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ralgervsrequests

MIT 3 1 153
1.2 thousand (month) Dec 22 2019 2.2.4(3 years ago)
51,138 30 271 Apache 2.0
2.31.0(10 months ago) Feb 14 2011 408.5 million (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.

The requests package is a popular library for making HTTP requests in Python. It provides a simple, easy-to-use API for sending HTTP/1.1 requests, and it abstracts away many of the low-level details of working with HTTP. One of the key features of requests is its simple API. You can send a GET request with a single line of code:

import requests
response = requests.get('https://webscraping.fyi/lib/requests/')
requests makes it easy to send data along with your requests, including JSON data and files. It also automatically handles redirects and cookies, and it can handle both basic and digest authentication. Additionally, it's also providing powerful functionality for handling exceptions, managing timeouts and session, also handling a wide range of well-known content-encoding types. One thing to keep in mind is that requests is a synchronous library, which means that your program will block (stop execution) while waiting for a response. In some situations, this may not be desirable, and you may want to use an asynchronous library like httpx or aiohttp. You can install requests package via pip package manager:
pip install requests
requests is a very popular library and has a large and active community, which means that there are many third-party libraries that build on top of it, and it has a wide range of usage.

Highlights


syncease-of-useno-http2no-asyncpopular

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
import requests

# get request:
response = requests.get("http://webscraping.fyi/")
response.status_code
200
response.text
"text"
response.content
b"bytes"

# requests can automatically convert json responses to Python dictionaries:
response = requests.get("http://httpbin.org/json")
print(response.json())
{'slideshow': {'author': 'Yours Truly', 'date': 'date of publication', 'slides': [{'title': 'Wake up to WonderWidgets!', 'type': 'all'}, {'items': ['Why <em>WonderWidgets</em> are great', 'Who <em>buys</em> WonderWidgets'], 'title': 'Overview', 'type': 'all'}], 'title': 'Sample Slide Show'}}

# for POST request it can ingest Python's dictionaries as JSON:
response = requests.post("http://httpbin.org/post", json={"query": "hello world"})
# or form data:
response = requests.post("http://httpbin.org/post", data={"query": "hello world"})

# Session object can be used to automatically keep track of cookies and set defaults:
from requests import Session
s = Session()
s.headers = {"User-Agent": "webscraping.fyi"}
s.get('http://httpbin.org/cookies/set/foo/bar')
print(s.cookies['foo'])
'bar'
print(s.get('http://httpbin.org/cookies').json())
{'cookies': {'foo': 'bar'}}

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