Numpy Scraper
Spider read numpy.org in 143 ms without a browser and returned 197 lines of clean markdown, including the section "ECOSYSTEM".
The fundamental package for scientific computing with Python# ECOSYSTEMNearly every scientist working in Python draws on the power of NumPy.NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use. With this power comes simplicity: a solution in NumPy is often clear and elegant.NumPy's API is the starting point when libraries are written to exploit innovative hardware, create specialized array types, or add capabilities beyond what NumPy provides.Capabilities & Application areasDistributed arrays and advanced parallelism for analytics, enabling performance at scale.NumPy-compatible array library for GPU-accelerated computing with Python.Composable transformations of NumPy programs: differentiate, vectorize, just-in-time compilation to GPU/TPU.Labeled, indexed multi-dimensional arrays for advanced analytics and visualization.NumPy-compatible sparse array library that integrates with Dask and SciPy's sparse linear algebra.Deep learning framework that accelerates the path from research prototyping to production deployment.An end-to-end platform for machine learning to easily build and deploy ML powered applications.A cross-language development platform for columnar in-memory data and analytics.Multi-dimensional arrays with broadcasting and lazy computing for numerical analysis.Manipulate JSON-like data with NumPy-like idioms.Python backend system that decouples API from implementation; unumpy provides a NumPy API.Tensor learning, algebra and backends to seamlessly use NumPy, PyTorch, TensorFlow or CuPy.Accelerated computation for in-memory, on-disk, or remote compressed arrays.NumPy lies at the core of a rich ecosystem of data science libraries. A typical exploratory data science workflow might look like:* **Extract, Transform, Load: **Pandas, Intake, PyJanitor* **Exploratory analysis: **Jupyter, Seaborn, Matplotlib, AltairThe 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 numpy.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://numpy.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.numpy.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 numpy.org costs to scrape.
The capture above cost $0.000073 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.
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- Balance never expires
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Start scraping numpy.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.