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Flask generated web page displaying a collection of mars info scraped from 4 other sites via Splinter and Beautiful Soup and stored in a MongoDB... written in Python, HTML and CSS with Jupyter Notebook and VSCode.

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miwermi/mission-to-mars

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                                                                                       Michelle Werner (6/12/2022)

Mission to Mars: Web Scraping for NASA


graphic: Amazing Mars

Project Overview

Junior data Scientist Robin does freelance astronomy work and hopes to work for NASA someday. She spends a lot of time reading web articles and is currently fascinated with Mars. For this project, I've helped Robin code a web scraper that puts together Mars data from several different sites:

Figure 1: Robin and her telescope looking up at amazing Mars!

graphic: Flask to Mongo Web Scraping

Tech Specs

To show Robin my work, I've created an HTML index that populates with Flask from Python code and a Mongo database. Each time my python code is run, each of the sites we're pulling from is visited and scraped with the help of Splinter and Beautiful Soup, and my Python code collects and stores as new batch of info in my Mongo database. Last but not least, the Mongo data is called back and my web page is rebuilt!

Figure 2: Web Scraping with Splinter, Flask, Mongo and Python

Webpage Preview

Since the page is only local at this point, I can't share a link to it, but I've included a preview below. I added some custom CSS in addition to the standard Bootstrap layout and swiped a google font, Secular One, and a Mars background image from the Mars NASA site (https://mars.nasa.gov/system/news_items/main_images/8944_1-PIA24546-1280.jpg) to jazz it all up. Each time a user clicks the button... the sites are scraped again and the page reloads with new info. I hope Robin likes it!

graphic: Mission to Mars Site Index

Figure 3: MISSION TO MARS page

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Flask generated web page displaying a collection of mars info scraped from 4 other sites via Splinter and Beautiful Soup and stored in a MongoDB... written in Python, HTML and CSS with Jupyter Notebook and VSCode.

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