Running Python

Running Python#

We use Google Colab to run the instructor-led Python lessons for the first three weeks. Google Colab makes it really convenient for others to run Jupyter notebooks. There’s no need to install or download anything in your computer. For many, Colab may also be all you need to use Python effectively for your SURP research project needs. However, some may find it necessary or convenient to set up a Python environment in your own computer or a project team server. The instructions below will help you get set up, and also provide a broader background.

Local installation and execution#

Anaconda distribution#

We recommend the use of the Anaconda Python distribution to simplify the Python installation and execution of Python code files and Jupyter notebooks (the Python lessons notebooks that run on Google Colab are also Jupyter notebooks). For notebooks, we recommend Jupyter tools and computational notebooks (see here, too), interacting with Python code via the JupyterLab application (see also here for a more detailed guide).

Installing Anaconda on your computer

Follow these instructions. The videos are somewhat dated, so the screenshots may look a bit different from what you will actually see during installation. But the steps are the same. Here is a more up-to-date, short installation video, for Windows only.

Running JupyterLab#

Use the Anaconda Navigator to launch JupyterLab. It’s included in the Anaconda installation, together with JupyterLab itself and many other applications and packages. Working with notebooks in JupyterLab will be familiar after you’ve used Google Colab.

The wider world of Python installation and development#

There are many options to install Python and software to develop and execute Python code! The second paragraph in this page is a very brief overview.

Yes, this can be pretty confusing!

Python itself can be installed directly from the official Python website, https://python.org. The installer includes Python, the ability to execute code at the “terminal”, and a very simple “Integrated Development Environment” (IDE) called IDLE.

An initial choice one has to make is the Python version. Typically it’s best to avoid the very latest versions, and choose a slightly but more stable and more widely supported version. Today (July 2025), that’s probably Python 3.12.

Anaconda is based on the open-source conda package management system. conda itself can be installed through different distributions, with different pros and cons. Other important distrubutions are miniconda, miniforge and mamba. The source of the conda packages also varies; the most widely used one in the scientific and other communities is conda-forge.

As everything released by the Jupyter project is open-source, the ability to run Jupyter notebooks is also implemented in other software not developed directly by the Jupyter project. For example, Visual Studio Code (VSCode), Spyder and PyCharm (PyCharm is not open-source but is freely available for education).

For complex software development tasks, IDE’s are practically necessary. These include VSCode, Spyder and PyCharm. Typically you will write Python code directly (*.py files) rather than Jupyter notebooks; or some combination. Out of these IDE’s, Spyder probably resembles the Matlab interface the most. VSCode has the advantage of also being a powerful, generic text editor. IDE’s provide lots of conveniences that make coding more efficient.

You can also write code (not Jupyter notebooks) directly, “by hand”, using a simple text editor; then run the code at the terminal. This is helpful in a pinch but pretty inefficient.

Simple, no-fuss options for continued learning#

If you’d like to polish, practice and extend your understaing of core Python language features, you can use Google Colab or one of these two no-fuss options:

  • Thonny, a straightforward desktop software that also installs Python for its own use. Simple installation, few distractions, and helpful tools.

  • Online Python execution sites. There are many options, but this one looks good: https://www.online-python.com

These two are great resources for initial learning, but not for Python code development for actual, more complex work.