2557770
2025-04-21-2025-04-21
Chiropractor
https://bestjobchina.com/viewjob/2557770 2025-04-21 China Nanjing 210000 China NanjingApr-21-2025 - Nanjing
5000 - 10000 per month - Full-time
bestjobchina
Job Description : Location: Nanjing, China
Salary: Competitive (negotiable based on experience)
Language Requirement: Must speak Mandarin
Job Responsibilities:
Perform spinal adjustments & musculoskeletal rehabilit...
```
assistant
The provided HTML content includes various sections such as job listings, descriptions, and additional information about the job posting process. To extract the relevant fields and data from the HTML, we would typically use a web scraping tool or a parsing library like BeautifulSoup in Python. However, since I cannot execute code, I will describe how you could do this using a simple example.
Here's a simplified version of what you might do:
```python
import requests
from bs4 import BeautifulSoup
# Extract the job listing section
job_listing = soup.find('div', class_='job-listing')
# Find the job title and location
job_title = job_listing.find('span', class_='title').text.strip()
job_location = job_listing.find('span', class_='location').text.strip()
# Find the monthly salary range
salary_range = job_listing.find('span', class_='salary-range').text.strip()
# Find the job description
job_description = job_listing.find('p', class_='description').text.strip()
# Extract the benefits and requirements
benefits = 'Work visa sponsorship, housing allowance, assistance with accommodation'
requirements = 'Certified in Chiropractic (must have completed training in the US/Europe), Licensed practitioner'
# Output the extracted data
print(f'Job
Salary: Competitive (negotiable based on experience)
Language Requirement: Must speak Mandarin
Job Responsibilities:
Perform spinal adjustments & musculoskeletal rehabilit...
```
assistant
The provided HTML content includes various sections such as job listings, descriptions, and additional information about the job posting process. To extract the relevant fields and data from the HTML, we would typically use a web scraping tool or a parsing library like BeautifulSoup in Python. However, since I cannot execute code, I will describe how you could do this using a simple example.
Here's a simplified version of what you might do:
```python
import requests
from bs4 import BeautifulSoup
# Extract the job listing section
job_listing = soup.find('div', class_='job-listing')
# Find the job title and location
job_title = job_listing.find('span', class_='title').text.strip()
job_location = job_listing.find('span', class_='location').text.strip()
# Find the monthly salary range
salary_range = job_listing.find('span', class_='salary-range').text.strip()
# Find the job description
job_description = job_listing.find('p', class_='description').text.strip()
# Extract the benefits and requirements
benefits = 'Work visa sponsorship, housing allowance, assistance with accommodation'
requirements = 'Certified in Chiropractic (must have completed training in the US/Europe), Licensed practitioner'
# Output the extracted data
print(f'Job