I'm a newbie in programming, I chose Python. I'm learning on my own. Currently I'm preparing code for a portfolio on github.

I will be grateful for any code review, especially in the subject of OOP: should I try to create classes? Does it make any sense with such a code? Is the code structure pythonic? I will be very grateful for any hint.

The goal is to find out what technologies are most often requested in those jobs listing where Python is required. One table contains mandatory technologies, the other: nice to have sorted in descending order. I chose the site https://nofluffjobs.com/ to find out which technologies are most in demand.

from bs4 import BeautifulSoup
import requests
import pandas as pd
from collections import Counter

# In[2]:

def get_page (page):
    #making the soup
    r= requests.get('https://nofluffjobs.com/pl/Python?page='+(str(page)))
    c= r.content
    soup= BeautifulSoup (c, 'html.parser')
    return soup

def display_technologies(requirements,tech):
    #making dataframe with the technologies
    tech_dict = {i: x for i, x in enumerate(Counter(requirements).items())}
    df = pd.DataFrame.from_dict(tech_dict, orient='index', columns=[tech, 'number'])
    pd.set_option('display.max_rows', None)
    df.sort_values(by=['number'], inplace=True, ascending= False)
    print (df)

def make_dict(list_req):
    #making dictionary to display list of technologies
    return [item.strip() for sublist in list_req for item in sublist]

page= 1
while True:
    #searching for subpages with offers 
    soup= get_page(page)
    if (soup.find('a', {'class': 'jobs-link'}))== None:
    job_sites= soup.find_all('a',{'target':'_self'}, href=True)
    for h in job_sites:
        link= h.get('href')
        if '/job/' in (link):
            #searching for title and requirements
                if requests.Response() == 200:
                r= requests.get('https://nofluffjobs.com/'+(str(link.lstrip('/pl/'))), timeout=1000)
                c= r.content
                soup_job= BeautifulSoup (c, 'html.parser')
                title= soup_job.find('h1').text
                requirements_must= list(l.text for l in list(soup_job.find_all('ul', {'class': 'mb-0 ng-star-inserted'})))
                rm = list((requirements_must)[0].split("  "))
                    requirements_nice_to_have= (str(l.text).strip() for l in (soup_job.find('section', {'id': 'posting-nice-to-have'})))
                    rn= (list(requirements_nice_to_have)[1].split("  "))
                except TypeError as e:
            except Exception as inst:
                print (inst)
    page +=1

display_technologies(make_dict(list_req_must), 'tech- must')
display_technologies(make_dict(list_req_nice), 'tech- nice_to_have')
  • 2
    \$\begingroup\$ As a first start, probably remove the ipython prompt and run the code through a simple linter like pycodestyle \$\endgroup\$
    – tripleee
    Jan 27, 2023 at 11:37
  • \$\begingroup\$ Yeah, run it through isort + black and update the question. Also bury the while True: loop in a named function. \$\endgroup\$
    – J_H
    Jan 27, 2023 at 17:48
  • \$\begingroup\$ I recommend against black, especially for a beginner. It's a steamroller and doesn't make you go through the work of learning each recommendation explicitly. \$\endgroup\$
    – Reinderien
    Jan 27, 2023 at 18:37
  • \$\begingroup\$ if requests.Response() == 200 doesn't do what you think it does. What did you intend here? \$\endgroup\$
    – Reinderien
    Jan 27, 2023 at 18:47
  • \$\begingroup\$ OMG !!! I was not here for two days (my little cats had have some veterinary procedure and I had to take care of them . They greet you all people :) ) and there is so much content ! Give me a sec, I'll will go through all of the hints and answers, I can see right now that I will have some questions !! Thank you !! \$\endgroup\$
    – Magda
    Jan 30, 2023 at 19:21

1 Answer 1


I do not think that Pandas is well-applied here. I suggest that you use a built-in such as pprint instead.

None of your comments make the code any more clean than if there were no comments at all - so you can delete them.

Add PEP484 typehints.

Do not write that much code in the global namespace. From page=1 onward, move that into multiple functions. Iterators can help you divide this work.

Loop like a native: do not manually increment page; instead, use itertools.count.

if (soup.find('a', {'class': 'jobs-link'}))== None: is not necessary. More necessary: check for the presence of a pagination link.

soup.find_all('a',{'target':'_self'}, href=True) is not a very narrow selector. Improve your choice of selectors by inspecting the DOM. Also, add soup strainers to narrow the parsing job of BeautifulSoup.

if requests.Response() == 200: does not do what you think it does. Instead, it creates a brand new response and checks its code. You're better off calling raise_for_status.

Do not split(). You should be selecting on each individual span.

Catching TypeError suggests a programming error; you should remove this try.

Use a requests.Session to better characterise what you're doing, with cross-request headers, cookies and connections, etc.

Add a __main__ guard.


import itertools
from pprint import pprint
from typing import Iterator

from bs4 import BeautifulSoup
from bs4.element import SoupStrainer
from collections import Counter

from requests import Session

    class_='list-container ng-star-inserted',
JOB_STRAINER = SoupStrainer(

def get_search_page(session: Session, country: str, language: str, page: int) -> BeautifulSoup:
    with session.get(
        params={'page': page},
        headers={'Accept': 'text/html'},
    ) as resp:
        return BeautifulSoup(markup=resp.text, features='html.parser', parse_only=SEARCH_STRAINER)

def get_job_page(session: Session, link: str) -> BeautifulSoup:
    with session.get(
        headers={'Accept': 'text/html'},
    ) as resp:
        return BeautifulSoup(markup=resp.text, features='html.parser', parse_only=JOB_STRAINER)

def get_links(
    session: Session, country: str, language: str,
) -> Iterator[str]:

    for page in itertools.count(1):
        soup = get_search_page(session, country, language, page)
        job_sites = soup.find_all(name='a', class_='posting-list-item')

        for anchor in job_sites:
            yield anchor.get('href').removeprefix(f'/{country}/')

        if soup.find(name='a', class_='page_link', label='Next') is None:

def get_musts(soup: BeautifulSoup) -> Iterator[str]:
    musts = soup.find(name='section', branch='musts')
    if musts is not None:
        for span in musts.find_all(name='span'):
            yield span.text.strip()

def get_nices(soup: BeautifulSoup) -> Iterator[str]:
    nices = soup.find(name='section', branch='nices')
    if nices is not None:
        for span in nices.find_all(name='span'):
            yield span.text.strip()

def scrape(
    session: Session, country: str = 'pl', language: str = 'Python',
) -> tuple[Counter[str], Counter[str]]:
    req_must = Counter()
    req_nice = Counter()

    for link in get_links(session, country, language):
        print(f'Scraping {link}...')
        soup = get_job_page(session, link)

    return req_must, req_nice

def display_technologies(req: Counter[str], title: str) -> None:

def main() -> None:
    with Session() as session:
        req_must, req_nice = scrape(session)
        display_technologies(req_must, 'tech- must')
        display_technologies(req_nice, 'tech- nice_to_have')

if __name__ == '__main__':


Scraping job/mid-senior-data-engineer-remote-devsdata-llc-ycq636sg...
Scraping job/junior-data-engineer-nix-tech-kft-budapest-7dbftgsu...
Scraping job/python-developer-rtb-house-remote-xm36kkv1...
Scraping job/senior-data-engineer-sigma-it-poland-remote-ai1un7ji...
Scraping job/data-engineer-addepto-remote-oo36be4q...
Scraping job/python-developer-freysoft-remote-zzqgols5...
Scraping job/site-reliability-engineer-python-or-c-rits-professional-services-remote-ys1kq0b5...
Scraping job/machine-learning-engineer-infracert-tsi-warszawa-4egsv3nx...
Scraping job/akademia-it-data-scientist-python-xtb-remote-7bj8ysfw...
Scraping job/python-developer-bazy-danych-team-connect-remote-zdhnplmv...
Scraping job/remote-senior-backend-python-developer-devopsbay-lpj6imsi...
Scraping job/junior-python-developer-green-minds-remote-h9e26nbg...
Scraping job/technical-data-steward-link-group-warszawa-38bkgutb...
Scraping job/remote-fullstack-developer-tappr-rayrqvhd...
Scraping job/junior-python-developer-optimo-development-lodz-hxa7lmsn...
Scraping job/python-developer-with-relocation-zoostation-thehague-jp1cuhz5...
Scraping job/remote-data-engineer-with-python-inuits-c1y8w8za...
Scraping job/python-team-lead-tagging-tools-lead-form-remote-tqmix3yd...
Scraping job/remote-machine-learning-python-developer-clurgo-dvlbyqx1...
Scraping job/data-scientist-python-developer-avenga-remote-tn4qfdr8...
tech- must
Counter({'Python': 21,
         'SQL': 10,
         'English': 9,
         'English (B2)': 7,
         'Polish': 7,
         'Git': 6,
         'Docker': 5,
         'PostgreSQL': 4,
         'AWS': 4,
         'Kubernetes': 4,
         'MongoDB': 3,
         'Django': 3,
         'MySQL': 2,
         'Oracle': 2,
         'ETL': 2,
         'Linux': 2,
         'Big Data': 2,
         'Azure': 2,
         'REST API': 2,
         'pandas': 2,
         'PyTorch': 2,
         'TensorFlow': 2,
         'Flask': 2,
         'JavaScript': 2,
         'Design Patterns': 2,
         'NoSQL': 2,
         'RDBMS': 1,
         'Airflow': 1,
         'Version Control Systems': 1,
         'AWS Glue': 1,
         'English (B1)': 1,
         'Snowflake': 1,
         'Data Integration': 1,
         'ETL tools': 1,
         'Spark': 1,
         'Celery': 1,
         'RabbitMQ': 1,
         'HTTP': 1,
         'K8s': 1,
         'Ansible': 1,
         'Terraform': 1,
         'scikit-learn': 1,
         'NumPy': 1,
         'Keras': 1,
         'Polish (NATIVE)': 1,
         'Data science': 1,
         'Umiejętności analityczne': 1,
         'Znajomość oprogramowania bazodanowego': 1,
         'Amazon Web Services': 1,
         'HTML': 1,
         'CSS': 1,
         'Data management': 1,
         'banking': 1,
         'Excel': 1,
         'Python scripts': 1,
         'TypeScript': 1,
         'REST': 1,
         'React': 1,
         'Bootstrap': 1,
         'JSON': 1,
         'SOAP': 1,
         'CI/CD': 1,
         'Jenkins': 1,
         'FastAPI': 1,
         'Angular': 1,
         'Next.js': 1,
         'Nuxt.js': 1,
         'Scala': 1,
         'database': 1,
         'Communication skills': 1,
         'Team player': 1,
         'English (C1)': 1,
         'Polish (C1)': 1,
         'PySpark': 1})

tech- nice_to_have
Counter({'GCP': 4,
         'AWS': 3,
         'Azure': 3,
         'Kafka': 3,
         'Airflow': 2,
         'Django': 2,
         'Elasticsearch': 2,
         'Proactivity': 2,
         'Problem solving': 2,
         'MLOps': 2,
         'Splunk': 2,
         'DevOps': 2,
         'BigQuery': 1,
         'OAuth': 1,
         'OIDC': 1,
         'Azure Data Factory': 1,
         'Celery': 1,
         'Redis': 1,
         'PostgreSQL': 1,
         'Google cloud platform': 1,
         'Java': 1,
         'Scala': 1,
         'NiFi': 1,
         'Databricks': 1,
         'Polish': 1,
         'MongoDB': 1,
         'pytest': 1,
         'Flask': 1,
         'PyMongo': 1,
         'C++': 1,
         'Oracle': 1,
         'SQL Server': 1,
         'TCP/IP': 1,
         'Scrum': 1,
         'Kanban': 1,
         'CI/CD Pipelines': 1,
         'AI / ML expertise': 1,
         'ML': 1,
         'Odoo': 1,
         'English': 1,
         'CSS': 1,
         'Vue.js': 1,
         'React': 1,
         'Docker': 1,
         'Kubernetes': 1,
         'Rancher': 1,
         'Sentry': 1,
         'Grafana': 1,
         'NoSQL': 1,
         'Apache Airflow': 1,
         'Apache Spark': 1,
         'Machine Learning': 1,
         'ML Ops concepts': 1})

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