README-Mission_to_Mars.md

Web Scraping Homework - Mission to Mars

In this challenge I built a web application that scrapes various websites for data related to the NASA Mission to Mars and displays the information in a single HTML page. The following outlines what was completed.

Step 1 - Web Scraping

Completed my initial scraping of the information below using Jupyter Notebook, BeautifulSoup, Pandas, and Requests/Splinter. Created a Jupyter Notebook file called mission_to_mars.ipynb to complete all of my scraping and analysis work. Used Splinter to navigate the sites when needed and BeautifulSoup to help find and parse out the necessary data.

NASA Mars News

• I scraped the NASA Mars News Site and used python script to collect the latest News Title and Paragraph Text.

JPL Mars Space Images - Featured Image

• Visited the url for JPL Featured Space Image here.

• 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.

Mars Weather

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

Mars Facts

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

Mars Hemispheres

• Visited the USGS Astrogeology site here to obtain high resolution images for each of Mar's 4 hemispheres.

• Used a Python dictionary to store both image url string for the full resolution hemisphere image, and the Hemisphere title containing the hemisphere name.

• Appended the dictionary with the image url string and the hemisphere title to a list with one dictionary for each hemisphere.

---

Step 2 - MongoDB and Flask Application

Used MongoDB with Flask templating to create a new HTML page that displays all of the information that was scraped from the URLs above.

• The Jupyter notebook was converted into a Python script with a function that executed all scraping code to return one Python dictionary containing all of the scraped data.

• Next, a route was created to import the Python dictionary and store the return value in Mongo as a Python dictionary.

• Created a root route to query the Mongo database and pass the mars data into an HTML template to be displayed.

• Created a template HTML file to take the mars data dictionary and display all of the data in the appropriate HTML elements.