Script for scraping pre-foreclosure real estate

scrape.py

from selenium import webdriver
import pandas as pd
import time
import numpy as np
from bs4 import BeautifulSoup

#Read the data from csv file, apply transformations
df = pd.read_csv('ParcelReport2.csv')
parcels = df['Parcel number']
parcels.apply(pd.to_numeric, errors='ignore')
print(df['Parcel number'])
parcel_list=[]
for i in range(len(parcels) - 1):
	print(int(parcels[i]))
	try:
		x = int(parcels[i])
		parcel_list.append(x)
	except:
		continue
print(parcel_list)

# Selenium
browser = webdriver.Firefox()
type(browser)

for parcel in parcel_list:
	browser.get('http://info.kingcounty.gov/finance/treasury/propertytax/')
	search_box = browser.find_element_by_id('cphContent_RealAccountNumber')
	submit_button = browser.find_element_by_id('cphContent_RealSearch')
	search_box.send_keys(7228502035)
	time.sleep(1)
	submit_button.click()
	time.sleep(1)
	# street_address = browser.find_element_by_id('cphContent_MailingAddress')
	owed_2017 = browser.find_element_by_xpath('/html/body/form/table/tbody/tr/td[2]/table/tbody/tr[2]/td/div/div[5]/div[1]/table[2]/tbody/tr/td[1]/table/tbody/tr[11]/td[2]/div/strong').get_attribute('innerHTML')
	owed_2016 = browser.find_element_by_xpath('/html/body/form/table/tbody/tr/td[2]/table/tbody/tr[2]/td/div/div[5]/div[1]/table[3]/tbody/tr/td[1]/table/tbody/tr[11]/td[2]/div/strong').get_attribute('innerHTML')
	owed_2015 = browser.find_element_by_xpath('/html/body/form/table/tbody/tr/td[2]/table/tbody/tr[2]/td/div/div[5]/div[1]/table[4]/tbody/tr/td[1]/table/tbody/tr[11]/td[2]/div/strong').get_attribute('innerHTML')
	# street_address_output = street_address.get_attribute('innerHTML')
	output2017 = owed_2017.get_attribute('innerHTML')
	output2016 = owed_2016.get_attribute('innerHTML')
	output2015 = owed_2015.get_attribute('innerHTML')
	print(f'Taxes owed for plot #{parcel}: 2017 = {owed_2017}, 2016 = {owed_2016}, 2015 = {owed_2015}')