#dataviz

This Talk is about interactive data visualization in React using the Plot library. Plot is a high-level library that simplifies the process of visualizing data by providing key concepts and defaults for layout decisions. It can be integrated with React using hooks like useRef and useEffect. Plot allows for customization and supports features like sorting and adding additional marks. The Talk also discusses accessibility concerns, SSR support, and compares Plot to other libraries like D3 and Vega-Lite.

PolarisViz is a collection of React components that provide consistent visual styles, motion design, and accessibility features. It aims to solve the problem of inconsistencies in visualization decisions made by different teams. The library is flexible for different visual styles and has centralized theme management. PolarisViz was integrated with React Native using a separate library called Polaris Viz core. Challenges included limitations in native apps and the need to share UI components between web and mobile platforms.

Welcome to GPU Accelerating Node.js Web Services and Visualization with Rapids. Rapids aims to bring high-performance data science capabilities to Node.js, providing a streamlined API to the Rapids platform without the need to learn a new language or environment. GPU acceleration in Node.js enables performance optimization and memory access without changing existing code. The demos showcase the power and speed of GPUs and rapids in ETL data processing, graph visualization, and point cloud interaction. Future plans include expanding the library, improving developer UX, and exploring native Windows support.

Jupyter Notebooks are important for data science, but maintaining them can be challenging. Visualizing data sets and using code quality tools like NBQA can help address these challenges. Tools like nbdime and Precommit can assist with version control and future code quality. Configuring NBQA and other code quality tools can be done in the PyProject.toml file. NBQA has been integrated into various projects' continuous integration workflows. Moving code from notebooks to Python packages should be considered based on the need for reproducibility and self-contained solutions.