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parsel is a library for parsing HTML and XML using selectors, similar to beautifulsoup. It is built on top of the lxml library and allows for easy extraction of data from HTML and XML files using selectors, similar to how you would use CSS selectors in web development. It is a light-weight library which is specifically designed for web scraping and parsing, so it is more efficient and faster than beautifulsoup in some use cases.

Some of the key features of parsel include:

  • CSS selector & XPath selector support:
    Two most common html parsing path languages are both supported in parsel. This allows selecting attributes, tags, text and complex matching rules that use regular expressions or XPath functions.
  • Modifying data:
    parsel allows you to modify the contents of an element, remove elements or add new elements to a document.
  • Support for both HTML and XML:
    parsel supports both HTML and XML documents and you can use the same selectors for both formats.

It is easy to use and less verbose than beautifulsoup, so it's quite popular among the developers who are working with Web scraping projects and parse data from large volume of web pages.

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 parsel import Selector

# this is our HTML page:
html = """
  <title>Hello World!</title>
  <div id="product">
    <h1>Product Title</h1>
    <p>paragraph 1</p>
    <span class="price">$10</span>

selector = Selector(html)

# we can use CSS selectors:
selector.css("#product .price::text").get()

# or XPath:

# or get all matching elements:
print(selector.css("#product p::text").getall())
["paragraph 1", "paragraph2"]

# parsel also comes with utility methods like regular expression parsing:

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

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