Ethical scraping!

Not all scraping is ethical or legal, our team knows exactly what tool to use and how to implement it to avoid potential issues with the website owner or your competition!

From restricting the amount of requests we send, to the way the spiders interact with the website by adding it to an automated faceless browser to mimic human behavior!

Who cares if this is ethical or not?

This question raises critical concerns, notably regarding intellectual property rights, personal data laws, and regulations.

Harvesting personal data from a website without consent can lead to severe consequences. Additionally, issues surrounding intellectual property and the intended use of the scraped data must be carefully addressed before initiating the scraping process.

It is essential to consider these factors well in advance to avoid legal and ethical dilemmas.

DATA IS DATA and it's available online!

What are the steps?

Although all data can be useful having too much could be a problem, suppose you want to scrape 10 different websites to get house listings, you should note that not all the websites will display the same information!

For example, some might tell you the energy rating and some might not mention it so it's important to know what data you're going to need and then start the process of scraping.

Meaning if energy ratings are vital for your project it's better to not waste time scraping websites that don't have it.

I. Understanding your needs

Ii. Understanding each website's robots text

It's crucial to read and understand each website's scraping policy and follow their online maps before starting the scrapping process.

Test various tools such as BS4, Scrapy, Selenium, or API calls to see how the website will respond to each method and incorporate rotating IPs/user agents or add headless browsers if needed.

Once everything is ready, then the scraping can start!

Iii. Store the data and clean it

After scraping the data, it should be stored in a structured way that will make analyzing it this could be in an Excel file, JSON, CSV, pandas datafram, or even in a SQL database.

Then, once the data is safely stored we can now move to the process of cleaning it by removing duplicates, timing data entries, checking the char type of each entry in every column, and ensuring that it complies with the norm.

EXAMPLES INCLUDED IN THE PROJECT SECTION!

A housing website based in the UK: Rightmove.uk

Getting all the listings from the website using Python, API calls while mimicking human behavior!

Instantly collecting gas prices in the US

Getting gas prices in the state of Georgia through the use of playwright alone!

Capture house prices on PRIMELOCATION.com

Getting all the listings from the website using Python, scrapy while mimicking human behavior!