Many businesses collect data using Python coding to get ahead in the market. Building a Python-based web scraping tool may be a worthwhile investment for your business as it enables every company to collect the data it needs to push forward.
Python and web scraping
Python is a coding language designed with high-level data structures in mind. It allows for packages of data to be easily gathered, sent, and read. Combine this with Python’s parsing speed and you have language that’s easy to use for everyone. One of the ways that a company can use this programming language in its favor is by setting up a web-scraping application.
Web scraping is a term that refers to gathering and compiling data from the internet. While the term can include tasks like copying text from a web page, a modern understanding of web scraping implies automation. Web scraping now involves creating an autonomous program that can gather and collect data from a plethora of websites.
While web scraping isn’t illegal, some websites try to make the practice difficult for scraping programs to crawl through. Companies must make sure they aren’t violating any Terms of Service a website may have before running a web scraper on the site.
Benefits of web scraping
Although web scraping has the obvious benefit of generating data, there’s more to the process than just that tidbit.
Creation of datasets
There are nearly 2 billion websites out there on the internet. When thinking about how many of these websites have multiple pages, the huge amount of data that’s out there becomes apparent.
A Python-based web scraper can crawl across these websites looking for data related to a specific industry. This data can then be collected into datasets and reviewed at a later time. Routing these datasets through other analytic software could give insights without all the manual work.
Data management capabilities
Since a Python-based web scraper has to be custom-built for every business, the parameters must be individualized for each company. Rather than collecting all data out there, each company can set extra parameters or specific keywords on the web scraper.
This way, companies only gather data they want. Excess or irrelevant data would introduce extra variables or confusion into datasets. That excess could slow down or complicate responses that a company would normally make with the data.
The main point of using a web scraper is to gain insights into a market or industry. By keeping an eye out for new trends in the gathered data, a company can know about what’s going on in its market.
Some web scrapers can alert the assigned users when a variable or dataset changes significantly. This automated response to trends could allow each business to respond to changes in the market in near real-time.
Running a web scraper is much more cost-effective than a series of data analysts. A scraper can run all hours of the day. It can also work much faster thanks to the speed at which Python can parse through text and code.
Datasets generated by the scraper can also be immediately dropped into whatever analytical software or cloud storage each business uses. No manual data transfer has to take place if the company doesn’t want it to.
A Python-based web scraper could be the tool each business needs to get to the next level of efficiency. By combining speed with modern tech, it could reduce the time and costs of data collection and refocus that effort into responding to market forces.