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Most of this data is unstructured data in an HTML format which is then converted into structured data in a spreadsheet or a database so that it can be used in various applications\n\n## 1. Web Scraping\n\n Web scraping is an automatic method to obtain large amounts of data from websites. Most of this data is unstructured \ndata in an HTML format which is then converted into structured data in a spreadsheet \nor a database so that it can be used in various applications. There are many different ways to perform web scraping to \nobtain data from websites. These include using online services, particular API’s or even creating your code for web \nscraping from scratch. Many large websites, like Google, Twitter, Facebook, StackOverflow, etc. have API’s that allow\nyou to access their data in a structured format. This is the best option, but there are other sites that don’t allow\nusers to access large amounts of data in a structured form or they are simply not that technologically advanced.\nIn that situation, it’s best to use Web Scraping to scrape the website for data.\n\n## 2. How  Web Scrapers Work?\nWeb Scrapers can extract all the data on particular sites or the specific data that a user wants. Ideally,\n it’s best if you specify the data you want so that the web scraper only extracts that data quickly. For \n example, you might want to scrape an Amazon page for the types of juicers available, but you might only \n want the data about the models of different juicers and not the customer reviews. \n\nSo, when a web scraper needs to scrape a site, first the URLs are provided. Then it loads all the HTML code \nfor those sites and a more advanced scraper might even extract all the CSS and Javascript elements as well. \nThen the scraper obtains the required data from this HTML code and outputs this data in the format specified \nby the user. Mostly, this is in the form of an Excel spreadsheet or a CSV file, but the data can also be saved \nin other formats, such as a JSON file.\n\n\n## 3. Different Types of Web Scrapers\nWeb Scrapers can be divided on the basis of many different criteria, including Self-built or Pre-built Web Scrapers,\n Browser extension or Software Web Scrapers, and Cloud or Local Web Scrapers.\n\neg:\n  1. Price Monitoring\n  2. Market Research\n  3. News Monitoring\n  4. Sentiment Analysis\n  5. Email Marketing\n\n## Reference Documents\n\nPypy beautifulsoup4 -  https://pypi.org/project/beautifulsoup4/\n\nBeautiful Soup Documentation - https://www.crummy.com/software/BeautifulSoup/bs4/doc/\n\nWeb scraping wikipedia - https://en.wikipedia.org/wiki/Web_scraping\n\nWeb scraping geek for geek - https://www.geeksforgeeks.org/what-is-web-scraping-and-how-to-use-it/\n\nexample web scraping youtube video - https://www.youtube.com/watch?v=ng2o98k983k\n\n## pip\n``` pip install beautifulsoup4 ```\n\n``` pip install requests ```\n\n```  pip install lxml  ```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmuhammedrahil%2Fweb-scraping-using-python","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmuhammedrahil%2Fweb-scraping-using-python","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmuhammedrahil%2Fweb-scraping-using-python/lists"}