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.
Jupyter might be a bit more popular than D3.js. We know about 205 links to it since March 2021 and only 159 links to D3.js. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
JupyterLab: JupyterLab is an interactive development environment that allows you to create and share documents containing live code, equations, visualizations, and narrative text. It's particularly well-suited for data science and research-oriented projects. - Source: dev.to / 9 days ago
Jupyter Lab web-based interactive development environment. - Source: dev.to / 20 days ago
Choosing IDE: Selecting a suitable Integrated Development Environment (IDE) is crucial for efficient coding. Consider popular options such as PyCharm, Visual Studio Code, or Jupyter Notebook. Install your preferred IDE and ensure it's configured to work with Python. - Source: dev.to / 15 days ago
Jupyter Notebooks is very popular among data people specially Python users. So, I tried to find a way to run the Groovy kernel inside a Jupyter Notebook, and to my surprise, I found a way, BeakerX! - Source: dev.to / 2 months ago
Note. Nowadays, there are many flavors of notebooks (Jupyter, VSCode, Databricks, etc.), but they’re all built on top of IPython. Therefore, the Magics developed should be reusable across environments. - Source: dev.to / 2 months 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 / 18 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
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