Mission to Mars - Web Scraping Challenge

Table of Contents

• Step 1 - Scraping
  • NASA Mars News
  • JPL Mars Space Images - Featured Image
  • Mars Weather
  • Mars Facts
  • Mars Hemispheres
• Step 2 - MongoDB and Flask Application

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.

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.

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.

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.

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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.