p { margin-bottom: 0.1in; direction: ltr; line-height: 115%; text-align: left; orphans: 2; widows: 2 } a:link { color: #0000ff 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. Please see attached word document for more detail. This requires using various Pythong, Flask, HTML, and other applications in combination.
Please comment the code thoroughly as I will need to explain it. Include all of the scrapping code in the Jupyter Notebook file. 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 you need to do. 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. # Example: news_title = "NASA's Next Mars Mission to Investigate Interior of Red Planet" news_p = "Preparation of NASA's next spacecraft to Mars, InSight, has ramped up this summer, on course for launch next May from Vandenberg Air Force Base in central California -- the first interplanetary launch in history from America's West Coast." 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. # Example: featured_image_url = 'https://www.jpl.nasa.gov/spaceimages/images/largesize/PIA16225_hires.jpg' 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. # 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": "..."}, ] 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. 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. 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.