Web-Scraping-Project--Mission-to-Mars

In this Project, I will 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 needed to do.

Step 1 - Scraping

Completed the 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 I needed to scrape.

NASA Mars News

• Scraped the Mars News Site and collected the latest News Title and Paragraph Text. Assigned the text to variables that I could reference later.

JPL Mars Space Images - Featured Image

• Visited the url for the Featured Space Image site 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.

• Made sure to find the image url to the full size .jpg image.

• Made sure to save a complete url string for this image.

# Example:
featured_image_url = 'https://spaceimages-mars.com/image/featured/mars2.jpg'

Mars Facts

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

• Used Pandas to convert the data to a HTML table string.

Mars Hemispheres

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

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

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

• Appended the dictionary with the image url string and the hemisphere title to a list. This list contained 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

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

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

• Next, created a route called /scrape that imported my scrape_mars.py script and called my scrape function.

  • Stored the return value in Mongo as a Python dictionary.

• Created a root route / that queried my Mongo database and passed the mars data into an HTML template to display the data.

• Created a template HTML file called index.html that would take the mars data dictionary and display all of the data in the appropriate HTML elements. Used the following as a guide for what the final product would look like.