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Web Scraping - Mission to Mars

mission_to_mars

Step 1 - Scraping

Complete your initial scraping using Jupyter Notebook, BeautifulSoup, Pandas, and Requests/Splinter.

  • Create a Jupyter Notebook file called mission_to_mars.ipynb and use this to complete all of your scraping and analysis tasks. The following outlines what you need to scrape.

NASA Mars News

  • Scrape the NASA Mars News Site and collect the latest News Title and Paragraph Text. Assign the text to variables that you can reference later.
# Example:
news_title = "NASA's Next Mars Mission to Investigate Interior of Red Planet"

news_p = "Preparation of NASA's next spacecraft to Mars, InSight, has ramped up this summer, on course for launch next May from Vandenberg Air Force Base in central California -- the first interplanetary launch in history from America's West Coast."

JPL Mars Space Images - Featured Image

  • Visit the url for JPL Featured Space Image here.

  • 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.

# Example:
featured_image_url = 'https://www.jpl.nasa.gov/spaceimages/images/largesize/PIA16225_hires.jpg'

Mars Facts

  • Visit the Mars Facts webpage here 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 here to obtain high resolution images for each of Mar's hemispheres.

  • You will need to click each of the links to the hemispheres in order to find the image url to the full resolution image.

  • 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.

# Example:
hemisphere_image_urls = [
    {"title": "Valles Marineris Hemisphere", "img_url": "..."},
    {"title": "Cerberus Hemisphere", "img_url": "..."},
    {"title": "Schiaparelli Hemisphere", "img_url": "..."},
    {"title": "Syrtis Major Hemisphere", "img_url": "..."},
]

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.

  • Start by converting your Jupyter notebook into a Python script called scrape_mars.py with a function called scrape that will execute all of your scraping code from above and return one Python dictionary containing all of the scraped data.

  • Next, create a route called /scrape that will import your scrape_mars.py script and call your scrape function.

    • Store the return value in Mongo as a Python dictionary.

  • Create a root route / that will query your 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. Use the following as a guide for what the final product should look like, but feel free to create your own design.

final_app_part1.png final_app_part2.png

  • Use Splinter to navigate the sites when needed and BeautifulSoup to help find and parse out the necessary data.

  • Use Pymongo for CRUD applications for your database.

  • Use Bootstrap to structure your HTML template.

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