Open-source alternatives to Ggpy

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

bqplot is an interactive data visualization library for Jupyter notebooks. It implements a grammar of graphics model, allowing users to build complex 2D charts by combining marks, scales, and axes. The library distinguishes itself with specialized toolkits for financial charting, such as OHLC candlesticks and time-series analysis, and geographic data visualization, including choropleths and custom map projections for TopoJSON and GeoJSON data. It enables deep interaction through tools like lasso selection, rectangular brushing, and the ability to manually manipulate plot points or line data.

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

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

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

Visual Insights is an automated exploratory data analysis platform and causal inference tool designed to discover patterns and cause-and-effect relationships within datasets. It functions as an interactive data visualization library using a grammar-of-graphics approach to generate multi-dimensional charts and dashboards. The project distinguishes itself through a natural language interface that translates plain-text questions into data answers and visualizations via a language model. It provides a specialized framework for causal discovery and inference, allowing users to identify variable li

Altair is a declarative data visualization library for Python that generates Vega-Lite specifications. It functions as a tool for mapping data to graphical marks using a high-level syntax, allowing users to describe the desired visual outcome instead of writing imperative drawing commands. The framework enables the creation of interactive charts and graphics, including linked views and filtered displays that respond to user input in real time. It supports the design of multi-view dashboards by combining visualizations into layered or faceted layouts. The library provides capabilities for sta

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 is a declarative charting library and data visualization framework for iOS, iPadOS, and macOS. It functions as a multi-type statistical charting engine that renders a variety of plot types, including line, bar, bubble, box plot, and polar charts. The framework utilizes a Core Graphics vector rendering engine to draw visual elements with precise pixel control. It provides a system for interactive data visualization featuring built-in support for animations, zooming, panning, and user interaction events. The library covers broad capabilities for statistical data plotting and custom

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

G2 is a declarative data visualization engine that constructs complex charts and graphical representations by mapping raw data to visual elements through a systematic grammar of graphics. It functions as a modular framework for building custom analytical visualizations, allowing users to define visual encodings and coordinate systems independently of the underlying data. The library distinguishes itself through a multi-backend rendering pipeline that supports Canvas, SVG, and WebGL, ensuring consistent graphical performance across different environments. Its architecture relies on a plugin-ba

Visx is a collection of modular, low-level primitives designed for building custom data visualizations within a React component architecture. It functions as a toolkit for mapping data to coordinate systems and geometric shapes, allowing developers to construct bespoke charts and graphs that integrate directly into the standard component lifecycle. The library utilizes a decoupled package architecture, enabling the use of specific visualization utilities without requiring the entire framework. By leveraging established mathematical primitives for geometric calculations and functional data tra

F2 is a cross-platform charting engine and grammar-based visualization tool designed to render interactive data visualizations. It functions as a declarative system that allows users to build complex charts by defining the relationships between data dimensions and visual encoding channels. The framework is specifically optimized for mobile data visualization, providing a toolkit for creating touch-optimized charts. It supports custom data visualization styling, enabling the use of personalized shapes and animations to define a unique visual identity. The engine provides a platform-agnostic r

Altair is a declarative data visualization library for Python based on the Vega-Lite grammar. It allows users to create statistical visualizations by mapping data fields to visual properties rather than writing imperative drawing code. The library focuses on interactive charting through a system of linked selections and filters that update multiple visualizations based on user input. It renders charts as JSON and HTML for display in web browsers and interactive notebooks. The project covers statistical data analysis and interactive data exploration, providing capabilities to export visuals a

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

ScrollableGraphView is a Swift data visualization library and iOS plotting framework used to render discrete numerical datasets as interactive graphs. It provides a scrollable user interface component that visualizes data points using a coordinate system with configurable layouts and styling. The framework is characterized by its adaptive graph scaling, which automatically adjusts the vertical axis to fit the visible data points as the user scrolls. It supports real-time data rendering, allowing graph views to update instantly as underlying datasets change through animated transitions. The l

KLineChart is a high-performance financial charting library and data visualization engine. It uses native browser canvas APIs to render interactive candlestick charts and time-series financial data for web and mobile applications. The library is built as a plugin-based framework, allowing for the integration of custom calculation logic and rendering routines. This modular architecture enables the addition of technical analysis visualization tools and custom indicators to financial dashboards. The system handles real-time market data updates and computes technical indicators to visualize mark

Text mode diagrams using UTF-8 characters and fancy colors

Python+Numpy+OpenGL: fast, scalable and beautiful scientific visualization

Python library that makes it easy for data scientists to create charts.

Python 3D library.

The Point Processing Toolkit (pptk) is a Python package for visualizing and processing 2-d/3-d point clouds.

This project is an exploratory data analysis library and profiling tool for Pandas and Spark DataFrames. It automates the initial investigation of datasets by generating comprehensive descriptive analysis reports, statistical summaries, and data quality warnings. The system functions as a data quality profiler to detect missing values, duplicate rows, and type inconsistencies. It includes a dataset comparison tool for identifying structural and content shifts between different versions of the same data, as well as specialized tools for time-series analysis to calculate auto-correlation and se

The power of Chart.js with Python

pyecharts is a Python visualization library and wrapper for the Echarts JavaScript engine. It translates Python data and configurations into JSON specifications to generate interactive web-based charts and graphs. The library provides specialized capabilities for geographic data mapping using a comprehensive library of map assets to visualize spatial information. It also includes utilities to capture rasterized snapshots of rendered web visualizations for export as static image files. The tool supports rendering interactive plots directly within data science notebook environments and exporti

missingno is a Python library for the visualization and analysis of missing data patterns. It provides a set of tools to profile dataset completeness, map data gaps, and quantify the volume of null values across variables. The library differentiates itself through a nullity correlation analyzer and a hierarchical data clustering tool. These components allow for the detection of systemic dependencies and trends by measuring how the absence of one variable relates to the absence of another. The toolset covers broader data quality auditing and exploratory analysis capabilities. It includes feat