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import requests
import re


def get_pages(link):
    links_to_visit = []
    links_to_visit.append(link)
    while links_to_visit:
        current_link = links_to_visit.pop(0)
        page = requests.get(current_link)
        for url in re.findall('<a href="([^"]+)">', str(page.content)):
            if url[0] == '/':
                url = current_link + url[1:]
            pattern = re.compile('https?')
            if pattern.match(url):
                links_to_visit.append(url)
        yield current_link


webpage = get_pages('http://sample.com')
for result in webpage:
    print(result)

# requests for fetching html of website
import requests
# Make the GET request to a url
r = requests.get('http://www.cleveland.com/metro/index.ssf/2017/12/case_western_reserve_university_president_barbara_snyders_base_salary_and_bonus_pay_tops_among_private_colleges_in_ohio.html')
# Extract the content
c = r.content


from bs4 import BeautifulSoup
# Create a soup object
soup = BeautifulSoup(c)
# Find the element on the webpage
main_content = soup.find('div', attrs = {'class': 'entry-content'})
# Extract the relevant information as text
content = main_content.find('ul').text


# Create a pattern to match names
name_pattern = re.compile(r'^([A-Z]{1}.+?)(?:,)', flags = re.M)
# Find all occurrences of the pattern
names = name_pattern.findall(content)

# Make school patttern and extract schools
school_pattern = re.compile(r'(?:,|,\s)([A-Z]{1}.*?)(?:\s\(|:|,)')
schools = school_pattern.findall(content)

# Pattern to match the salaries
salary_pattern = re.compile(r'\$.+')
salaries = salary_pattern.findall(content)

# Messy salaries
salaries = ['$876,001', '$543,903', '$2453,896']
# Convert salaries to numbers in a list comprehension 
salaries = [int(''.join(s[1:].split(','))) for s in salaries]

# Make a horizontal bar chart
df.plot(kind='barh', x = 'President', y = 'salary')