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For a straightforward scraper, fetch a page with an HTTP client and parse its HTML; use a crawler framework when you need crawl management, and browser automation when a page depends on rendering or interaction in a browser. Before collecting data, check the site’s rules, keep requests bounded, and treat every response as untrusted input. A robots.txt file gives crawler instructions—it does not grant access.
How do I scrape a website?
First check whether the site offers an API, export, feed, or documented way to access the data. If it does, prefer that route when it meets your needs. For ordinary static HTML, separate the work into two jobs: retrieve the response, then parse the fields you need.
- Define the scope. Identify the target pages, exact fields, intended use, and how often you need to collect them. Avoid gathering unrelated data.
- Check the rules. Review the site’s terms and access restrictions, applicable privacy obligations, and its robots.txt rules for your crawler’s user-agent. These checks are distinct; a robots.txt file is not authorization.
- Fetch conservatively. Make requests at a bounded rate and concurrency, identify your crawler clearly, and handle errors without repeatedly retrying in a way that burdens the site.
- Parse and validate. Extract only the necessary fields, then normalize and validate values before storing them. Keep retrieval time and source provenance where useful.
- Monitor and reassess. Track failures and page changes. Stop or revisit the project if the site blocks access, signals distress, or the basis for collection changes.
Python example: Requests and Beautiful Soup
Requests performs the HTTP request; Beautiful Soup parses the returned HTML. The following example fetches one page and prints its title. Replace the URL and extraction logic with a permitted target and the fields your project needs.
import requests
from bs4 import BeautifulSoup
url = "https://example.com/"
response = requests.get(
url,
headers={"User-Agent": "ExampleResearchBot/1.0 (contact: you@example.com)"},
timeout=20,
)
response.raise_for_status()
soup = BeautifulSoup(response.text, "html.parser")
title = soup.title.get_text(strip=True) if soup.title else None
print(title)Set a meaningful contact address in the user-agent, handle timeouts and HTTP errors, and inspect the page’s actual response before relying on selectors. Requests and Beautiful Soup documentation: Requests and Beautiful Soup.
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Which web scraping tool should I use?
Choose according to how the site delivers content and how much crawl management the project needs. No one library is best for every task.
| Need | Starting point | Why it fits |
|---|---|---|
| A few pages with data already in the HTTP response | HTTP client plus HTML parser, such as Requests and Beautiful Soup | Separates retrieval from parsing and avoids browser runtime when the response contains the needed data. |
| A recurring or larger crawl needing framework-level request handling | Scrapy | Provides a crawler framework; assess project structure, coordination, operational controls, and security configuration. |
| Pages requiring browser rendering or interaction | Playwright | Automates a browser for workflows that depend on browser behavior, at the cost of additional setup and runtime overhead. |
| Checking robots.txt rules from Python | urllib.robotparser | Exposes robots rule checks; confirm its behavior covers the needs of your crawler. |
Compare options against page rendering, request volume and frequency, pagination, resilience to page changes, data sensitivity, and operational complexity. Official documentation: Scrapy, Playwright for Python, and urllib.robotparser.
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Do I need browser automation?
Not if the HTML response already contains the information you need. Start by inspecting the response from a normal HTTP request. If required content appears only after client-side rendering or you must perform browser interactions, a browser automation tool such as Playwright is a better fit. Its browser setup adds runtime and operational overhead, so use it for browser-dependent behavior rather than as the default for every page.
If you need a rendered screenshot rather than parsed fields, ScreenshotNeo is a website screenshot API and MCP server for developers. It can capture a page as PNG, JPEG, WebP, or PDF; its options include full-page capture, CSS-selector element capture, waits, custom JavaScript, and request blocking. See ScreenshotNeo.
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Or skip the browser setup
For a screenshot, one GET request can return the image or PDF. Example using cURL:
curl -G "https://api.screenshotneo.com/v1/shot" -d access_key=YOUR_API_KEY --data-urlencode url=https://stripe.com -o shot.webpSee the ScreenshotNeo API documentation for parameters and response details. Before capture, it accepts cookie or consent banners as a visitor and removes 60+ known consent platforms, newsletter popups, and chat widgets; each step can be turned off. Bot checks, blank pages, timeouts, failed loads, and cache hits are not billed, and response headers identify the page verdict and billing status. An MCP server offers take_screenshot, get_page_info, and capture_pdf for AI agents and other MCP clients. The free plan includes 1,000 shots per month with no card; paid plans start at $5 for 3,000 shots.
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How should I handle robots.txt?
The Internet Engineering Task Force’s RFC 9309, published in September 2022, standardizes the Robots Exclusion Protocol. It says: “These rules are not a form of access authorization.” Robots.txt communicates crawler instructions; it does not itself grant permission to access a resource.
- Rules are grouped by user-agent. Apply the rules that match your crawler and use the most specific matching path rule; equivalent Allow and Disallow rules favor Allow.
- When retrieval succeeds, parse the file and follow its parseable rules. The standard recommends not using a cached copy for more than 24 hours in ordinary circumstances, unless the file is unreachable.
- A 4xx response makes robots.txt “unavailable”; RFC 9309 says a crawler may access resources in that case. A 5xx response or network failure makes it “unreachable”; the crawler must assume complete disallow while that condition applies. Do not treat all fetch failures alike.
- If an implementation sets a parsing limit, RFC 9309 requires support for at least 500 kibibytes.
Python’s urllib.robotparser can help evaluate rules, but your implementation still needs to retrieve the correct file, handle fetch failures appropriately, and apply the result to the relevant user-agent.
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How do I keep a scraper safe and reliable?
Keep load and scope under control
- Collect only the fields required for the stated purpose and limit crawl scope, concurrency, and request frequency.
- Handle timeouts, status codes, and transient failures deliberately. Avoid unbounded retries.
- Record useful provenance and retrieval timestamps so you can investigate stale, missing, or changed data.
Treat pages as untrusted input
Do not execute fetched scripts or unsafely deserialize content. Validate extracted values before using them, especially if they could influence commands, database queries, or filesystem paths. Apply response-size limits when appropriate: Scrapy warns that constructing a full in-memory tree from a response can use substantial memory for large responses. See Scrapy’s security guidance.
Plan for change
HTML structure and site behavior can change. Make parsing failures visible, validate outputs rather than silently accepting malformed records, and reassess when pages begin returning blocks, errors, or unexpected content. A change in access restrictions or intended use may also require a new review.
Is web scraping legal?
There is no universal answer based only on whether a page is publicly visible. The relevant facts can include jurisdiction, site terms, technical access restrictions, whether personal data is involved, copyright, the purpose of collection, and downstream use. Evaluate the actual project rather than treating robots.txt as a legal permission or prohibition.
The cited EU court material concerns GDPR processing in a specific case; GDPR processing can require a legal basis and remains subject to data-protection requirements. The U.S. Department of Justice material refers to particular hiQ litigation concerning access to a publicly accessible website and the Computer Fraud and Abuse Act. Neither source settles contract, privacy, copyright, or other legal questions for every scraping project. See the Court of Justice of the European Union material and the U.S. Department of Justice statement. For a consequential project, get advice appropriate to the relevant jurisdictions and facts.
Quick Recap
Common scraping problems and fixes
- The extracted fields are empty. Inspect the HTTP response and confirm the data is present in its HTML. If it appears only after browser rendering or interaction, use browser automation rather than assuming a parser failure.
- The robots.txt request fails. Distinguish a 4xx unavailable response from a 5xx or network-level unreachable condition. Under RFC 9309, the latter requires assuming complete disallow while it persists.
- The process uses too much memory. Avoid retaining unnecessary response bodies or building full trees for oversized pages; limit response sizes and parse only the needed content.
- Selectors break after a site update. Validate expected fields and record parsing failures, then inspect the changed page before updating extraction logic.
- Requests are blocked or the site signals distress. Stop and reassess access, rate, scope, and the project’s permission basis rather than increasing request volume.
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