Jupyter notebooks

Objectives

  • Know what it is

  • Create a new notebook and save it

  • Open existing notebooks from the web

  • Be able to create text/markdown cells, code cells, images, and equations

  • Know when to use a Jupyter notebook for a Python project and when perhaps not to

Instructor note

  • 15 min discussion/demo

  • 15 min exercise

  • 5 min summary

[this lesson is adapted from https://coderefinery.github.io/jupyter/01-motivation/]

Motivation for Jupyter notebooks

Galileo's drawings of Jupiter and its Medicean Stars from Sidereus Nuncius

One of the first notebooks: Galileo’s drawings of Jupiter and its Medicean Stars from Sidereus Nuncius. Image courtesy of the History of Science Collections, University of Oklahoma Libraries (CC-BY).


Where we want to get at the end

Discovery of gravitational waves

Discovery of gravitational waves.

As a case example, let us have a look at the analysis published together with the discovery of gravitational waves. This page lists the available analyses and presents several options to browse them.

  • A quick look at short segments of data can be found at https://github.com/losc-tutorial/quickview

  • The notebook can be opened and interactively explored using Binder by clicking the “launch Binder” button.

  • How does the Binder instance know which Python packages to load?

For more examples, head over to the Gallery of interesting Jupyter Notebooks.


Our first notebook

  • We all open up a notebook and rename it

  • Together we create few cells and run them

  • Most important shortcut: Shift + Enter, to run current cell and create a new one below

  • We create a function to compute the mean (we will make sense of the Python code later)

    def arithmetic_mean(sequence):
        s = 0.0
        for element in sequence:
            s += element
        n = len(sequence)
        return s / n
    
  • In a different cell we call the function

    arithmetic_mean([1, 2, 3, 4, 5])
    
  • Save the notebook

Exercises

Exercise Jupyter-1: create a notebook (15 min)

  • Open a new notebook (on Windows: open Anaconda Navigator, then launch JupyterLab; on macOS/Linux: you can open JupyterLab from the terminal by typing jupyter-lab)

  • Rename the notebook

  • Create a markdown cell with a section title, a short text, an image, and an equation

    # Title of my notebook
    
    Some text.
    
    ![Jupyter logo](https://jupyter.org/assets/main-logo.svg)
    
    $E = mc^2$
    
  • Create a code cell where you define and call the arithmetic_mean function (above)

  • Run all cells


Use cases for notebooks

[adapted from https://coderefinery.github.io/jupyter/01-motivation/#common-use-cases]

  • Really good for linear workflows (e.g. read data, filter data, do some statistics, plot the results)

  • Experimenting with new ideas, testing new libraries/databases

  • As an interactive development environment for code, data analysis, and visualization

  • Interactive work on HPC clusters

  • Sharing and explaining code to colleagues

  • Teaching (programming, experimental/theoretical science)

  • Learning from other notebooks

  • Keeping track of interactive sessions, like a digital lab notebook

  • Supplementary information with published articles

  • Slide presentations using Reveal.js

Pitfalls and situations where notebooks can be less useful

  • Programs with non-linear code flow

  • Large codebase (however it can make sense to use Jupyter as interface to the large codebase and import the codebase as a module)

  • You cannot easily write a notebook directly in your text editor (but you can do that with R Markdown)

Good practices

Run all cells before sharing/saving to verify that the results you see on your computer were not due to cells being run out of order.

We will demonstrate why this is important after we have discussed a bit of Python (next episode).