Create Your Own Web Scraper. Now you know why web scrapers and Python are cool. Next, we will be going through the steps to creating our web scraper. Choose the page you want to scrape. In this example, we will scrape Footshop for some nice sneaker models and their prices. Then, we’ll store the data in CSV format for further use. You got an idea of multiple tools for web scraping. You learned to make web scraper using Scrapy - The Complete Python Web Scraping Framework, Extracting and Exporting data from web to any format. Learn python programming for effective web scraping, data extraction and automation. Create your first web scraper with Scraper API and Python Recently I come across a tool that takes care of many of the issues you usually face while scraping websites. The tool is called Scraper API which provides an easy to use REST API to scrape a different kind of websites (Simple, JS enabled, Captcha, etc) with quite an ease. Jul 30, 2020 An easy way to scrape data using Python is using a package or library called Beautiful Soup. Let’s do a pip install and import it like so:!pip install beautifulsoup4 from bs4 import BeautifulSoup.

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.

What is web scraping?

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.

Example of web scraping

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.

Why use a proxy for web scraping?

• 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:

***start of code***

import socket

HOST = ‘www.mysite.com’ # Server hostname or IP address

PORT = 80 # Port

client_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)

server_address = (HOST, PORT)

client_socket.connect(server_address)

request_header = b’GET / HTTP/1.0rnHost: www.mysite.comrnrn’

client_socket.sendall(request_header)

response = ”

while True:

recv = client_socket.recv(1024)

if not recv:

break

response += str(recv)

print(response)

client_socket.close()

***end of code***

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:

***start of code***

import requests

proxies = {

“http”: “http://10.XX.XX.10:8000”,

“https”: “http://10.XX.XX.10:8000”,

}

r = requests.get(“http://toscrape.com”, proxies=proxies)

***end of code***

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:

***start of 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(“http://toscrape.com”)

***end of code***

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

***start of code***

import requests

s = requests.Session()

Making A Web Scraper In Python Tutorial

s.proxies = {

“http”: “http://10.XX.XX.10:8000”,

“https”: “http://10.XX.XX.10:8000”,

}

r = s.get(“http://toscrape.com”)

***end of code***

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.

***start of code***

import grequests

BATCH_LENGTH = 10

# An array having the 100 URLs for scraping

urls = […]

# results will be stored in this empty results array

Web Scraper Free

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 = grequests.map(rs)

# appending results to our main results array

results += batch_results

# removing fetched URLs from urls

urls = urls[BATCH_LENGTH:]

print(results)

# [<Response [200]>, <Response [200]>, …, <Response [200]>, <Response [200]>]

***end of code***

Final Thoughts

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.

APIs are not always available. Sometimes you have to scrape data from a webpage yourself. Luckily the modules Pandas and Beautifulsoup can help!

Related Course:Complete Python Programming Course & Exercises

Web scraping

Pandas has a neat concept known as a DataFrame. A DataFrame can hold data and be easily manipulated. We can combine Pandas with Beautifulsoup to quickly get data from a webpage.

Web Scraper Google Chrome

If you find a table on the web like this:

We can convert it to JSON with:

And in a browser get the beautiful json output:

Converting to lists

Rows can be converted to Python lists.
We can convert it to a dataframe using just a few lines:

Pretty print pandas dataframe

You can convert it to an ascii table with the module tabulate.
This code will instantly convert the table on the web to an ascii table:
This will show in the terminal as: