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ralgervsgazpacho

MIT 3 1 153
1.2 thousand (month) Dec 22 2019 2.2.4(3 years ago)
730 1 14 MIT
1.1(3 years ago) Dec 28 2012 9.6 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.

gazpacho is a Python library for scraping web pages. It is designed to make it easy to extract information from a web page by providing a simple and intuitive API for working with the page's structure.

gazpacho uses the requests library to download the page and the lxml library to parse the HTML or XML code. It provides a way to search for elements in the page using CSS selectors, similar to BeautifulSoup.

To use gazpacho, you first need to install it via pip by running pip install gazpacho. Once it is installed, you can use the gazpacho.get() function to download a web page and create a gazpacho object. For example:

from gazpacho import get, Soup

url = "https://en.wikipedia.org/wiki/Web_scraping"
html = get(url)
soup = Soup(html)
print(soup.find('title').text)
You can also use gazpacho.get() with file-like objects, bytes or file paths.

Once you have a gazpacho object, you can use the find() and find_all() methods to search for elements in the page using CSS selectors, similar to BeautifulSoup.

gazpacho also supports searching using the select() method, which returns the first matching element, and the select_all() method, which returns all matching elements.

Example Use


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
from gazpacho import get, Soup

# gazpacho can retrieve web pages
url = "https://webscraping.fyi/"
html = get(url)
# and parse them:
soup = Soup(html)
print(soup.find('title').text)

# search for elements like beautifulsoup:
body = soup.find("div", {"class":"item"})
print(body.text)

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