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HTML tables are a very common format for displaying information. When building scrapers you often need to extract data from HTML tables on web pages and turn it into some different structured format, for example, JSON, CSV, or Excel. In this article, we discuss how to extract data from HTML tables using Python and Scrapy.

Web crawlers are becoming increasingly popular in the era of big data, especially now with the advent of Large Language Models (LLMs) such as ChatGPT and LLaMA. The sheer amount of data that is publicly available from the web has a wide variety of applications including market research, sentiment analysis, and predictive modeling.

Whether you're trying to analyze market trends or gather data for research, web scraping can be a useful skill to have. This technique allows you to extract specific pieces of data from websites automatically and process them for further analysis or use.

Let's start with what is XPath? XPath is a powerful language that is often used for scraping the web. It allows you to select nodes or compute values from an XML or HTML document and is actually one of the languages that you can use to extract web data using Scrapy.