Data Scraping Using Python

Data is the most precious thing in the 21st century and the foundation of most big organizations. Lots of information is freely available on the Internet in the form of data. Organizations can use that data for analysis and making future decisions. But the main problem is that it is difficult to copy and paste every time, and this creates a problem when you are working on real-time data. The copy-paste method is not practical at scale. So, what is the solution?

1. Web Crawling

2. Web Scraping

Most of this information is free on the internet, so we can use it for research, forecasting, and insight into an organisation, depending on your goals. This post focuses on Python for web scraping. Python is a general-purpose language, and we can use several of its libraries for scraping. Let's discuss each one.

What is Web Scraping?

Web scraping is the method of extracting data from a webpage using its element IDs and paths. If you want to use public data available on the internet for further analysis, you can use web scraping to extract that data from the website and store it in your own database — provided the site's terms of service allow it and you are not collecting personal data without a lawful basis to do so.

Using Python, the Requests library is one of the most effective libraries for web scraping. All you need is the URL of the webpage; pass it into the Requests library and it will fetch the whole HTML page into your terminal. After that, you can extract the information you need using the element ID or the XPath.

What is Web Crawling?

Like web scraping, web crawling is a method of extracting valuable information from a web page by automating navigation between URLs, rather than reading a single page. It means building a bot to move through a site's pages and reach the page you actually want. (This is generally only appropriate on pages you're authorised to access — building a bot to get past a login page without permission raises both legal and terms-of-service concerns.)

Web crawling is used to extract the necessary information while filtering out the rest. For example, automated crawling of a large site like Google or an e-commerce platform to reach a specific page and scrape data from it should only be done within that platform's terms of service — most large platforms explicitly restrict or licence this kind of automated access.

Python plays a major role in scraping data. There are multiple libraries available for the statistics and analysis of the data collected.

Why?

These methods let you work with real-time data, which is helpful for analysis. You can build indicators to track progress:

Content aggregation: web scraping can be used to gather content from multiple websites and aggregate it into a single location, such as a news aggregator.

Market research: web scraping can be used to gather publicly available data on customer behaviour, such as what products people are interested in or what they search for, to understand preferences and improve strategy. Where this touches personal data, it needs to be handled under the same privacy obligations as any other collection of personal data — see the note below.

Conclusion:

Web scraping and web crawling can be valuable tools for collecting data from the internet and extracting insights from it. They can be used to gather information that is not available through other means, and to automate data collection processes.

Risk:

It's important to use web scraping and web crawling responsibly, as they can have legal and ethical implications if not used properly.

Organisations should be aware of the potential risks of web scraping and web crawling, such as violating a site's terms of service or collecting personal data without a lawful basis or consent. They should also invest in the proper tools and resources to help them collect, clean, and analyse the data effectively. With these considerations in mind, web scraping and web crawling can be powerful tools for gathering insights and making data-driven decisions.