Decoding Web Scraping with Python

Decoding Web Scraping with Python

Web scraping has become an important technique for extracting valuable information from websites. With the growing need for data-driven insights, web scraping provides a powerful means to gather data from various sources on the internet.

In this blog post, we will delve into the world of web scraping with Python, exploring its definition, differences from web crawling, traditional methods, implementation with Python, how can the data be utilized, importance, and ethical considerations.

What is Web Scraping?

Web scraping refers to the automated extraction of data from websites. It involves parsing the HTML structure of web pages, extracting specific data elements, and storing them in a desired format, such as a csv or database. By automating the retrieval of data, web scraping saves time and effort compared to manual data collection.

Web Scraping Vs Web Crawling

Although the terms web scraping and web crawling are frequently used interchangeably, they are not the same thing.

Web scraping typically involves extracting specific data from targeted web pages, whereas web crawling entails traversing the web systematically in order to index or analyse web content.

A web crawler starts with a seed, which is a list of URLs to visit. The crawler finds links in the HTML for each URL, filters those links based on specific criteria, and then passes those links to a scraper so that the desired information can be extracted from them.

Web scraping is a subset of web crawling that serves more specific purposes such as obtaining product information, obtaining customer reviews, or gathering news articles.

Traditional Methods of Web Scraping

Before the libraries with Python came into picture, the go to methods to get data from the internet included:

  1. Regular Expressions: To extract data from structured HTML documents, regular expressions were commonly used. We could use regex syntax to define patterns that would match specific data elements within the HTML source code. Regex-based scraping, while powerful, was limited to cases where the HTML structure was predictable and consistent. Handling complex or nested structures with regular expressions can be difficult and error-prone.

  2. Manual Copying and Pasting: Manually copying and pasting data from websites into a local file or spreadsheet was one of the earliest and simplest methods of web scraping. This method worked well for scraping small amounts of data, but it became inefficient and time-consuming for larger-scale scraping tasks.

These traditional methods had drawbacks. They worked best with static websites with simple HTML structures and struggled with dynamic or JavaScript-rendered content. Furthermore, these methods were less scalable and necessitated a significant amount of manual labour, making them inefficient for large-scale data extraction tasks.

Web Scraping with Python

Some widely used Python libraries for web scraping include BeautifulSoup, Scrapy, Selenium & Extruct.


BeautifulSoup is used for parsing HTML and XML documents. Beautiful doesn't directly interact with the server of the url we are trying to scrape. We need to use libraries like request to get the response data from the url. Once that is done, we can use parser like html.parser or lxml's parser to get the HTML content. Once we have the HTML content, we can fetch the required data.

We can use Beautiful when we need to extract data from a single webpage or webpages of the same HTML structure which don't require complex navigation. One drawback with BeautifulSoup is, it works only for static web pages.

import requests
from bs4 import BeautifulSoup

url = ""
response = requests.get(url)

# Parse the HTML content
soup = BeautifulSoup(response.content, "html.parser")

# Extract data in the title tag from the parsed HTML
title = soup.title.text
print("Title of the page is ", title)


Selenium is a web testing framework that allows automated browser interactions. It can be used for web scraping by controlling a browser instance and extracting data from dynamic or JavaScript-driven web pages which is one of it's major advantages.

from selenium import webdriver

# Configure the chrome webdriver
driver = webdriver.Chrome()

# Load the web page
url = ""

# Extract data in the title tag using Selenium
title_element = driver.find_element_by_tag_name("title")
title = title_element.text
print("Page title:", title)

# Close the browser

We can also combine Selenium with BeautifulSoup to get content rendered dynamically. Selenium automates web browser interaction. Hence the data rendered by JavaScript links can be made available with Selenium and then can be extracted using Beautiful Soup. Below is a snippet on how to use the combination of two.

soup = BeautifulSoup(driver.page_source, 'html.parser')
title = soup.title.text


Python's extruct library is useful for extracting structured data from web pages. The extruct library comes in handy when you need to extract structured data from web pages, such as microdata or json-ld. It makes it simple to access and process structured information embedded in HTML. We need requests to load the web page data, just like BeautifulSoup.

import requests
from extruct.jsonld import JsonLdExtractor

# Make a request to the website
url = ""
response = requests.get(url)

# Extract JSON-LD structured data from the HTML content
extractor = JsonLdExtractor()
data = extractor.extract(response.text)


Scrapy offers an integrated method for following links and extracting data from multiple pages. Scrapy is typically used to scrape data from multiple pages, follow links in web crawling, handle pagination, and perform more complex scraping tasks. It includes advanced features such as middleware, pipelines, and built-in asynchronous request handling. One disadvantage of Scrapy is that it does not support JavaScript by default, instead relying on Splash.

import scrapy

class MySpider(scrapy.Spider):
  name = "example_spider"
  start_urls = [""]

  def parse(self, response):
    # Extract title tag data from the response
    title = response.css("title::text").get()
    print("Page title:", title)

Analyzing & Storing Data

Now that we have the data from the web, we can save it in the formats we want, such as CSV or databases. We can use Python libraries such as Pandas for data cleaning, transformation, and obtaining the final version of our preprocessed data.

Next, we can use Matplotlib or Seaborn to understand the trends, patterns, or correlations in the scraped data.

We can use Natural Language Processing to perform sentiment analysis on data containing customer reviews or movie reviews.

There are numerous applications for Machine Learning in scraped data.

Importance of Web Scraping

Web scraping is important in many industries. It aids in the monitoring of product prices, the analysis of customer reviews, and the tracking of competitors in e-commerce. Web scraping is used in finance for stock market analysis, tracking economic indicators, and collecting financial data. It is useful in investigative reporting and data journalism.

The applications are numerous, and web scraping enables businesses to remain competitive and make data-driven decisions.

Ethical Consideration & Best Practices

Some Ethical & Best Practices for web scraping include:

  1. Respecting website policies: Check the website's terms of service and robots.txt file to ensure compliance with their guidelines.

  2. Rate limiting: Implement delays between requests to avoid overwhelming the target website's server and potentially causing disruption.

  3. Adding User Agents: Include a user agent in your HTTP requests that identifies your web scraping script. This allows website owners to contact you if needed.

  4. Scraping public data: Focus on scraping publicly available data and avoid sensitive information or private areas of websites.

Hope this article helps you in getting a brief overview on Web Scraping & how it can be achieved using Python.

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