2024-10-26 –, CLASS #5 - 3B
Language: English
Polars is popular these days. The code looks similar to pandas however they are different libraries. To know how to efficiently use Polars, we need to dive deeper into how these libraries are different. In this talk, we will do that and provide tips to migrate from pandas to Polars.
Despite the similarity of the Python API, Polars are written in Rust and have a different mechanism at their backend. Most users praise the robustness of using RAM and multi-threading of Polars over pandas. To make good use of them, we cannot write our code just like we do with pandas. Also, since the backend of Polars is not NumPy, there may be an extra step of conversion when using libraries that use NumPy, like TensorFlow and other libraries.
Goal
To provide tips and educational information for data scientists and engineers about Polars and to help them fully use the robust features provided by Polars.
Target Audience
This talk assumes little to no knowledge of Polars, however, we assume audiences are familiar with pandas and Python.
Outline
- Fun facts about Polars
- How Polars different from pandas
- Tips for faster code with Polars
- Tips for using Polars with other libraries
- Conclusion and Q&A
After having a career in Data Scientist and Developer Relations, Cheuk dedicated her work to the open-source community and founded CMD Limes, a Python consultants cooperation. She has also co-founded Humble Data, a beginner Python workshop that has been happening around the world. She has served the EuroPython Society board for two years and is now a fellow and director of the Python Software Foundation.