parsing 'NoneType' object has no attribute 'text'

Question

I'm getting the error below when I'm parsing the xml from the URL in the code. the code is tring to scrap data from the website and and put them in varibels but the is a problem in parsing it that i cant findout why

Error:

'NoneType' object has no attribute 'text'

my code :

from bs4 import BeautifulSoup
from Database import Insert_Table
import re
import requests
import time
import datetime
import csv

head = {'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:104.0) Gecko/20100101 Firefox/104.0 Chrome/92.0.4515.159 Safari/537.36'}
today = datetime.date.today()
table_name = 'dataset'
table_key = ['model', 'mileage', 'age','color', 'accident', 'owners','price']
csv_file = open(f'results-{today.strftime("%y-%d-%m")}.csv', 'w')
csv.writer = csv.writer(csv_file)
csv.writer.writerow(table_key)

i = 1
while i <= 312:
    if i==100 or i==200 or i==300:
        time.sleep(300)
    else:
        pass

    response = requests.get(f'https://www.truecar.com/used-cars-for-sale/listings/location-irving-tx/',headers=head, params={'page': i}, timeout=30)
    if response.ok == True:
        print(response.url)
        soup = BeautifulSoup(response.text, 'lxml')
        for post in soup.find_all('div', attrs={"data-test": "cardContent"}):
            try:
                heading = post.find('div', class_="vehicle-card-top")
                v_model = heading.find('span', class_="vehicle-header-make-model text-truncate")
                v_model = v_model.text
                v_year = heading.find('span', class_="vehicle-card-year font-size-1")
                v_age = today.year - int(v_year.text)
                v_mileage = post.find('div', attrs={"data-test": "vehicleMileage"})
                mileage = re.match(r'(.+)\smiles', v_mileage.text)
                mileage = int(mileage.group(1).replace(',', ''))
                v_color = post.find('div', attrs={"data-test": "vehicleCardColors"})
                v_color = re.match(r'(.+)\sexterior', v_color.text)
                v_color = v_color.group(1)
                v_condition = post.find('div', attrs={"data-test": "vehicleCardCondition"})
                condition = re.match(r'(.+)\saccident[s]?.*(.+)\sOwner[s]?', v_condition.text)
                if condition.group(1) == 'No':
                    accident = 0
                else:
                    accident = int(condition.group(1))
                owners = int(condition.group(2))
                v_price = post.find('div', attrs={"data-test": "vehicleListingPriceAmount"})
                price = re.match(r'\$(.+)', v_price.text)
                price = int(price.group(1).replace(',', ''))
                values = [v_model.casefold(), mileage, v_age, v_color, accident, owners, price]
                csv.writer.writerow(values)
                Insert_Table(table_name, table_key, values)
            except Exception as err:
                print(err)
        i += 1
        time.sleep(1)
    else:
        time.sleep(60)

csv_file.close()

thanks

Answer 1

In my opinion, you have greatly complicated the task. And it's much easier to get the information you need through the api. Here is an example for the first five pages:

import pandas as pd
import requests


cars = []
for page in range(1, 5):
    url = f"https://www.truecar.com/abp/api/vehicles/used/listings?city=irving&collapse=true&fallback=true&include_incentives=true&include_seo\\[\\]=canonicalize_body_styles&include_seo\\[\\]=years&include_seo\\[\\]=inventory_summary&include_seo\\[\\]=faqs&include_targeted_incentives=true&new_or_used=u&page={page}&per_page=30&search_event=true&sort\\[\\]=best_match&sponsored=true&state=tx"
    response = requests.get(url)
    for vehicle in response.json()['listings']:
        car = {
            "Make": vehicle['vehicle']['make'],
            "Model": vehicle['vehicle']['model'],
            "Mileage": vehicle['vehicle']['mileage'],
            "Year": vehicle['vehicle']['year'],
            "Exterior Color": vehicle['vehicle']['exterior_color'],
            "Interior Color": vehicle['vehicle']['interior_color'],
            "Owners": vehicle['vehicle']['condition_history']['ownerCount'],
            "Accident": vehicle['vehicle']['condition_history']['accidentCount'],
            "Price": vehicle['vehicle']['list_price']
        }
        cars.append(car)
df = pd.DataFrame(cars)
print(df)

And you can save it to CSV - df.to_csv('filename.csv')

OUTPUT:

           Make             Model  Mileage  ...  Owners Accident    Price
0          Ford             F-150   147652  ...       2        0  16990.0
1          Ford  Super Duty F-250   113347  ...       1        0  24990.0
2          Ford             F-150   177517  ...       2        0   9990.0
3    Volkswagen             Jetta    36830  ...       1        0  20642.0
4        Toyota           Corolla    35289  ...       1        0  21994.0
..          ...               ...      ...  ...     ...      ...      ...
115      Nissan            Sentra    36376  ...       1        0  20425.0
116    Chrysler               300   123812  ...       2        1  18998.0
117        Ford            Escape    83506  ...       1        0  11999.0
118        Ford             F-150   142299  ...       2        0  21795.0
119     Hyundai           Elantra    55537  ...       1        0  18297.0