cssselectvsralger
cssselect is a BSD-licensed Python library to parse CSS3 selectors and translate them to XPath 1.0 expressions.
XPath 1.0 expressions can be used in lxml or another XPath engine to find the matching elements in an XML or HTML document.
cssselect is used by other popular Python packages like parsel and scrapy but can also be used on it's own to generate
valid XPath 1.0 expressions for parsing HTML and XML documents in other tools.
Note that because XPath selectors are more powerful than CSS selectors this translation is only possible one way. Converting XPath to CSS selectors is impractical and not supported by cssselect.
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 cssselect import GenericTranslator, SelectorError
translator = GenericTranslator()
try:
expression = translator.css_to_xpath('div.content')
print(expression)
'descendant-or-self::div[@class and contains(concat(' ', normalize-space(@class), ' '), ' content ')]'
except SelectorError as e:
print(f'Invalid selector {e}')
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