Marco Gorelli

Polars is a dataframe library which has taken the world by storm over the last 4-5 years. Because people love benchmarks, people often compare it with SQL-like engines such as DuckDB, PySpark, Daft, and others.

Suppose you want to write a data science tool to do feature engineering. Your experience may go like this: - Expectation: you can focus on state-of-the art techniques for feature engineering.

Polars is the new dataframe on the block taking the world by storm. You'll learn: - what Polars is, and what it can do for you - Polars basics and core concepts - how to work with different datatypes - inteoperability with other tools

If you were writing a data science tool in 2015, you'd have ensured it supported pandas and then called it a day. But it's not 2015 anymore, we've fast-forwarded to 2025.

Narwhals is an extremely lightweight and extensible compatibility layer between dataframe libraries, and it needs your help! An open source sprint is the perfect opportunity to make your first contribution to open source.

When it comes to dataframes, pandas is the go-to library for many people. Yet Polars is taking the world by storm, and so many data practitioners are curious about trying it out.

If you're writing a library which consumes dataframes, should you choose to support pandas, Polars, cuDF, modin, vaex, pyspark, dask, or something else? Don't choose - learn how the DataFrame Interchange Protocol and/or Narwhals enable you support them all!

Time series analysis is ubiquitous in applied data science because of the value it delivers. In order to do effective time series analysis, you need to know your tools well.

In 2023, we saw several libraries - which had previously only supported pandas - add support for other dataframe libraries such as Polars, Modin, and cuDF. - How did they do it? - Are there any drawbacks to how they did it? - What comes next, and what other solutions are there? This talk could...

Polars is the new dataframe on the block taking the world by storm. You'll learn: - what Polars is, and what it can do for you - Polars basics and core concepts (including expressions and lazy computation) - how to work with different datatypes, and how the List datatype gives you superpowers -...

"You should never, ever deal with time zones if you can help it" Tom Scott Instead, you should let your software deal with time zones for you. Polars is a Dataframe library with full support for time zones - come and learn how to leverage it to its full potential!

There exists a tonne of Python code quality tools which can help catch bugs and fix stylistic issues. Unfortunately, the vast majority don't work out-of-the-box on Jupyter Notebooks.