Resources: python & jupyter

Posted on 2026-01-01 by George McNinch

Overview

Our course will use the python programming language to perform some machine computations needed to carry out mathematical modeling tasks.

And parts of the notes of the course lectures will be made available as jupyter notebooks.

There are a few ways that you should learn to interact with python and jupyter for our class:

  1. via Google’s colab
  2. by executing python code in a python interpreter on your computer
  3. (optionally) using jupyter notebooks on your computer.

Here is a bit of what you need to know to enable these interactions:

Before diving into the details, let me try to answer a few question that you may have:

Q - do I need to install juptyer on my computer, or I just use colab?

A - It is probably possible to just use colab. colab provides a reasonable environment for reading the course material, though for code execution it may (?) be a tad slow.

But: using python exclusively through colab is a somewhat imperfect environment for producing code, and at least in the long run you’ll be better served by learning to work with these tools on your own computer.

An overview of jupyter notebooks

Jupyter notebooks provide a way to package written material – with possible mathematical markup – together with an interactive environment for executing computer code.

The material for this course will mostly be presented in the form of these notebooks. You can recognize notebook files by their filename extension – their file name has the form *.ipynb.

On the course site, notebooks will be posted with two links – one to colab, and one to an *.ipynb file.

  • The simplest way to interact with jupyter notebooks is to use Google’s colab. Clicking one of the links will open the notebook in the cloud via the site colab.research.google.com.

  • The other one link permits you to download the notebook file to your computer. In order to use this file, you will need to have a working installation of python and jupyter. See below for some discussion of how to set up the required tools and how to use jupyter.

Interacting with jupyter notebooks via Google’s CoLab

As mentioned above, the course material will be presented in the form of jupyter notebooks, and for each notebook there will be a link to a copy of this notebook available via colab.

Colab is a web-based free-to-use Jupyter notebook environment that runs in the cloud and stores its notebooks on Google Drive. When you view a notebook in colab containing code, that code can be executed. The code execution occurs in the cloud, which basically means on some google computer. Thus, google’s computer has python and the relevant libraries installed and available.

You can choose to interact with the course notebooks on colab, abd create your own notebooks there.

A good starting point to learn how to use colab (and jupyter notebooks in general) is the document Overview of Colaboratory Features.

Installing the tools on your computer

Here I hope to answer the questions: “What do I need to do to be able to view and execute jupyter notebooks on my computer?”

(The instructions I’m about to give differ somewhat from instructions I gave e.g. in Spring 2025).

We are going to use a tool called uv for managing python and it’s libraries and packages.

To begin with, install uv following the instructions here.

Open a terminal on your computer. We are going to use uv to create a directory on your file system, so if you’d like that directory to be placed somewhere in particular, you might changed directories in your terminal:

george@valhalla:~$ cd math87
george@valhalla:~/math87$

(You should only type the cd jupyter bit. And you may need to type a more complicated string if the directory you created is nested below your home directory. In these instructions going forward I’m going to suppress writing the full prompt displayed by the terminal).

We now use uv to create a directory for jupyter and python related packages by executing the following command in the terminal:

$ uv init jupyter

You should see some output saying something like

Initialized project `jupyter` at `/home/george/math87/jupyter`.

Now change to the directory just created:

$ cd jupyter

And install relevant packages:

$ uv add jupyter matplotlib nbconvert numpy pandas scipy sympy

Now, we can use uv to start a jupyter server on your computer:

$ uv run jupyter-lab

After running this command, you should see a bunch of output in the terminal (which you can largely ignore). And a tab should open in your browser with a URL like

http://localhost:8888/lab

And in that tab you can interact with the jupyter server running on your computer. You can stop that server from running by stopping the uv run juypter-lab job (say be typing ctrl-c in the terminal, or by closing the terminal completely…)

Interacting with jupyter on your computer

You can always view and interact with jupyter notebooks on colab. But if you carried out the above installation instructions, you can now use jupyter notebooks on your own computer.

As explained in the installation notes (above), you can start jupyter from the command-line/terminal via uv run jupyter-lab in a terminal, after first making sure you are in the correct directory).

In fact, there are various versions of the jupyter interface - you could instead use the command uv run jupyter notebook. Honestly, I’m not completely sure I understand why there are two different interfaces; you should experiment for yourself.

To run jupyter-lab in your terminal when the current directory is not your project directory, you can use something like

$ uv run --project /home/george/math87/jupyter jupyter-lab

which enables you to provide the path to the project directory.

The page that opens will have a URL that looks something like:

http://localhost:8888/lab

This URL tells you that there is now a server running on your local machine, and that your web browser is communicating with that server.

In the web page at this location, you should see in particular an interface where you can interact with jupyter notebooks. You can create a new notebook from the File menu: New -> Notebook. If you are asked to Select Kernel, you should select Python 3.

You are now editing a file name Untitled.ipynb.

If you save a jupyter notebook (i.e. a ipynb file) in your directory tree, you can open it using the File menu: Open from path

You can end the jupyter notebook session by typing C-c (twice) in the terminal where you started the command, or by menu-driven commands in the web-interface.

Some learning resources for python and scientific computing

(Honestly, I just borrowed this list from a previous instructor, but the links look useful).