In the Big Data world, Web Scraping or Data Extraction services are the main requisites for Big Data Analytics. Extracting data from the web has become very necessary for companies to survive in business.
Web scraping is also known as web harvesting or data extraction. It is used for extracting data from the website. Web scraping a web page included fetching and extracting. Fetching is something what we are downloading. So we can say that web crawling is a most important component of web scraping, to fetch pages for later processing. Once fetched, then the extraction takes place. It is also used for contact scrapping, and as a component of applications used for web indexing, web mining, and data mining.
Data can be extracted from the web source using a numerous methods, popular websites like Google, Facebook, and Twitter to extract the available data in a structured manner. This prevents the use of other methods that may not be preferred by the API provider.
To carry out web scraping using Python, the very first step is to install the Python Environment, which enables to run code written in the python language. It understands the website and how the things are laid out and Python we are using because it is ridiculous fast as well as easy to this kind of web- scraping.
The very first step is to check the website's Terms and Conditions before scrapping. Should read all the important legal use of tha data. One should take care that the fetched data should not be used for commercial purposes.
Don't request data from the website too aggressively with your program termed as spamming, as this may break the websiteand it is the voilaton of the rules. The program should behave in a correct manner that means it should work as a human. One request for one webpage per second is good practice.
The layout of a website may change from time to time, so make sure to revisit the site and rewrite your code as needed.
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