Pandas is the de-facto standard for data manipulation in python, which I personally love for its flexible syntax and interoperability. But Pandas has well-known drawbacks such as memory in-efficiency, inconsistent missing data handling and lacking multicore-support. Multiple open-source projects aim to solve those issues, the most interesting is Polars.

Polars uses Rust and Apache Arrow to win in all kinds of performance-benchmarks and evolves fast. But is it already stable enough to migrate an existing Pandas' codebase? And does it meet the high-expectations on query language flexibility of long-time Pandas-lovers?

In this talk, I will explain, how Polars can be that fast, and present my insights on where Polars shines and in which scenarios I stay with pandas (at least for now!)

In the recent years we saw an explosion of usage of Python in the browser: Pyodide, CPython on WASM, PyScript, etc. All of this is possible thanks to the powerful functionalities of the underlying platform, WebAssembly, which is essentially a virtual CPU inside the browser.

In this keynote, I will share the lessons learned from using Python in 4 industries. Apart from machine learning applications that I build in my day to day as a data scientist and machine learning engineer, I also use Python to develop games for my own gaming company, Quill Game Studios. There is a lot of versatility in Python, and it's been my pleasure to use it to solve many interesting problems. I hope that this talk can give inspiration to various types of applications in your own industry as well.

Python packaging is quickly evolving and new tools pop up on a regular basis. Lots of talks and posts on packaging exist but none of them give a structured, unbiased overview of the available tools.

This talk will shed light on the jungle of packaging and environment management tools, comparing them on a basis of predefined features.

Snowflake as a data platform is the core data repository of many large organizations.
With the introduction of Snowflake's Snowpark for Python, Python developers can now collaborate and build on one platform with a secure Python sandbox, providing developers with dynamic scalability & elasticity as well as security and compliance.

In this talk I'll explain the core concepts of Snowpark for Python and how they can be used for large scale feature engineering and data science.

Speeding up Python code has traditionally been achieved by writing C/C++ — an alien world for most Python users. Today, you can write high performance code in Julia instead, which is much much easier for Python users. This tutorial will give you hands-on experience writing a Python library that incorporates Julia for performance optimization.

The industrial environment offers a lot of interesting use cases for data enthusiasts. There are myriads of interesting challenges that can be solved by data scientists. However, collecting industrial data in general and industrial IoT (IIoT) data in particular, is cumbersome and not really appealing for anyone who just wants to work with data. Apache StreamPipes addresses this pitfall and allows anyone to extract data from IIoT data sources without messing around with (old-fashioned) protocols. In addition, StreamPipes newly developed Python client now gives Pythonistas the ability to programmatically access and work with them in a Pythonic way.

This talk will provide a basic introduction into the functionality of Apache StreamPipes itself, followed by a deeper discussion of the Python client. Finally, a live demo will show how IIoT data can be easily derived in Python and used directly for visualization and ML model training.

The nightmare before data science production: You found a working prototype for your problem using a Jupyter notebook and now it's time to build a production grade solution from that notebook. Unfortunately, your notebook looks anything but production grade. The good news is, there's finally a cure!

The open-source python package LineaPy aims to automate data science workflow generation and expediting the process of going from data science development to production. And truly, it transforms messy notebooks into data pipelines like Apache Airflow, DVC, Argo, Kubeflow, and many more. And if you can't find your favorite orchestration framework, you are welcome to work with the creators of LineaPy to contribute a plugin for it!

In this talk, you will learn the basic concepts of LineaPy and how it supports your everyday tasks as a data practitioner. For this purpose, we will transform a notebook step by step together to create a DVC pipeline. Finally, we will discuss what place LineaPy will take in the MLOps universe. Will you only have to check in your notebook in the future?

The materials presented during this tutorial are open source and can be used by coaches and tutors who want to teach their students how to use Python for text processing and text classification. (A minimal understanding of programming (in any language) is required by the students)