Almost 70% of the world’s population uses the internet, with a 20% increase in the last six years alone. We generate an astronomical amount of data, not to mention the information created due to online services and commerce.
Web scraping can help filter out the noise, leaving only useful information that will let you advance your goals. Python is the most adaptable and robust programming language for that purpose. This article uncovers why and explores eight scraping use cases where Python shines.
Why Python?
Python is the most popular and widely-used programming language for web scraping. It’s among the easier languages to get into, thanks to clear syntax. Python is constantly evolving and doing so faster than other widespread programming languages.
Web scraping is more efficient with Python due to the extensive use of specialized libraries and frameworks. Some contribute to automating the process, while others help you sort and make sense of the data once you possess it.
Google is an indispensable tool when you’re looking to scrape the content of multiple related sites. Python is one of the company’s cornerstone languages, so creating code that interacts with its existing structure is straightforward.
Different Applications of Python for Web Scraping
The practical applications of data scraping are nearly limitless, and Python can help with all of them! Here are just the most widespread & practical examples to get you started.
1. Data extraction
Scraping's main goal is to search web pages for content and copy it to a local file or database. It replaces the tedious process of manually copying and pasting data. While doing so is sometimes still necessary, writing a few lines of Python code can extract the information it would take a person ages to do in seconds.
Even extracting data indiscriminately can yield results. Maybe you're trying to preserve an old archive without straightforward download options. Or maybe, you expect not to have internet access but still need all the information on a website to continue working, etc.
If you are keen on trying it yourself, follow a tutorial for building web scrapers in Python to make the entire process easier.
2. Automation
Automation is one of the most potent uses for web scraping. It lets you create a script that executes one or a series of actions periodically, leaving you free to do other things.
A simple automation would be to write a script that scrapes a blog and then creates an Excel spreadsheet with posts in chronological order.
Sports betting is an excellent example of more advanced automation. Sports betting sites may automatically update match results and team stats after each game. These results may offer insights into a team’s expected performance and adjust payout odds accordingly.
3. Creating APIs
Some websites protect themselves from scraping and will stop conventional scripts. Luckily, you can use Python to create APIs that interact with websites in a more sophisticated manner. Think of APIs as waiters that take your order at a restaurant. They forward that order to the website and return the data you requested.
APIs are more user-friendly than basic scripts. They let users who may not be expert coders access needed information. APIs also bypass CAPTCHAs, location-based locks, and other hurdles websites may place.
4. Lead generation
Besides ad campaigns and targeted outreach, companies also attract potential clients with cold calls and emails. For example, a company that sells prosthetics may use web scraping to get contact information for hundreds of physicians. The company will then reach out to these contacts and try to sell their services. It doesn’t always work, but the chances of getting a new client far outweigh the costs.
Lead generation is a pivotal aspect of business growth, encompassing various strategies to attract and convert potential customers into loyal clients. Understanding how to handle cookies in web scraping is essential for effective lead generation, ensuring that user data is managed ethically and aligns with privacy regulations. By employing targeted campaigns, engaging content, and ethical data practices, businesses can optimize lead generation efforts, fostering valuable connections and driving sustainable growth.
5. Website monitoring
Monitoring the competition's every move can help your business gain the upper hand. It's possible to use Python to write software that tracks competitors' prices and alerts you when new products become available. This is also useful for online shopping or getting the best travel deals.
6. Data analysis
A single website doesn't offer all there is to know about a given topic. Scraping makes extracting relevant data from multiple independent sources straightforward.
Once you're working with a comprehensive dataset, it becomes easier to identify and exploit patterns. For instance, regularly scraping real estate market websites and highlighting trends can uncover investment opportunities.
7. Machine learning
Machine learning models need vast quantities of data to evolve. Properly configured, a Python-based web scraping tool can provide the necessary quantity and quality of data for such models.
8. Natural language processing
NLP is among web scraping’s most sophisticated applications. Having access to countless examples of human-generated text on a topic allows one to perform general sentiment analysis and keyword research.
It's also useful for creating more natural-sounding chatbots and monitoring user feedback. Companies rely on these to craft streamlined and more pleasant online customer experiences.
Conclusion
Web scraping has become essential whether you want to automate boring data collection or make informed business operation decisions. Harnessing Python's scraping-specific toolset will give you access to meaningful data you can use to your advantage.