What is Web Scraping?

If you need data, one of the best ways of obtaining it is by accessing the internet. You could go to a website and gather all the information you need about a particular topic. But, what if you needed to gather millions of data to train a machine learning model, or simply for comprehensive data analysis?

Well, it is inefficient to navigate through sites manually to gather millions of data points.

At times like this, you need to look into automation, and, one of the best techniques to collecting data automatically is web scraping.

What is Web Scraping?

Web scraping is a technique that lets users collect data from various websites across the internet. This can include data like product information, vehicle data or even information about people. You can collect such data in the form of a JSON or a CSV file which can then be fed into other systems for data analysis tasks or even for machine learning jobs.

Use Cases of Web Scraping

Based on above definition, it’s clear that web scraping has various applications in the industry. Some of the key use cases of web scraping include:

1. E-commerce price monitoring

If you’re running a business, keeping track of what your competitors are charging can be a significant advantage to adjust your pricing to attract more customers. In such cases, you can leverage web scraping to scrape through the competitor’s websites and collect a list of products and the prices that they are selling it for. You can later use this scraped data to revise your price list.

2. Market research

Next, you might be trying to understand how people feel about a particular product. Customers generally express their feelings regarding a product through reviews. So, with web scraping, you can scrape through product reviews across several websites and gain an overall list of data on how people react to a product.

You can then analyze this data and gain insight into the issues people face, along with what’s working well to better serve your customers.

3. Academic research

Web scraping can be helpful in academics as well. For instance, if you’re a researcher, you’ll likely have to work with different types of articles from different publications and journals. And, finding research data can be quite challenging.

You can use a web scraping script to scrape through the internet to find the types of articles that you’re looking for. So, you spend more time analyzing the data, than actually finding it.

4. News aggregation

Web scraping can help by collecting articles from different news websites. You can get an aggregated news feed organized by topic, date, or source, giving you a comprehensive view of what’s happening around the world.

5. Sentiment analysis

Web scraping can be used to understand how people feel about your brand or product. You can collect data from social media posts or online reviews and use sentiment analysis to analyze the language and tone used. This helps you analyze the public sentiment—whether it’s positive, negative, or neutral.

How Does Web Scraping Work?

So, now that we have an idea on what web scraping is, and what it’s used for, it’s important to understand how it really works. There are five steps involved in web scraping:

Step 01: Sending an HTTP request

First, you need to send an HTTP request to the website you want to scrape. This request asks the server to send back the HTML content of the webpage.

Step 02: Retrieving HTML Content

Once the server responds, you get the HTML content of the webpage. Ideally, this is the raw data that you will work with.

Step 03: Parsing HTML

Out of the box, you won’t really be able to make sense of this data as it will be a giant HTML blob. So, you’ll need to leverage libraries like BeautifulSoup to perform DOM Queries on the HTML to actually identify what you need to obtain from the HTML.

Step 04: Extracting Required Data

After you identify the HTML elements (tags, classes, IDs) that contain the data you’re interested in, you can use BeautifulSoup to extract this data.

Step 05: Data Cleaning

After extraction, the data might need cleaning and processing to make it useful. This typically involves removing unwanted characters, converting data types, or structuring the data into a format suitable for analysis.

All of these steps can be executed in a few lines of Python code using BeautifulSoup. In fact, here’s how you can scrape for data using Python:

import requests
from bs4 import BeautifulSoup

# Step 1: Send an HTTP request
url = 'http://example.com'
response = requests.get(url)

# Step 2: Retrieve HTML content
html_content = response.content

# Step 3: Parse the HTML with BeautifulSoup
soup = BeautifulSoup(html_content, 'html.parser')

# Step 4: Extract the necessary data
# For example, extracting all paragraph texts
paragraphs = soup.find_all('p')
for p in paragraphs:
    print(p.get_text())

# Step 5: Clean and process the data
# In this simple example, we are just printing the text, but you might clean/process it further.

This example starts by sending an HTTP request to a website and retrieves the HTML content of the page. It then uses BeautifulSoup to parse the HTML. Next, it finds all paragraph (<p>) elements and prints their text content.

Tools and Libraries for Web Scraping

It’s not only BeautifulSoup that’s available for web scraping. There’s plenty of other tools that are available free of charge for you to build efficient web scrapers.

1. Scrapy

Scrapy is an open-source web crawling framework for Python. It is used to extract data from websites and process it as per user requirements. It’s key features include:

• Built-in support for handling requests and responses.
• Asynchronous scraping, which makes it fast.
• Robust and scalable for large-scale scraping projects.

2. Puppeteer

Puppeteer is a Node.js library which provides a high-level API to control Chrome or Chromium over the DevTools Protocol. Some of its key features include;

• Headless browser automation.
• Capable of handling dynamic content.
• Powerful features for interaction with web pages (clicks, form submissions).

3. Selenium

Selenium is a suite of tools for automating web browsers. It is widely used for testing web applications but can also be used for web scraping. Some of its key features includes:

• Cross-browser compatibility.
• Can handle JavaScript-heavy websites.
• Supports headless browser mode.

Legal and Ethical Considerations

However, even though there are so many tools for web scraping, there is still a grey area.

For example, a commonly asked question is it actually legal to scrape for company data?

You might find this confusing because the data that you’re actually scraping for is owned by the company but visible to the public. So, there’s always the question of what’s allowed to be shared on the Internet. As a rule of thumb, it’s always recommended to verify the terms of service of the website you’re scraping to ensure that they are okay with it.

Another issue in scraping is that you’re exerting the resources of the website owner. So, there can be chances where you can cause outages for the website owner while you scrape for data. So, it’s something you need to consider before you actually scrape.

Next, many people who start scraping seem to ignore the presence of the robots.txt file. It tells you what the website prefers to keep to themselves while letting everything else be shared with the public. So, you must respect the robots.txt and scrape only what’s allowed.

Challenges of Web Scraping

But this doesn’t mean that you only have to look at the ethical aspect of it. In fact, there are many other challenges to web scraping. Some common challenges of web scraping include:

Anti-Scraping Measures

Generally, most sites place anti-scraping measures to prevent scraper bots from scraping data of their sites. This is usually done with the help of CAPTCHAs and IP Blocking.

Scrapers need to either bypass CAPTCHAs or find ways to solve them automatically, which can be complex and time-consuming.

Additionally, to avoid IP blocking, scrapers need to use proxy servers to rotate IP addresses.

Data Quality

Sometimes, extracted data can be messy and inconsistent, requiring significant cleaning and validation before it can be used.

Scrapers need to implement robust data validation and cleaning processes to ensure the accuracy and consistency of the extracted data. This involves handling missing values, duplicates, and inconsistencies in data formats.

Script Maintenance

Ultimately, web scraping is a presentation layer integration. This means that you have no control over the UI that you’re scraping on. Websites tend to frequently update their layout and structure, which can break existing scraping scripts.

As a result, scrapers need to regularly maintain and update their scripts to adapt to changes in website structures. This involves re-analyzing the HTML layout and modifying the scraping logic accordingly.

Web Scraping Best Practises

Here are some of the best practices you can use to avoid the challenges we discussed above:

1. Respect robots.txt: Always check a site’s robots.txt to see what is allowed.
2. Use Proxies: Rotate IP addresses to avoid blocking.
3. Handle CAPTCHAs: Use services or machine learning to solve CAPTCHAs.
4. Implement Throttling: Add delays between requests to reduce server load.
5. Use Headless Browsers: For dynamic content, use Puppeteer or Selenium.
6. Integrate with APIs: When available, use APIs for structured data.
7. Leverage Machine Learning: Improve data extraction accuracy.
8. Employ Distributed Scraping: Use tools like Apache Spark for efficiency.

Concluding thoughts

Web scraping is a powerful technique when used correctly. It lets you scan over millions of data on the internet and make quick data driven decisions by analyzing the data you scrape. Although it is pretty easy to start web scraping, there are certain aspects to consider in terms of the overall ethical and performance aspects to ensure that the scraper you build is legal and fast.

So, overall, web scraping can give you access to a lot of useful information, but it’s important to use it responsibly and follow the rules.