Jupyter Scraper
Spider read jupyter.org in 235 ms without a browser and returned 93 lines of clean markdown, including sections like "Interactive output", "Big data integration" and "Pluggable authentication".
The Jupyter Foundation call for funding proposals is now open.### Interactive outputYour code can produce rich, interactive output: HTML, images, videos, LaTeX, and custom MIME types.### Big data integrationLeverage big data tools, such as Apache Spark, from Python, R, and Scala. Explore that same data with pandas, scikit-learn, ggplot2, and TensorFlow.A multi-user version of the notebook designed for companies, classrooms and research labs### Pluggable authenticationManage users and authentication with PAM, OAuth or integrate with your own directory service system.### Centralized deploymentDeploy the Jupyter Notebook to thousands of users in your organization on centralized infrastructure on- or off-site.### Container friendlyUse Docker and Kubernetes to scale your deployment, isolate user processes, and simplify software installation.### Code meets dataDeploy the Notebook next to your data to provide unified software management and data access within your organization.Learn more about JupyterHub## Voilà: Share your resultsVoilà helps communicate insights by transforming notebooks into secure, stand-alone web applications that you can customize and share.### Currently in use at## Open Standards for Interactive ComputingProject Jupyter promotes open standards that third-party developers can leverage to build customized applications. Think HTML and CSS for interactive computing on the web.### Notebook Document FormatJupyter Notebooks are an open document format based on JSON. They contain a complete record of the user's sessions and include code, narrative text, equations, and rich output.### Interactive Computing ProtocolThe Notebook communicates with computational Kernels using the Interactive Computing Protocol, an open network protocol based on JSON data over ZMQ, and WebSockets.### The KernelThe same call, in code.
The capture above came back as markdown. These examples add a key, so you get browser rendering, proxies, and concurrency on jupyter.org.
import { SpiderBrowser } from "spider-browser";
const spider = new SpiderBrowser({
apiKey: process.env.SPIDER_API_KEY!,
});
await spider.connect();
const page = spider.page!;
await page.goto("https://jupyter.org");
// No selectors, no schema. Spider reads the page and names the fields.
const data = await page.scrape();
console.log(data);
await spider.close();import { Spider } from "@spider-cloud/spider-client";
const spider = new Spider({ apiKey: process.env.SPIDER_API_KEY! });
const result = await spider.scrapeUrl("https://www.jupyter.org", {
return_format: "markdown",
});
console.log(result);Ready for volume? Get an API key →
Fields you can pull.
Spider names these from the page. The capture above came back as markdown; the same
call with return_format: "json" returns them as keys.
What jupyter.org costs to scrape.
The capture above cost $0.000064 to fetch. Pricing is $1 per GB of pre-transformation bandwidth plus $0.001 per CPU minute, so a page like this one lands at a fraction of a cent. Failed requests are billed at $0.
- Free balance on signup
- No card required to test
- Balance never expires
Run it keyless, no account
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Start scraping jupyter.org.
You already have the call. A key raises the rate limit and turns on browser rendering, proxies, and concurrency. Balance never expires, and top-ups go through secure checkout.