GET numpy.org HTTP 200143 ms14.7 KB$0.000073 captured Aug 7, 2026
numpy.org, as data
One API turns any numpy.org page into markdown, structured JSON, its link graph or a screenshot, and crawls the whole site the same way. The panel shows the real response we captured.
- Render mode
- Plain HTTP, no browser needed
- Response
- HTTP 200 in 143 ms
- Page size
- 14.7 KB of markdown, 197 lines
- Fields
- Business Name, Address, Phone, Category and 1 more
- Captured
- Aug 7, 2026
Free balance on signup, no card. This numpy.org page cost $0.000073 to fetch.
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, AltairWhat Spider does on numpy.org
Same key, five endpoints. The numbers under a cell were measured on this page.
Page to markdown
Clean text for RAG and LLM context, boilerplate removed. The lane that runs without a key.
143 ms · $0.000073Page to JSON
Spider reads the page and names the fields. Pass your own schema when you need exact keys.
Rendered capture
Real Chromium, full-page PNG. The same call also returns the rendered HTML.
The call behind the panel
This request produced the response above. Paste it with your key and you get the same bytes.
curl -X POST https://api.spider.cloud/scrape \
-H "Authorization: Bearer $SPIDER_API_KEY" \
-H "Content-Type: application/json" \
-d '{"url": "https://numpy.org/", "return_format": "markdown"}'from spider import Spider
app = Spider()
params = {"return_format": "markdown"}
page = app.scrape_url("https://numpy.org/", params=params)
print(page[0]["content"])import { Spider } from "@spider-cloud/spider-client";
const app = new Spider();
const [page] = await app.scrapeUrl("https://numpy.org/", {
return_format: "markdown",
});
console.log(page.content);What numpy.org costs
Multiplied from the measured 14.7 KB page. Estimates round to the cent.
1 GB of transfer costs $1, plus $0.001 per CPU minute, and failed requests cost $0. Crawling daily? The Unlimited plan is a flat monthly rate.
Point this at the rest of numpy.org.
The capture above took 143 ms and cost $0.000073. The same call takes any URL on numpy.org.