This repository contains public notebooks and datasets to accompany my Data Analysis with Polars course on Udemy.

If you want to join me on the course please use this referral link for a 50% discount: https://www.udemy.com/course/data-analysis-with-polars/?couponCode=POLARS_HALF_PRICE

For a general introduction to Polars see this workshop I lead on youtube and the workshop materials in the notebooks/ml directory of this repository.

Notebooks

Introduction

• intro/00-KeyConcepts.ipynb - an introduction to the big ideas behind Polars
• intro/02-LazyPolarsIntro.ipynb - an introduction to lazy mode in Polars

Filtering rows

• filtering_rows/02-SelectingRowsFilter.ipynb - a key lecture where we learn how to use the Expression API to filter rows in Polars

Time series

• time_series/01-IntroTimeSeries.ipynb - the time series dtypes in Polars
• time_series/02-TimeZones.ipynb - converting between time zones in Polars

Machine learning

• ml/CatboostElectricityForecasting.ipynb - introductory notebook with a simple example of using Polars with Catboost to forecast electricity demand

Want to know more about Polars for high performance data science and ML? Then you can:

• follow me on twitter
• connect with me at linkedin
• check out my youtube channel

Installation instructions

Binder

I recommend cloning the repository and runnng the notebooks locally on your computer. However, you can run the notebooks interactively using binder by clicking the following link:

Docker

I run the code using docker. If you have docker installed then you:

• run the deploy.sh with bash as ./deploy.sh
• when the container starts in interactive mode run jlab to start the notebooks (jlab is an alias for jupyterlab I define in the Dockerfile)
• when jupyter is ready it gives you a web address that you can enter in your browser like http://127.0.0.1:8888/lab?token=e81f5485724f9107e2252fcf71d0eb2c76a027c1be1d10b7

Install with pip

Mac OS

• Run the pip-deploy.sh script
• Activate the virtual environment with source course_env/bin/activate
• Confirm the virtual environment is activated with which python. The output should end with course_env/bin/python
• Start the Jupyter notebooks with jupyter lab. This will either open the notebook in your browser or print a URL that you can use to open the notebook in your browser
• Check your installation by opening notebooks/TestInstallation.ipynb

Windows

• Open powershell in this directory
• Run the powershell script create-course-environment.ps1
• Run the powershell script activate-course-environment.ps1
• Confirm that the virtual environment is working: it should say course_env at the start of your powershell prompt
• Run: jupyter lab
• Check your installation by opening notebooks/TestInstallation.ipynb