Webscraping with BeautifulSoup create a dictionary containing author name, car model and all paragraphs with review

I have such a code which gets the values of all paragraphs from a div and inserts them into a list as a new element for each car model year. I wanted to add the possibility of creating a dictionary which would contain values in such form

d = { 'reviewer_name': 'xyz', 'car_model' : '2017 Audi A4', 'review_content' : 'all paragraphs from the div which is already visible in the code' } 

this dictionary should contain values for different years, so if I specify years to 2017 and 2018 I would like entries in the dictionary for both of those years.

from bs4 import BeautifulSoup
import requests
import pandas as pd
import time
from fake_useragent import UserAgent
import random

articles = []
ua = UserAgent()
header = {'User-Agent':str(ua.safari)}
for i in range(2017, 2019):
    url = f'https://www.caranddriver.com/audi/a4-{i}' 
    response = requests.get(url, headers=header)
    print(response)
    html_soup = BeautifulSoup(response.text, 'lxml')
    article = html_soup.find('div', attrs={'class': 'review-body-content'}).findAll('p')
    article_text = ''

    for element in article:
      article_text = article_text + 'n' + ''.join(element.findAll(text = True))
    articles.append(article_text)

Answer

Here you go, just add it to a dictionary then append the dictionary into your list.

from bs4 import BeautifulSoup
import requests
import pandas as pd
import time
from fake_useragent import UserAgent
import random
import re

articles = []
ua = UserAgent()
header = {'User-Agent':str(ua.safari)}
for i in range(2017, 2020):
    url = f'https://www.caranddriver.com/audi/a4-{i}' 
    response = requests.get(url, headers=header)
    print(response)
    html_soup = BeautifulSoup(response.text, 'lxml')
    article = html_soup.find('div', attrs={'class': 'review-body-content'}).findAll('p')
    article_text = ''

    for element in article:
      article_text = article_text + 'n' + ''.join(element.findAll(text = True))
      article_text = re.sub('.css.*}', '', article_text)
    article_text = article_text.strip()
    
    car_model = html_soup.find('div', class_= re.compile("^review-header-inner")).find('h1').text
    
    try:
        reviewer_name = html_soup.find('a', {'href':re.compile("^/author")}).text
    except:
        reviewer_name = 'NA'
        
    row = {
        'reviewer_name': reviewer_name, 
        'car_model' : car_model, 
        'review_content' : article_text } 
    
    
    articles.append(row)

df = pd.DataFrame(articles)

Output:

print(df)
  reviewer_name  ...                                     review_content
0            NA  ...  The A4 embodies everything we love about Audi:...
1            NA  ...  The 2018 Audi A4 is perhaps the most well-roun...
2   Drew Dorian  ...  Audi's A4 has proven to be a wündercar that ou...

[3 rows x 3 columns]