Data Analysis with Polars
What you'll learn
- Taking advantage of parallel and optimised analysis with Polars
- Working with larger-than-memory data
- Using Polars expressions for analysis that is easy to read and write
- Loading data from a wide variety of data sources
- Combining data from different datasets using fast joins operations
- Grouping and parallel aggregations
- Deriving insight from time series
- Preparing data for machine learning pipelines
- Visualising data with Matplotlib, Seaborn, Altair & Plotly
- Using Polars with Scikit-learn
Requirements
- Computer with Windows/Linux/MacOS and a python installation
Description
In this course I show you how to take advantage of Polars - the fast-growing open source dataframe library that is becoming the go-to dataframe library for data scientists in python. I am a Polars contributor with a focus on making Polars accessible to new users and I keep this course up-to-date with new releases of Polars - updated to version 1.5
"A thorough introduction to Polars" - Ritchie Vink, creator of Polars
"Thank you for your great work with this course - I've optimized some code thanks to it already!" Maiia Bocharova
The course is for data scientists who have some familiarity with a dataframe library like Pandas but who want to move to Polars because it is easier to write and faster to run. The core materials are Jupyter notebooks that examine each topic in depth. Each notebook comes with a set of exercises to help you develop your understanding of the core concepts. For many key topics this course is the only source of documentation for learners and comes from my time examining the Polars source code.
An important note about videos: this is a primarily a notebook course and not a video course. Not all of the lectures have videos and some of the videos may have components that are not up-to-date. Why? Because the Polars API has changed too often to allow me to keep videos up-to-date. Instead I focus on keeping the notebooks up-to-date with an extensive automated testing system that alerts me to changes in the API. I release an updated version of the course about twice a month in response to changes in Polars.
The course introduces the syntax of Polars and shows you the many ways that Polars allows you to produce queries that are easy to read and write. However, the course also delves deeper to help you understand and exploit the algorithms that drive the outstanding performance of Polars.
By the end of the course you will have optimised ways to:
load and transform your data from CSV, Excel, Parquet, cloud storage or a database
run your analysis in parallel
understand optimal patterns for building queries
work with larger-than-memory datasets
carry out aggregations on your data
combine your datasets with joins and concatenations
work with nested dtypes including lists and structs
optimise the speed and memory usage of your queries
work with string and categorical data
visualise your outputs with Matplotlib, Seaborn, Plotly, hvPlot & Altair
prepare your data for machine learning pipelines with sklearn
Who this course is for:
- Data scientists with no familiarity with Polars and want to get up and running
- Data scientists with some familiarity with Polars but want a deeper understanding
- Pandas or other dataframe library users
Instructor
- 4.7 Instructor Rating
- 355 Reviews
- 2,600 Students
- 1 Course
I'm Liam and I'm the best person in the world to learn Polars with. I have gained an intimate knowledge of the Polars code base from 2 years as a Polars contributor. I was the world's first Polars trainer and have taught thousands of data professionals how to unleash the full power of Polars.
As well as delivering Polars training I am also a lead data scientist. I have been putting Polars intro production in my machine learning pipelines for over a year and the course material reflects my real world experience with the library. In addition I share Polars best practices on social media and have written dozens on blog posts on using Polars.