Mission_to_Mars_Challenge.py

#!/usr/bin/env python
# coding: utf-8

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# Import Splinter, BeautifulSoup, and Pandas
from splinter import Browser
from bs4 import BeautifulSoup as soup
import pandas as pd
from webdriver_manager.chrome import ChromeDriverManager


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# Set the executable path and initialize Splinter
executable_path = {'executable_path': ChromeDriverManager().install()}
browser = Browser('chrome', **executable_path, headless=False)


# ### Visit the NASA Mars News Site

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# Visit the mars nasa news site
url = 'https://redplanetscience.com/'
browser.visit(url)

# Optional delay for loading the page
browser.is_element_present_by_css('div.list_text', wait_time=1)


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# Convert the browser html to a soup object and then quit the browser
html = browser.html
news_soup = soup(html, 'html.parser')

slide_elem = news_soup.select_one('div.list_text')


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slide_elem.find('div', class_='content_title')


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# Use the parent element to find the first a tag and save it as `news_title`
news_title = slide_elem.find('div', class_='content_title').get_text()
news_title


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# Use the parent element to find the paragraph text
news_p = slide_elem.find('div', class_='article_teaser_body').get_text()
news_p


# ### JPL Space Images Featured Image

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# Visit URL
url = 'https://spaceimages-mars.com'
browser.visit(url)


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# Find and click the full image button
full_image_elem = browser.find_by_tag('button')[1]
full_image_elem.click()


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# Parse the resulting html with soup
html = browser.html
img_soup = soup(html, 'html.parser')
img_soup


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# find the relative image url
img_url_rel = img_soup.find('img', class_='fancybox-image').get('src')
img_url_rel


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# Use the base url to create an absolute url
img_url = f'https://spaceimages-mars.com/{img_url_rel}'
img_url


# ### Mars Facts

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df = pd.read_html('https://galaxyfacts-mars.com')[0]
df.head()


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df.columns=['Description', 'Mars', 'Earth']
df.set_index('Description', inplace=True)
df


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df.to_html()


# # D1: Scrape High-Resolution Mars’ Hemisphere Images and Titles

# ### Hemispheres

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# 1. Use browser to visit the URL 
url = 'https://marshemispheres.com/'
browser.visit(url)


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# 2. Create a list to hold the images and titles.
hemisphere_image_urls = []

# 3. Write code to retrieve the image urls and titles for each hemisphere.

## Create an HTML object, assigned to the html variable:
html = browser.html
## Use BeautifulSoup (as soup) to parse the html object:
hemi_soup = soup(html, 'html.parser')


# Create a list with all relevant elements (4 elements for 4 hemispheres) in it:
divs = hemi_soup.find_all('div', class_='description')
for div in divs:
    a = div.find_all('a', class_='itemLink product-item')
    # From each specific element, get the href (to go to a specific hemisphere's own page):
    for ana in a:
        href = ana.get('href')
        print(href)
        
        # Create dictionary:
        hemispheres = {}
        
        # From the specific href, string together a complete URL (to be able to visit the specific hemisphere's own page):
        next_pg_url = url + href
        print(next_pg_url)
        browser.visit(next_pg_url)

        # Parse the new (a specific hemisphere's) page:    
        html = browser.html
        # Use BeautifulSoup (as soup) to parse the html object:
        sphere_soup = soup(html, 'html.parser')

        # Get the title for that hemisphere:
        title = sphere_soup.find('h2', class_='title').get_text()
        print(title)

        # Get the (complete) image URL for that hemisphere (and visit the full image thereby):
        img_rel_url = sphere_soup.find('img', class_='wide-image').get('src')
        print(img_rel_url)
        img_url = url + img_rel_url
        print(img_url)
        browser.visit(img_url)

        # Add the image URL and title to the dictioanry:
        hemispheres['img_url'] = img_url
        hemispheres['title'] = title
        hemisphere_image_urls.append(hemispheres)  


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# 4. Print the list that holds the dictionary of each image url and title.
hemisphere_image_urls


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# 5. Quit the browser
browser.quit()


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