Mission to Mars

A web-scraping application, which aggregates information from various online resources and displays the results on a landing page

This application utilized Jupyter Notebook, BeautifulSoup, Pandas, Splinter, PyMongo, Flask Web Templating, & requests. CRUD

Scraping

Scraping was developed in Jupyter Notebook File mission_to_mars.ipynb, which was later converted into a function called scrape within a new file called scrape_mars.py

Information was gathered from:

• NASA Mars News Site, Scraped: Latest News Title & Paragraph Text,
• Jet Propulsion Laboratories (JPL), Scraped: Featured Image,
• Mars Weather - Twitter Account, Scraped: Latest Weather Tweet,
• Space Facts Website, Scraped: Tables Containing Planet Attributes & Information,
• United States Geological Survery (USGS), Scraped: Hi-Res Images of each of Mars' Hemispheres,

MongoDB and Flask Application

The file app.py contains the main application, which populates an HTML page from the contents of a database.

The aggregated information from above Scraping is stored in a MongoDB database, and is updated with the latest information every time the application is run (CRUD).

Flask web-templating was used to create a few routes, including a new landing page, which displays the gathered information from the database.

The routes are:

• / - The Landing Page (Home), which queries the database, & populates the contents,
• /scrape - Runs scrape function from scrape_mars.py, and updates database,