Awesome GitHub RepositoriesDeclarative Visualization Frameworks

This project serves as a comprehensive textbook and educational resource for data analysis using the Python ecosystem. It provides a structured guide to manipulating, cleaning, and processing datasets, focusing on the core tools required for numerical computing and statistical analysis. The repository distinguishes itself by offering a collection of practical code examples and workflows that demonstrate how to perform complex data tasks. It covers the application of vectorized numerical computations, the management of time-indexed data, and the creation of statistical visualizations to commun

PlotNeuralNet is a programmatic tool designed to generate high-quality visual representations of neural network architectures. It functions as a declarative visualization framework that converts structural definitions into professional-grade graphical output, specifically tailored for technical documentation and academic research papers. The project distinguishes itself by utilizing a layer-centric procedural modeling approach, which applies standardized geometric templates to network components to ensure consistent visual styling. By leveraging a domain-specific macro language and a LaTeX-ba

This project is a declarative visualization library and geospatial framework designed for rendering large-scale data sets within web browsers. It functions as a high-performance graphics engine that leverages hardware acceleration to display complex 2D and 3D visual layers, enabling the visualization of millions of data points through a structured, component-based syntax. The framework distinguishes itself through its ability to synchronize custom data visualizations with third-party mapping platforms. By managing camera states and coordinate systems, it allows developers to overlay high-perf

c3 is a charting library for creating reusable data visualizations and interactive charts based on the D3 JavaScript framework. It functions as a declarative visualization framework that generates complex charts through high-level configurations rather than manual SVG manipulation. The project provides a reusable chart component library and a tool for converting raw datasets into scalable vector graphics. These capabilities allow for the implementation of interactive data visualizations and web-based data reporting using standardized templates. The library supports the development of custom

metrics-graphics is a data visualization library and declarative graphics framework designed to create principled data graphics and layouts. It functions as a statistical graphics engine that maps raw data to geometric shapes and structured objects to render complex, data-driven layouts. The toolkit specializes in rendering time-series data through line charts and scatterplots using a consistent layout system. It also provides capabilities for statistical distribution mapping, including the creation of rug plots to represent one-dimensional data density. The system covers a broad surface of

ggpy is a Python library for statistical data visualization based on the grammar of graphics. It functions as a declarative framework for building complex charts by mapping data variables to visual properties through a structured coordinate system. The library enables the construction of composite visualizations by layering geometric shapes and statistical summaries. It utilizes a system of continuous and discrete scales to translate raw data into visual attributes and supports facet-based plotting to segment a single visualization into a grid of subplots based on variable categories. Visual

Ce projet est une ressource pédagogique fournissant une base mathématique en probabilités et statistiques pour l'apprentissage automatique. Il offre une collection de notebooks interactifs et de manuels conçus pour expliquer les théories statistiques fondamentales et les principes de la science des données à travers des exemples de code pratiques. Le contenu est structuré en chapitres modulaires qui permettent un apprentissage à son propre rythme de sujets tels que l'inférence bayésienne et les distributions de probabilité. En utilisant l'exécution basée sur le navigateur et la visualisation déclarative, le projet permet aux utilisateurs de manipuler des variables et d'observer les résultats mathématiques en temps réel, transformant des concepts abstraits en représentations graphiques. Le dépôt sert de guide complet pour construire la base statistique requise pour comprendre et implémenter des algorithmes d'apprentissage automatique. Tous les supports sont compilés dans une structure web navigable pour fournir un chemin d'apprentissage clair pour les étudiants et les praticiens.