Python, BeuatifulSoup, Pandas, Splinter to scrape NASA Mars News and save data in Mongodb
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.
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Created a new repository for this project called
web-scraping
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Cloned the new repository to the computer.
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Inside the local git repository, created a directory for the web scraping activity. Used a folder name to correspond to the challenge: Missions_to_Mars.
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Added the Jupyter notebook files to this folder as well as the flask app.
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Pushed the above changes to GitHub or GitLab.
Completed the initial scraping using Jupyter Notebook, BeautifulSoup, Pandas, and Splinter.
- Created a Jupyter Notebook file called
mission_to_mars.ipynb
and used this to complete all of the scraping and analysis tasks. The following outlines what was scraped.
- Scraped the Mars News Site and collected the latest News Title and Paragraph Text. Assigned the text to variables that were referenced 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."
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Visited the url for the Featured Space Image site here.
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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.
# Example:
featured_image_url = 'https://spaceimages-mars.com/image/featured/mars2.jpg'
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Visited the Mars Facts webpage here and use Pandas to scrape the table containing facts about the planet including Diameter, Mass, etc.
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Used Pandas to convert the data to a HTML table string.
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Saved a 'mars_table.html' file to view results that were retrieved, then saved in notebook to view any udpates
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Visited the astrogeology site here to obtain high resolution images for each of Mar's hemispheres.
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Needed to click each of the links to the hemispheres in order to find the image url to the full resolution image.
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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
andtitle
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Appended the dictionary with the image url string and the hemisphere title to a list. This list will 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": "..."},
]
Used MongoDB with Flask templating to create a new HTML page that displays all of the information that was scraped from the URLs above.
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Started by converting the Jupyter notebook into a Python script called
scrape_mars.py
with a function calledscrape
that executes all of the scraping code from above and returns one Python dictionary containing all of the scraped data. -
Next, created a route called
/scrape
that imports thescrape_mars.py
script and called thescrape
function.- Stored the return value in Mongo as a Python dictionary.
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Created a root route
/
that queries the Mongo database and passed the mars data into an HTML template to display the data. -
Created a template HTML file called
index.html
that takes 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 should look like, but feel free to create your own design.
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Use Splinter to navigate the sites when needed and BeautifulSoup to help find and parse out the necessary data.
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Use Pymongo for CRUD applications for your database. One 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.
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Responsive (chrome) - some aspects
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