EuroSciPy 2026

When writing Python code for scientific applications, you may reach a point where you want to share your work with collaborators that don't code. But what if you do not want to require them to set up Python environments or act as their tech support?

There are tools to avoid that by converting your Python code into executable that can be run without Python setup, and with all dependencies included.
In this talk I will share my experience with using some of those tools like PyInstaller and Conda constructor and how you could do the same.
Showed solutions might be used for both CLI script and application with graphical Interface.
Importantly, I will also mention some of the callenges that you might encounter and how to solve them.

If you work with scientific data, chances are that visualization is one of your strongest tools and biggest time sinks. Whether you're dealing with images from microscopes or telescopes, complex surface reconstructions, 3D point clouds, or n-dimensional feature embeddings from neural networks, some requirements are always the same: performance, interactivity, and extensibility.
napari is a Python library for the visualization and annotation of scientific data that focuses on addressing these needs, staying cross-field and un-specialized at the core, while providing an easy way to develop powerful specialized plugins.
In this tutorial, we will learn the basics of interacting with napari and its features and how to use napari to effectively navigate n-dimensional data. Armed with this knowledge, we will simulate a typical exploratory approach to developing a new image processing workflow in Python and converting it to an easily shearable napari plugin.