requests-htmlvsralger
requests-html is a Python package that allows you to easily make HTTP requests and parse the HTML content of web pages. It is built on top of the popular requests package and uses the html parser from the lxml library, which makes it fast and efficient. This package is designed to provide a simple and convenient API for web scraping, and it supports features such as JavaScript rendering, CSS selectors, and form submissions.
It also offers a lot of functionalities such as cookie, session, and proxy support, which makes it an easy-to-use package for web scraping and web automation tasks.
In short requests-html offers:
- Full JavaScript support!
- CSS Selectors (a.k.a jQuery-style, thanks to PyQuery).
- XPath Selectors, for the faint of heart.
- Mocked user-agent (like a real web browser).
- Automatic following of redirects.
- Connection–pooling and cookie persistence.
- The Requests experience you know and love, with magical parsing abilities.
- Async Support
ralger is a small web scraping framework for R based on rvest and xml2.
It's goal to simplify basic web scraping and it provides a convenient and easy to use API.
It offers functions for retrieving pages, parsing HTML using CSS selectors, automatic table parsing and auto link, title, image and paragraph extraction.
Example Use
from requests_html import HTMLSession
session = HTMLSession()
r = session.get('https://www.example.com')
# print the HTML content of the page
print(r.html.html)
# use CSS selectors to find specific elements on the page
title = r.html.find('title', first=True)
print(title.text)
library("ralger")
url <- "http://www.shanghairanking.com/rankings/arwu/2021"
# retrieve HTML and select elements using CSS selectors:
best_uni <- scrap(link = url, node = "a span", clean = TRUE)
head(best_uni, 5)
#> [1] "Harvard University"
#> [2] "Stanford University"
#> [3] "University of Cambridge"
#> [4] "Massachusetts Institute of Technology (MIT)"
#> [5] "University of California, Berkeley"
# ralger can also parse HTML attributes
attributes <- attribute_scrap(
link = "https://ropensci.org/",
node = "a", # the a tag
attr = "class" # getting the class attribute
)
head(attributes, 10) # NA values are a tags without a class attribute
#> [1] "navbar-brand logo" "nav-link" NA
#> [4] NA NA "nav-link"
#> [7] NA "nav-link" NA
#> [10] NA
#
# ralger can automatically scrape tables:
data <- table_scrap(link ="https://www.boxofficemojo.com/chart/top_lifetime_gross/?area=XWW")
head(data)
#> # A tibble: 6 × 4
#> Rank Title `Lifetime Gross` Year
#> <int> <chr> <chr> <int>
#> 1 1 Avatar $2,847,397,339 2009
#> 2 2 Avengers: Endgame $2,797,501,328 2019
#> 3 3 Titanic $2,201,647,264 1997
#> 4 4 Star Wars: Episode VII - The Force Awakens $2,069,521,700 2015
#> 5 5 Avengers: Infinity War $2,048,359,754 2018
#> 6 6 Spider-Man: No Way Home $1,901,216,740 2021