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scraplingvsrvest

BSD-3-Clause 7 2 36,206
397.4 thousand (month) Aug 01 2024 0.4.5(2026-04-07 04:22:27 ago)
1,517 1 38 MIT
Nov 22 2014 534.7 thousand (month) 1.0.5(2024-02-12 21:10:00 ago)

Scrapling is an adaptive web scraping framework for Python that introduces "self-healing" selectors — selectors that can track and find elements even when the website's DOM structure changes. This solves one of the biggest maintenance headaches in web scraping: broken selectors after website updates.

Key features include:

  • Self-healing selectors Scrapling uses smart element matching that can identify target elements even after the page structure changes. It builds a fingerprint of the element based on multiple attributes (text, position, siblings, attributes) and uses fuzzy matching to relocate it.
  • Multiple parsing backends Supports different parsing engines including lxml (fast) and a custom engine, allowing you to choose the right balance of speed and features.
  • Scrapy-like Spider API Provides a familiar Spider class pattern for organizing crawling logic, similar to Scrapy but with the added benefit of adaptive selectors.
  • CSS and XPath selectors Full support for CSS selectors and XPath, plus the adaptive matching system on top.
  • Type hints and modern Python Built with full type annotations and 92% test coverage for reliability.
  • Async support Supports asynchronous crawling for efficient concurrent scraping.

Scrapling gained massive traction in 2025 as one of the most starred new Python scraping libraries. It is particularly useful for scraping targets that frequently update their HTML structure, where traditional selector-based scrapers would break.

rvest is a popular R library for web scraping and parsing HTML and XML documents. It is built on top of the xml2 and httr libraries and provides a simple and consistent API for interacting with web pages.

One of the main advantages of using rvest is its simplicity and ease of use. It provides a number of functions that make it easy to extract information from web pages, even for those who are not familiar with web scraping. The html_nodes and html_node functions allow you to select elements from an HTML document using CSS selectors, similar to how you would select elements in JavaScript.

rvest also provides functions for interacting with forms, including html_form, set_values, and submit_form functions. These functions make it easy to navigate through forms and submit data to the server, which can be useful when scraping sites that require authentication or when interacting with dynamic web pages.

rvest also provides functions for parsing XML documents. It includes xml_nodes and xml_node functions, which also use CSS selectors to select elements from an XML document, as well as xml_attrs and xml_attr functions to extract attributes from elements.

Another advantage of rvest is that it provides a way to handle cookies, so you can keep the session alive while scraping a website, and also you can handle redirections with handle_redirects

Highlights


css-selectorsxpathfastpopular

Example Use


```python from scrapling import Fetcher, StealthFetcher, PlayWrightFetcher # Simple fetching with adaptive parsing fetcher = Fetcher() page = fetcher.get("https://example.com/products") # CSS selectors work as expected products = page.css(".product-card") for product in products: name = product.css_first(".name").text() price = product.css_first(".price").text() print(f"{name}: {price}") # Adaptive selector - finds the element even if DOM changes # Uses element fingerprinting for resilient matching element = page.find("Product Title", auto_match=True) # Stealth fetching with anti-bot bypass stealth = StealthFetcher() page = stealth.get("https://protected-site.com") # Playwright-based fetching for JS-rendered pages pw = PlayWrightFetcher() page = pw.get("https://spa-example.com", headless=True) ```
```r library("rvest") # Rvest can use basic HTTP client to download remote HTML: tree <- read_html("http://webscraping.fyi/lib/r/rvest") # or read from string: tree <- read_html(' ') # to parse HTML trees with rvest we use r pipes (the %>% symbol) and html_element function: # we can use css selectors: print(tree %>% html_element(".products>a") %>% html_text()) # "[1] "\nCat Food\nDog Food\n"" # or XPath: print(tree %>% html_element(xpath="//div[@class='products']/a") %>% html_text()) # "[1] "\nCat Food\nDog Food\n"" # Additionally rvest offers many quality of life functions: # html_text2 - removes trailing and leading spaces and joins values print(tree %>% html_element("div") %>% html_text2()) # "[1] "Cat Food Dog Food"" # html_attr - selects element's attribute: print(tree %>% html_element("div") %>% html_attr('class')) # "products" ```

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