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/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).

Code, text, equations, figures, plots, etc. are interleaved, creating a computational narrative.
“an environment in which users execute code, see what happens, modify and repeat in a kind of iterative conversation between researcher and data”
The name “Jupyter” derives from Julia+Python+R, but today Jupyter kernels exist for dozens of programming languages.
Gallery of interesting Jupyter Notebooks.

Our first notebook

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.

![Photo of Galilei's manuscript](https://upload.wikimedia.org/wikipedia/commons/b/b3/Galileo_Galilei_%281564_-_1642%29_-_Serenissimo_Principe_-_manuscript_with_observations_of_Jupiter_and_four_of_its_moons%2C_1610.png)

$E = mc^2$

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

Create a code cell where you define the arithmetic_mean function:

def arithmetic_mean(sequence):
    s = 0.0
    for element in sequence:
        s += element
    n = len(sequence)
    return s / n

In a different cell, call the function:
```
arithmetic_mean([1, 2, 3, 4, 5])
```
Run all cells.
Save the notebook.

Use cases for notebooks

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
Keeping track of interactive sessions, like a digital lab notebook
Supplementary information with published articles

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.