# Data Visualization

## Links

* [Vega](http://vega.github.io/) - Declarative format for creating, saving, and sharing visualization designs. ([Code](https://github.com/vega/vega))
* [Vega-Lite](https://vega.github.io/vega-lite/) - High-level grammar of interactive graphics. Provides a concise JSON syntax for rapidly generating visualizations to support analysis. ([Code](https://github.com/vega/vega-lite)) ([HN](https://news.ycombinator.com/item?id=24937954))
* [Vega-Lite API](https://github.com/vega/vega-lite-api) - JavaScript API for Vega-Lite.
* [Vega Editor](https://vega.github.io/editor/#/)
* [VegaGraphs](https://github.com/JuliaGraphs/VegaGraphs.jl) - Implements graph visualization with Vega-Lite.
* [react-vega](https://github.com/vega/react-vega) - Convert Vega spec into React class conveniently.
* [altair\_recipes](https://github.com/piccolbo/altair_recipes) - Collection of ready-made statistical graphics for vega.
* [XVega](https://github.com/QuantStack/xvega) - C++ API to Vega-Lite.
* [Fundamentals of Data Visualization book (2019)](https://serialmentor.com/dataviz/)
* [Most cited scientific papers within data visualization (2019)](https://www.reddit.com/r/dataisbeautiful/comments/am2xk4/most_cited_scientific_papers_within_data/)
* [Visualization of related subreddits](https://github.com/anvaka/sayit)
* [Draco](https://github.com/uwdata/draco) - Formal framework for representing design knowledge about effective visualization design as a collection of constraints.
* [Observable](https://observablehq.com/) - Magic notebook for visualization.
* [Bl.ocks](https://bl.ocks.org/) - Simple viewer for sharing code examples hosted on GitHub Gist.
* [react-vis](https://github.com/uber/react-vis) - Data Visualization Components.
* [deck.gl](https://deck.gl/#/) - WebGL-powered framework for visual exploratory data analysis of large datasets.
* [Flourish](https://flourish.studio/) - Quickly turn your spreadsheets into stunning online charts, maps and interactive stories.
* [Dash App Gallery](https://dash-gallery.plotly.host/Portal/)
* [Scientific Visualization – Python & Matplotlib book](https://github.com/rougier/scientific-visualization-book)
* [Awesome Interactive Journalism](https://github.com/wbkd/awesome-interactive-journalism)
* [SandDance](https://github.com/microsoft/SandDance) - Visually explore, understand, and present your data.
* [Bokeh](https://github.com/bokeh/bokeh) - Interactive Data Visualization in the browser, from Python.
* [In praise of scatter plots (2019)](https://johnwickerson.wordpress.com/2019/10/22/scatter-plots/)
* [Perspective](https://github.com/finos/perspective/) - Streaming pivot visualization via WebAssembly.
* [Bar Chart Race, Explained (2019)](https://observablehq.com/@d3/bar-chart-race-explained)
* [owid-grapher](https://github.com/owid/owid-grapher) - Interactive data visualizations done quickly.
* [HoloViz](http://holoviz.org/) - High-level tools to simplify visualization in Python.
* [Datashader](https://datashader.org/) - Graphics pipeline system for creating meaningful representations of large datasets quickly and flexibly.
* [Perception in Visualization](https://www.csc2.ncsu.edu/faculty/healey/PP/index.html)
* [Prize winning JS solution for the Telegram April 2019 coding contest](https://github.com/Finesse/telegram-chart)
* [New technique for making responsive, JavaScript-free charts (2020)](https://dev.to/richharris/a-new-technique-for-making-responsive-javascript-free-charts-gmp)
* [HiPlot](https://github.com/facebookresearch/hiplot) - Lightweight interactive visualization tool to help AI researchers discover correlations and patterns in high-dimensional data. ([Article](https://ai.facebook.com/blog/hiplot-high-dimensional-interactive-plots-made-easy/)) ([HN](https://news.ycombinator.com/item?id=22270727))
* [Explorabl.es](https://explorabl.es/) - Hub for learning through play. ([HN](https://news.ycombinator.com/item?id=22368323))
* [Interactive Data Visualization course (2020)](http://vis.csail.mit.edu/classes/6.894/)
* [Vega-Embed](https://github.com/vega/vega-embed) - Makes it easy to embed interactive Vega and Vega-Lite views into web pages.
* [Facette](https://github.com/facette/facette) - Time series data visualization software.
* [Experimental 3D axis for matplotlib](https://github.com/rougier/matplotlib-3d)
* [Our World in Data](https://ourworldindata.org/) - Research and data to make progress against the world’s largest problems.
* [PlotJuggler](https://github.com/facontidavide/PlotJuggler) - QT5 based application to display time series in plots, using an intuitive "drag and drop" interface. ([Web](https://plotjuggler.io/)) ([HN](https://news.ycombinator.com/item?id=25357714))
* [Lightweight Charts](https://github.com/tradingview/lightweight-charts) - Financial lightweight charts built with HTML5 canvas.
* [Glue](https://github.com/glue-viz/glue) - Python project to link visualizations of scientific datasets across many files. ([Web](http://glueviz.org/index.html)) ([Multi Dimensional Linked Data Exploration with Glue](https://www.youtube.com/watch?v=TkMZ9gZ8xtk))
* [Learning Observable](https://www.youtube.com/playlist?list=PLOHIJAFwtkEfEsafMd9BY3KWsRrz7FV5e)
* [BizCharts](https://github.com/alibaba/BizCharts) - Powerful data visualization library based on G2 and React.
* [Bar Chart Race](https://github.com/dexplo/bar_chart_race/) - Make animated bar chart races in Python with matplotlib. ([Docs](https://www.dexplo.org/bar_chart_race/)) ([Article](https://www.dunderdata.com/blog/official-release-of-bar_chart_race-a-python-package-for-creating-animated-bar-chart-races))
* [Gallery of Concept Visualization](http://conceptviz.github.io/#/e30=) - Features projects which use pictures to communicate complex and difficult ideas.
* [Vida](https://vida.io/) - Software tool that reads JSON files and turns them into compelling dashboards. ([Code](https://github.com/vidalab/vida))
* [Best of the visualization web (2020)](https://www.visualisingdata.com/2020/07/best-of-the-visualisation-web-april-2020/)
* [Data Visualisation Catalogue](https://datavizcatalogue.com/index.html) ([HN](https://news.ycombinator.com/item?id=23915694))
* [Muze](https://muzejs.org/) - Free data visualization library to create Tableau-like charts in browser.
* [Beautiful News](https://informationisbeautiful.net/beautifulnews/) - Unseen trends, uplifting stats - daily.
* [Data-Visualization Books Everyone Should Read](https://informationisbeautiful.net/visualizations/dataviz-books/)
* [Information is Beautiful](https://informationisbeautiful.net/)
* [e-sum](https://anvaka.github.io/e-sum/) - Visualization of exponential sums. ([Code](https://github.com/anvaka/e-sum))
* [Matplot++](https://github.com/alandefreitas/matplotplusplus) - C++ Graphics Library for Data Visualization.
* [How to pick more beautiful colors for your data visualizations (2020)](https://blog.datawrapper.de/beautifulcolors/) ([HN](https://news.ycombinator.com/item?id=24391049))
* [Kyrix](https://github.com/tracyhenry/Kyrix) - Interactive details-on-demand data visualizations at scale.
* [Dash Deck](https://github.com/plotly/dash-deck) - Library for building 3D interactive maps and graphical visualizations directly in Dash.
* [Apache ECharts](https://github.com/apache/incubator-echarts) - Powerful, interactive charting and data visualization library for browser. ([Web](https://echarts.apache.org/en/index.html))
* [plotly.py](https://github.com/plotly/plotly.py) - Interactive graphing library for Python (includes Plotly Express).
* [Gnuplot 5 book](https://alogus.com/g5script/gnuplot5/)
* [Pandas Profiling](https://github.com/pandas-profiling/pandas-profiling) - Create HTML profiling reports from pandas DataFrame objects.
* [ImPlot](https://github.com/epezent/implot) - Immediate mode, GPU accelerated plotting library for Dear ImGui.
* [Edward Tufte: Seeing with Fresh book (2020)](https://www.edwardtufte.com/tufte/seeing-with-fresh-eyes)
* [Why We're Building Observable (2020)](https://observablehq.com/@observablehq/why-were-building-observable) ([HN](https://news.ycombinator.com/item?id=25161409))
* [Global population density graphic (2020)](https://twitter.com/undertheraedar/status/1332733136715780096)
* [Lux](https://github.com/lux-org/lux) - Python API for Intelligent Visual Data Discovery.
* [ETE (Environment for Tree Exploration)](https://github.com/etetoolkit/ete) - Python programming toolkit that assists in the automated manipulation, analysis and visualization of phylogenetic trees. ([Web](http://etetoolkit.org/))
* [From data to Viz](https://www.data-to-viz.com/) - Find the graphic you need. ([Code](https://github.com/holtzy/data_to_viz))
* [hvPlot](https://github.com/holoviz/hvplot) - High-level plotting API for pandas, dask, xarray, and networkx built on HoloViews. ([Docs](https://hvplot.holoviz.org/))
* [HoloViz](https://holoviz.org/) - High-level tools to simplify visualization in Python. ([GitHub](https://github.com/holoviz))
* [tikzplotlib](https://github.com/nschloe/tikzplotlib) - Convert matplotlib figures to TikZ/PGFplots for smooth integration into LaTeX.
* [Chartify](https://github.com/spotify/chartify) - Python library that makes it easy for data scientists to create charts.
* [G6](https://github.com/antvis/G6) - Graph Visualization Framework in TypeScript.
* [Ask HN: What low-code “dashboarding“ SaaS would you recommend in 2021?](https://news.ycombinator.com/item?id=25579379)
* [Datashader](https://github.com/holoviz/datashader) - Quickly and accurately render even the largest data. ([Docs](https://datashader.org/))
* [VisPy](https://github.com/vispy/vispy) - Interactive scientific visualization in Python. ([Web](http://vispy.org/))
* [ChartSeer](https://github.com/jeffjianzhao/ChartSeer) - Visualization tool that helps analysts steer their exploratory visual analysis (EVA) through dynamic visualization and recommendation of charts.
* [dygraphs](https://github.com/danvk/dygraphs) - Interactive visualizations of time series using JavaScript and the HTML canvas tag. ([Web](https://dygraphs.com/))
* [Raincloud Plots](https://github.com/RainCloudPlots/RainCloudPlots) - Code and tutorials to visualise your data that is both beautiful *and* statistically valid.
* [Data Visualization and the Modern Imagination](https://exhibits.stanford.edu/dataviz/) ([HN](https://news.ycombinator.com/item?id=25916646))
* [Awesome visualization research](https://github.com/mathisonian/awesome-visualization-research)
