How web scraping is done can be a little complex to understand to begin with.
However, understanding them can allow your business to access a large amount of information that could enhance your ability to offer competitive and attractive products and services for your customers.
In short, website scraping is the process of downloading structured data from the internet, which can later be used for other activities, such as for analyzes.
It is an automated process and is often used by businesses looking for market research data.
We’ll be looking at how you can use web scraping Python to get the best results from your web scraping efforts for data collection and analysis for those who already understand this topic.
What Is The General Use of Web Scraping?
When it comes to getting the best results from your data extraction attempts, you must clearly understand the process.
This method can be used by businesses (or individuals, although the process is usually used in a commercial sense) to scour the internet for data that could be useful to their goals.
This data could include numerous different things, such as competitors’ product prices and offers and market research.
Automated data extraction can technically be carried out manually by opening up individual web pages and manually excerpting the relevant data from them.
However, this is a long and tedious process.
To get enough information for your retail attempt to be effective, doing this manually would be difficult; therefore, there are now automated processes implemented by web scraping companies that are more reliable and able to provide data and statistics in real-time for your business analysis.
Why is Python The Best Solution for Scraping?
Web scraping can actually be used for many different goals.
Most commonly, it is used by businesses wanting to collect data on the market in which they operate.
This data is commonly related to competitors’ products or even goal becomes stock prices scraping;
it can be analyzed to determine your business’ competitiveness and product affordability.
Furthermore, it can also be used to great effect in many market research objectives.
Instead of looking at the job site every day, you can use Python to help automate your job search’s repetitive parts.
This can be a solution to speed up the data collection process.
You write your code once, and it will get the information you want many times and from many pages.
Rightfully, it is the preferred language because it can effectively handle almost all processes related to data extraction.
And to perform everything smoothly, python has advanced libraries.
We firmly believe that the real reason why Python is the most popular language is because of Scrapy and Beautiful Soup, two of the most widely employed frameworks based on Python.
Beautiful Soup is a Python library designed for fast and efficient data extraction.
Scrapy is another popular web scraping and web crawling framework performing thanks to the Twisted library and carries a set of amazing debugging tools.
Pythonic idioms for navigation, searching, and modifying a parse tree are also quite useful.
In short, Python web scraping is primarily used to find out new information about your rival firms’ products and pricing.
By collecting this data in real-time, you will have an up-to-date outlook on how well your own business is performing compared to your rival companies.
In turn, this should allow your business to be more competitive and effective!
Web Scraping Strategies
Web scraping is a somewhat complicated topic to understand if you’ve never done it before, so it’s easier to break it down into the two components that make it work.
These are the web crawler and the web scraper; working together allows you to extract the data your business needs.
The web crawler’s job is to crawl the internet, searching for your target demographic’s websites.
The automated nature of crawlers allows them to gather many URLs for relevant web content in a short span of time.
These URLs are then passed onto the web scraper from the web crawler.
The web scraper extracts relevant data from the HTML code of the web page provided to the user.
To simplify the task, developers sometimes do manual research on the topic and feed scraper with predefined sources.
That is an easier way to target a specific market where you understand your goals.
It is also important to consider the time and power required to run software effectively.
Hence it is advised to use multi-threading, which allows running scraper simultaneously.
To avoid confusion with the source, you might need to use proxy servers and rotate on time to secure data flow and protections against anti-bot detections.
Most importantly, it would be best if you always considered the legal aspect of the web scraping task.
How is it Done on A Large Scale?
Web scraping is done in a few simple steps.
The automated process means that the user doesn’t need to do much once the data extraction tools are established and running.
As such, the entire process is highly automated and straightforward to extract data from.
There is a little change when a task goes on a larger scale.
You might have software or custom code to scrape desired data from the web, but when you have to create requests for the website multiple times, things can go wrong.
For example, if you are using a personal computer, your wireless-router might not be prepared for massive tension and data flow, and it can fail.
Considering potential accidents and handling all the errors is a difficult task to do.
Imagine writing software to scrape the contact information from millions of companies, you have already tested everything to make sure it works as intended, you could even be using a dedicated server like Google Cloud or Amazon AWS,
which may require weeks to scrape everything required, and suddenly after a day of running the software, the IP address gets suspended from connecting to the source website.
Even though it worked really well on a smaller scale, after weeks of running the software, the data is half empty, if not worse!
Always consider the worst-case scenario. You don’t know what can go wrong unless it does.
Plan ahead, choose how to store data, the information you need from the source, consider grabbing information that you don’t need but won’t disturb the scraper, don’t discard it but scrape anyway.
You don’t know what kind of ideas might come to your mind after looking at a huge dataset.
Always use error handlers and dedicated servers (usually more useful), consider proxy rotation, multi-threading, be aware that you need to control the process to make sure quality is consistently high.
It takes a lot of experience, and still, every new source of scraping might be a new challenge.
The smartest thing to do here is not to rush it.
Practical Examples of Web Scraping in Python
There Are several different ways to use web scrapers, and you probably already have many amazing ideas of how to implement this potential for your benefit.
We will present some unique and trendy ideas of how it can spark some more interesting thoughts.
Frankly, everything in the existing world of information can be scraped and analyzed. Practical usages can vary from business-related interests, such as comparison of production to your competitors, Scraping stock prices into an app API to find reported analyzes of price fluctuation in real-time;
Scraping data from YellowPages to generate leads;
Scraping data from a store locator to create a list of business locations;
Scraping product data from sites like Amazon or eBay for competitor analysis; Scraping financial data for market research and insights;
You can absolutely use this tool to gain information for personal usage as well.
For example, a very surprising but arguably brilliant thing some people do is scraping sports stats for betting or fantasy leagues;
Some people scrape site data before a website migration;
You can also do Scraping of product details for comparison shopping and many, many more.
How Is Web Scraping Done In A Nutshell
Web scraping strategies can be useful for allowing your business to collect data from competitor companies in real-time automatically.
This automated process means that data is gathered rapidly and efficiently, helping to make your business production more effective and affordable.
This is in comparison to data extraction attempts in the past, which was slow and largely unreliable.