Sharing notebooks

Objectives

  • Learn how to share notebooks with colleagues and the community?

Instructor note

  • 10 min teaching

  • 20 min exercises

Sharing dynamic notebooks on Binder

Exercise (20 min): Making your notebooks reproducible by anyone via Binder

  • Create a new GitHub repository.

  • Commit and push the notebook which we have created earlier to this repository. If you got stuck earlier, you can download this notebook (right-click, “Save as …”). You can also try Binder with a different notebook.

  • Create a requirements.txt file which contains (adapt this if your notebook has other dependencies):

    matplotlib==3.4.1

  • Commit and push also this file to your notebook repository.

  • Visit https://mybinder.org:

    Screenshot of Binder web interface

    Screenshot of Binder web interface.

  • Copy-paste the markdown text for the mybinder badge into a README.md file in your notebook repository.

  • Check that your notebook repository now has a “launch binder” badge in your README.md file on GitHub.

  • Try clicking the button and see how your repository is launched on Binder (can take a minute or two). Your notebooks can now be expored and executed in the cloud.

  • Enjoy being fully reproducible!

  • Even better would be to get a DOI to your notebook and point Binder to the DOI.

Optional exercises

Importance of requirements file

(Optional) Exercise: what happens without requirements.txt?

Let’s look at the same activity inequality repository.

  • Start the repository in Binder using this link.

  • fig3/fig3bc.ipynb is a Python notebook, so it works in Binder. Most others are in R, which also works in Binder. But how?

  • Try to run the notebook. What happens?

  • Most likely the run brakes down immediately in the first cell

    %matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style="whitegrid")
from itertools import cycle

    with a long list of ModuleNotFoundError messages. This is because the required Python packages have not been installed and can not be imported. The missing packages include, at least, pandas and matplotlib mentioned in the error message.

  • To install the missing requirements, add a new code cell to the beginning of the notebook with the contents

    !python3 -m pip install pandas matplotlib

    and run the notebook again. What happens now?

  • Again, the run brakes down due to missing packages. This time the culprit is the seaborn package. Modify the first cell to also install it with

    !python3 -m pip install pandas matplotlib seaborn

    and try to run the notebook for the third time. Does it finally work? What could have been done differently by the developer?

  • A good way to make a notebook more usable is to create a requirements.txt file containing the necessary packages to run the notebook and add it next to the notebook in the repository.

  • In this case, the requirements.txt could look like this

    # requirements.txt
pandas
matplotlib
seaborn

    and to make sure the packages are installed, one could add a code cell to the beginning of original notebook with the line

    !python3 -m pip install -r requirements.txt

Sharing an interactive notebook on Binder

(Optional) Exercise: share an interactive (ipywidgets) notebook via Binder

  • Take the solution from the exercise “Widgets for interactive data fitting” in the Examples of Jupyter features episode and paste it into a notebook.

  • Push the notebook to a GitHub/GitLab repository.

  • Create a requirements.txt file in your notebook repository, e.g.:

    ipywidgets==7.4.2
numpy==1.16.4
matplotlib==3.1.0

  • Try to deploy this example via Binder in the same way as the above exercise.

Sharing R Markdown/Studio projects

(Optional) Exercise: share R Markdown/R Studio project via Binder

This exercise is for those who use Rmd files instead of Jupyter notebooks.

  • Put your Rmd file into a GitHub repository.

  • To this repository add a file runtime.txt which specifies the R version you want to use:

    r-3.6-2020-10-13

  • To this repository add a file install.R which lists the dependencies, for instance:

    install.packages(c("readr", "ggplot2"))

  • After you have done that, visit https://mybinder.org/v2/gh/myuser/myrepo/mybranch?urlpath=rstudio (adapt “myuser”, “myrepo”, and “mybranch”).

  • For more information, see this guide.

Different ways to share a notebook

  • You can enter a URL, GitHub repo or username, or GIST ID in nbviewer and view a rendered Jupyter notebook

  • Read the Docs can render Jupyter Notebooks via the nbsphinx package

  • Binder creates live notebooks based on a GitHub repository

  • EGI Notebooks (see also https://egi-notebooks.readthedocs.io)

  • JupyterLab supports sharing and collaborative editing of notebooks via Google Drive. Recently it also added support for Shared editing with collaborative notebook model.

  • JupyterLite creates a Jupyterlab environment in the browser and can be hosted as a GitHub page.

  • Notedown, Jupinx and DocOnce can take Markdown or Sphinx files and generate Jupyter Notebooks

  • Voilà allows you to convert a Jupyter Notebook into an interactive dashboard

  • The jupyter nbconvert tool can convert a (.ipynb) notebook file to:

    • python code (.py file)

    • an HTML file

    • a LaTeX file

    • a PDF file

    • a slide-show in the browser

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