curl-cffivscrul
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.
crul is a R library for sending HTTP requests and web scraping. It is designed to be simple and easy to use, while still providing powerful functionality for working with HTTP requests and scraping web pages.
One of the main features of crul is its intuitive and easy-to-use syntax for sending HTTP requests. It allows you to easily specify the HTTP method, headers, and body of a request, and also provides a simple way to handle the response.
crul also has the ability to handle different types of requests and responses, including GET, POST, PUT, DELETE, and PATCH. It also support for handling redirects, cookies, and authentication.
Another feature of crul is its support for web scraping. The library provides a simple and efficient way to extract data from web pages, using a syntax similar to that of the XML and httr libraries. It also allows to easily filter the extracted data based on a specific criteria.
crul also supports parallel scraping, which allows to make multiple requests at the same time, thus speeding up the scraping process.
In addition to these features, crul has a good compatibility with other R packages such as tidyverse and purrr which facilitates the manipulation of the data obtained after scraping.
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(crul)
# Sending a GET request to a website
response <- HttpClient$new("https://www.example.com")$get()
# Sending a POST request to a website
request_body <- list(param1 = "value1", param2 = "value2")
response <- HttpClient$new("https://www.example.com")$post(body = request_body)
# Extracting the status code and body of the response
status_code <- response$status_code()
body <- response$body()
# crul also allows easy asynchronous requests:
urls <- c("https://www.example1.com", "https://www.example2.com", "https://www.example3.com")
# Creating a list of request objects from urls
requests <- lapply(urls, function(url) {
HttpClient$new(url)$get()
})
# Sending the requests asynchronously
responses <- async(requests)
# Extracting the status code and body of the responses
status_codes <- lapply(responses, function(response) response$status_code())
bodies <- lapply(responses, function(response) response$body())