Imagine you are launching an eCommerce store, and you wish to prepare a list of all the products that your competitor sells. There are two ways to know this.
The first method is visiting your competitor site, copying the data of every product listing, and pasting them on a spreadsheet. This is a time-consuming method if there are thousands of products on the site.
The second method is, using a scraping program that will automatically scrape all the required product data from your competitor website and paste them on a spreadsheet. This method is a real time-savior because you only need to run the program to copy hundreds or millions of product data.
Hence, web scraping is the best method to copy data from the web. In this article, we will learn how to scrape the web using Python. But, before we discuss the code, let’s understand some basics.
What is web scraping?
Web scraping or data scraping is an automated data collection method that extracts unstructured data from websites and stores it in a structured form. You can use various web scraping software to automate the data retrieval task for you, or you can write your own code.
How does web scraping work?
Web scraping software uses the HTTP protocol and GET request to scrape the target URL. The web server allows the program to read the contents of the page. The content is copied, and the data is stored in formats like XML, SQL, or excel sheet.
Here are the four steps needed for web scraping:
● Step 1: Identify and define the data that you need to scrape.
● Step 2: Crawl the HTML of the page having the raw data and use a proxy server to prevent IP banning.
● Step 3: Run a scraping program defining all the variables you need from the scraped pages.
● Step 4: Convert the unstructured data into a structured form and save it in a format like CSV, SQL, XML, or any other format as needed.
Choosing a proxy for web scraping
It is always recommended to scrape the web by using a proxy. Sending too many GET requests from the same IP address might result in an IP ban. Website servers are quick to react in this regard, and they readily block any bots that send too many GET requests in a short interval of time.
For web scraping purposes, you will need to send several requests to extract data. When so many requests are initiated, a proxy will prevent the server from tracking down your IP address. Without IP tracking, the server won’t be able to block your program, and you can smoothly perform your scraping task.
Several proxies are available for scraping, but it is better to choose residential proxies because they offer IP addresses belonging to real residential addresses. Residential IP provides the best security, and the chances of IP blocking is very low.
What are the advantages of web scraping?
There are several advantages offered by web scraping like:
● It is easy to implement and saves a lot of time. Manual copy-pasting is time-consuming and requires high investment.
● Scraping programs are accurate because they are written keeping in mind the specific data needed from a collection of web pages. Accurate extraction of data is necessary to fulfill your scraping goals.
● Raw data is parsed and can be stored in a variety of formats like CSV, JSON, XML, and SQL.
What is Python?
Python is a high-level programming language used as a scripting language to automate a series of tasks. Python is easy to learn and offers a range of modules and packages to reuse large chunks of code. It becomes easier to program when you pre include libraries. Python has some of the best collections of libraries.
Why use Python for web scraping?
Python is the most preferred web scraping language because it offers frameworks like Beautiful Sop and Scrapy that makes scraping a smooth process. Python has some of the finest libraries for web scraping like Scrapy, Requests, Urllib, Beautiful Sop, Selenium,
How to web scrape with Python?
Here are the steps to follow to scrape the web using Python:
Step 1: List the URL that you wish to scrape and identify the tag that has the data. You can check the data by right-clicking on the URL and then clicking on inspect.
Step 2: For example, if we wish to scrape all the Pizza store names and addresses of a particular location from a Pizza directory page, we will run the following code in Ubuntu:
gedit myscrapedata.py #create a file named myscrapedata to store your code
from selenium import webdriver
from BeautifulSoup import BeautifulSoup
import pandas as pd
driver = webdriver.Chrome(“/usr/lib/chromium-browser/chromedriver”) #set the browser as Chrome
Step 3: Now, we will create files that will store the extracted data. Here is the code:
name=[] #List to store the name of the pizza stores
address=[] #List to store the address of the pizza stores
driver.get(“<a href=”https://www.pizzadirectorysample.com/”>”) #open the URL to extract the data
Step 4: Now, we will add the data extraction code to our file:
content = driver.page_source
soup = BeautifulSoup(content)
for a in soup.findAll(‘a’,href=True, attrs={‘class’:’_31qSD5′}): #list the respective class name
name=a.find(‘div’, attrs={‘class’:’_6hU59m’})
address=a.find(‘div’, attrs={‘class’:’_7bC6KE _2rQ-NK’})
name.append(name.text)
address.append(address.text)
Step 5: Thereafter, we will add the code to store the data in CSV format:
df = pd.DataFrame({Store Name’:name,’Address’:address,})
df.to_csv(‘products.csv’, index=False, encoding=’utf-8′)
Step 6: Now, we will run the code stored in the file:
python myscrapedata.py
This will extract the data and return an excel sheet containing a list of items.
Conclusion
Web scraping with Python is easy. You just need to know the URLs and prepare a simple code by importing the Selenium and BeautifulSoup libraries. Start using Python for all your web scraping needs, and do not forget to use a proxy server before you try extracting data from several URLs at a time because not doing so might result in IP blocking.
