title: parsing 1 table from html that has multiple tables
body: <p>i've built a crawl for a page that has only 1 table and is already setup with columns and such.  Pretty straight forward.  this website has 3 different tables, broken out in random cells through out.  I only need info from the first table.  I've created a list of the info i need.  Not sure how to organize it and get it to run by pulling urls from a csv file. </p>
  
  <p>if i break it down to just one url i can print the info from the license.  But i can't get it to work for multiple urls. i feel like i'm totally over complicating things. </p>
  
  <p>Here are some examples of the urls i'm trying to run:</p>
  
  <pre><code>http://search.ccb.state.or.us/search/business_details.aspx?id=221851
  http://search.ccb.state.or.us/search/business_details.aspx?id=221852
  http://search.ccb.state.or.us/search/business_details.aspx?id=221853
  </code></pre>
  
  <p>The code is all jacked up, but here's what i've got</p>
  
  <p>I appreciate any and all help</p>
  
  <pre><code>import csv
  from urllib.request import urlopen
  import pandas as pd
  from bs4 import BeautifulSoup as BS
  from email import encoders
  import time
  import os
  import smtplib
  from email.mime.multipart import MIMEMultipart
  from email.mime.text import MIMEText
  from email.mime.base import MIMEBase
  
  def get_page():
      contents = []
      with open('OR_urls.csv','r') as csvf:
      urls = 'csv.reader(csvf)'
      r = requests.get(url)
  
  
  data = {}
  
  data['biz_info_object'] = soup(id='MainContent_contractornamelabel')[0].text.strip()
  data['lic_number_object'] = soup(id='MainContent_licenselabel')[0].text.strip()
  data['lic_date_object']  = soup(id='MainContent_datefirstlabel')[0].text.strip()
  data['lic_status_object']  = soup(id='MainContent_licensestatuslabel')[0].text.strip()
  data['lic_exp_object']  = soup(id='MainContent_licenseexpirelabel')[0].text.strip()
  data['biz_address_object']  = soup(id='MainContent_addresslabel')[0].text.strip()
  data['biz_phone_object']  = soup(id='MainContent_phonelabel')[0].text.strip()
  data['biz_address_object']  = soup(id='MainContent_endorsementlabel')[0].text.strip()
  
  
  with open('OR_urls.csv','r') as csvf: # Open file in read mode
      urls = csv.reader(csvf)
      for url in urls:
          page = ('get_page')
          df1 = pd.read_html(page)
  </code></pre>