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Cheuk Ting Ho

How to be Pythonic? Design a Query Language in Python We created Python API calls that let you make queries and manipulate data in our graph database. We thought about what will be best for Pythonistas? What will be the most Pythonic way to do it? (Is it a thing?) Here's our journey in making WOQLpy and we want to make it useful to you.

Sümer Cip

Hunting Performance in Python Code A typical program spends almost all its time in a small subset of its code. Profilers lead us straight to the functions where we should spend our effort on. I aim to make the audience more knowledgeable about the differences and capabilities between various profilers in the Python ecosystem.

Aaron Bassett

Reduce, Reuse, Recycle - Persisting WebSocket connections with SharedWorkers WebSockets have many advantages, reduced latency, lower bandwidth, and reduced costs. But when our browser starts duplicating WebSocket connections, we quickly begin to lose these advantages. In this talk, we'll see how we can share one WebSocket connection across all browser contexts.

Christian Schramm

How To Write Python Code For Production Environments There's code, and then there's production code – the one that makes or breaks your company's business. A few battle-tested guidelines with real-world examples can help you get the important stuff correct from the get-go, and retain flexibility as the code base grows or requirements change.

Neslihan Wittek

Birds of a feather flock together - Tracking pigeons with Python and OpenCV In this talk, I want to demonstrate how to use OpenCV to implement basic animal movement tracking use cases. And everything without any fancy machine learning or neuronal networks ;)

Robson J√∫nior

Mastering a data pipeline with Python: 6 years of learned lessons from mistakes to success This talk is a demystification of years of experience and painful mistakes using Python as a core to create data pipelines and manage insanely amount of data. How each python piece fits into this puzzle? best practices and possible issues. PySpark vs Dask and Pandas, Airflow, and Apache Arrow.

Izzet Pembeci

asyncio Lessons Learnt, Coding Patters Discovered This talk is about a project that required performant real-time data processing and traffic where IO between a REST API, MySQL, Redis, RabbitMQ server and a SPA client needs to be orchestrated. asyncio was used extensively to achieve this and we want to share lessons learnt and patterns discovered.

Tania Allard

Docker and Python: making them play nicely and securely for Data Science and ML The existence of Docker has helped with the "your laptop is not a production environment" (to some extent). It also has a huge potential to improve the way we develop and deploy Machine Learning and data science projects. In this talk, I will explore how you can leverage Docker for Data Science!