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.

TL;DR Learn how to turn your Python functions into interactive web applications using open-source tools. By the end, each of us will have deployed a portfolio (or store) with multiple web applications and learned how to reproduce it easily later on.

Tell me more Work not shown is work lost. Many excellent scientists and engineers are not always adept at showcasing their work. This results in many interesting scientific ideas that have never been brought to light.

However, using today's tools, one no longer has to leave the Python ecosystem to create classy, complete prototypes using modern data visualization and web development tools. With over five years of experience building and presenting data solutions at huge science companies, we show it doesn't have to be challenging. We provide a walkthrough of the primary web application frameworks and showcase Fast Dash, an open-source Python library that we built to address specific prototyping needs.

This tutorial is designed for all data professionals who value the ability to quickly convert their scientific code into web applications. Participants will learn about the leading frameworks, their strengths and limitations, and a decision flowchart for picking the best one for a given task. We will go through some day-to-day applications and hands-on Python coding throughout the session. Whether you bring your use-cases and datasets, or pick from our suggestions, you'll have a reproducible portfolio (app store) of deployed web applications by the end!

This tutorial will explore GPU-accelerated clustering techniques using RAPIDS cuML, optimizing algorithms like K-Means, DBSCAN, and HDBSCAN for large datasets. Traditional clustering methods struggle with scalability, but GPU acceleration significantly enhances performance and efficiency.

Participants will learn to leverage dimensionality reduction techniques (PCA, T-SNE, UMAP) for better data visualization and apply hyperparameter tuning with Optuna and cuML. The session also includes real-world applications like topic modeling in NLP and customer segmentation. By the end, attendees will be equipped to implement, optimize, and scale clustering algorithms effectively, unlocking faster and more powerful insights in machine learning workflows.

This tutorial is an introduction to data visualization using the popular Vega-Altair Python library. Vega-Altair provides a simple and expressive API, enabling authors to rapidly create a wide range of interactive charts.

Participants will explore the fundamentals of effective chart design and gain hands-on experience building a variety of visualizations using Vega-Altair's declarative API. Furthermore, this tutorial will introduce users to advanced topics such as data transformations and interaction design. We will finish off by covering practical workflows such as integrating Vega-Altair into dashboarding systems, publishing visualizations, and creating reusable, themed charting libraries. By the end of the session, attendees will have the skills to leverage Vega-Altair for both rapid prototyping and production-ready visualizations in diverse environments