Scrape tables with python

Question

I am trying to scrape tables and convert them into data.tables in python, but I have little luck of election data in USA. This is html of the data I want to scrape.

Answer 1

So after some time I managed to scrape all data from this website. So the main problem was, that website was embedded in JavaScript, so I could not scrape with Beautifulsoup. So I used selenium + beautifulsoup4, to convert page into html and scrape it.

from selenium import webdriver
import time
import os
from bs4 import BeautifulSoup
chrome_path = r"C:\Users\Desktop\chromedriver_win32\chromedriver.exe"
driver = webdriver.Chrome(chrome_path)
driver.get('http://www.politico.com/2016-election/primary/results/map/president/arizona/')
time.sleep(80)
driver.execute_script("window.scrollTo(0, document.body.scrollHeight);")
time.sleep(5)
html = driver.page_source
soup = BeautifulSoup(html,'html.parser')
for posts in soup.findAll('table',{'class':'results-table'}):
for tr in posts.findAll('tr'):
    popular = [td for td in tr.stripped_strings]
    print(popular)

Because it is dynamic webpage, I needed to simulate some things with selenium. Like scrolling page down. I used time.sleep(60) so the page could load. It loads really slowly, so I set time to 60s. Hope it helps someone.

Answer 2

import requests, bs4

r = requests.get('http://www.politico.com/2016-election/results/map/president/alabama/')
soup = bs4.BeautifulSoup(r.text, 'lxml')
contents = soup.find(class_='contrast-white')
for table in contents.find_all(class_='results-group'):
    title = table.find(class_='title').text
    for tr in table.find_all('tr'):
        _, name, percentage, popular = [td for td in tr.stripped_strings]
        print(title, name, percentage, popular)

out:

Autauga County D. Trump 73.4% 18,110
Autauga County H. Clinton 24.0% 5,908
Autauga County G. Johnson 2.2% 538
Autauga County J. Stein 0.4% 105
Baldwin County D. Trump 77.4% 72,780
Baldwin County H. Clinton 19.6% 18,409
Baldwin County G. Johnson 2.6% 2,448
Baldwin County J. Stein 0.5% 453
Barbour County D. Trump 52.3% 5,431
Barbour County H. Clinton 46.7% 4,848
Barbour County G. Johnson 0.9% 93
Barbour County J. Stein 0.2% 18
Bibb County D. Trump 77.0% 6,733
Bibb County H. Clinton 21.4% 1,874
Bibb County G. Johnson 1.4% 124
Bibb County J. Stein 0.2% 17
Blount County D. Trump 89.9% 22,808
Blount County H. Clinton 8.5% 2,150
Blount County G. Johnson 1.3% 337
Blount County J. Stein 0.4% 89
Bullock County H. Clinton 75.1% 3,530
Bullock County D. Trump 24.2% 1,139
Bullock County G. Johnson 0.5% 22
Bullock County J. Stein 0.2% 10
Butler County D. Trump 56.3% 4,891
Butler County H. Clinton 42.8% 3,716
Butler County G. Johnson 0.7% 65
Butler County J. Stein 0.1% 13
Calhoun County D. Trump 69.2% 32,803
Calhoun County H. Clinton 27.9% 13,197
Calhoun County G. Johnson 2.4% 1,114
Calhoun County J. Stein 0.6% 262
Chambers County D. Trump 56.6% 7,803
Chambers County H. Clinton 41.8% 5,763
Chambers County G. Johnson 1.2% 168
Chambers County J. Stein 0.3% 44
Cherokee County D. Trump 83.9% 8,809
Cherokee County H. Clinton 14.5% 1,524
Cherokee County G. Johnson 1.4% 145
Cherokee County J. Stein 0.2% 25

The rest is empty, nothing in there.