Polars - Lightning-fast DataFrame library for Rust and Python

04/25/23 • 54 min

In this episode of Inspiring Computing we talked to Ritchie Vink was the author of polars. Polars is a Python package written in rust and anyone who is ever used pandas will for sure find this episode interesting, as polars has incredible speedups compared to pandas. But in order to do so, it is interesting to hear from , Ritchie, where he got his inspiration from, how did he go about creating such a powerful package and where is he actually going in the future?
And this episode, he talks about some of the optimizations that he implemented.

Support the show

Subscribe to mailing list here.

Support the show

Subscribe to mailing list here.

Previous Episode

Prodrive Technologies - Creating meaningful technologies that make the world work

In this episode, we talk to Thomas who works at a company called Prodrive Technologies. We explore how the company started with these AI platform and what it meant for the company to go from simple proof of concepts of applying machine learning or deep learning techniques to actually adding business value.
We listen to the journey that Thomas and his team had to take to understand, and then reveal all the common gotches around implementing MLOps. If you're interested in understanding the tools, the challenges, the story, the motivation, the obstacles that pro drivers overcome in the recent few years in this journey.
During this episode we mention a few neat packages:
- MLFlow
- PyTorch
- TensorFlow
- Django
- anomalib

Support the show

Subscribe to mailing list here.

Next Episode

Dexter Forecast & Trade Optimization Powered by AI

In this podcast episode, we delve into the intricacies of power markets and energy forecasting with Tom Lemmens who has firsthand experience in the field. Starting his career at an energy company, our guest explains the complexities of short-term power markets, focusing on generation forecasting for wind and solar power, as well as price forecasting.
We learn about the crucial role of forecasting prices as a proxy for balancing the grid, and the importance of portfolio optimization in maximizing asset value. After transitioning from a data science consultant back to the energy sector, our guest became one of the early joiners at Dexter Energy, a company providing generation forecasting and trade optimization services.
Dexter Energy specializes in forecasting solar and wind power generation, along with short-term power prices, to help companies make informed trade strategies and optimize their assets. The guest highlights the significance of utilizing Python in their work and explains the process of translating data into expected power output using machine learning models.
Moreover, we explore the challenges and rapid changes in the energy transition, particularly in regions with increasing adoption of renewable energy sources like solar panels. Tom shares insights into the continuous evolution of their models and the technology stack used at Dexter Energy, including Python, Google Cloud, Airflow, and various databases.
Finally, we uncover the data sources for weather data, essential for accurate forecasting, and the iterative process of determining model usefulness through backtesting. This episode provides a comprehensive overview of the dynamic energy market and the vital role of data-driven solutions in optimizing energy trading strategies.

Support the show

Subscribe to mailing list here.