Extract data with CSS selectors

When you know exactly which elements you want, send a map of names to CSS selectors with css_extractor and Zenrows returns those values as JSON. For common data such as tables, links, emails or headings, outputs does it with no selectors at all. And when you’d rather keep your own parser, fetch the HTML and run BeautifulSoup or Cheerio on it. Uses: Fetch with css_extractor, outputs and Adaptive Stealth Mode (mode=auto)

Ask your AI agent

With the Zenrows MCP server connected to Claude, Cursor or any MCP client, paste this prompt. Your agent picks the right Zenrows tools and settings on its own.

Use Zenrows to read https://www.scrapingcourse.com/ecommerce/ with these CSS selectors:
names from .product-name, prices from .product .price, and links from the href of
a.woocommerce-LoopProduct-link. Return a table of name, price and URL.

Extract elements with css_extractor

Send css_extractor as a JSON object: each key is the name you want in the output, each value is a CSS selector. Fetch applies the selectors to the rendered page and returns JSON instead of HTML.

import json
import requests

css_extractor = {
    "title": "h1",
    "names": ".product-name",
    "prices": ".product .price",
    "links": "a.woocommerce-LoopProduct-link @href",
    "next_page": "a.next @href",
}

params = {
    "apikey": "YOUR_ZENROWS_API_KEY",
    "url": "https://www.scrapingcourse.com/ecommerce/",
    "mode": "auto",
    "css_extractor": json.dumps(css_extractor),
}

response = requests.get("https://api.zenrows.com/v1/", params=params, timeout=180)
response.raise_for_status()
print(json.dumps(response.json(), indent=2))

const cssExtractor = {
  title: "h1",
  names: ".product-name",
  prices: ".product .price",
  links: "a.woocommerce-LoopProduct-link @href",
  next_page: "a.next @href",
};

const params = new URLSearchParams({
  apikey: "YOUR_ZENROWS_API_KEY",
  url: "https://www.scrapingcourse.com/ecommerce/",
  mode: "auto",
  css_extractor: JSON.stringify(cssExtractor),
});

const response = await fetch(`https://api.zenrows.com/v1/?${params}`, {
  signal: AbortSignal.timeout(180_000),
});
if (!response.ok) throw new Error(`${response.status}: ${await response.text()}`);

console.log(JSON.stringify(await response.json(), null, 2));

curl "https://api.zenrows.com/v1/?apikey=YOUR_ZENROWS_API_KEY&url=https%3A%2F%2Fwww.scrapingcourse.com%2Fecommerce%2F&mode=auto&css_extractor=%7B%22title%22%3A%22h1%22%2C%22names%22%3A%22.product-name%22%2C%22prices%22%3A%22.product%20.price%22%2C%22links%22%3A%22a.woocommerce-LoopProduct-link%20%40href%22%2C%22next_page%22%3A%22a.next%20%40href%22%7D"

zenrows extract https://www.scrapingcourse.com/ecommerce/ --css '{"names":".product-name","prices":".product .price","next_page":"a.next @href"}'

In cURL, URL-encode the JSON. The Python and Node.js clients encode it for you. In the CLI, zenrows extract --css sends css_extractor and --outputs sends outputs.

Example output

Trimmed to three products:

{
  "title": "Shop",
  "names": ["Abominable Hoodie", "Adrienne Trek Jacket", "Aeon Capri"],
  "prices": ["$69.00", "$57.00", "$48.00"],
  "links": [
    "https://www.scrapingcourse.com/ecommerce/product/abominable-hoodie/",
    "https://www.scrapingcourse.com/ecommerce/product/adrienne-trek-jacket/",
    "https://www.scrapingcourse.com/ecommerce/product/aeon-capri/"
  ],
  "next_page": [
    "https://www.scrapingcourse.com/ecommerce/page/2/",
    "https://www.scrapingcourse.com/ecommerce/page/2/"
  ]
}

How selectors map to JSON

You write	You get
`"title": "h1"`	The element’s text. One match returns a string.
`"names": ".product-name"`	Every match, as an array of strings.
`"links": "a.woocommerce-LoopProduct-link @href"`	The `href` attribute instead of the text. Use `@src`, `@content`, `@data-id` or any attribute name the same way.
`"next_page": "a.next @href"`	An array when the page repeats the element, as this pagination does at the top and bottom. Take the first item.

Any CSS selector works: attribute filters such as a[href*='/product/'], combinators such as .product .price, and pseudo-classes such as li:first-child. Test a selector in your browser’s console with document.querySelectorAll(...) before you send it.

Get common data with outputs

For data every site shares, skip selectors. outputs takes a comma-separated list of filters: emails, phone_numbers, headings, images, audios, videos, links, menus, hashtags, metadata, tables and favicon, or * for all of them. Tables come back as rows keyed by their column headings.

import json
import requests

params = {
    "apikey": "YOUR_ZENROWS_API_KEY",
    "url": "https://www.scrapingcourse.com/table-parsing",
    "mode": "auto",
    "outputs": "headings,tables",
}

response = requests.get("https://api.zenrows.com/v1/", params=params, timeout=180)
response.raise_for_status()

data = response.json()
print(data["headings"])
print(json.dumps(data["tables"][0]["Content"][:2], indent=2))

curl "https://api.zenrows.com/v1/?apikey=YOUR_ZENROWS_API_KEY&url=https%3A%2F%2Fwww.scrapingcourse.com%2Ftable-parsing&mode=auto&outputs=headings,tables"

zenrows extract https://www.scrapingcourse.com/table-parsing --outputs headings,tables

Output (trimmed)

{
  "headings": ["Table Parsing Challenge", "Product Catalog", "Challenge"],
  "tables": [
    {
      "Dimensions": { "Rows": 15, "Cols": 5, "Headings": true },
      "Headings": ["Product ID", "Name", "Category", "Price", "In Stock"],
      "Content": [
        { "Product ID": "001", "Name": "Laptop", "Category": "Electronics", "Price": "$999.99", "In Stock": "Yes" },
        { "Product ID": "002", "Name": "Smartphone", "Category": "Electronics", "Price": "$599.99", "In Stock": "Yes" }
      ]
    }
  ]
}

Using external libraries

When you need parsing logic of your own, such as reading one product card at a time so each name stays paired with its price, request the HTML and parse it with your favorite library. Zenrows still handles rendering and anti-bot protection.

Python with BeautifulSoup

Python (Requests)

# pip install requests beautifulsoup4
import requests
from bs4 import BeautifulSoup

params = {
    "apikey": "YOUR_ZENROWS_API_KEY",
    "url": "https://www.scrapingcourse.com/ecommerce/",
    "mode": "auto",
}

response = requests.get("https://api.zenrows.com/v1/", params=params, timeout=180)
response.raise_for_status()

soup = BeautifulSoup(response.text, "html.parser")
products = [
    {
        "name": item.select_one(".product-name").get_text(strip=True),
        "price": item.select_one(".price").get_text(strip=True),
        "url": item.select_one("a.woocommerce-LoopProduct-link")["href"],
    }
    for item in soup.select("li.product")
]

print(len(products), products[:2])

Output

16 [{'name': 'Abominable Hoodie', 'price': '$69.00', 'url': 'https://www.scrapingcourse.com/ecommerce/product/abominable-hoodie/'}, {'name': 'Adrienne Trek Jacket', 'price': '$57.00', 'url': 'https://www.scrapingcourse.com/ecommerce/product/adrienne-trek-jacket/'}]

JavaScript with Cheerio

Node.js

// npm install cheerio
import * as cheerio from "cheerio";

const params = new URLSearchParams({
  apikey: "YOUR_ZENROWS_API_KEY",
  url: "https://www.scrapingcourse.com/ecommerce/",
  mode: "auto",
});

const response = await fetch(`https://api.zenrows.com/v1/?${params}`, {
  signal: AbortSignal.timeout(180_000),
});
if (!response.ok) throw new Error(`${response.status}: ${await response.text()}`);

const $ = cheerio.load(await response.text());
const products = $("li.product")
  .map((_, item) => ({
    name: $(item).find(".product-name").text().trim(),
    price: $(item).find(".price").text().trim(),
    url: $(item).find("a.woocommerce-LoopProduct-link").attr("href"),
  }))
  .get();

console.log(products.length, products.slice(0, 2));

Which method to choose

Method	Reach for it when
Extract (`extract=auto`)	You want a page’s data as JSON with no selectors to maintain.
`css_extractor`	You know the exact elements, on one site, and want JSON back.
`outputs`	You need tables, links, emails, images or other common data from any page.
BeautifulSoup or Cheerio	You need custom logic, such as grouping fields per item or cleaning values as you parse.

Customize it

Change	How
Wait for an element before extracting	Use `js_render=true` with `wait_for=.product` instead of `mode=auto`, see Wait for selector
Content from a specific country	Add `proxy_country=us`, see Proxy country
Every page of a paginated list	Follow `next_page`, or collect the page URLs with Crawl
The same selectors on thousands of URLs	Put `css_extractor` in a Batch job’s `zenrows_params`, see Scrape a list of URLs
Test selectors visually	Try them in the Request Builder, which also generates code

Troubleshooting

A key comes back as an empty string: the selector matched nothing in the final HTML. Check it against the page with your browser’s console, and if the element loads late, switch to js_render=true with wait_for set to that selector.
You get HTML instead of JSON: the css_extractor value isn’t valid JSON or isn’t encoded. Build it with json.dumps or JSON.stringify, and URL-encode it in cURL.
Values from different products don’t line up: separate arrays lose the grouping when one card lacks a field. Parse one card at a time with BeautifulSoup or Cheerio.
413 response: the page is very large. css_extractor or outputs returns just the data you ask for and keeps responses small.

Get structured JSON from any page

Let Extract find the fields for you, no selectors needed.

Crawl a website from a start URL

Collect every product URL, then extract each page.

Scrape a list of URLs

Apply the same selectors to thousands of pages in one job.

Turn any web page into LLM-ready markdown

Hand a whole page to an LLM as clean markdown.

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Extract data with CSS selectors

Ask your AI agent

Extract elements with css_extractor

Example output

How selectors map to JSON

Get common data with outputs

Using external libraries

Python with BeautifulSoup

JavaScript with Cheerio

Which method to choose

Customize it

Troubleshooting

Get structured JSON from any page

Crawl a website from a start URL

Scrape a list of URLs

Turn any web page into LLM-ready markdown

​Ask your AI agent

​Extract elements with css_extractor

​Example output

​How selectors map to JSON

​Get common data with outputs

​Using external libraries

​Python with BeautifulSoup

​JavaScript with Cheerio

​Which method to choose

​Customize it

​Troubleshooting

​Related recipes

Get structured JSON from any page

Crawl a website from a start URL

Scrape a list of URLs

Turn any web page into LLM-ready markdown

Ask your AI agent

Extract elements with css_extractor

Example output

How selectors map to JSON

Get common data with outputs

Using external libraries

Python with BeautifulSoup

JavaScript with Cheerio

Which method to choose

Customize it

Troubleshooting

Related recipes