この記事では、Python、Nu シェル、ChatGPT の組み合わせを活用して、LinkedIn からジョブ データを抽出して分析し、ワークフローを合理化し、強化するプロセスについて詳しく説明します。
調査を実行するために私が行った手順を説明し、これらのテクニックを使用してさまざまな国や他の分野の雇用市場を調査する方法を示します。これらのツールと方法を組み合わせることで、データを収集して分析し、興味のあるあらゆる雇用市場について貴重な洞察を得ることができます。
Python が選ばれた理由は、その多用途なライブラリ、特に linkedin_jobs_scraper と openai です。これらのパッケージは、ジョブ データのスクレイピングと処理を合理化しました。
Nu シェルは、その機能を従来の bash スタックと比較するために実験されました。この実験は、データの処理と操作における潜在的な利点を探ることを目的としました。
ChatGPT は、経験年数、学位要件、技術スタック、役職レベル、中核的な責任など、収集されたデータから特定の職務の特徴を抽出するのに役立ちます。
開始するには、いくつかのデータが必要です。 LinkedIn は私の頭に浮かんだ最初の Web サイトであり、Python パッケージを使用する準備ができていました。サンプル コードをコピーし、少し変更して、スクリプトを使用してジョブ説明のリストを含む JSON ファイルを取得する準備をしました。ソースは次のとおりです:
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()
Chrome ドライバーをダウンロードするために、次の bash スクリプトを作成しました:
#!/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"
私の .env ファイルは次のようになります:
CHROMEDRIVER_PATH="chromedriver-linux64/chromedriver" LI_AT_COOKIE=
linkedin_jobs_scraper はジョブを次の DTO にシリアル化します:
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] = []
サンプルの例 (読みやすくするために、説明は ... に置き換えられました):
| 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.
以上がソフトウェアエンジニアの雇用市場の探索の詳細内容です。詳細については、PHP 中国語 Web サイトの他の関連記事を参照してください。
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