Polars, the Fastest Dataframe Library You Never Heard of. - Ritchie Vink | PyData Global 2021

preview_player
Показать описание
Polars, the Fastest Dataframe Library You Never Heard of.
Speaker: Ritchie Vink

Summary
This talk will introduce Polars, a blazingly fast DataFrame library written in Rust on top of Apache Arrow. Its a DataFrame library that brings exploritory data analysis closer to the lessons learned in database research.

It's the library that puts python in top for OLAP queries. See the excellent query benchmark performances.

Description
This talk will introduce Polars a blazingly fast DataFrame library written in Rust on top of Apache Arrow. Its a DataFrame library that brings exploratory data analysis closer to the lessons learned in database research.

CPU's today's come with many cores and with their superscalar designs and SIMD registers allow for even more parallelism. Polars is written from the ground up to fully utilize the CPU's of this generation.

Besides blazingly fast algorithms, cache efficient memory layout and multi-threading, it consist of a lazy query engine, allowing Polars to do several optimizations that may improve query time and memory usage.

Read more:

Join the talk to learn more.

Ritchie Vink's Bio

PyData Global 2021

PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.

PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.

00:00 Welcome!
00:10 Help us add time stamps or captions to this video! See the description for details.

Рекомендации по теме
Комментарии
Автор

Tried the library. its really good and very fast. Just waiting for this library to be as widespread as pandas. Also, waiting to see more books on the use of this library.

vigneshpadmanabhan
Автор

please add a subtitle
it has poor audio and it is hard to understand for non English people

amirreza
Автор

Offensively bad audio. Shouldn’t have even uploaded.

JOHNSMITH-verq