Skip to content

pycurlvsralger

LGPL/MIT 13 9 1,048
1.4 million (month) Feb 25 2003 7.45.3(3 months ago)
152 1 3 MIT
Dec 22 2019 1.1 thousand (month) 2.2.4(3 years ago)

PycURL is a Python interface to libcurl, a multi-protocol file transfer library written in C. PycURL allows developers to use a variety of network protocols in their Python programs, including HTTP, FTP, SMTP, POP3, and many more.

PycURL is often used in web scraping, data analysis, and automation tasks, as it allows developers to send and receive data over the internet. It can be used to perform various types of requests, such as GET, POST, PUT, and DELETE, and can also handle file uploads and downloads, cookies, and redirects.

One of the key features of PycURL is its support for SSL and proxy servers, which allows developers to securely transfer data over the internet and work around any network restrictions. PycURL also supports a wide range of authentication methods, such as Basic, Digest, and NTLM, and allows developers to easily set custom headers and query parameters.

Just like cURL itself, PycURL is also highly configurable and allows for fine-grained control over various aspects of the transfer, such as timeouts, retries, buffer sizes, and verbosity levels. Additionally, PycURL also provides easy access to the underlying libcurl library, which allows developers to access advanced functionality that is not exposed by the PycURL API.

It's important to note that PycURL is a wrapper around the libcurl library and therefore provides the same functionality and performance as libcurl.

Main strengths of PycURL is that it uses cURL which is one of the most feature rich low-level http clients. The downside is that it's a very low-level client (see the examples below) with complex API making use in web scraping very difficult and niche.

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


uses-curlhttp2multi-partresponse-streaminghttp-proxy

Example Use


import pycurl
from io import BytesIO

buf = BytesIO()
headers = BytesIO()

c = pycurl.Curl()
c.setopt(c.HTTP_VERSION, c.CURL_HTTP_VERSION_2_0)  # set to use http2
# set proxy
c.setopt(c.PROXY, 'http://proxy.example.com:8080') 
c.setopt(c.PROXYUSERNAME, 'username')
c.setopt(c.PROXYPASSWORD, 'password')

# make a request
c.setopt(c.URL, 'https://httpbin.org/get')
c.setopt(c.WRITEFUNCTION, buf.write)  # where to save response body
c.setopt(c.HEADERFUNCTION, headers.write)  # where to save response headers
# to make post request enable POST option:
# c.setopt(c.POST, 1)
# c.setopt(c.POSTFIELDS, 'key1=value1&key2=value2')
c.perform()  # send request

# read response
data = buf.getvalue().decode()
headers = headers.getvalue().decode()  # headers as a string
headers = dict([h.split(': ') for h in headers.splitlines() if ': ' in h])  # headers as a dict
c.close()

# multiple concurrent requests can be made using CurlMulti object:
# Create a CurlMulti object
multi = pycurl.CurlMulti()
# Set the number of maximum connections
multi.setopt(pycurl.MAXCONNECTS, 5)

# Create a list to store the Curl objects
curls = []

# Add the first request
c1 = pycurl.Curl()
c1.setopt(c1.URL, 'https://httpbin.org/get')
c1.setopt(c1.WRITEFUNCTION, BytesIO().write)
multi.add_handle(c1)
curls.append(c1)

# Add the second request
c2 = pycurl.Curl()
c2.setopt(c2.URL, 'https://httpbin.org/')
c2.setopt(c2.WRITEFUNCTION, BytesIO().write)
multi.add_handle(c2)
curls.append(c2)

# Start the requests
while True:
    ret, _ = multi.perform()
    if ret != pycurl.E_CALL_MULTI_PERFORM:
        break

# Close the connections
for c in curls:
    multi.remove_handle(c)
    c.close()
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

Alternatives / Similar


Was this page helpful?