Web-Scraping-to-HTML-with-Database

This program uses Web Scraping with Jupyter Notebooks, MongoDB, PyMongo, Flask, and Splinters - all in order to render data on a dashboard and store it in a MongoDB database.

To perform this task, I 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. The following outlines the steps required.

Step 1 - Scraping

• Scraped data for the application using Jupyter Notebook, BeautifulSoup, Pandas, and Requests/Splinter.

• Created a Jupyter Notebook file called mission_to_mars.ipynb and used this to complete all scraping and analysis tasks. The following outlines what data needed to be scraped.

NASA Mars News

• Scraped the NASA Mars News Site and collected the latest News Title and Paragraph Text. Assigned the text to variables that can be referenced later.

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 assigned 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.

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

• Found the image urls 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 contains one dictionary for each hemisphere.

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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.

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

• Created a route called /scrape that will import scrape_mars.py script and call scrape function.

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

• Created a root route / that will query Mongo database and pass the mars data into an HTML template to display the data.

• Created 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.