WebScrapingMarsInfo
In this project I utilized Jupyter Notebook, BeautifulSoup, Pandas, and Requests/Splinter to scrape information about Mars from several different websites, and utilized MongoDB with Flask templating to create an HTML page with all the information that was scraped.
Overview:
The project involves utilizing various tools and technologies like Jupyter Notebook, BeautifulSoup, Pandas, and Requests/Splinter to scrape information about Mars from multiple websites. The scraped data is then stored in MongoDB and displayed on an HTML page using Flask templating.
Features:
- Web Scraping: The project leverages libraries like BeautifulSoup and Requests/Splinter to extract information from different websites.
- Data Storage: The scraped data is stored in a MongoDB database for easy retrieval and manipulation.
- Data Display: The project utilizes Flask templating to dynamically generate an HTML page with the scraped data.
- Multiple Websites: Information is collected from several websites, providing comprehensive data about Mars.
Summary:
The project demonstrates the use of web scraping techniques to gather data about Mars from various websites. It uses Jupyter Notebook, BeautifulSoup, Pandas, and Requests/Splinter to extract the required information. The data is stored in MongoDB and presented on an HTML page through Flask templating. This project can serve as a good reference for scraping data from multiple sources and displaying it in a user-friendly format.