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.

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

• Created a Jupyter Notebook file called mission_to_mars.ipynb and used this to complete scraping and analysis tasks.

• Scrape the NASA Mars News Site and collect the latest News Title and Paragraph Text. Assign the text to variables that can be referenced later.

• Visit the url for JPL's Featured Space Image (https://www.jpl.nasa.gov/spaceimages/?search=&category=Mars).

• 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.

• 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.

• 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.

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

• 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": "..."},
]

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

• Next, create a route called /scrape that will import 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 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 Splinter to navigate the sites when needed and BeautifulSoup to help find and parse out the necessary data.

• Use Pymongo for CRUD applications for database. For this homework, can simply overwrite the existing document each time the /scrape url is visited and new data is obtained.

• Use Bootstrap to structure HTML template.