探索軟體工程師的就業市場
- Patricia Arquette原創
- 2024-09-19 16:15:03853瀏覽
介紹
在本文中，我們深入研究從 LinkedIn 提取和分析工作資料的過程，利用 Python、Nu shell 和 ChatGPT 的組合來簡化和增強我們的工作流程。
我將引導您完成我進行研究的步驟，向您展示如何使用這些技術來探索不同國家甚至其他領域的就業市場。透過結合這些工具和方法，您可以收集和分析數據，以獲得對您感興趣的任何就業市場的寶貴見解。
技術概述
Python
Python 因其多功能庫而被選中，特別是 linkedin_jobs_scraper 和 openai。這些套件簡化了作業資料的抓取和處理。
核殼
對 Nu shell 進行了實驗，以將其功能與傳統的 bash 堆疊進行比較。該實驗旨在探索其在處理和操作數據方面的潛在好處。
聊天GPT
ChatGPT 用於協助從收集的資料中提取特定的工作特徵，例如經驗年限、學位要求、技術堆疊、職位層級和核心職責。
資料擷取
啟動需要一些資料。 LinkedIn 是我想到的第一個網站，並且已經準備好使用 Python 套件。我複製了範例程式碼，對其進行了一些修改，並準備使用腳本來獲取包含職位描述清單的 JSON 檔案。來源如下：
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()
為了下載 chrome 驅動程序，我製作了以下 bash 腳本：
#!/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"
我的 .env 檔案看起來像這樣：
CHROMEDRIVER_PATH="chromedriver-linux64/chromedriver" LI_AT_COOKIE=
linkedin_jobs_scraper 將作業序列化到以下 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] = []
範例（為了更好的可讀性，描述已替換為 ...）：
| 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
Last steps before analysis
We already have several ready to use features (title and skills), but I want more:
- Years of experience
- Degree
- Tech stack
- Position
- Responsibilities
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 │ ╰────┴──────────────────╯
Analysis
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.
Conclusion
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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