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botasaurusvsrvest

MIT 52 5 4,321
35.5 thousand (month) Oct 01 2023 4.0.97(2026-01-06 07:45:54 ago)
1,517 1 38 MIT
Nov 22 2014 534.7 thousand (month) 1.0.5(2024-02-12 21:10:00 ago)

Botasaurus is an all-in-one Python web scraping framework that combines browser automation, anti-detection, and scaling features into a single package. It aims to simplify the entire web scraping workflow from development to deployment.

Key features include:

  • Anti-detect browser Ships with a stealth-patched browser that passes common bot detection tests. Automatically handles fingerprinting, user agent rotation, and other anti-detection measures.
  • Decorator-based API Uses Python decorators (@browser, @request) to define scraping tasks, making code clean and easy to organize.
  • Built-in parallelism Easy parallel execution of scraping tasks across multiple browser instances with configurable concurrency.
  • Caching Built-in caching layer to avoid re-scraping pages during development and debugging.
  • Profile persistence Can save and reuse browser profiles (cookies, localStorage) across scraping sessions for maintaining login state.
  • Output handling Automatic output to JSON, CSV, or custom formats with built-in data filtering.
  • Web dashboard Includes a web UI for monitoring scraping progress, viewing results, and managing tasks.

Botasaurus is designed for developers who want a batteries-included framework that handles anti-detection automatically, without needing to manually configure stealth settings or manage browser fingerprints.

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


anti-detectstealthlarge-scale

Example Use


```python from botasaurus.browser import browser, Driver from botasaurus.request import request, Request # Browser-based scraping with anti-detection @browser(parallel=3, cache=True) def scrape_products(driver: Driver, url: str): driver.get(url) # Wait for content to load driver.wait_for_element(".product-list") # Extract product data products = [] for el in driver.select_all(".product-card"): products.append({ "name": el.select(".product-name").text, "price": el.select(".product-price").text, "url": el.select("a").get_attribute("href"), }) return products # HTTP-based scraping (no browser needed) @request(parallel=5, cache=True) def scrape_api(req: Request, url: str): response = req.get(url) return response.json() # Run the scraper results = scrape_products( ["https://example.com/page/1", "https://example.com/page/2"] ) ```
```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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