Why is Python the Best Language for Web Scraping?

What is Python Web Scraping?

Python Web Scraping is an automatic method of collecting data from the web and its different websites, and performing further operations on the data. These may include storing the data in a database for future references, analyzing the data for business purposes, and providing a continuous stream of data from different sources in a single place.

Some common methods of web scraping

• High performance

• Simple syntax

• Available existing frameworks

• Universality of Python

• Useful data representation

Let us take a detailed look.

Reason 1: High Performance

Python scripts written for web scraping are highly efficient. Web scraping is limited to just retrieving data from other sources in some languages, whereas in some others, it involves sourcing the data in an unstructured format and appending it together, followed by parsing and saving it as a dataset. Scripts written in Python do all this, as well as representing the scraped data visually with Python libraries like Matplotlib.

Syntax

tree = html.fromstring(response.text)
text_in_site = tree.xpath(‘IP address/text()’)
for title in blog_titles:
   print(title)

Here we are seeing a scraping script using the lxml library of Python. This library contains a html module to work with HTML, although it needs the HTML string first which is retrieved using the Requests library. This parsed data is then stored in a tree object, and exact data items can be accessed by creating queries using the Xpath() function, from which desired components like text or body of the website can be extracted using appropriate tags.

Algorithm

Step 1 − Import the lxml library

Step 2 − Retrieve the HTML string using Requests library

Step 3 − Parse the scraped data from target website

Step 4 − Obtain individual data elements by using queries

Step 5 − Printing the required data, or using it for further purposes

Example

# After response = requests.get()
from lxml import html
tree = html.fromstring(response.text)
blog_titles=tree.xpath('//h2[@class="blog-card__content-title"]/text()')
for title in blog_titles:
   print(title) 

This script only runs in dedicated Python IDEs such as Jupyter Notebook/terminals.

Output

Blog title 1
Blog title 2
Blog title 3

Reason 2: Simple Syntax

The Python language has one of the easiest and most simple syntaxes of the programming world. This is what makes it one of the easiest languages to learn for beginners. Thus, web scraping scripts written in Python are very small and simple, compared to other languages like C# and C++. This is what makes web scraping using Python so easy to write and execute.

Syntax

pip install requests
import requests
response = requests.get("https://www.python.org/”)
print(response.text)

Here we use the Requests library to perform web scraping, which has one of the easiest and shortest code scripts to execute. The library sends a HTTP request using the GET() function, and then prints the scraped data for the user. This can be used as the basic syntax for the Requests library and can be modified as needed.

Algorithm

Step 1 − Install the Requests library using the console

Step 2 − Send HTTP request to the website server using the REQUESTS.GET() command

Step 3 − Print the received scraped data or use it for necessary representation purposes.

Example

import requests
from bs4 import BeautifulSoup
res = requests.get('https://www.tutorialspoint.com/tutorialslibrary.htm')
print("\n")
soup_data = BeautifulSoup(res.text, 'html.parser')
print(soup_data.title)
print("\n")
print(soup_data.find_all('h4')) 

This script only runs in dedicated Python IDEs such as Jupyter Notebook/terminals.

Output

[
Academic
, 
Computer Science
, 
Digital Marketing
, 
Monuments
, 
Machine Learning
, 
Mathematics
, 
Mobile Development
, 
SAP
, 
Software Quality
, 
Big Data & Analytics
, 
Databases
, 
Engineering Tutorials
, 
Mainframe Development
, 
Microsoft Technologies
, 
Java Technologies
, 
XML Technologies
, 
Python Technologies
, 
Sports
, 
Computer Programming
, 
DevOps
, 
Latest Technologies
, 
Telecom
, 
Exams Syllabus
, 
UPSC IAS Exams
, 
Web Development
, 
Scripts
, 
Management
, 
Soft Skills
, 
Selected Reading
, 
Misc
] 

Reason 3: Available Existing Frameworks

The Python language has an extensive collection of frameworks for a wide range of functions and use cases, which includes web scraping as well. Libraries such as Beautiful Soup, lxml, Requests and Scrapy. Using these frameworks for web scraping can be very efficient and effective, and can also support Xpath, HTML and other. These libraries also contain debugging methods, which help in smooth and secure programming.

Syntax

driver = Chrome(executable_path='/path/to/driver')
driver.get('https://oxylabs.io/blog') 

Here we are using Selenium for web scraping, which supports parsing using Javascript, thereby allowing crawling on dynamic websites. Here we require a driver for the browser being used. In today’s era of the entire internet being programmed on Javascript, this library is essential for web scraping.

Algorithm

Step 1 − Installing the Selenium library

Step 2 − Importing the appropriate class for the browser used

Step 3 − Object of the browser is created using the driver

Step 4 − Load the required webpage using the get() method

Step 5 − Extract the neccessary elements from the website, if required

Step 6 − Close the browser object

Example

from selenium import webdriver
from selenium.webdriver.chrome.options import Options
options = Options()
options.headless = True
options.add_argument("--window-size=1920,1200")
DRIVER_PATH = '/path/to/chromedriver'
driver = webdriver.Chrome(options=options,
executable_path=DRIVER_PATH)
driver.get("https://www.google.com/")
print(driver.page_source)
driver.quit()

This script only runs in dedicated Python IDEs such as Jupyter Notebook/terminals.

Output

<title>Oxylabs Blog | Oxylabs</title>

Reason 4: Universality of Python

Python is one of the most universally used programming languages in today’s world, and is also widely accepted in different aspects. The biggest data collectors and companies in the world use Python, and scripts written in Python can be used with programs written in other languages as well.

Syntax

pip import requests
import requests
response = requests.get("https://oxylabs.io/”)
print(response.text)

Here we use a web scraping script using the Requests library, which can be used in sync with scripts written in other languages and programming environments as well, thereby making Python scripts universal.

Algorithm

Step 1 − Install the Requests library using the console

Step 2 − Send HTTP request to the website server using the REQUESTS.GET() command

Step 3 − Print the received scraped data or use it for necessary representation purposes.

Example

pip import requests
import requests
response = requests.get("https://oxylabs.io/")
print(response.text)

This script only runs in dedicated Python IDEs such as Jupyter Notebook/terminals.

Output

<title>Oxylabs Blog | Oxylabs</title>

Reason 5: Useful Data Representation

The web scraping libraries used in Python can perform not just web crawling and data parsing, they can execute useful representation of data for purposes like business analysis, research and market analysis, and understanding customer feedback. Beautiful Soup is the best for scraping data which can be then displayed via Matplotlib, Plotly and similar libraries.

Syntax

response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')

This is the syntax for a script in Beautiful Soup, where we first get the target url using the Requests library, as shown in earlier examples. Then we search and find the required element from the website in the second line. This received data can be then represented using the appropriate libraries.

Algorithm

Step 1 − Install the Beautiful Soup library

Step 2 − Receive the website url by sending request

Step 3 − Extract the element required from the website

Step 4 − Perform necessary operations with the data like printing/storing etc.

Step 5 − Pass the data to Matplotlib for representation purposes

Example

import requests
url='https://oxylabs.io/blog'
response = requests.get(url)
from bs4 import BeautifulSoup
soup = BeautifulSoup(response.text, 'html.parser')
print(soup.title)
blog_titles = soup.select('h2.blog-card__content-title')
for title in blog_titles:
   print(title.text) 

This script only runs in dedicated Python IDEs such as Jupyter Notebook/terminals.

Output

<title>Oxylabs Blog | Oxylabs</title>

Conclusion

Thus here we see how web scraping is done using various methods in Python, and also the way in which these methods make Python the best for web scraping. There are several other smaller reasons why Python is great for web scraping, but here we have only mentioned a few. To get a detailed lesson on each of the methods, you can visit their individual learning pages respectively. Python is thus arguably one of the best languages to perform web scraping.