Python Guide to Build Web Scraping Apps

Want to harness data from multiple external platforms to gain accuracy in decisions and operations across your business ecosystems?

Data Scraping involves employing a critical algorithm or a program to retrieve, structure, and process huge amounts of data from the web. On the flip side, at the heart of data scraping app development is Python, a programming language popular for its ease of use, extensive libraries, and faster development tools.

Whether you want to build eCommerce intelligence, generate leads, conduct market research, enhance social media strategies, or monitor brand presence, Python for data scraping solutions makes the app development journey more agile, faster, and above all easier to integrate trending technologies.

This blog is a quick ride through the ‘how’, ‘what’, and ‘why’ to use Python for developing data scraping apps. Let’s get started!

How Does Web Scraping Work?

A single Python script sends an HTTP request to a website. Which then receives a raw HTTP response that needs to be parsed using the Beautiful Soup- one of the Python libraries for data scraping. It helps turn the raw response into structured data. This structured data is then processed to use it in the script to identify the text content in the data.

Here, a combination of scrapers and crawlers is used to fetch data from websites. By browsing a website, the web crawler indexes the content. Parallely, the web scraper extracts the data you have requested. You can mention the file format in which you want the data results to be visualized along with the storage option that you want a web scraper to save the data.

Prerequisites to Build Data Scraping Apps Using Python

To develop a scalable Python web scraping app, explore the chief essentials, making it seamless to retrieve data from the web efficiently.

Selenium

It is one of the most popular open-source web development tools for automating the web browsing functions allowing users to extract data and interact with the website seamlessly.

Playwright

It is the latest cross-language and cross-platform web browser automation tool used to scrape web apps and dynamic web content. Using Playwright, it’s easy for web headless browsers like Chrome, Safari, and Microsoft Edge to navigate the web just like humans do.

Types of Data Scraping

Developers use varied web scraping techniques like HTTP programming, HTML parsing, Human copy-and-paste, DOM parsing, text pattern matching, computer vision web page analysis, or vertical aggregation based on the type and purpose to harness data from the web. Data scraping approaches vary based on the data sources and task complexities. Explore different types of data scraping that are high-in-demand among the innovators.

Web Scraping

It encompasses text, prices, images, or any other data on the web pages which are used to gather market intelligence, monitor competitors, or track product prices.

Screen Scraping

It includes data extraction from the display output of other programs when the data is not accessible directly through databases or APIs.

Social Media Scraping

Data extraction from social media platforms to leverage data related to user profiles, comments, posts, or other relevant data. It is used in market research, sentiment analysis, and understanding customer preferences.

Email Scraping

Extract email addresses from the websites to build mailing lists along with implementing legal and ethical implications.

How To Scrape Critical Data From the Web?

1. Analyze Web Structure Using Development Tools

Understanding the website structure will help determine the exact location of the data you want to extract. Based on the type of website, it has a variety of categories, listings, pricing, ratings, or other data.

Firstly, open the developer’s tools and try selecting the content element on the webpage, you will discover the tags and classes of the selected content elements. This data is critical as it helps compile all other elements with such types of details.

Now you know which class to target, you need to get the HTML from the website.

2. Get The HTML From The Website

After understanding the website structure, to get the HTML from the website, ‘Requests’- a Python library is used to send a GET request to the targeted website URL.

import requests

# Base URL
base_url = 'https://watch.toscrape.com/'

# Send GET request to the base URL
response = requests.get(base_url)

# Get the HTML content
html_content = response.text

# Print the HTML content
print(html_content)

This script will give a status code but you need to focus on the actual HTML content. Through response.text, you will get the HTML content of the website homepage that will serve as your initiation point to extract the data. This process varies based on the type of website you want to scrape data from.

Static websites don’t need any login credentials but dynamic websites do. Headless browsers like Selenium, Playwright, or Puppeteer are used in case of data extraction from dynamic websites.

Scrape Data From Dynamic Websites

For instance, to scrape data from a dynamic platform that majorly relies on JavaScript for content rendering.

First, install Playwright using pip, the Python package manager. It’s simple; just type pip install playwright in the command prompt and press ‘Enter’. Then, you need to install the necessary browser binaries by running playwright install.

Use Playwright in your script after importing it.

# Import Playwright
from playwright.sync_api import sync_playwright

# Use Playwright to open a browser, navigate to the website, and get the HTML source
with sync_playwright() as p:

    # Set up the browser. In this example, we're using Chromium.
    browser = p.chromium.launch()
    page = browser.new_page()

    # Navigate to the website
    page.goto("https://quotes.toscrape.com/js/")

    # Get the HTML source
    html_source = page.content()

    print("HTML source of the website:", html_source)

# Close the browser to free up resources
browser.close()

You have your HTML ready to process it using the Python library – playwright.

Scrape Data When the Website Structure Changes Unexpectedly

Website structure may change due to the dynamic elements that alter the CSS or HTML of the webpages, or if the website is redesigned, or updated. To ensure that the data scraping code doesn’t miss essential information-

• Choose the right selectors to navigate and extract the data you want, depending on how the website is structured and coded. The right approach is to use the selectors based on class names, unique identifiers, or semantic meaning as these attributes are most likely to remain consistent across varied website versions.
• To handle unexpected website structure changes, use logging and error handling in the web scraping code. It detects and rectifies the exceptions that occur during code execution. ‘Try-except blocks’ in Python are used for handling exceptions as well as catching that involve parsing errors, connection failures, or timeout errors. This exception handling process helps avoid crashing the code, discover and fix real causes and analyze and monitor the web scraping process.
• Fallback strategies involve using different selectors for a single data point and selecting the best one out of all. By using BeautifulSoup, Scrapy, or lxml, Python libraries for data scraping helps source varied formats of data from the same website such as XML, JSON, or RSS feeds and using cookies to bypass varied versions of the website or bypass the anti-scraping measures.
• Implement ethical data scraping principles to handle website changes. These principles respect the interests of both data providers and consumers. These principles include robots.text rules, respects the website’s privacy policies, avoids excessive web scraping and seeks data provider’s permission before scraping data from their website.

Exception Handling

Even the best laid plans go amiss which is also true with the web scraping process. Sometimes you might fetch incomplete data from the website. When the web scraping script encounters these loopholes, it throws errors that can be fixed through Python’s ‘try-except blocks’. It lets us dictate how the program must react to the errors, ensuring it doesn’t burn or crash.

Another way to handle the exceptions is to check the HTTP status codes or implement the retry mechanism to handle exceptions. Using timeouts in your network requests, log errors, and robots.text help handle the web scraping exceptions gracefully.

Proxies

A proxy server acts as an intermediary for requests received from clients needing resources from the servers that offer those resources. As web scraping involves multiple requests sent to the server from an IP address, the server might detect multiple requests and block the IP address to avoid further scraping of data. This is where proxies are used to continue the scraping as IP addresses change and create anonymity by hiding the IP address.

Bypassing Captcha and 2FA

Some websites have highly complicated authentication methods such as Captcha, CSRF tokens, or even two-factor authentication (2FA). It’s important to adopt the web scraping script to handle these complexities.

You can parse the login page firsthand to extract the CSRF token and add it to the login request. Headless browsers like Selenium, Playwright, or Puppeteer are used in case of data extraction from websites with Captcha or 2FA authentication methods.

3. Parsing HTML Content and Data Extraction

After fetching the HTML content, it’s time to structure this data how you need using ‘BeautifulSoup’, one of the best Python libraries for data scraping apps. This Python library is mainly used for pulling data out of XML and HTML files.

Firstly, convert the HTML into a BeautifulSoup object.

from bs4 import BeautifulSoup

# Parse the HTML content using BeautifulSoup
soup = BeautifulSoup(html_content, 'html.parser')

After converting the HTML, use find_all() method that helps return the list of all the data examples of the specific tag and its related attributes. To know which tags to consider, ID, Xpath, or Class are the few ways to locate the elements.

Now it’s time to fetch specific details out of the raw data sets, using find() to search within the elements. For example, to get the price, you need to look for the ‘p’ tag with class.

Next, to start filtering the data, you need to add an if statement informing Python to check for the exact data you are looking for.

# Extract watch information from the parsed HTML
for watch in soup.find_all('article', {'class': 'product_pod'}):
    if watch.find('p', {'class': 'star-rating Five'}):
        title = watch.find('h3').find('a')['title']
        price = watch.find('p', {'class': 'price_color'}).text[1:]
        watch_url = base_url + watch.find('h3').find('a')['href']
        image_url = base_url + watch.find('img')['src'][3:]

Review the whole script that navigates webpage data and filters it with the exact details you want.

Pagination in Web Scraping

Previous steps showed extracting data from the first page of the website. What if you want to scrape data from the whole website?

Here, the website URL structure gives us hints on how to initiate pagination in data scraping. By using Python’s ‘for-loop’, it reads through each page, and removes the page number from the URL along with the iteration index.

Now, merge that script into the existing script to scrape all the pages on the website.

4. Storing Scraped Data in CSV File

If you want to save this structured data for future use, you can store the data in CSV or other file formats.

Note, Python will save this data file in the existing working directory if you don’t specify another location to store the CSV file.

Firstly, import the CSV library and create a file. You are ready to start data scraping by looping all the webpages. After running the script, you will get a nice CSV of the structured data you want to fetch from the website.

Storing Scraped Data in a JSON File

If you need a JSON file, the process is similar to that of writing it to a CSV file. The only difference is that you need to add the details to a list and write that list to a JSON file.

Python makes it super easy to organize and save data scraped from the web.

It is recommended to partner with a trusted software development company critical to ensure the design and development choices are made in line with the best data scraping practices. This also involves leveraging sufficient skillsets and capabilities to develop essential data models for your unique software project.

Your Trusted Data Scraping App Development Company

Python development team at TOPS makes sure of the data quality, accurately articulates business needs, uses legit methods to scrape data without annoying the external platforms, and helps embed the core use of data scraping apps in your business processes and across the team.

Focusing on the larger to even smaller level of ‘high-value’ use cases for our client to leverage the scraped data has been our USP. Being the top Python development company for 9 years, we have exhaustively worked on the web scraping development environments and made it a forte.