Awesome GitHub RepositoriesData Visualization Scales

D3 is a modular library providing low-level primitives for creating data-driven visualizations. It functions as a flexible framework that allows for direct control over visual presentation by mapping abstract data dimensions to graphical properties, such as position, color, and size, without imposing predefined chart abstractions. The library distinguishes itself by offering specialized tools for complex data representation, including algorithmic layouts for hierarchical structures and geographic projection utilities for mapping spherical coordinates. It also includes a comprehensive suite fo

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

Seaborn is a Python library designed for statistical data visualization. It functions as a high-level interface built on the Matplotlib ecosystem, providing specialized routines to explore and communicate complex patterns within datasets. The framework enables users to generate informative graphics through automated statistical aggregation, multi-plot faceting, and integrated regression modeling. The library distinguishes itself through a declarative approach to data mapping, which translates raw inputs into visual properties like color, size, and position. It includes a robust statistical tr

Kepler.gl is a web-based geospatial visualization framework designed for rendering large-scale location datasets. It functions as a modular React mapping component that enables developers to embed interactive, high-performance geographic visualizations into web applications, serving as a comprehensive engine for building browser-based GIS dashboards. The library distinguishes itself through a highly extensible architecture that centers on centralized state management. By utilizing a predictable state-driven model, it allows for the programmatic control of map layers, filters, and viewport set

gpui-component is a native desktop UI kit and component library built for the GPUI framework. It provides a collection of reusable user interface elements, a desktop layout engine for organizing application space, and a specialized data visualization library for rendering quantitative information. The project is distinguished by its high-performance rendering systems, including a virtualized data grid and list system designed to handle large datasets with low memory overhead. It also features a comprehensive data visualization toolkit for rendering charts, axes, and coordinate scales using li

chroma.js is a JavaScript library for color manipulation, space conversion, and the generation of data visualization color scales. It provides a system for transforming colors between formats including RGB, HSL, Lab, LCH, OKLab, and CMYK, alongside tools for parsing and modifying colors across these spaces. The library specializes in data-driven color mapping, using algorithms such as Cubehelix, Bezier interpolation, and ColorBrewer palettes to translate numeric data into visual gradients. It includes a perceptual color difference calculator that utilizes CIE 2000 Delta E and Euclidean metric

ggplot2 is a data visualization library for R based on a formal grammar of graphics. It provides a declarative plotting framework that allows users to create complex graphics by combining geometric objects, statistical summaries, and coordinate systems. The system is distinguished by a layered approach to composition, where visualizations are built incrementally by stacking independent geometric, statistical, and coordinate layers. It utilizes a hierarchical styling engine to manage non-data elements such as backgrounds, fonts, and margins, and includes a multi-panel faceting tool for splitti

ggplot2 is an R data visualization library and statistical graphics engine. It implements a grammar of graphics that functions as a declarative plotting framework, allowing users to specify what a plot should contain rather than how to draw it. The system builds visualizations by mapping data variables to visual aesthetics through a structured set of layering rules. This approach enables the composition of complex graphics by stacking independent components, such as geometric objects and scales, on top of a shared coordinate system. The framework supports scientific plotting and exploratory

This is a grammar of graphics visualization library used to build charts by mapping tabular data to visual marks. It functions as an SVG data visualization tool and an exploratory data analysis API, allowing users to render complex visualizations and geographic maps. The library features a GeoJSON map renderer that projects spherical coordinates into two-dimensional pixel space and an Apache Arrow visualization interface for high-efficiency data processing. Its capability surface covers data transformation through binning and grouping, visual encoding via automatic scale inference and color

Vega-Lite is a high-level declarative language for specifying interactive, multi-view visualizations. It compiles a concise JSON specification into a full Vega visualization, automatically inferring scales, axes, and legends from encoding declarations. The grammar-of-graphics encoding maps data fields to visual channels such as position, color, size, and shape, while a multi-view composition grammar enables layered, faceted, concatenated, and repeated layouts. Reactive parameter binding links named parameters to input widgets, selections, and expressions for dynamic updates. The project suppo

AAChartKit iOS, iPadOS और macOS के लिए एक घोषणात्मक (declarative) चार्टिंग लाइब्रेरी और डेटा विज़ुअलाइज़ेशन फ्रेमवर्क है। यह एक मल्टी-टाइप सांख्यिकीय चार्टिंग इंजन के रूप में कार्य करता है जो लाइन, बार, बबल, बॉक्स प्लॉट और पोलर चार्ट सहित विभिन्न प्लॉट प्रकारों को रेंडर करता है। फ्रेमवर्क सटीक पिक्सेल नियंत्रण के साथ दृश्य तत्वों को खींचने के लिए एक Core Graphics वेक्टर रेंडरिंग इंजन का उपयोग करता है। यह एनिमेशन, ज़ूमिंग, पैनिंग और यूज़र इंटरैक्शन इवेंट्स के लिए इन-बिल्ट सपोर्ट की विशेषता वाला इंटरैक्टिव डेटा विज़ुअलाइज़ेशन के लिए एक सिस्टम प्रदान करता है। लाइब्रेरी सांख्यिकीय डेटा प्लॉटिंग और कस्टम चार्टिंग के लिए व्यापक क्षमताओं को कवर करती है, जिसमें एरर बार और वॉटरफॉल चार्ट जैसे जटिल प्रकारों को रेंडर करने की क्षमता शामिल है। यह डायनामिक डेटा एनिमेशन, मल्टी-चार्ट सिंक्रोनाइज़ेशन और अनुकूलन योग्य टूलटिप्स और वैल्यू रेंज मार्कर्स के साथ इंटरैक्टिव डैशबोर्ड के निर्माण का सपोर्ट करती है।

Plotnine 'Grammar of Graphics' पर आधारित Python के लिए एक डेटा विज़ुअलाइज़ेशन लाइब्रेरी है। यह एक घोषणात्मक (declarative) सांख्यिकीय प्लॉटिंग फ्रेमवर्क और मल्टी-पैनल प्लॉटिंग इंजन के रूप में कार्य करता है, जो यूज़र्स को डेटा वेरिएबल्स को स्थिति, रंग और आकार जैसे विज़ुअल गुणों से मैप करके जटिल चार्ट बनाने की अनुमति देता है। प्रोजेक्ट को इसके लेयर्ड कंपोजिशन मॉडल और सांख्यिकीय ट्रांसफॉर्मेशन इंजन द्वारा प्रतिष्ठित किया गया है जो विज़ुअल्स रेंडर करने से पहले एग्रीगेशन और गणना करता है। इसमें मल्टी-पैनल फैसेटिंग के लिए एक व्यापक सिस्टम है, जो श्रेणीबद्ध वेरिएबल्स के आधार पर एक एकल विज़ुअलाइज़ेशन को सब-प्लॉट्स के ग्रिड में विभाजित करने में सक्षम बनाता है। लाइब्रेरी वितरण, क्षेत्र और स्कैटर प्लॉट्स के लिए विविध ज्यामितीय प्रतिनिधित्व, और भौगोलिक सीमाओं को रेंडर करने के लिए जियोस्पेशियल विज़ुअलाइज़ेशन सहित क्षमताओं की एक विस्तृत श्रृंखला को कवर करती है। यह डेटा-संचालित तत्वों को गैर-डेटा सौंदर्य गुणों से अलग करने के लिए स्केल मैपिंग, कोऑर्डिनेट प्रोजेक्शन और थीम-आधारित स्टाइलिंग के लिए व्यापक टूल प्रदान करती है। फ्रेमवर्क रेंडरिंग के लिए Matplotlib बैकएंड का उपयोग करता है और पाइपिंग ऑपरेशंस के माध्यम से टैबुलर डेटाफ़्रेम के साथ एकीकृत होता है।

bqplot is an interactive data visualization library for IPython and Jupyter notebooks that utilizes a grammar of graphics. It functions as a tool for creating 2D charts and maps with real-time updates and bidirectional communication between the kernel and frontend. The library is distinguished by its ability to act as a geographic data visualization tool, rendering choropleth maps and spatial data via GeoJSON and custom projections. It also serves as a financial charting tool for producing OHLC and candle bar charts, and as an interactive dashboard framework for combining plotting widgets wit

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

Makie.jl is a high-performance Julia data visualization library and hardware-accelerated plotting engine used to create interactive 2D and 3D visualizations. It functions as a reactive visualization framework where plots update automatically via observables and compute graphs, and as a vector graphics generator for high-resolution academic output. The system is distinguished by its backend-agnostic rendering pipeline, which supports OpenGL, WebGL, and ray-traced scenes. It employs a grammar-of-graphics approach to map variables to aesthetic attributes and utilizes a hierarchical scene graph t

Unovis is a modular SVG and Canvas data visualization library used to build interactive charts, maps, and network graphs. It provides a framework-agnostic set of primitives for creating data dashboards and specialized visualizations. The library is distinguished by its dedicated toolkits for different visualization domains, including an XY charting library for coordinated plots, a network graph framework for relational data, and a geospatial visualization toolkit for TopoJSON-based mapping. Its capability surface covers a wide range of data representations, including linear, area, and bar ch