Mission-to-Mars

Background

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

Objectives

Step 1 - Scraping

NASA Mars News

• Scrape the NASA Mars News Site and collect the latest News Title and Paragraph Text

JPL Mars Space Images - Featured Image

• Visit the URL for the JPL Featured Space Image
• Use Splinter to navigate the site and find the image URL for the current Featured Mars Image and assign the URL string to a variable called featured_image_url
• Make sure to find the image URL to the full size .jpg image
• Make sure to save a complete URL string for this image

Mars Weather

• Visit the Mars Weather Twitter account and scrape the latest Mars Weather Tweet from the page
  • Save the Tweet text for the weather report as a variable called mars_weather

Mars Facts

• Visit the Mars Facts webpage and use Pandas to scrape the table containing facts about the planet including Diameter, Mass, etc.
• Use Pandas to convert the data to a HTML table string

Mars Hemispheres

• Visit the USGS Astrogeology site to obtain high resolution images for each of Mar's hemispheres
• Save both the image URL string for the full resolution hemisphere image, and the Hemisphere title containing the hemisphere name.
  • Use a Python dictionary to store the data using the keys img_url and title
• Append the dictionary with the image URL string and the hemisphere title to a list
  • This list will contain one dictionary for each hemisphere

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
• Convert Jupyter Notebook into a Python Script called scrape_mars.py with a function called scrape that will execute all of the scraping code from above and return one Python Dictionary containing all of the scraped data
• Create a route called /scrape that will import the scrape_mars.py script and call the scrape function
  • Store the return value in Mongo as a Python Dictionary
• Create a root route / that will query the Mongo database and pass the Mars Data into an HTML template to display the data
• Create 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