# Jupyter Notebooks

Python programs (`.py` files) are widely used in data analysis pipelines and software development projects that require
a more structured approach.  In this course, however, we will be using mainly Jupyter Notebooks to write our code.  

Jupyter Notebooks are a Python way of doing **literate programming**, that is, incorporating natural language, *e.g.,* English with 
your source code.  So rather than having a word processing document containing all your text, and a .py file containing 
your source code, Jupyter Notebooks combines functionality of both into one document. A Jupyter Notebook is a web application to make 
our analysis/code reproducible, one of the qualities that are being promoted in research to be compliant with FAIR 
standards (**F**inadble **A**ccessible **I**nteroperable and **R**eproducible).

The name Jupyter is a play on Jupyter's core programming languages: **Ju**lia, **Pyt**hon, **R**.  The circles in the Jupyter 
logo are attributed to the Galileo's moons of Jupiter discovery.  
```{figure} images/jupyter.png
---
name: jupyter
---
Jupyter logo.  Image from jupyter.org.
```

Jupyter Notebooks support different programming languages.  Each programming language has a **kernel** which executes the code cells
in the Jupyter Notebook.  For Python, the kernel is called **IPython**.  A Jupyter Notebook has an `.ipynb` extension which 
stands for **IPy**thon **N**ote**b**ook.  

```{figure} images/jupyter-components.png
---
name: jupyter-components
---
The different components of the Jupyter ecosystem that enable execution of a Jupyter Notebook [^1].
```
{numref}`jupyter-components` shows the different components that interact with a Jupyter Notebook file and process it to be 
displayed in a web browser. The Jupyter Notebook file, kernel and browser communicate together via the Jupyter Notebook Server.


[^1]: Image from https://docs.jupyter.org/en/latest/projects/architecture/content-architecture.html