文件列表:

.DS_Store (10244, 2018-05-11)
.ipynb_checkpoints (0, 2018-05-11)
.ipynb_checkpoints\mission_to_mars-checkpoint.ipynb (15652, 2018-05-11)
Images (0, 2018-05-11)
Images\final_app_part1.png (1783414, 2018-05-11)
Images\final_app_part2.png (1847066, 2018-05-11)
Images\mission_to_mars.jpg (167381, 2018-05-11)
Images\requirements.txt (32, 2018-05-11)
__pycache__ (0, 2018-05-11)
__pycache__\scrape_mars.cpython-36.pyc (2596, 2018-05-11)
app.py (551, 2018-05-11)
index.html (3200, 2018-05-11)
mission_to_mars.ipynb (15652, 2018-05-11)
scrape_mars.py (3840, 2018-05-11)
table.html (1099, 2018-05-11)
templates (0, 2018-05-11)
templates\index.html (3300, 2018-05-11)

# Mission to Mars  In this assignment, 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. ## Step 1 - Scraping I completed 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 you need to scrape. ### NASA Mars News * Scrape the [NASA Mars News Site](https://github.com/seaaaaany/Mission_to_Mars/blob/master/https://mars.nasa.gov/news/) and collect the latest News Title and Paragragh Text. Assign the text to variables that you can reference later. ```python # Example: news_title = "NASA's First Deep-Space CubeSats Say: 'Polo!'" news_p = "MarCO is a pair of tiny spacecraft that launched with NASA's InSight lander today." ``` ### JPL Mars Space Images - Featured Image * Visit the url for JPL's Featured Space Image [here](https://github.com/seaaaaany/Mission_to_Mars/blob/master/https://www.jpl.nasa.gov/spaceimages/?search=&category=Mars). * 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. ```python # Example: featured_image_url = 'https://www.jpl.nasa.gov/spaceimages/images/largesize/PIA16225_hires.jpg' ``` ### Mars Weather * Visit the Mars Weather twitter account [here](https://github.com/seaaaaany/Mission_to_Mars/blob/master/https://twitter.com/marswxreport?lang=en) and scrape the latest Mars weather tweet from the page. Save the tweet text for the weather report as a variable called `mars_weather`. ```python # Example: mars_weather = 'Sol 1801 (Aug 30, 2017), Sunny, high -21C/-5F, low -80C/-112F, pressure at 8.82 hPa, daylight 06:09-17:55' ``` ### Mars Facts * Visit the Mars Facts webpage [here](https://github.com/seaaaaany/Mission_to_Mars/blob/master/http://space-facts.com/mars/) 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 Hemisperes * Visit the USGS Astrogeology site [here](https://github.com/seaaaaany/Mission_to_Mars/blob/master/https://astrogeology.usgs.gov/search/results?q=hemisphere+enhanced&k1=target&v1=Mars) 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 hemipshere 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. ```python # 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. For this homework, 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. ## Copyright Coding Boot Camp 2017. All Rights Reserved.

近期下载者：

相关文件：

评论：[我要评论] [举报此文件]

收藏者：