Data Analysis with Polars and Python

This repo contains the datasets and Jupyter Notebooks for the Data Analysis with Polars and Python course.

Setup Instructions

1. Install the uv command-line program for managing Python projects.
   • Follow the instructions at https://docs.astral.sh/uv/getting-started/installation/.
2. Download or clone this repository locally.

If you have Git installed, execute git clone git@github.com:paskhaver/data-analysis-with-polars-and-python.git

If you do not have Git installed, click the green Code button, then click Download ZIP.

If downloading directly from GitHub, you'll receive a compressed .zip file. You'll need to unzip it to access its contents.

• On macOS, double-click the data-analysis-with-polars-and-python zip folder to unzip it.
• On Windows, right-click the zipped data-analysis-with-polars-and-python folder, select Extract All..., and choose your destination folder.

3. Open your command-line application (i.e., Terminal, PowerShell) and navigate to the data-analysis-wth-polars-and-python folder.
4. Execute uv sync. This command will:
   • Download Python (the base language)
   • Download Polars (the data analysis library)
   • Download Jupyter Lab (the development environment)

The exact version of Python, Polars, and all other libraries can be found in the pyproject.toml file.

Two Folders

The incomplete and complete folders contain the exact same files.

The Jupyter Notebooks in the incomplete folder have empty code cells. Watch along with the videos and practice writing the code inside the Notebook. You'll complete them as you progress through the course. Feel free to experiment!

The complete folder contains my completed Jupyter Notebooks from the recording of the course. I recommend using these as a backup resource if a code sample does not work or if you want to compare our results.

Startup and Shutdown

1. Execute uv run jupyter-lab to launch Jupyter Lab. Jupyter Lab should launch in your default web browser (i.e., Chrome). If it doesn't open, navigate to the link localhost:8888 in your browser.
2. In Jupyter Lab, use the left-hand file explorer to navigate into the incomplete folder.
3. Open the Jupyter Notebook for your current course section.
4. Locate the section of the Notebook corresponding to the current lesson video. There is a Table of Contents icon on the left-hand side of Jupyter Lab if you'd like to navigate to any of the headings in the Notebook. The lessons appear in the same order as they do in the video course.
5. When you're done learning, save your work (Cmd + S on macOS, Ctrl + S on Windows, or press File > Save All).
6. Close the tabs corresponding to your open Jupyter Notebooks.
7. Click the Stop icon on the left-hand navigation bar (second button from the top).
8. In the Kernels section, click Shut Down All. Confirm your choice in the modal that appears.
9. Close the browser.
10. In your terminal, shut down your Jupyter Lab server with the shortcut Ctrl + C. Close your Terminal.