curl-cffivsralger
Curl-cffi is a Python library for implementing curl-impersonate which is a
HTTP client that appears as one of popular web browsers like:
- Google Chrome
- Microsoft Edge
- Safari
- Firefox
Unlike requests and httpx which are native Python libraries, curl-cffi uses cURL and inherits it's powerful features
like extensive HTTP protocol support and detection patches for TLS and HTTP fingerprinting.
Using curl-cffi web scrapers can bypass TLS and HTTP fingerprinting.
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.
Highlights
Example Use
from curl_cffi import requests
response = requests.get('https://httpbin.org/json')
print(response.json())
# or using sessions
session = requests.Session()
response = session.get('https://httpbin.org/json')
# also supports async requests using asyncio
import asyncio
from curl_cffi.requests import AsyncSession
urls = [
"http://httpbin.org/html",
"http://httpbin.org/html",
"http://httpbin.org/html",
]
async with AsyncSession() as s:
tasks = []
for url in urls:
task = s.get(url)
tasks.append(task)
# scrape concurrently:
responses = await asyncio.gather(*tasks)
# also supports websocket connections
from curl_cffi.requests import Session, WebSocket
def on_message(ws: WebSocket, message):
print(message)
with Session() as s:
ws = s.ws_connect(
"wss://api.gemini.com/v1/marketdata/BTCUSD",
on_message=on_message,
)
ws.run_forever()
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