D3 allows you to bind arbitrary data to a Document Object Model (DOM), and then apply data-driven transformations to the document. For example, you can use D3 to generate an HTML table from an array of numbers. Or, use the same data to create an interactive SVG bar chart with smooth transitions and interaction.
D3 is not a monolithic framework that seeks to provide every conceivable feature. Instead, D3 solves the crux of the problem: efficient manipulation of documents based on data. This avoids proprietary representation and affords extraordinary flexibility, exposing the full capabilities of web standards such as HTML, SVG, and CSS. With minimal overhead, D3 is extremely fast, supporting large datasets and dynamic behaviors for interaction and animation. D3’s functional style allows code reuse through a diverse collection of official and community-developed modules.
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Example notebooks are included in the repo and can be previewed using nbviewer:. Source: over 1 year ago
Nbviewer (https://nbviewer.org/): very easy to use for smaller jupyter notebook that does not require heavy rendering. Source: over 1 year ago
Nbconvert renders everything exactly as it looks in your notebook app into a read-only HTML version and is what GitHub uses for notebooks. Interactive plots from Bokeh, Holoviews, etc can still work if you trust the JS, and since editing notebooks while showing them during a meeting usually doesn't go well, read-only is probably good enough (eager to hear feedback on this point though). The nice thing is that... Source: over 1 year ago
Just as a heads up, I used plotly to generate a lot of the charts, so you'll need to view it from an nbviewer like nbviewer.org. Source: about 2 years ago
I used a lot of plotly not knowing that Github wouldn't show it, so you'll need notebook viewer like nbviewer.org to see some of the charts. Source: about 2 years ago
Yes this was done with a combination of GSAP Scrolltrigger https://gsap.com/docs/v3/Plugins/ScrollTrigger/ and https://d3js.org/. - Source: Hacker News / 15 days ago
d3 - very power visualization library enabling dynamic visualizations. docs. - Source: dev.to / about 1 month ago
Yep, Evidence is doing good work. We were most directly inspired by VitePress; we spent months rewriting both D3’s docs (https://d3js.org) and Observable Plot’s docs (https://observablehq.com/plot) in VitePress, and absolutely loved the experience. But we wanted a tool focused on data apps, dashboards, reports — observability and business intelligence use cases rather than documentation. Compared to Evidence, I’d... - Source: Hacker News / 3 months ago
They are images so it could be any number of things, datawrapper, charts.js, d3.js to name a few options. Source: 5 months ago
I made this interactive visualization that attempts to show the real-time frequency and location of births around the world. A country’s annual births (i.e. The country’s population times its birthrate) were distributed across all of the populated locations in each country, weighted by the population distribution (i.e. More populated areas got a greater fraction of the births). Data Sources and... Source: 5 months ago
Observable - Interactive code examples/posts
Chart.js - Easy, object oriented client side graphs for designers and developers.
Jupyter - Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. Ready to get started? Try it in your browser Install the Notebook.
Plotly - Low-Code Data Apps
RunKit - RunKit notebooks are interactive javascript playgrounds connected to a complete node environment right in your browser. Every npm module pre-installed.
Highcharts - A charting library written in pure JavaScript, offering an easy way of adding interactive charts to your web site or web application