Web Scraping - Mission to Mars

This challenge demonstrates to build a web application that scrapes various websites for data related to the Mission to Mars and displays the information in a single HTML page.

The technologies used in this project are:

BeautifulSoup | requests | Selenium | Splinter | MongoDB | Pandas

Websites scraped:

• NASA Mars News Site : Collect the latest News Title and Paragraph Text.
• JPL Featured Space Image : Find the image url for the current Featured Mars Image.
• Mars Weather twitter account : Scrape the latest Mars weather tweet from the page.
• Mars Facts webpage : Scrape the table containing facts about the planet including Diameter, Mass, etc using Pandas.
• USGS Astrogeology site : Obtain high resolution images for each of Mar's hemispheres.

This challenge is done in 2 steps:

Step 1 - Scraping

Completed initial scraping using BeautifulSoup, Pandas, Requests, Selenium and Splinter.

Step 2 - MongoDB and Flask Application

Use MongoDB with Flask templating to create a new HTML page that displays all of the information that was scraped from the URLs above.

• Created a route called /scrape that will import scrape_mars.py script and call the scrape function.
• Store the return value in Mongo as a Python dictionary.
• Created a root route / that will query your Mongo database and pass the mars data into an HTML template to display the data.
• Created a template HTML file called index.html that will take the mars data dictionary and display all of the data in the appropriate HTML elements.