selectolaxvsralger
selectolax is a fast and lightweight library for parsing HTML and XML documents in Python. It is designed to be a drop-in replacement for the popular BeautifulSoup library, with significantly faster performance.
selectolax uses a Cython-based parser to quickly parse and navigate through HTML and XML documents. It provides a simple and intuitive API for working with the document's structure, similar to BeautifulSoup.
To use selectolax, you first need to install it via pip by running pip install selectolax``.
Once it is installed, you can use theselectolax.html.fromstring()function to parse an HTML document and create a selectolax object.
For example:
selectolax.html.fromstring()from selectolax.parser import HTMLParser
html_string = "<html><body>Hello, World!</body></html>"
root = HTMLParser(html_string).root
print(root.tag) # html
with file-like objects, bytes or file paths,
as well asselectolax.xml.fromstring()`` for parsing XML documents.
Once you have a selectolax object, you can use the select() method to search for elements in the document using CSS selectors,
similar to BeautifulSoup. For example:
body = root.select("body")[0]
print(body.text()) # "Hello, World!"
Like BeautifulSoups find and find_all methods selectolax also supports searching using the search()`` method, which returns the first matching element,
and thesearch_all()`` method, which returns all matching elements.
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 selectolax.parser import HTMLParser
html_string = "<html><body>Hello, World!</body></html>"
root = HTMLParser(html_string).root
print(root.tag) # html
# use css selectors:
body = root.select("body")[0]
print(body.text()) # "Hello, World!"
# find first matching element:
body = root.search("body")
print(body.text()) # "Hello, World!"
# or all matching elements:
html_string = "<html><body><p>paragraph1</p><p>paragraph2</p></body></html>"
root = HTMLParser(html_string).root
for el in root.search_all("p"):
print(el.text())
# will print:
# paragraph 1
# paragraph 2
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