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untanglevsralger

MIT 24 2 608
154.1 thousand (month) Jun 09 2011 1.2.1(1 year, 10 months ago)
152 1 3 MIT
Dec 22 2019 1.1 thousand (month) 2.2.4(3 years ago)

untangle is a simple library for parsing XML documents in Python. It allows you to access data in an XML file as if it were a Python object, making it easy to work with the data in your code.

To use untangle, you first need to install it via pip by running pip install untangle``. Once it is installed, you can use theuntangle.parse()`` function to parse an XML file and create a Python object.

For example:

import untangle

obj = untangle.parse("example.xml")
print(obj.root.element.child)

You can also pass a file-like object or a string containing XML data to the untangle.parse() function. Once you have an untangle object, you can access elements in the XML document using dot notation.

You can also access the attributes of an element by using attrib property, eg. `obj.root.element['attrib_name']`` untangle also supports xpath-like syntax to access the elements, obj.root.xpath("path/to/element")

It also supports iteration over the elements using obj.root.element.children

for child in obj.root.element.children:
    print(child)

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


import untangle

obj = untangle.parse("example.xml")

print(obj.root.element.child)
# access attributes:
print(obj.root.element['attrib_name'])
# use xpath:
element = obj.root.xpath("path/to/element")
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

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