The data that you can find online can provide various insights to businesses. The only problem is that it is scattered all over the place. That is where web scraping comes in. If you were researching various web scraping options, the chances are that Python has popped up during your research.
Web scraping articles often mention the Python programming language as one of the best options. You probably wonder how to scrape with Python and why you should do it. Here is everything you need to know about using Python to scrape data from online resources.
What is Python web scraping?
To understand web scraping with Python, you first need to understand what web scraping is in general.
Web scraping refers to collecting and parsing the data available online. To do web scraping, you will need a scraping bot.
A scraping bot is a software solution or a script that can find the raw target data online, collect it, and parse it. That brings us to web scraping with Python. It refers to using the Python programming language to create bots able to scrape online data. Visit this page for an in-depth technical Python web scraping tutorial.
The importance of using Python for scraping
Python quickly got ahead of the competition, although not designed from the ground up to accommodate complex web scraping needs. Using Python for scraping is an excellent choice because it is the most popular language for scraping. It also features automatic memory management for high efficiency.
With Python, you will be able to scrape data at lightning-fast speeds. You don’t have to have extensive experience or write sophisticated code. Plus, since it’s one of the most popular high-level programming languages, you will be able to access numerous guides and tutorials.
Most importantly, Python comes with pre-built libraries or frameworks such as BeautifulSoup, Scrapy, and Requests. Thanks to the libraries, you will be able to achieve different scraping goals.
For instance, BeautifulSoup is excellent for high-speed tasks, Scrapy is perfect for dynamic pages, and Requests is ideal for bypassing anti-scraping measures.
Finally, you have access to additional libraries to manipulate the extracted data, properly structure it, and get insights from it right away.
What can you scrape with Python?
Most commonly, the target of web scraping is text on the websites. However, some scraping methods fall short when scraping both static and dynamic pages. That’s where Python excels. Even if a site uses JavaScript to load the content, you can still use Python to access and extract the data.
Python web scraping has found many use cases throughout industries.
Companies most commonly use it for price and competition monitoring. They use it to extract price data from competitor websites. It can help them streamline dynamic pricing strategies, find vendors with the most affordable prices, and run predictive analyses.
Companies also extract data from social media and consumer forums. It helps them gauge customer sentiment to learn how their brand stands in a given target market. They can custom-tailor their approach to improve their image and attract more customers with these insights.
Web scraping with Python has also found applications in search engine optimization (SEO). Companies use it to identify the best keywords to target to improve their SERP ratings. Scraping sites of highly ranked competitors can help you see which keywords they’ve been using and discover the density for every keyword.
Furthermore, businesses use Python to extract data for big ML projects. To efficiently train ML models, companies need access to big data. The only way to obtain massive data is to harvest it from online resources. Since Python is highly efficient, especially at scale, it can help streamline these projects.
Is Python better for scraping than other languages?
Every programming language offers unique features that can potentially align with your web scraping goals.
Python has cut through the noise and become the best language for web scraping because it’s easy to use, has scraping and data manipulation libraries, and quickly structures data. It is highly efficient, making it perfect for scraping projects at scale.
C# is another popular language for web scraping projects. Although not as flexible and lightweight as Python, it also offers impressive data harvesting. It is excellent for extracting data from GUIs and applications.
Node.js is next in line. It can execute scraping tasks at incredible speeds. Due to its unique features, Node.js is perfect for simultaneously extracting data from multiple websites.
Conclusion
Python web scraping is one of the most popular methods both individuals and businesses use. Python has a massive community, and there are many free resources you can use to learn how to create scraping bots from scratch. With access to so many libraries, you won’t need to write hundreds of lines of code, which can significantly speed up your scraping projects.
