html5-parservsralger
html5-parser is a Python library for parsing HTML and XML documents.
A fast implementation of the HTML 5 parsing spec for Python. Parsing is done in C using a variant of the gumbo parser. The gumbo parse tree is then transformed into an lxml tree, also in C, yielding parse times that can be a thirtieth of the html5lib parse times. That is a speedup of 30x. This differs, for instance, from the gumbo python bindings, where the initial parsing is done in C but the transformation into the final tree is done in python.
It is built on top of the popular lxml library and provides a simple and intuitive API for working with the document's structure.
html5-parser uses the HTML5 parsing algorithm, which is more lenient and forgiving than the traditional XML-based parsing algorithm. This means that it can parse HTML documents with malformed or missing tags and still produce a usable parse tree.
To use html5-parser, you first need to install it via pip by running pip install html5-parser.
Once it is installed, you can use the html5_parser.parse() function to parse an HTML document and create a parse tree. For example:
from html5_parser import parse
html_string = "<html><body>Hello, World!</body></html>"
root = parse(html_string)
print(root.tag) # html
Once you have a parse tree, you can use the find() and findall() methods to search for elements
in the document similar to BeautifulSoup.
html5-parser also supports searching using xpath, similar to lxml.
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 html5_parser import parse
html_string = "<html><body>Hello, World!</body></html>"
root = parse(html_string)
print(root.tag) # html
body = root.find("body")
# or find all
print(body.text) # "Hello, World!"
for el in root.findall("p"):
print(el.text) # "Hello
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