### Seaborn - Statistical data visualization using matplotlib

•        113

Seaborn is a Python visualization library based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics.Online documentation is available at seaborn.pydata.org. Installation requires numpy, scipy, pandas, and matplotlib. Some functions will optionally use statsmodels if it is installed.

http://seaborn.pydata.org

 Tags data-visualization visualization statistics Implementation Python License BSD-3-Clause Platform Windows Linux

## hypertools - A Python toolbox for gaining geometric insights into high-dimensional data

•    Python

HyperTools is designed to facilitate dimensionality reduction-based visual explorations of high-dimensional data. The basic pipeline is to feed in a high-dimensional dataset (or a series of high-dimensional datasets) and, in a single function call, reduce the dimensionality of the dataset(s) and create a plot. The package is built atop many familiar friends, including matplotlib, scikit-learn and seaborn. Our package was recently featured on Kaggle's No Free Hunch blog. For a general overview, you may find this talk useful (given as part of the MIND Summer School at Dartmouth). Check the repo of Jupyter notebooks from the HyperTools paper.

## gramm - Gramm is a complete data visualization toolbox for Matlab

•    Matlab

Gramm is a powerful plotting toolbox which allows to quickly create complex, publication-quality figures in Matlab, and is inspired by R's ggplot2 library by Hadley Wickham. As a reference to this inspiration, gramm stands for GRAMmar of graphics for Matlab. Gramm is a data visualization toolbox for Matlab that allows to produce publication-quality plots from grouped data easily and flexibly. Matlab can be used for complex data analysis using a high-level interface: it supports mixed-type tabular data via tables, provides statistical functions that accept these tables as arguments, and allows users to adopt a split-apply-combine approach (Wickham 2011) with rowfun(). However, the standard plotting functionality in Matlab is mostly low-level, allowing to create axes in figure windows and draw geometric primitives (lines, points, patches) or simple statistical visualizations (histograms, boxplots) from numerical array data. Producing complex plots from grouped data thus requires iterating over the various groups in order to make successive statistical computations and low-level draw calls, all the while handling axis and color generation in order to visually separate data by groups. The corresponding code is often long, not easily reusable, and makes exploring alternative plot designs tedious.

## Apache ECharts - An Open Source JavaScript Visualization Library

•    Typescript

Apache ECharts is an open-sourced JavaScript visualization tool, which can run fluently on PC and mobile devices. It is compatible with most modern Web Browsers. The basic chart types ECharts supports include line series, bar series, scatter series, pie charts, candle-stick series, boxplot series for statistics, map series, heatmap series, lines series for directional information, graph series for relationships, treemap series, sunburst series, parallel series for multi-dimensional data, funnel series, gauge series.

## g2 - G2 (The Grammar of Graphics)

•    Javascript

G2 is a visualization grammar, a data-driven visual language with a high level of usability and scalability. It provides a set of grammars, takes users beyond a limited set of charts to an almost unlimited world of graphical forms. With G2, users can describe the visual appearance of a visualization just by one statement. Special thanks to Leland Wilkinson, the author of The Grammar Of Graphics, whose book served as the foundation for G2.

## data-science-with-ruby - Practical Data Science with Ruby based tools.

•    Ruby

Data Science is a new "sexy" buzzword without specific meaning but often used to substitute Statistics, Scientific Computing, Text and Data Mining and Visualization, Machine Learning, Data Processing and Warehousing as well as Retrieval Algorithms of any kind. This curated list comprises awesome tutorials, libraries, information sources about various Data Science applications using the Ruby programming language.

## GRASS GIS - Geographic Resources Analysis Support System

•    C++

Geographic Resources Analysis Support System, commonly referred to as GRASS GIS, is a Geographic Information System (GIS) used for data management, image processing, graphics production, spatial modelling, and visualization of many types of data. GRASS supports raster and vector data in two and three dimensions. The vector data model is topological, meaning that areas are defined by boundaries and centroids; boundaries cannot overlap within a single layer.

## Apache Superset is a Data Visualization and Data Exploration Platform

•    Python

Superset is fast, lightweight, intuitive, and loaded with options that make it easy for users of all skill sets to explore and visualize their data, from simple line charts to highly detailed geospatial charts. It easily integrates your data, using either our simple no-code viz builder or state of the art SQL IDE. Superset can query data from any SQL-speaking datastore or data engine (e.g. Presto or Athena) that has a Python DB-API driver and a SQLAlchemy dialect.

## Facets - Visualizations for machine learning datasets

•    Typescript

The facets project contains two visualizations for understanding and analyzing machine learning datasets: Facets Overview and Facets Dive. The visualizations are implemented as Polymer web components, backed by Typescript code and can be easily embedded into Jupyter notebooks or webpages.

## vaex - Lazy Out-of-Core DataFrames for Python, visualize and explore big tabular data at a billion rows per second

•    Python

Vaex is a python library for Out-of-Core DataFrames (similar to Pandas), to visualize and explore big tabular datasets. It can calculate statistics such as mean, sum, count, standard deviation etc, on an N-dimensional grid up to a billion (109) objects/rows per second. Visualization is done using histograms, density plots and 3d volume rendering, allowing interactive exploration of big data. Vaex uses memory mapping, zero memory copy policy and lazy computations for best performance (no memory wasted).

## vaex - Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualize and explore big tabular data at a billion rows per second 🚀

•    Python

Vaex is a high performance Python library for lazy Out-of-Core DataFrames (similar to Pandas), to visualize and explore big tabular datasets. It calculates statistics such as mean, sum, count, standard deviation etc, on an N-dimensional grid for more than a billion (10^9) samples/rows per second. Visualization is done using histograms, density plots and 3d volume rendering, allowing interactive exploration of big data. Vaex uses memory mapping, zero memory copy policy and lazy computations for best performance (no memory wasted). HDF5 and Apache Arrow supported.

## Dex - Dex : The Data Explorer -- A data visualization tool written in Java/Groovy/JavaFX capable of powerful ETL and publishing web visualizations

•    Javascript

Dex : The data explorer is a data visualization tool written in Java/JavaFX capable of powerful ETL and data visualization. There are 2 main ways to install Dex.

## giojs - 🌏 A Declarative 3D Globe Data Visualization Library built with Three.js

•    Javascript

Gio.js is an open source library for web 3D globe data visualization built with Three.js. What makes Gio.js different is that it is simple to use Gio.js to customize a 3D data visualization model in a declarative way, add your own data, and integrate it into your own modern web application. Gio.js is an open source library for web 3D globe data visualization built with Three.js. What makes Gio.js different is that it is simple to use Gio.js to customize a 3D data visualization model in a declarative way, add your own data, and integrate it into your own modern web application.

## statsviz - :rocket: Instant live visualization of your Go application runtime statistics (GC, MemStats, etc

•    Go

Instant live visualization of your Go application runtime statistics (GC, MemStats, etc.). Statsviz serves 2 HTTP handlers.

## rweekly.org - R Weekly

•    R

R weekly provides weekly updates from the R community. You are welcome to contribute as long as you follow our code of conduct and our contributing guide. Update the draft post, and create a pull request.

## Kibana Enhanced Table - Kibana visualization like a Data Table, but with enhanced features like computed columns, filter bar and Split Cols bucket

•    Javascript

This Kibana visualization plugin is like a Data Table, but with enhanced features like computed columns, filter bar and pivot table. Now the plugin contains a second visualization named 'Document Table'. This visualization does the same thing than 'Enhanced Table' visualization, but for single documents (not aggregations). It especially allows to have enhanced features, compared to a saved search (like column custom labels, computed columns and filter bar)

## party-mode - An experimental music visualizer using d3.js and the web audio api.

•    Javascript

Using the web audio api, I can get an array of numbers which corresponds to the waveform of the sound an html5 audio element is producing. There's a good tutorial on how to do this. Then, using requestAnimationFrame (with a little frame limiting for performance reasons) I'm updating that array as the music changes. I then normalize the data a bit (or transform it slightly depending on the visualization) and redraw the screen based on the updated array. I'm using d3.js to draw and redraw SVG based on this normalized data. Each visualization uses the data a bit differently -- it was mostly trial and error to get some stuff I liked looking at. Since I'm using D3 -- which is just drawing SVG -- I was able to style everything in CSS (no images are used at all, including icons). There are a handful of differently colored themes for each visualization, and I do some rudimentary CSS namespacing by updating a class applied to the html element. eg. <html class='theme_1'>. This lets me override or substitute CSS rules pretty trivially. I can add some additional variation to each theme by messing with pseudo selectors. For example, I can use :nth-of-type to hide every nth SVG rectangle or making every odd child have a different stroke-dasharray, etc.

## datoviz - ⚡ High-performance GPU interactive scientific data visualization with Vulkan

•    C

Datoviz is an open-source high-performance interactive scientific data visualization library leveraging the graphics processing unit (GPU) for speed, visual quality, and scalability. It supports both 2D and 3D rendering, as well as minimal graphical user interfaces (using the Dear ImGUI library). Written in C, Datoviz has been designed from the ground up for performance. It provides native Python bindings (based on Cython). Bindings to other languages could be developed thanks to community efforts (Julia, R, MATLAB, Rust, C#, and so on). Datoviz uses the Vulkan graphics API created by the Khronos consortium, successor of OpenGL. Supporting other modern graphics API, such as WebGPU, would constitute interesting developments.

## Knime - Data Analytics Platform

•    Java

KNIME, pronounced [naim], is a modern data analytics platform that allows you to perform sophisticated statistics and data mining on your data to analyze trends and predict potential results. Its visual workbench combines data access, data transformation, initial investigation, powerful predictive analytics and visualization. KNIME also provides the ability to develop reports based on your information or automate the application of new insight back into production systems.

•    Ruby

Analyze Facebook copy of your data. Download zip file from Facebook and get info about friends, ranking by message, vocabulary, contacts, friends added statistics and more. It won't work if you use different language because of date formatting, different titles on pages. This script uses nokogiri internally to parse data.

## muze - Composable data visualisation library for web with a data-first approach now powered by WebAssembly

•    Javascript

Muze is a free data visualization library for creating exploratory data visualizations (like Tableau) in browser, using WebAssembly. It uses a layered Grammar of Graphics (GoG) to create composable and interactive data visualization for web. It is ideal for use in visual analytics dashboards & applications to create highly performant, interactive, multi-dimensional, and composable visualizations. It uses a data-first approach to define the constructs and layers of the chart, automatically generates cross-chart interactivity, and allows you to over-ride any behavior or interaction on the chart.

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