokeh, so you should go with his advice first, however for the notebook rendering on anaconda.org (which I have been involved with developing), I am fairly sure we are simply using `jupyter nbconvert` from the command line to create that content. And as you can see from anaconda.org examples the Bokeh plots display properly and are interactive, e.g.

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