Mission to Mars

In this assignment, I built a web application that scrapes various websites for data related to the Mission to Mars and displays the information in a single HTML page.

Step 1 - Scraping

I completed initial scraping using Jupyter Notebook, BeautifulSoup, Pandas, and Requests/Splinter.

• Created 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 Paragragh Text. Assign the text to variables that you can reference later.

# Example:
news_title = "NASA's First Deep-Space CubeSats Say: 'Polo!'"

news_p = "MarCO is a pair of tiny spacecraft that launched with NASA's InSight lander today."

JPL Mars Space Images - Featured Image

• Visit the url for JPL's 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 Weather

• Visit the Mars Weather twitter account here and scrape the latest Mars weather tweet from the page. Save the tweet text for the weather report as a variable called mars_weather.

# Example:
mars_weather = 'Sol 1801 (Aug 30, 2017), Sunny, high -21C/-5F, low -80C/-112F, pressure at 8.82 hPa, daylight 06:09-17:55'

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 Hemisperes

• 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 hemipshere 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": "..."},
]

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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.

• 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.

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Hints

• 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. For this homework, you can simply overwrite the existing document each time the /scrape url is visited and new data is obtained.

• Use Bootstrap to structure your HTML template.

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