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MIT 3 1 152
1.4 thousand (month) Dec 22 2019 2.2.4(2 years ago)
660 1 1 Apache 2.0
0.4.12(3 months ago) Jun 03 2007 169.0 thousand (month)

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.

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
You can also use `html5_parser.parse()`` with file-like objects, bytes or file paths.

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.

Example Use


url <- ""

# 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 = "",
  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 ="")

#> # 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
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

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