Keywords: Python web scraping, data extraction, web scraping tutorial, BeautifulSoup, Selenium, Scrapy, Requests
Introduction
Web scraping means collecting information from websites. It is an automatic process done by a computer program. It replaces slow manual work with quick scripts. These scripts gather data fast and without errors. Most online data is unorganized and messy. Scraping helps to make it clean and structured. Python is easy to read and simple to write. It has many tools for web scraping. Hence, Python is mainly used for data extraction and automation (Crazeneurons – Applications of Python 2025).
Why Python
Python is very simple and clear. It is easy to understand even for beginners. Many people use Python for automation and analysis (Crazeneurons – What is Automation, Trends, Future and Impact on Jobs). It has many ready-made libraries. These libraries make scraping quite easy. Python is popular because it saves time and effort. Libraries like Requests, BeautifulSoup, Selenium, and Scrapy are very useful. They help to collect, clean, and store data easily. Hence, Python is a complete language for web scraping (Crazeneurons – Data Analysis Tools and Data Visualization Software).
Common Libraries Used in Web Scraping
Scrapy: This is a strong and fast web scraping framework. It is used for large and complex projects. It helps build spiders and crawlers easily. Hence, it is useful for big data collection (Crazeneurons – Future of Data Analytics in 2025).
Requests: This library connects Python to websites. It helps download web pages easily. It is fast and simple to use. It works well with BeautifulSoup (Crazeneurons – Why Learning Data Analysis, Data Science and AI Matters).
BeautifulSoup: This library reads and understands HTML code. It finds tags, text, and links on the page. It makes data extraction easy and clean (Crazeneurons – Types of Data in Data Science).
Selenium: This tool controls a browser like Chrome or Firefox. It is used for websites that load content using JavaScript. It can click buttons and scroll pages automatically (Crazeneurons – AI Everywhere India).
Applications of Web Scraping
Web scraping is used in many areas.
In e-commerce, it helps track prices and reviews.
In market research, it collects data about customers and products.
In media, it gathers news and online stories.
In recruitment, it finds and updates job listings.
In finance and real estate, it collects live market prices.
Hence, web scraping is important in many fields today.
Steps for Web Scraping Using Python
Step 1: Import Required Libraries
We first import Requests and BeautifulSoup. Requests connects to the website. BeautifulSoup reads the HTML content. Together, they make data extraction easy.
Step 2: Define the Target URL
We store the website link in a variable. For example, the link “https://www.wikiwand.com/en/articles/Artificial_intelligence”. It is the Wikiwand version of the Artificial Intelligence article. This is the page from which we collect data.
Step 3: Send an HTTP Request
We use requests.get(url) to download the page. It sends a request to the website. If the request works, the data is received. The command raise_for_status() checks for errors.
Step 4: Parse the HTML Document
We pass the HTML code to BeautifulSoup. It reads the page structure. It helps us find text, links, and headings. Hence, the data becomes easier to use.
Step 5: Extract the Title
We find the <h1> tag in the HTML. This tag holds the page title. We use get_text(strip=True) to clean it. It removes spaces and unwanted characters.
Step 6: Extract the First Paragraph
We find the main content section. It has the class name “mw-parser-output”. Inside it, we find the first <p> tag. It holds the article’s introduction. We extract this paragraph for analysis.
Step 7: Extract Section Headings
We find all <h2> and <h3> tags. These are the section titles. They show different topics of the article. We skip headings like “Contents”. The rest are stored in a list.
Step 8: Display or Store the Results
We print the title, paragraph, and headings. We can also save them to a file. Sometimes we store them in a database. Hence, the results are ready for study.
Example: Python Code to Scrape the “Artificial Intelligence” Wiki Page
import requests
from bs4 import BeautifulSoup
url = “https://www.wikiwand.com/en/articles/Artificial_intelligence”
response = requests.get(url)
response.raise_for_status()
soup = BeautifulSoup(response.text, “html.parser”)
title_tag = soup.find(“h1”)
title = title_tag.get_text(strip=True) if title_tag else None
content_div = soup.find(“div”, class_=”mw-parser-output”)
first_para = None
if content_div:
p = content_div.find(“p”)
if p:
first_para = p.get_text(strip=True)
headings = []
for h in soup.find_all([“h2”, “h3”]):
text = h.get_text().strip()
if text and text.lower() not in (“contents”,):
headings.append(text)
print(“Title:”, title)
print(“First paragraph:”, first_para)
print(“Headings:”)
for h in headings:
print(” -“, h)
Sample Output
Title: Artificial intelligence
First paragraph: Artificial intelligence (AI) is intelligence shown by machines.
It is different from human or animal intelligence.
Headings:
– History
– Approaches to artificial intelligence
– Symbolic AI
– Machine learning
– Deep learning
– Applications
– Criticism
– Future directions
– See also
– References
– External links
Explanation of the Code
This Python code is very easy to understand. It starts by importing Requests and BeautifulSoup. The website link is stored in a variable. A request is sent to the website. The page content is received as HTML. BeautifulSoup reads this HTML file. It finds the title and first paragraph. Then it collects all section headings. The results are printed one by one. Hence, the code shows how to scrape data using Python.
Challenges and Ethics:
Scraping must be done carefully. We should always follow website rules. We should not collect private or personal data. We must respect the robots.txt file. Too many requests can harm servers. Hence, scraping should be fair and responsible.
Guidance for Beginners
Start with simple and static web pages. Understand the page structure before scraping. Save data in a text or CSV file. Then move to dynamic websites. Use tools like Selenium or Scrapy when needed. Clean and analyze your data for better results. Hence, learning scraping step by step is best.
Perspective
Python is very powerful for web scraping. It is simple, fast, and widely used. It helps beginners learn easily. It connects data collection with analysis. Hence, Python is the main tool for scraping. Automation is growing every day. But human judgment is still important. We must collect data with care and ethics.
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