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
- Computer with Windows/Linux/MacOS and a python installation
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.
"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. I have focused on producing Jupyter notebooks to allow anyone taking the course to start using the full power of Polars. As a consequence the video content is limited. More videos that go beyond the notebooks will be added in the coming months once the core functionality has been documented in the notebooks.
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
work with larger-than-memory datasets
carry out aggregations on your data
combine your datasets
visualise your outputs with Matplotlib, Seaborn, Plotly & Altair and
prepare your data for machine learning pipelines
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
I have a PhD in climate physics from the University of Oxford. Before founding my own company I was lead data scientist at a startup working on problems in NLP, time series and geospatial analysis.
I am a data science communicator and I help to spread the work on exciting new technology on my youtube channel and twitter.