Find span element based on text written inside li Bs4 scraping

In general, it would be good to check if the tag you are looking for exists before applying the text method:

title = title.text.strip() if (title := soup.find('h1')) else None

To select tag by text and check if it exists, you can go with css selectors and -soup-contains():

floor = tag.text.strip() if (tag := soup.select_one('ul.list-group li:-soup-contains("Floor") span')) else None

Above works well for some tags, but to go generic and get rid of these confusing property selections, I would suggest the following - Use a dict to store the information in a list of dicts. So you are save if you create a dataframe based on it and a propertiy is missing. Pandas will fill this outomatically with nan.

data = {}
data['title'] = soup.find('h1').text.strip()
data['loc'] = soup.find('span', {'property':'name'}).find_next('span').find_next('span').find_next('span').find_next('span').find_next('span').text.replace('\n','')
data['sub_loc'] = soup.find('span', {'property':'name'}).find_next('span').find_next('span').find_next('span').find_next('span').find_next('span').find_next('span').text.replace('\n','')
data.update(dict([li.stripped_strings for li in soup.select('ul.list-group li')]))

Benefits - You can do adjustments simple, filter if you like and export results in a strucured way.

Example

import requests
from bs4 import BeautifulSoup as bs
import pandas as pd
    
baseurl = 'https://aqarmap.com.eg'
headers = { 
    'User_Agent' : 
        'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.81 Safari/537.36'
    }

data = []

def scrape(test_link):
    r = requests.get(test_link , headers=headers)
    soup = bs(r.content,'lxml')

    data = {}
    data['title'] = soup.find('h1').text.strip()
    data['loc'] = soup.find('span', {'property':'name'}).find_next('span').find_next('span').find_next('span').find_next('span').find_next('span').text.replace('\n','')
    data['sub_loc'] = soup.find('span', {'property':'name'}).find_next('span').find_next('span').find_next('span').find_next('span').find_next('span').find_next('span').text.replace('\n','')
    data.update(dict([li.stripped_strings for li in soup.select('ul.list-group li')]))
    return data

urlList = ['https://aqarmap.com.eg/en/listing/3138984-for-rent-cairo-new-cairo-el-narges-el-narges-omarat',
           'https://aqarmap.com.eg/en/listing/3124476-for-rent-cairo-new-cairo-el-narges-el-narges-omarat?source=related-listing-source']

for url in urlList:
    data.append(scrape(url))

pd.DataFrame(data)

Output

You might find the following approach useful, particularly an alternative way to extract items from the "bread crumb" list at the top:

import requests
from bs4 import BeautifulSoup as bs


# Set base url & headers :
    
baseurl = 'https://aqarmap.com.eg'
headers = { 
    'User_Agent' : 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.81 Safari/537.36'
}
test_link = 'https://aqarmap.com.eg/en/listing/3138984-for-rent-cairo-new-cairo-el-narges-el-narges-omarat'
r = requests.get(test_link , headers=headers)
soup = bs(r.content,'lxml')

# Store all available items in a dictionary
ul_list_group = soup.find('ul', class_='list-group')
data = {}

for li_item in ul_list_group.find_all('li'):
    items = list(li_item.stripped_strings)
    
    if len(items) == 2:
        data[items[0]] = items[1]

req_elements = [
    'Floor', 'Room', 'Baths', 
    'Finish Type', 'Size (in meters)', 'Listing ID', 
    'Publish Date', 'Price', 'Seller Role', 
    'Payment Method', 'Price Per Meter',
    'Swimming pool',     # test case to show missing item
]

# Store all parts of the breadcrump in a list (last 3 required)
ul_breadcrumb = soup.find('ul', class_='breadcrumb')
breadcrumb_data = [li.get_text(strip=True) for li in ul_breadcrumb.find_all('li')]

title = breadcrumb_data[-1]
sub_loc = breadcrumb_data[-2]    
loc = breadcrumb_data[-3]
    
# Build a suitable row of data (give missing entries 'N/A')
row = [title, sub_loc, loc] + [data.get(element, 'N/A') for element in req_elements]
print(row)

Giving you:

['Furnished Apartment For rent in El Narges Omarat', 'El Narges Omarat', 'El Narges', '3', '3', '2', 'SUPER_LUX', '180 M²', 'EG-3138984', '09/01/2022', '19,000 EGP', 'Agent', 'Cash', '106 EGP/M²', 'N/A']