Workshop Series: Intermediate Python

UC Davis DataLab
Author & Maintainer: Nick Ulle <[email protected]>

• Reader

These five standalone workshops aim to help Python users understand language features, packages, and programming strategies that will enable them to write more efficient code, be more productive when writing code, and debug code more effectively. This is not an introduction to Python and is appropriate for motivated intermediate to advanced users who want a better understanding of working with Python for their research. Researchers from all domains at UC Davis who meet the workshop prerequisites are welcome to enroll.

Learning Objectives

See the reader for learning objectives.

Prerequisites

Participants are expected to have taken DataLab’s Python Basics workshop series and/or have prior experience using Python, be comfortable with basic Python syntax, and have it pre-installed and running on their laptops.

Contributing

The course reader is a live webpage, hosted through GitHub, where you can enter curriculum content and post it to a public-facing site for learners.

To make alterations to the reader:

1. Run git pull, or if it's your first time contributing, see the Setup section of this document.

2. Edit an existing chapter file or create a new one. Chapter files are Markdown files (.md) in the chapters/ directory. Enter your text, code, and other information directly into the file. Make sure your file:

   • Follows the naming scheme ##_topic-of-chapter.md (the only exception is index.md, which contains the reader's front page).
   • Begins with a first-level header (like # This). This will be the title of your chapter. Subsequent section headers should be second-level headers (like ## This) or below.

   Put any supporting resources in data/ or img/. For large files, see the Large Files section of this document. You do not need to add resources generated by your code (such as plots). The next step saves these in docs/ automatically.

3. Run the command jupyter-book build . in a shell at the top level of the repo to regenerate the HTML files in the _build/.

4. When you're finished, git add:

   • Any files you edited directly
   • Any supporting media you added to img/
   • The .gitattributes file (if you added a large file)

   Then git commit and git push. This updates the main branch of the repo, which contains source materials for the web page (but not the web page itself).

5. Run the command ghp-import -n -p -f _build/html in a shell at the top level of the repo to update the gh-pages branch of the repo. This uses the ghp-import Python package, which you will need to install first (pip install ghp-import). The live web page will update automatically after 1-10 minutes.

Setup

We strongly recommend using pixi, a fast package manager based on the conda ecosystem, to install the packages required to build this reader. To install pixi, follow the official instructions. If you prefer not to use pixi, it's also possible to manually install the packages using conda or mamba.

The pixi.toml file in this repo lists required packages, while the pixi.lock file lists package versions for each platform. When the lock file is present, pixi will attempt to install the exact versions listed. Deleting the lock file allows pixi to install other versions, which might help if installation fails (but beware of inconsistencies between package versions).

To install the required packages, open a terminal and navigate to this repo's directory. Then run:

pixi install

This will automatically create a virtual environment and install the packages.

To open a shell in the virtual environment, run:

pixi shell

You can run the pixi shell command from the repo directory or any of its subdirectories. Use the virtual environment to run any commands related to building the reader. When you're finished using the virtual environment, you can use the exit command to exit the shell.

Note

If you're using Windows and Git Bash, the pixi shell command is not yet supported. Instead, you can use the pixi run command to run commands in the virtual environment. See the pixi documentation for examples of how to use pixi run.