The array API standard is unifying the ecosystem of Python array computing, facilitating greater interoperability between code written for different array libraries, including NumPy, CuPy, PyTorch, JAX, and Dask.

But what are all of these "array-api-" libraries for? How can you use these libraries to 'future-proof' your libraries, and provide support for GPU and distributed arrays to your users? Find out in this talk, where I'll guide you through every corner of the array API standard ecosystem, explaining how SciPy and scikit-learn are using all of these tools to adopt the standard. I'll also be sharing progress updates from the past year, to give you a clear picture of where we are now, and what the future holds.

JupyterGIS facilitates collaborative editing of GIS files, including the QGIS format, through a web-based interface built on JupyterLab. It also provides a programmatic interface tailored for Jupyter notebooks, making use of the advanced capabilities of the Jupyter rich display system.

In this presentation, we will first provide a high-level overview of the project’s main features.

We will then explore the latest developments, including the integration with the xarray stack and the Pangeo ecosystem, and the support for STAC geographical asset catalogs.

We conclude the talk with a forward-looking presentation of the ongoing development, such as the story maps feature, and the integration with the R programming language.

In March 2025, PEP 751 got accepted, proposing an new format how lockfiles should be structured. The talk will give a brief history of this PEP (and it's rejected predecessor), introduce you to the proposed pylock.toml format and discuss (subjective) highlights of this PEP. Afterwards, a practical example how this PEP could improve managing your environments will be discussed.

See how Quarto can transform your Jupyter notebooks into stakeholder-ready web pages or PDFs, published online with just one command. This session features practical demonstrations of publishing with quarto publish, applying custom styles tailored to your organization thanks to brand.yml, and leveraging new features for reproducible research.

Designed for anyone looking to share their work, this talk requires only basic Python and notebook familiarity. You’ll walk away with the skills to elevate your reporting workflow and share insights professionally.

Challenges in economics and governance models for open-source scientific projects

In this presentation, the CEOs of two companies at the forefront of open-source scientific software development - Sylvain Corlay of QuantStack and Yann Lechelle of Probabl - examine the intricate challenges of open-source funding and governance and reflect on how these two aspects interconnect.

We start by reflecting on the origins of the open-source movement within the scientific community, and delve into the contemporary challenges of operating businesses and identifying sustainable economic models that both leverage and contribute to open-source software.

In particular, we highlight the unique approaches and experiences of QuantStack and Probabl, which primarily contribute to multi-stakeholder scientific projects such as scikit-learn, Jupyter, Apache Arrow, or conda-forge.

If you worked with large amounts of tabular data, chances are you have dealt with Parquet files. Apache Parquet is an open source, column-oriented data file format designed for efficient storage and retrieval. It employs high performance compression and encoding schemes to handle complex data at scale and is supported in many programming language and analytics tools. This talk will give a technical overview of Parquet format file structure, explain how the data is represented and stored in Parquet and why and how some of the possible configuration options might better match your specific use case.

We will also highlight some recent developments the and discussions in the Parquet community including Hugging Face's proposed content defined chunking - an approach that reduces required storage space by ten percent on realistic training datasets. We will also examine the geometry and geography types added to the Parquet specification in 2025, which enable efficient storage of spatial data and have catalyzed Parquet's growing adoption within the geospatial community.

Four years ago, I had no idea what PyArrow was—or how open source development worked. But through mentorship, collaboration, and learning in public, I found not just a place in the community, but a sense of how open source evolves and connects.

In this keynote, I’ll share my experience on how complex projects like Apache Arrow evolve through shared protocols, cross-project conversations, and the people behind them. Along the way, we’ll look at the human side of technical work, the quiet strength of standards, and how imposter syndrome, while uncomfortable, has sharpened my curiosity and helped me find my own way of contributing.

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