Many notable PyData projects including Jupyter Hub, Matplotlib and JAX follow a versioning scheme called EffVer, where instead of making promises around backward compatibility they communicate the likelihood and magnitude of the work required to adopt a new version.

In this talk we will dive into EffVer, what it is and what it means for developers and users. We will discuss how to apply EffVer to your own projects and how to depend on projects that use it.

Matplotlib is already a favorite plotting library for creating static data visualizations in Python. Here, we discuss the development of a new DataContainer interface and accompanying transformation pipeline which enable easier dynamic data visualization in Matplotlib. This improves the experience of plotting pure functions, automatically recomputing when you pan and zoom. Data containers can ingest data from a variety of sources, including structured data such as Pandas Dataframes or Xarrays, up to live updating data from web services or databases. The flexible transformation pipeline allows for control over how your data is encoded into a plot.

The rapid expansion of the geospatial industry and accompanying increase in availability of geospatial data, presents unique opportunities and challenges in data science. As the need for skilled data scientists increases, the ability to manipulate and interpret this data becomes crucial. This workshop introduces the essentials of geospatial data manipulation and data visualisation, emphasizing hands-on techniques to transform, analyze and visualise diverse datasets effectively.

Throughout the workshop, attendees will explore the extensive ecosystem of geospatial Python libraries. Key tools include GeoPandas, Shapely and Cartopy for vector data, GDAL, Rasterio and rioxarray for raster data and participants will also learn to integrate these with popular plotting libraries such as Matplotlib, Bokeh, and Plotly for visualizations.

This tutorial will cover three primary topics: visualizing geospatial shapes, managing raster datasets, and synthesizing multiple data types into unified visual representations. Each section will incorporate data manipulation exercises to ensure attendees not only visualize but also deeply understand geospatial data.

Targeting both beginners and advanced practitioners, the workshop will employ real-world examples to guide participants through the necessary steps to produce striking and informative geospatial visualizations. By the end, attendees will be equipped with the knowledge to leverage advanced data science techniques in their geospatial projects, making them proficient in both the analysis and communication of spatial information.

PyVista is a general purpose 3D visualization library used for over 2000+ open source projects for the visualization of everything from computer aided engineering and geophysics to volcanoes and digital artwork.

PyVista exposes a Pythonic API to the Visualization Toolkit (VTK) to provide tooling that is immediately usable without any prior knowledge of VTK and is being built as the 3D equivalent of Matplotlib, with plugins to Jupyter to enable visualization of 3D data using both server- and client-side rendering.

Working with data can be challenging: it often doesn’t come in the best format for analysis, and understanding it well enough to extract insights requires both time and the skills to filter, aggregate, reshape, and visualize it. This session will equip you with the knowledge you need to effectively use pandas – a powerful library for data analysis in Python – to make this process easier.

Pandas makes it possible to work with tabular data and perform all parts of the analysis from collection and manipulation through aggregation and visualization. While most of this session focuses on pandas, during our discussion of visualization, we will also introduce at a high level Matplotlib (the library that pandas uses for its visualization features, which when used directly makes it possible to create custom layouts, add annotations, etc.) and Seaborn (another plotting library, which features additional plot types and the ability to visualize long-format data).