The ultimate guide for using proxies for Web Scraping with Python

The ultimate guide for using proxies for Web Scraping with Python

The ultimate guide for using proxies for Web Scraping with Python

Posted at October 06, 2020 in Web scraping

Python is a high-level programming language that is used for web development, mobile application development, and also for scraping the web.

Python is considered as the finest programming language for web scraping because it can handle all the crawling processes smoothly. When you combine the capabilities of Python with the security of a web proxy, then you can perform all your scraping activities smoothly without the fear of IP banning.

In this article, you will understand how proxies are used for web scraping with Python. But, first, let’s understand the basics.


Web scraping is the method of extracting data from websites. Generally, web scraping is done either by using a HyperText Transfer Protocol (HTTP) request or with the help of a web browser.

Web scraping works by first crawling the URLs and then downloading the page data one by one. All the extracted data is stored in a spreadsheet. You save tons of time when you automate the process of copying and pasting data. You can easily extract data from thousands of URLs based on your requirement to stay ahead of your competitors.


An example of a web scraping would be to download a list of all pet parents in California. You can scrape a web directory that lists the name and email ids of people in California who own a pet. You can use web scraping software to do this task for you. The software will crawl all the required URLs and then extract the required data. The extracted data will be kept in a spreadsheet.


  • Proxy lets you bypass any content related geo-restrictions because you can choose a location of your choice.
  • You can place a high number of connection requests without getting banned.
  • It increases the speed with which you request and copy data because any issues related to your ISP slowing down your internet speed is reduced.
  • Your crawling program can smoothly run and download the data without the risk of getting blocked.

Now that you have understood the basics of web scraping and proxies. Let’s learn how you can perform web scraping using a proxy with the Python programming language.

Configure a proxy for Web Scraping with Python


Scraping using Python starts by sending an HTTP request. HTTP is based on a client/server model where your Python program (the client) sends a request to the server for seeing the contents of a page and the server returns with a response.

The basic method of sending an HTTP request is to open a socket and send the request manually:

import socket;
HOST = '' # Server hostname or IP address
PORT = 80 # Port
client_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
server_address = (HOST, PORT)
request_header = b'GET / HTTP/1.0\r\nHost:\r\n\r\n'
response = ''
while True:
    recv = client_socket.recv(1024)
    if not recv:
    response += str(recv)

You can also send HTTP requests in Python using built-in modules like urllib and urllib2. However, using these modules isn’t easy.

Hence, there is a third-option called Request, which is a simple HTTP library for Python.

You can easily configure proxies with Requests.

Here is the code to enable the use of proxy in Requests:

import requests
proxies = {
    "http": "http://10.XX.XX.10:8000",
    "https": "http://10.XX.XX.10:8000"
r = requests.get("", proxies=proxies)

In the proxies section, you have to specify the proxy address and the port address.

If you wish to include sessions and use a proxy at the same time, then you need to use the below code:

import requests
s = requests.Session()
s.proxies = {
 "http": "http://10.XX.XX.10:8000",
 "https": "http://10.XX.XX.10:8000"
r = s.get("")

Sometimes, you might need to create a new session and add a proxy. To do this, a session object should be created.

import requests
s = requests.Session()
s.proxies = {
 "http": "http://10.XX.XX.10:8000",
 'https": "http://10.XX.XX.10:8000",
r = s.get("")

However, using the Requests package might be slow because you can scrape just one URL with every request. Now, imagine you have to scrape 100 URLs then you will have to send the request 100 times only after the previous request is completed.

To solve this problem and to speed up the process, there is another package called the grequests that allows you to send multiple requests at the same time. Grequests is an asynchronous Python API that is widely used for building web apps.

Here is the code that explains the working of grequests. In this code, we are using an array to keep all the URLs to scrape in an array. Let’s suppose we have to scrape 100 URLs. We will keep all the 100 URLs in an array and use the grequest package specifying the batch length to 10. This will require sending just 10 requests to complete the scraping of 100 URLs instead of sending 100 requests.

import grequests
# An array having the 100 URLs for scraping
urls = […] 
# results will be stored in this empty results array
results = []
while urls:
    # this is the first batch of 10 URLs
    batch = urls[:BATCH_LENGTH]
    # create a set of unsent Requests
    rs = (grequests.get(url) for url in batch)
    # send all the requests at the same time
    batch_results =
    # appending results to our main results array
    results += batch_results
    # removing fetched URLs from urls
    urls = urls[BATCH_LENGTH:]
# [<Response [200]>, <Response [200]>, …, <Response [200]>, <Response [200]>]


Web scraping is a necessity for several businesses, especially eCommerce websites. Real-time data needs to be captured from a variety of sources to make better business decisions at the right time. Python offers different frameworks and libraries that make web scraping easy. You can extract data fast and efficiently. Moreover, it is crucial to use a proxy to hide your machine’s IP address to avoid blacklisting. Python along with a secure proxy should be the base for successful web scraping.