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Pagination not showing up in parsed content (BeautifulSoup)

I am new to python programming and I have a problem with pagination while using beautiful soup. all the parsed content show up except the pagination contents. image of content not showing up I have highlighted the lines which does not show up.
Website link.
from bs4 import BeautifulSoup
import requests
import time
import pandas as pd
from lxml import html
url = "https://www.yellowpages.lk/Medical.php"
result = requests.get(url)
time.sleep(5)
doc = BeautifulSoup(result.content, "lxml")
time.sleep(5)
Table = doc.find('table',{'id':'MedicalFacility'}).find('tbody').find_all('tr')
Page = doc.select('.col-lg-10')
C_List = []
D_List = []
N_List = []
A_List = []
T_List = []
W_List = []
V_List = []
M_List = []
print(doc.prettify())
print(Page)
while True:
for i in range(0,25):
Sort = Table[i]
Category = Sort.find_all('td')[0].get_text().strip()
C_List.insert(i,Category)
District = Sort.find_all('td')[1].get_text().strip()
D_List.insert(i,District)
Name = Sort.find_all('td')[2].get_text().strip()
N_List.insert(i,Name)
Address = Sort.find_all('td')[3].get_text().strip()
A_List.insert(i,Address)
Telephone = Sort.find_all('td')[4].get_text().strip()
T_List.insert(i,Telephone)
Whatsapp = Sort.find_all('td')[5].get_text().strip()
W_List.insert(i,Whatsapp)
Viber = Sort.find_all('td')[6].get_text().strip()
V_List.insert(i,Viber)
MoH_Division = Sort.find_all('td')[7].get_text().strip()
M_List.insert(i,MoH_Division)
I tried using .find() with class and .select('.class') to see if the pagination contents show up so far nothing has worked

The pagination is more or less superfluous in that page: the data is loaded anyway, and Javascript is generating pagination just for display purposes: Requests will get full data anyway.
Here is one way of getting that information in full:
import requests
from bs4 import BeautifulSoup as bs
import pandas as pd
pd.set_option('display.max_columns', None)
pd.set_option('display.max_colwidth', None)
headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/105.0.0.0 Safari/537.36'
}
url = 'https://www.yellowpages.lk/Medical.php'
r = requests.get(url, headers=headers)
soup = bs(r.text, 'html.parser')
table = soup.select_one('table[id="MedicalFacility"]')
df = pd.read_html(str(table))[0]
print(df)
Result in terminal:
Category District Name Address Telephone WhatsApp Viber MoH Division
0 Pharmacy Gampaha A & B Pharmacy 171 Negambo Road Veyangoda 0778081515 9.477808e+10 9.477808e+10 Aththanagalla
1 Pharmacy Trincomalee A A Pharmacy 350 Main Street Kanthale 0755576998 9.475558e+10 9.475558e+10 Kanthale
2 Pharmacy Colombo A Baur & Co Pvt Ltd 55 Grandpass Rd Col 14 0768200100 9.476820e+10 9.476820e+10 CMC
3 Pharmacy Colombo A Colombo Pharmacy Ug 93 97 Peoples Park Colombo 11 0773771446 9.477377e+10 NaN CMC
4 Pharmacy Trincomalee A R Pharmacy Main Street Kinniya-3 0771413838 9.477500e+10 9.477500e+10 Kinniya
... ... ... ... ... ... ... ... ...
1968 Pharmacy Ampara Zam Zam Pharmacy Main Street Akkaraipattu 0672277698 9.477756e+10 9.477756e+10 Akkaraipattu
1969 Pharmacy Batticaloa Zattra Pharmacy Jummah Mosque Rd Oddamawadi-1 0766689060 9.476669e+10 NaN Oddamavady
1970 Pharmacy Puttalam Zeenath Pharmacy Norochcholei 0728431622 NaN NaN Kalpitiya
1971 Pharmacy Puttalam Zidha Pharmacy Norochcholei 0773271222 NaN NaN Kalpitiya
1972 Pharmacy Gampaha Zoomcare Pharmacy & Grocery 182/B/1 Rathdoluwa Seeduwa 0768378112 NaN NaN Seeduwa
1973 rows × 8 columns
See pandas documentation here. Also BeautifulSoup documentation, and lastly, Requests documentation.

If you are using pandas, all you need is just a couple of lines of code to put the entire table into a dataframe.
All you need is pandas.read_html() function as follows:
Code:
import pandas as pd
df = pd.read_html("https://www.yellowpages.lk/Medical.php")[0]
print(df)
Output:

BeautifultSoup Python get content

I don't usually play with BeautifulSoup in Python so I am struggling to find the value 8.133,00 that matches with the Ibex 35 in the web page: https://es.investing.com/indices/indices-futures
So far I am getting all the info of the page, but I can't filter to get that value:
site = 'https://es.investing.com/indices/indices-futures'
hardware = {'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64; rv:106.0) Gecko/20100101
Firefox/106.0'}
request = Request(site,headers=hardware)
page = urlopen(request)
soup = BeautifulSoup(page, 'html.parser')
print(soup)
I appreciate a hand to get that value.
Regards

Here is a way of getting that bit of information - a dataframe with all the info in that table containing IBEX 35, DAX, and so on, you can then slice that dataframe as you wish.
import pandas as pd
from bs4 import BeautifulSoup as bs
import cloudscraper
scraper = cloudscraper.create_scraper(disableCloudflareV1=True)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_colwidth', None)
url = 'https://es.investing.com/indices/indices-futures'
r = scraper.get(url, headers=headers)
soup = bs(r.text, 'html.parser')
table = soup.select_one('table[class="datatable_table__D_jso quotes-box_table__nndS2 datatable_table--mobile-basic__W2ilt"]')
df = pd.read_html(str(table))[0]
print(df)
Result in terminal:
0 1 2 3 4
0 IBEX 35derived 8.098,10 -3510 -0,43% NaN
1 US 500derived 3.991,90 355 +0,90% NaN
2 US Tech 100derived 11.802,20 1962 +1,69% NaN
3 Dow Jones 33.747,86 3249 +0,10% NaN
4 DAXderived 14.224,86 7877 +0,56% NaN
5 Índice dólarderived 106255 -1837 -1,70% NaN
6 Índice euroderived 11404 89 +0,79% NaN
See https://pypi.org/project/cloudscraper/

Web scraping multiple pages in python

So I'm trying to web scrape a website that has around 500 pages for used cars and each page has around 22 cars, I managed to extract the first 22 cars from the first page, but how can make my code iterate through all the pages so I can get all cars? (I'm a beginner so sorry if my code is not well structured)
from bs4 import BeautifulSoup
import requests
import pandas as pd
import numpy as np
website = 'https://ksa.yallamotor.com/used-cars/search'
headers = {
'User-Agent' : 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:103.0) Gecko/20100101 Firefox/103.0'
}
response = requests.get(website, headers=headers)
links = []
car_name = []
model_year = []
cars = []
soup = BeautifulSoup(response.text, 'lxml')
cars = soup.find_all('div', class_='singleSearchCard m24t p12 bg-w border-gray border8')
for c in cars:
l = "https://ksa.yallamotor.com/" + c.find('a', class_='black-link')['href']
links.append(l)
for i in range(0,22):
url = links[i]
session_object = requests.Session()
result = session_object.get(url, headers=headers)
soup = BeautifulSoup(result.text, 'lxml')
name = soup.find('h1', class_="font24")
car_name.append(name.text)
y = soup.find_all('div', class_="font14 text-center font-b m2t")[0]
model_year.append(y.text)

Website is under Cloudflare protection, so you would need something like cloudscraper (pip install cloudscraper). The following code will get you your data (you can further analyse each car, get the details you need, etc):
import cloudscraper
from bs4 import BeautifulSoup
scraper = cloudscraper.create_scraper()
for x in range(1, 501):
r = scraper.get(f'https://ksa.yallamotor.com/used-cars/search?page={x}&sort=updated_desc')
soup = BeautifulSoup(r.text, 'html.parser')
cars = soup.select('.singleSearchCard')
for car in cars:
url = car.select_one('a.black-link')
print(url.get_text(strip=True), url['href'])
Result printed in terminal:
Used BMW 7 Series 730Li 2018 /used-cars/bmw/7-series/2018/used-bmw-7-series-2018-jeddah-1294758
Used Infiniti QX80 5.6L Luxe (8 Seats) 2020 /used-cars/infiniti/qx80/2020/used-infiniti-qx80-2020-jeddah-1295458
Used Chevrolet Suburban 5.3L LS 2WD 2018 /used-cars/chevrolet/suburban/2018/used-chevrolet-suburban-2018-jeddah-1302084
Used Chevrolet Silverado 2016 /used-cars/chevrolet/silverado/2016/used-chevrolet-silverado-2016-jeddah-1297430
Used GMC Yukon 5.3L SLE (2WD) 2018 /used-cars/gmc/yukon/2018/used-gmc-yukon-2018-jeddah-1304469
Used GMC Yukon 5.3L SLE (2WD) 2018 /used-cars/gmc/yukon/2018/used-gmc-yukon-2018-jeddah-1304481
Used Chevrolet Impala 3.6L LS 2018 /used-cars/chevrolet/impala/2018/used-chevrolet-impala-2018-jeddah-1297427
Used Infiniti Q70 3.7L Luxe 2019 /used-cars/infiniti/q70/2019/used-infiniti-q70-2019-jeddah-1295235
Used Chevrolet Tahoe LS 2WD 2018 /used-cars/chevrolet/tahoe/2018/used-chevrolet-tahoe-2018-jeddah-1305486
Used Mercedes-Benz 450 SEL 2018 /used-cars/mercedes-benz/450-sel/2018/used-mercedes-benz-450-sel-2018-jeddah-1295830
[...]

Scraping using BeautifulSoup, value is not clean

I'm trying to scrape a nutrient tag (http://smartlabel.generalmills.com/41196891218). and I'm having a hard time getting a clean gram value for each category.
For example, this is how it comes out for fat
('fat': '\n 1 g\n ',)\
Any way to get something like this("fat": 1g)?
I just started learning bs4 yesterday, any help will be appreciated!.
My code is
def minenutrition1(link):
driver = webdriver.Chrome()
driver.get(link)
# noticed there is an ad here, sleep til page fully loaded.
time.sleep(1)
soup = BeautifulSoup(driver.page_source)
driver.quit()
calories=soup.find_all("span",{"class":"header2"})[0].text
fat=soup.find_all("span",{"class":"gram-value"})[0].text
satfat=soup.find_all("span",{"class":"gram-value"})[1].text
cholesterol=soup.find_all("span",{"class":"gram-value"})[3].text
sodium=soup.find_all("span",{"class":"gram-value"})[4].text
carb=soup.find_all("span",{"class":"gram-value"})[5].text
Total_sugar=soup.find_all("span",{"class":"gram-value"})[7].text
protein=soup.find_all("span",{"class":"gram-value"})[9].text
name = soup.find_all('div',{'class': 'product-header-name header1'})[0].text
upc=soup.find_all("div",{"class":"upc sub-header"})
upc=upc[0].text

You get normal string "\n 1 g\n " so you can use string functions to clean/change it.
Using "\n 1 g\n ".strip() you can get "1 g"
So you can add .strip() at the end of this line
fat = soup.find_all("span",{"class":"gram-value"})[0].text.strip()
or do it later
fat = fat.strip()
BS has also function .get_text(strip=True) which you can use instead of .text
fat = soup.find_all("span",{"class":"gram-value"})[0].get_text(strip=True)
Minimal working code.
I display fat with > < to see if there are spaces, tabs, enters (new lines).
from selenium import webdriver
from bs4 import BeautifulSoup
import time
url = 'http://smartlabel.generalmills.com/41196891218'
driver = webdriver.Chrome()
#driver = webdriver.Firefox()
driver.get(url)
# noticed there is an ad here, sleep til page fully loaded.
time.sleep(1)
soup = BeautifulSoup(driver.page_source)
driver.quit()
items = soup.find_all("span", {"class": "gram-value"})
fat = items[0].text
print('>{}<'.format(fat))
fat = items[0].text.strip()
print('>{}<'.format(fat))
fat = items[0].get_text(strip=True)
print('>{}<'.format(fat))
Result:
>
1 g
<
>1 g<
>1 g<

For this, I would not use Selenium. Not that you can't, but the site is static, and you can get the html source straight away with requests. So this is a little bit of a stretch since you are beginning with BeautifulSoup, but if you open Dev Tools (Ctrl-Shift-I) and reload the page, you will notice the requests made in the right panel under Network -> XHR. There is a requeset to GetNutritionalDetails.
Withibn there, you'll see the request url, and the the requests headers, and at the bottom the payload. You will also see it's a POST request (usually you'll use GET.
The data is within a list (<li> tags). So it's not just a mater of getting all those tags, then iterate through each of those, to pull out the other data.
You can append that data into a list, and then that list into a table/dataframe with pandas.
Code:
import requests
from bs4 import BeautifulSoup
import pandas as pd
url = 'http://smartlabel.generalmills.com/GTIN/GetNutritionalDetails'
headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.149 Safari/537.36'}
payload = {
'id': '41196891218',
'servingSize': 'AS PACKAGED'}
response = requests.post(url, headers=headers, params=payload)
soup = BeautifulSoup(response.text, 'html.parser')
listItems = soup.find_all('li')
labels = []
gramValues = []
percValues = []
for each in listItems:
label = each.find('label').text.strip()
if label == 'Includes':
label += ' Added Sugar'
gram = each.find('span', {'class':'gram-value'}).text.strip()
if each.find('span', {'class':'dv-result'}):
perc = each.find('span', {'class':'dv-result'}).text.strip()
else:
perc = ''
labels.append(label)
gramValues.append(gram)
percValues.append(perc)
df = pd.DataFrame({
'Label':labels,
'Grams':gramValues,
'Percent':percValues})
Output:
print (df)
Label Grams Percent
0 Total Fat 1 g 1 %
1 Saturated Fat 0 g 0 %
2 Trans Fat 0 g
3 Cholesterol 0 mg 0 %
4 Sodium 810 mg 35 %
5 Total Carbohydrate 17 g 6 %
6 Dietary Fiber 2 g 6 %
7 Total Sugar 2 g
8 Includes Added Sugar 2 g 3 %
9 Protein 4 g
10 Vitamin D 0 ?g 0 %
11 Calcium 60 mg 4 %
12 Iron 1.2 mg 6 %
13 Potassium 0 mg 0 %

Searching Dataframe for Specific Values to Be Stored

I'm new to programming and Python. I'm adopting code(https://github.com/rileypredum/East-Bay-Housing-Web-Scrape/blob/master/EB_Room_Prices.ipynb) to scrape Craiglist. My goal is to retrieve and store all the automotive posts in Chicago. I am able to store the Post Title, Post Time, Price, and Neighborhood. My next goal is to create a new column adding only the make of the vehicle, i.e. Toyota, Nissan, Honda, etc by searching the Post Title. How do I do this?
I believe this would be where I would add logic here: In [13]" for a variable "post_make" to search "post_title".
#build out the loop
from time import sleep
from random import randint
from warnings import warn
from time import time
from IPython.core.display import clear_output
import numpy as np
#find the total number of posts to find the limit of the pagination
results_num = html_soup.find('div', class_= 'search-legend')
results_total = int(results_num.find('span', class_='totalcount').text)
pages = np.arange(0, results_total, 120)
iterations = 0
post_timing = []
post_hoods = []
post_title_texts = []
post_links = []
post_prices = []
for page in pages:
#get request
response = get("https://sfbay.craigslist.org/search/eby/roo?"
+ "s="
+ str(page)
+ "&hasPic=1"
+ "&availabilityMode=0")
sleep(randint(1,5))
#throw warning for status codes that are not 200
if response.status_code != 200:
warn('Request: {}; Status code: {}'.format(requests, response.status_code))
#define the html text
page_html = BeautifulSoup(response.text, 'html.parser')
#define the posts
posts = html_soup.find_all('li', class_= 'result-row')
#extract data item-wise
for post in posts:
if post.find('span', class_ = 'result-hood') is not None:
#posting date
#grab the datetime element 0 for date and 1 for time
post_datetime = post.find('time', class_= 'result-date')['datetime']
post_timing.append(post_datetime)
#neighborhoods
post_hood = post.find('span', class_= 'result-hood').text
post_hoods.append(post_hood)
#title text
post_title = post.find('a', class_='result-title hdrlnk')
post_title_text = post_title.text
post_title_texts.append(post_title_text)
#post link
post_link = post_title['href']
post_links.append(post_link)
post_price = post.a.text
post_prices.append(post_price)
iterations += 1
print("Finished iteration: " + str(iterations))
Trying to figure out how to show the output.
Current output in excel is:
posted, neighborhood, post title, url, price
My goal is to add "post make" after the price.
I'm also looking for advice on how to show output from Jupyter notebooks here.

It's rather tricky to pull that out. I gave it a shot using another package Spacy to try to pull out the entities that are linked to organisations/car companies. It's not perfect, but it's a start:
Code:
import pandas as pd
import requests
from bs4 import BeautifulSoup
import spacy
nlp = spacy.load("en_core_web_sm")
req_url = 'https://chicago.craigslist.org/search/cta'
headers = {'User-Agent': 'Mozilla/5.0 (Linux; Android 6.0; Nexus 5 Build/MRA58N) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/77.0.3865.90 Mobile Safari/537.36'}
payload = {
's': '0',
'query': 'automotive',
'sort': 'rel'}
response = requests.get(req_url, headers=headers, params=payload)
soup = BeautifulSoup(response.text, 'html.parser')
total_posts = int(soup.find('span',{'class':'totalcount'}).text)
pages = list(range(0, total_posts, 120))
iterations = 0
post_timing = []
post_hoods = []
post_title_texts = []
post_links = []
post_prices = []
post_makes = []
post_models = []
for page in pages:
payload = {
's': page,
'query': 'automotive',
'sort': 'rel'}
response = requests.get(req_url, headers=headers, params=payload)
soup = BeautifulSoup(response.text, 'html.parser')
posts = soup.find_all('li', class_= 'result-row')
#extract data item-wise
for post in posts:
if post.find('span', class_ = 'result-hood') is not None:
#posting date
#grab the datetime element 0 for date and 1 for time
post_datetime = post.find('time', class_= 'result-date')['datetime']
post_timing.append(post_datetime)
#neighborhoods
post_hood = post.find('span', class_= 'result-hood').text
post_hoods.append(post_hood)
#title text
post_title = post.find('a', class_='result-title hdrlnk')
post_title_text = post_title.text
post_title_texts.append(post_title_text)
#post link
post_link = post_title['href']
post_links.append(post_link)
post_price = post.a.text.strip()
post_prices.append(post_price)
try:
# Used Spacy and Named Entity Recognition (NER) to pull out makes/models within the title text
post_title_text = post_title_text.replace('*', ' ')
post_title_text = [ each.strip() for each in post_title_text.split(' ') if each.strip() != '' ]
post_title_text = ' '.join( post_title_text)
doc = nlp(post_title_text)
model = [ent.text for ent in doc.ents if ent.label_ == 'PRODUCT']
make_model_list = [ent.text for ent in doc if ent.tag_ == 'NNP']
doc = nlp(' '.join(make_model_list))
make = [ent.text for ent in doc.ents if ent.label_ == 'ORG']
post_make = make[0]
post_makes.append(post_make)
post_model = model[0]
post_models.append(post_model)
except:
post_makes.append('')
post_models.append('')
iterations += 1
print("Finished iteration: " + str(iterations))
data = list(zip(post_timing,post_hoods,post_title_texts,post_links,post_prices,post_makes,post_models))
df = pd.DataFrame(list(zip(post_timing,post_hoods,post_title_texts,post_links,post_prices,post_makes,post_models)),
columns = ['time','hood','title','link','price','make','model'])
Output:
print (df.head(20).to_string())
time hood title link price make model
0 2019-10-03 07:12 (TEXT 855-976-4304 FOR CUSTOM PAYMENT) 2015 Ford Focus SE Sedan 4D sedan Dk. Gray - F... https://chicago.craigslist.org/chc/ctd/d/chica... $11500 Ford Focus SE
1 2019-10-03 06:03 (EVERYBODY DRIVES IN SOUTH ELGIN) $174/mo [][][] 2013 Hyundai Sonata BAD CREDIT OK https://chicago.craigslist.org/nwc/ctd/d/south... $174 Sonata BAD
2 2019-10-03 00:04 (EVERYBODY DRIVES IN SOUTH ELGIN) $658/mo [][][] 2016 Jeep Grand Cherokee BAD CR... https://chicago.craigslist.org/nwc/ctd/d/south... $658 Hyundai
3 2019-10-02 21:04 (EVERYBODY DRIVES IN SOUTH ELGIN) $203/mo [][][] 2010 Chevrolet Traverse BAD CRE... https://chicago.craigslist.org/nwc/ctd/d/south... $203 Jeep Grand Cherokee BAD Traverse BAD
4 2019-10-02 20:24 (DENVER) 2017 Jeep Cherokee Latitude 4x4 4dr SUV SKU:60... https://chicago.craigslist.org/chc/ctd/d/denve... $8995 Cherokee
5 2019-10-02 20:03 ( Buy Here Pay Here!) Good Credit, Bad Credit, NO Credit = NO Problem https://chicago.craigslist.org/nwc/ctd/d/chica... $0 Chevrolet
6 2019-10-02 20:03 ( Buy Here Pay Here!) Aceptamos Matricula!!! Te pagan en efectivo?? ... https://chicago.craigslist.org/wcl/ctd/d/chica... $0 Jeep
7 2019-10-02 20:02 ( Buy Here Pay Here!) Good Credit, Bad Credit, No Credit = No Problem https://chicago.craigslist.org/chc/ctd/d/vista... $0 Credit Bad Credit
8 2019-10-02 20:00 ( Buy Here Pay Here!) Good Credit, Bad Credit, No Credit= No Problem https://chicago.craigslist.org/sox/ctd/d/chica... $0
9 2019-10-02 19:15 (* CHRYSLER * TOWN AND COUNTRY * WWW.YOURCHOI... 2013*CHRYSLER*TOWN & COUNTRY*TOURING LEATHER K... https://chicago.craigslist.org/nwc/ctd/d/2013c... $9499
10 2019-10-02 19:09 (*CADILLAC* *DTS* WWW.YOURCHOICEAUTOS.COM) 2008*CADILLAC*DTS*1OWNER LEATHER SUNROOF NAVI ... https://chicago.craigslist.org/sox/ctd/d/2008c... $5999 Credit Bad Credit
11 2019-10-02 18:59 (WAUKEGANAUTOAUCTION.COM OPEN TO PUBLIC OVER ... 2001 *GMC**YUKON* XL DENALI AWD 6.0L V8 1OWNER... https://chicago.craigslist.org/nch/ctd/d/2001-... $1200
12 2019-10-02 18:47 (*GMC *SAVANA *CARGO* WWW.YOURCHOICEAUTOS.COM) 1999 *GMC *SAVANA *CARGO*G2500 SHELVES CABINET... https://chicago.craigslist.org/sox/ctd/d/1999-... $2999 Credit Bad Credit
13 2019-10-02 18:04 ( Buy Here Pay Here!) GoodCredit, Bad Credit, No credit = No Problem https://chicago.craigslist.org/nwc/ctd/d/chica... $0
14 2019-10-02 18:05 ( Buy Here Pay Here!) Rebuild your credit today!!! https://chicago.craigslist.org/sox/ctd/d/chica... $0 CHRYSLER
15 2019-10-02 18:03 ( Buy Here Pay Here!) Rebuild your credit today!!! Repo? No Problem!... https://chicago.craigslist.org/chc/ctd/d/vista... $0
16 2019-10-02 17:59 (* ACURA * TL * WWW.YOURCHOICEAUTOS.COM) 2006 *ACURA**TL* LEATHER SUNROOF CD KEYLES ALL... https://chicago.craigslist.org/sox/ctd/d/2006-... $4499
17 2019-10-02 18:00 ( Buy Here Pay Here!) Buy Here Pay Here!!! We Make it Happen!! Bad C... https://chicago.craigslist.org/wcl/ctd/d/chica... $0
18 2019-10-02 17:35 (ST JOHN) 2009 NISSAN VERSA https://chicago.craigslist.org/nwi/ctd/d/saint... $4995
19 2019-10-02 17:33 (DENVER) 2013 Scion tC Base 2dr Coupe 6M SKU:065744 Sci... https://chicago.craigslist.org/chc/ctd/d/denve... $5995 GoodCredit Bad Credit