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Beyond Pandas: A dive into Python's high-performance dataframe library Polars
Description
Polars is a relatively new library to handle data through dataframes. It gained popularity very quickly due to its simplicity of APIs, performance in terms of both speed and memory handling. Polars is written in Rust and python bindings are released through PyO3. This particularly helps Polars to be memory safe. Polars took advantage of pyarrow as its backendpyarrow like polars search backend to provide comparatively higher performance as well. Polars entered quite late into the community, when Pandas is ruling the world of dataframe libraries and made the authors of pandas to rewrite the entire backend from numpy to pyarrow.
Table of contents
Introduction to Polars - Background, advantages, disadvantages
I am currently leading the Data Engineering team at Adavrisk. Recently, I've begun engaging with community events and venturing into areas outside my usual work scope after attending PyCon 2023. Now I am not able to shut my mouth from sharing the interesting things happening in the Python and Data Engineering space.
Hello @FluffyDietEngine, thank you for the proposal. Will you be able to present it in this month's meetup which is on 10th Feb?
The location: Prabhat Road (I will be updating the details on meetup.com soon).
Title of the talk
Beyond Pandas: A dive into Python's high-performance dataframe library Polars
Description
Polars is a relatively new library to handle data through dataframes. It gained popularity very quickly due to its simplicity of APIs, performance in terms of both speed and memory handling. Polars is written in Rust and python bindings are released through PyO3. This particularly helps Polars to be memory safe. Polars took advantage of
pyarrow as its backendpyarrow like polars search backend to provide comparatively higher performance as well. Polars entered quite late into the community, when Pandas is ruling the world of dataframe libraries and made the authors of pandas to rewrite the entire backend from numpy to pyarrow.Table of contents
Duration (including Q&A)
20 + 5 mins
Prerequisites
A
virtualenv
with polars installed, if they want to follow along.Polars - https://pypi.org/project/polars/
Speaker bio
I am currently leading the Data Engineering team at Adavrisk. Recently, I've begun engaging with community events and venturing into areas outside my usual work scope after attending PyCon 2023. Now I am not able to shut my mouth from sharing the interesting things happening in the Python and Data Engineering space.
You can reach out to me by
Twitter - https://twitter.com/SolomonSanthosh
LinkedIn - https://www.linkedin.com/in/santhosh-solomon/
github - https://github.com/FluffyDietEngine
The talk/workshop speaker agrees to
Share the slides, code snippets and other material used during the talk
If the talk is recorded, you grant the permission to release
the video on PythonPune's YouTube
channel
under CC-BY-4.0
license
Not do any hiring pitches during the talk and follow the Code
of
Conduct
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