Web Scraping - Mission to Mars

The task here is 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 following outlines what I did.

Step 1 - Scraping

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

• I created a Jupyter Notebook file called mission_to_mars.ipynb and used this to complete all of the scraping and analysis tasks:

NASA Mars News

• I scraped the NASA Mars News Site and collected the latest News Title and Paragraph Text. Assigned the text to variables that I 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

• I visited the url for JPL Featured Space Image here.

• I used 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.

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

Mars Facts

• I visited the Mars Facts webpage here and used Pandas to scrape the table containing facts about the planet including Diameter, Mass, etc.

• I used Pandas to convert the data to a HTML table string.

Mars Hemispheres

• I visited the USGS Astrogeology site here to obtain high resolution images for each of Mar's hemispheres.

• I set up splinter to click each of the links to the hemispheres in order to find the image url to the full resolution image.

• Then I saved both the image url string for the full resolution hemisphere image, and the Hemisphere title containing the hemisphere name. I created a Python dictionary to store the data using the keys img_url and title.

• Next, I appended the dictionary with the image url string and the hemisphere title to a list. This list 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": "..."},
]

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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.

• I started by converting the Jupyter notebook into a Python script called scrape_mars.py with a function called scrape that executes all of your scraping code from above and returns one Python dictionary containing all of the scraped data.

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

  • It stores the return value in Mongo as a Python dictionary.

• I created a root route / that queries the Mongo database and passes the mars data into an HTML template to display the data.

• I created a template HTML file called index.html that takes the mars data dictionary and displays all of the data in the appropriate HTML elements.
  Thi is how the final product looks like:

Feel free to reach out to me if you have any questions.

Marina Ercoli

E-mail: maercoli2017@gmail.com