In this article, we dive into the process of extracting and analyzing job data from LinkedIn, leveraging a combination of Python, Nu shell, and ChatGPT to streamline and enhance our workflow.
I’ll walk you through the steps I took to carry out my research, showing how you can use these techniques to explore job markets in different countries or even in other fields. By combining these tools and methods, you can gather and analyze data to gain valuable insights into any job market you're interested in.
Python was chosen for its versatile libraries, particularly linkedin_jobs_scraper and openai. These packages streamlined the scraping and processing of job data.
Nu shell was experimented with to compare its functionality against the traditional bash stack. This experiment aimed to explore its potential benefits in handling and manipulating data.
ChatGPT was employed to assist in the extraction of specific job features from the collected data, such as years of experience, degree requirements, tech stack, position levels, and core responsibilities.
To start some data is required. LinkedIn was the first website that came to my mind and there was ready to use Python package. I've copied example code, modified it a little and got ready to use script to get a JSON file with a list of job descriptions. Here it's source:
import json
import logging
import os
from threading import Lock
from dotenv import load_dotenv
# linkedin_jobs_scraper loads env statically
# So dotenv should be loaded before imports
load_dotenv()
from linkedin_jobs_scraper import LinkedinScraper
from linkedin_jobs_scraper.events import EventData, Events
from linkedin_jobs_scraper.filters import ExperienceLevelFilters, TypeFilters
from linkedin_jobs_scraper.query import Query, QueryFilters, QueryOptions
CHROMEDRIVER_PATH = os.environ["CHROMEDRIVER_PATH"]
RESULT_FILE_PATH = "result.json"
KEYWORDS = ("Python", "PHP", "Java", "Rust")
LOCATIONS = ("South Korea",)
TYPE_FILTERS = (TypeFilters.FULL_TIME,)
EXPERIENCE = (ExperienceLevelFilters.MID_SENIOR,)
LIMIT = 500
logging.basicConfig(level=logging.INFO)
log = logging.getLogger(__name__)
def main():
result_lock = Lock()
result = []
def on_data(data: EventData):
with result_lock:
result.append(data._asdict())
log.info(
"[JOB]",
data.title,
data.company,
len(data.description),
)
def on_error(error):
log.error("[ERROR]", error)
def on_end():
log.info("Scraping finished")
if not result:
return
with open(RESULT_FILE_PATH, "w") as f:
json.dump(result, f)
queries = [
Query(
query=keyword,
options=QueryOptions(
limit=LIMIT,
locations=[*LOCATIONS],
filters=QueryFilters(
type=[*TYPE_FILTERS],
experience=[*EXPERIENCE],
),
),
)
for keyword in KEYWORDS
]
scraper = LinkedinScraper(
chrome_executable_path=CHROMEDRIVER_PATH,
headless=True,
max_workers=len(queries),
slow_mo=0.5,
page_load_timeout=40,
)
scraper.on(Events.DATA, on_data)
scraper.on(Events.ERROR, on_error)
scraper.on(Events.END, on_end)
scraper.run(queries)
if __name__ == "__main__":
main()
To download chrome driver I've made the following bash script:
#!/usr/bin/env bash
stable_version=$(curl 'https://googlechromelabs.github.io/chrome-for-testing/LATEST_RELEASE_STABLE')
driver_url=$(curl 'https://googlechromelabs.github.io/chrome-for-testing/known-good-versions-with-downloads.json' \
| jq -r ".versions[] | select(.version == \"${stable_version}\") | .downloads.chromedriver[0] | select(.platform == \"linux64\") | .url")
wget "$driver_url"
driver_zip_name=$(echo "$driver_url" | awk -F'/' '{print $NF}')
unzip "$driver_zip_name"
rm "$driver_zip_name"
And my .env file looks like that:
CHROMEDRIVER_PATH="chromedriver-linux64/chromedriver" LI_AT_COOKIE=
linkedin_jobs_scraper serializes jobs to the following DTO:
class EventData(NamedTuple):
query: str = ''
location: str = ''
job_id: str = ''
job_index: int = -1 # Only for debug
link: str = ''
apply_link: str = ''
title: str = ''
company: str = ''
company_link: str = ''
company_img_link: str = ''
place: str = ''
description: str = ''
description_html: str = ''
date: str = ''
insights: List[str] = []
skills: List[str] = []
Example sample (description was replaced with ... for better readability):
| query | location | job_id | job_index | link | apply_link | title | company | company_link | company_img_link | place | description | description_html | date | insights | skills |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Python | South Korea | 3959499221 | 0 | https://www.linkedin.com/jobs/view/3959499221/?trk=flagship3_search_srp_jobs | Senior Python Software Engineer | Canonical | https://media.licdn.com/dms/image/v2/C560BAQEbIYAkAURcYw/company-logo_100_100/company-logo_100_100/0/1650566107463/canonical_logo?e=1734566400&v=beta&t=emb8cxAFwBnOGwJ8nTftd8ODTFDkC_5SQNz-Jcd8zRU | Seoul, Seoul, South Korea (Remote) | ... | ... | [Remote Full-time Mid-Senior level, Skills: Python (Programming Language), Computer Science, 8 more, See how you compare to 18 applicants. Try Premium for RSD0, , Am I a good fit for this job?, How can I best position myself for this job?, Tell me more about Canonical] | [Back-End Web Development, Computer Science, Engineering Documentation, Kubernetes, Linux, MLOps, OpenStack, Python (Programming Language), Technical Documentation, Web Services] |
Was generated with the following nu shell command:
# Replaces description of a job with elipsis
def hide-description [] {
update description { |row| '...' }
| update description_html { |row| '...' }
}
cat result.json
| from json
| first
| hide-description
| to md --pretty
We already have several ready to use features (title and skills), but I want more:
So let's add them with help of ChatGPT!
import json
import logging
import os
from dotenv import load_dotenv
from linkedin_jobs_scraper.events import EventData
from openai import OpenAI
from tqdm import tqdm
load_dotenv()
client = OpenAI(
api_key=os.environ["OPENAI_API_KEY"],
)
with open("result.json", "rb") as f:
jobs = json.load(f)
parsed_descriptions = []
for job in tqdm(jobs):
job = EventData(**job)
chat_completion = client.chat.completions.create(
model="gpt-4o-mini",
messages=[
{
"role": "user",
"content": """
Process given IT job description.
Output only raw JSON with the following fields:
- Experience (amount of years or null)
- Degree requirement (str if found else null)
- Tech stack (array of strings)
- Position (middle, senior, lead, manager, other (describe it))
- Core responsibilites (array of strings)
Output will be passed directrly to the
Python's `json.loads` function. So DO NOT APPLY MARKDOWN FORMATTING
Example:
```
{
"experience": 5,
"degree": "bachelor",
"stack": ["Python", "FastAPI", "Docker"],
"position": "middle",
"responsibilities": ["Deliver features", "break production"]
}
```
Here is a job description:
"""
+ "\n\n"
+ job.description_html,
}
],
)
content = chat_completion.choices[0].message.content
try:
if not content:
print("Empty result from ChatGPT")
continue
result = json.loads(content)
except json.decoder.JSONDecodeError as e:
logging.error(e, chat_completion)
continue
result["job_id"] = job.job_id
parsed_descriptions.append(result)
with open("job_descriptions_analysis.json", "w") as f:
json.dump(parsed_descriptions, f)
Do not forget to add OPENAI_API_KEY to the .env file
Now we can merge by job_id results with data from LinkedIn:
cat job_descriptions_analysis.json | from json | merge (cat result.json | from json) | to json | save full.json
Our data is ready to analyze!
cat full.json | from json | columns ╭────┬──────────────────╮ │ 0 │ experience │ │ 1 │ degree │ │ 2 │ stack │ │ 3 │ position │ │ 4 │ responsibilities │ │ 5 │ job_id │ │ 6 │ query │ │ 7 │ location │ │ 8 │ job_index │ │ 9 │ link │ │ 10 │ apply_link │ │ 11 │ title │ │ 12 │ company │ │ 13 │ company_link │ │ 14 │ company_img_link │ │ 15 │ place │ │ 16 │ description │ │ 17 │ description_html │ │ 18 │ date │ │ 19 │ insights │ │ 20 │ skills │ ╰────┴──────────────────╯
For the start
let df = cat full.json | from json
Now we can see technologies frequency:
$df | get 'stack' | flatten | uniq --count | sort-by count --reverse | first 20 | to md --pretty
| value | count |
|---|---|
| Python | 185 |
| Java | 70 |
| AWS | 65 |
| Kubernetes | 61 |
| SQL | 54 |
| C++ | 46 |
| Docker | 42 |
| Linux | 41 |
| React | 37 |
| Kotlin | 34 |
| JavaScript | 30 |
| C | 30 |
| Kafka | 28 |
| TypeScript | 26 |
| GCP | 25 |
| Azure | 24 |
| Tableau | 22 |
| Hadoop | 21 |
| Spark | 21 |
| R | 20 |
With Python:
$df | filter-by-intersection 'stack' ['python'] | get 'stack' | flatten | where $it != 'Python' # Exclude python itself | uniq --count | sort-by count --reverse | first 10 | to md --pretty
| value | count |
|---|---|
| Java | 44 |
| AWS | 43 |
| SQL | 40 |
| Kubernetes | 36 |
| Docker | 27 |
| C++ | 26 |
| Linux | 24 |
| R | 20 |
| GCP | 20 |
| C | 18 |
Without Python:
$df | filter-by-intersection 'stack' ['python'] --invert | get 'stack' | flatten | uniq --count | sort-by count --reverse | first 10 | to md --pretty
| value | count |
|---|---|
| React | 31 |
| Java | 26 |
| Kubernetes | 25 |
| TypeScript | 23 |
| AWS | 22 |
| Kotlin | 21 |
| C++ | 20 |
| Linux | 17 |
| Docker | 15 |
| Next.js | 15 |
The most of the jobs require Python, but there are some front-end, Java and C++ jobs
Magic filter-by-intersection function is a custom one and allow filtering list values that include given set of elements:
# Filters rows by intersecting given `column` with `requirements`
# Case insensitive and works only if ALL requirements exist in a `column` value
# If `--invert` then works as symmetric difference
def filter-by-intersection [
column: string
requirements: list<string>
--invert (-i)
] {
let required_stack = $requirements | par-each { |el| str downcase }
let required_len = if $invert { 0 } else { ($requirements | length )}
$in
| filter { |row|
$required_len == (
$row
| get $column
| par-each { |el| str downcase }
| where ($it in $requirements)
| length
)
}
}
What about experience and degree requirement for each position in Python?
$df
| filter-by-intersection 'stack' ['python']
| group-by 'position' --to-table
| insert 'group_size' { |group| $group.items | length }
| where 'group_size' >= 10
| insert 'experience' { |group|
$group.items
| get 'experience'
| uniq --count
| sort-by 'count' --reverse
| update 'value' { |row| if $row.value == null { 0 } else { $row.value }}
| rename --column { 'value': 'years' }
| first 3
}
| insert 'degree_requirement' { |group|
$group.items
| each { |row| $row.degree != null }
| uniq --count
| sort-by 'value'
| rename --column { 'value': 'required' }
}
| sort-by 'group_size' --reverse
| select 'group' 'group_size' 'experience' 'degree_requirement'
Output:
╭───┬────────┬────────────┬───────────────────────┬──────────────────────────╮ │ # │ group │ group_size │ experience │ degree_requirement │ ├───┼────────┼────────────┼───────────────────────┼──────────────────────────┤ │ 0 │ senior │ 83 │ ╭───┬───────┬───────╮ │ ╭───┬──────────┬───────╮ │ │ │ │ │ │ # │ years │ count │ │ │ # │ required │ count │ │ │ │ │ │ ├───┼───────┼───────┤ │ ├───┼──────────┼───────┤ │ │ │ │ │ │ 0 │ 5 │ 30 │ │ │ 0 │ false │ 26 │ │ │ │ │ │ │ 1 │ 0 │ 11 │ │ │ 1 │ true │ 57 │ │ │ │ │ │ │ 2 │ 7 │ 11 │ │ ╰───┴──────────┴───────╯ │ │ │ │ │ ╰───┴───────┴───────╯ │ │ │ 1 │ other │ 14 │ ╭───┬───────┬───────╮ │ ╭───┬──────────┬───────╮ │ │ │ │ │ │ # │ years │ count │ │ │ # │ required │ count │ │ │ │ │ │ ├───┼───────┼───────┤ │ ├───┼──────────┼───────┤ │ │ │ │ │ │ 0 │ 0 │ 8 │ │ │ 0 │ false │ 12 │ │ │ │ │ │ │ 1 │ 5 │ 1 │ │ │ 1 │ true │ 2 │ │ │ │ │ │ │ 2 │ 3 │ 1 │ │ ╰───┴──────────┴───────╯ │ │ │ │ │ ╰───┴───────┴───────╯ │ │ │ 2 │ lead │ 12 │ ╭───┬───────┬───────╮ │ ╭───┬──────────┬───────╮ │ │ │ │ │ │ # │ years │ count │ │ │ # │ required │ count │ │ │ │ │ │ ├───┼───────┼───────┤ │ ├───┼──────────┼───────┤ │ │ │ │ │ │ 0 │ 0 │ 5 │ │ │ 0 │ false │ 6 │ │ │ │ │ │ │ 1 │ 10 │ 4 │ │ │ 1 │ true │ 6 │ │ │ │ │ │ │ 2 │ 5 │ 1 │ │ ╰───┴──────────┴───────╯ │ │ │ │ │ ╰───┴───────┴───────╯ │ │ │ 3 │ middle │ 10 │ ╭───┬───────┬───────╮ │ ╭───┬──────────┬───────╮ │ │ │ │ │ │ # │ years │ count │ │ │ # │ required │ count │ │ │ │ │ │ ├───┼───────┼───────┤ │ ├───┼──────────┼───────┤ │ │ │ │ │ │ 0 │ 3 │ 4 │ │ │ 0 │ false │ 4 │ │ │ │ │ │ │ 1 │ 5 │ 3 │ │ │ 1 │ true │ 6 │ │ │ │ │ │ │ 2 │ 2 │ 2 │ │ ╰───┴──────────┴───────╯ │ │ │ │ │ ╰───┴───────┴───────╯ │ │ ╰───┴────────┴────────────┴───────────────────────┴──────────────────────────╯
Extraction of the most common requirements wasn't as easy as previous steps. So I've met a classification problem, and I'm going to describe my solution in the next chapter of this article.
We successfully extracted and analyzed job data from LinkedIn using the linkedin_jobs_scraper package. Responsibilities in the actual dataset are too sparse and need better processing to make functional classes that will help in CV creation. But the given steps already help me a lot with monitoring and applying to the jobs in half-auto mode.
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