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JobSpy
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enh: full description param (#85)

pull/88/head v1.1.35
Cullen Watson 2024-01-22 20:22:32 -06:00 committed by GitHub
parent 2ec3b04777
commit 5b3627b244
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8 changed files with 115 additions and 50 deletions
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1
README.md
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@ -67,6 +67,7 @@ Optional
├── job_type (enum): fulltime, parttime, internship, contract
├── proxy (str): in format 'http://user:pass@host:port' or [https, socks]
├── is_remote (bool)
├── full_description (bool): fetches full description for Indeed / LinkedIn (much slower)
├── results_wanted (int): number of job results to retrieve for each site specified in 'site_type'
├── easy_apply (bool): filters for jobs that are hosted on LinkedIn
├── country_indeed (enum): filters the country on Indeed (see below for correct spelling)

2
pyproject.toml
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@ -1,6 +1,6 @@
[tool.poetry]
name = "python-jobspy"
version = "1.1.34"
version = "1.1.35"
description = "Job scraper for LinkedIn, Indeed, Glassdoor & ZipRecruiter"
authors = ["Zachary Hampton <zachary@bunsly.com>", "Cullen Watson <cullen@bunsly.com>"]
homepage = "https://github.com/Bunsly/JobSpy"

2
src/jobspy/__init__.py
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@ -40,6 +40,7 @@ def scrape_jobs(
country_indeed: str = "usa",
hyperlinks: bool = False,
proxy: Optional[str] = None,
full_description: Optional[bool] = False,
offset: Optional[int] = 0,
) -> pd.DataFrame:
"""
@ -74,6 +75,7 @@ def scrape_jobs(
is_remote=is_remote,
job_type=job_type,
easy_apply=easy_apply,
full_description=full_description,
results_wanted=results_wanted,
offset=offset,
)

1
src/jobspy/scrapers/__init__.py
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@ -19,6 +19,7 @@ class ScraperInput(BaseModel):
is_remote: bool = False
job_type: Optional[JobType] = None
easy_apply: bool = None # linkedin
full_description: bool = False
offset: int = 0
results_wanted: int = 15

129
src/jobspy/scrapers/glassdoor/__init__.py
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@ -5,8 +5,12 @@ jobspy.scrapers.glassdoor
This module contains routines to scrape Glassdoor.
"""
import json
from typing import Optional, Any
import requests
from bs4 import BeautifulSoup
from typing import Optional
from datetime import datetime, timedelta
from concurrent.futures import ThreadPoolExecutor, as_completed
from ..utils import count_urgent_words, extract_emails_from_text
from .. import Scraper, ScraperInput, Site
from ..exceptions import GlassdoorException
@ -66,50 +70,70 @@ class GlassdoorScraper(Scraper):
jobs_data = res_json["data"]["jobListings"]["jobListings"]
jobs = []
for i, job in enumerate(jobs_data):
job_url = res_json["data"]["jobListings"]["jobListingSeoLinks"][
"linkItems"
][i]["url"]
if job_url in self.seen_urls:
continue
self.seen_urls.add(job_url)
job = job["jobview"]
title = job["job"]["jobTitleText"]
company_name = job["header"]["employerNameFromSearch"]
location_name = job["header"].get("locationName", "")
location_type = job["header"].get("locationType", "")
age_in_days = job["header"].get("ageInDays")
is_remote, location = False, None
date_posted = (datetime.now() - timedelta(days=age_in_days)).date() if age_in_days else None
if location_type == "S":
is_remote = True
else:
location = self.parse_location(location_name)
compensation = self.parse_compensation(job["header"])
job = JobPost(
title=title,
company_name=company_name,
date_posted=date_posted,
job_url=job_url,
location=location,
compensation=compensation,
is_remote=is_remote
)
jobs.append(job)
with ThreadPoolExecutor(max_workers=self.jobs_per_page) as executor:
future_to_job_data = {executor.submit(self.process_job, job): job for job in jobs_data}
for future in as_completed(future_to_job_data):
job_data = future_to_job_data[future]
try:
job_post = future.result()
if job_post:
jobs.append(job_post)
except Exception as exc:
raise GlassdoorException(f'Glassdoor generated an exception: {exc}')
return jobs, self.get_cursor_for_page(
res_json["data"]["jobListings"]["paginationCursors"], page_num + 1
)
def process_job(self, job_data):
"""Processes a single job and fetches its description."""
job_id = job_data["jobview"]["job"]["listingId"]
job_url = f'{self.url}/job-listing/?jl={job_id}'
if job_url in self.seen_urls:
return None
self.seen_urls.add(job_url)
job = job_data["jobview"]
title = job["job"]["jobTitleText"]
company_name = job["header"]["employerNameFromSearch"]
location_name = job["header"].get("locationName", "")
location_type = job["header"].get("locationType", "")
age_in_days = job["header"].get("ageInDays")
is_remote, location = False, None
date_posted = (datetime.now() - timedelta(days=age_in_days)).date() if age_in_days else None
if location_type == "S":
is_remote = True
else:
location = self.parse_location(location_name)
compensation = self.parse_compensation(job["header"])
try:
description = self.fetch_job_description(job_id)
except Exception as e :
description = None
job_post = JobPost(
title=title,
company_name=company_name,
date_posted=date_posted,
job_url=job_url,
location=location,
compensation=compensation,
is_remote=is_remote,
description=description,
emails=extract_emails_from_text(description) if description else None,
num_urgent_words=count_urgent_words(description) if description else None,
)
return job_post
def scrape(self, scraper_input: ScraperInput) -> JobResponse:
"""
Scrapes Glassdoor for jobs with scraper_input criteria.
:param scraper_input: Information about job search criteria.
:return: JobResponse containing a list of jobs.
"""
scraper_input.results_wanted = min(900, scraper_input.results_wanted)
self.country = scraper_input.country
self.url = self.country.get_url()
@ -143,6 +167,43 @@ class GlassdoorScraper(Scraper):
return JobResponse(jobs=all_jobs)
def fetch_job_description(self, job_id):
"""Fetches the job description for a single job ID."""
url = f"{self.url}/graph"
body = [
{
"operationName": "JobDetailQuery",
"variables": {
"jl": job_id,
"queryString": "q",
"pageTypeEnum": "SERP"
},
"query": """
query JobDetailQuery($jl: Long!, $queryString: String, $pageTypeEnum: PageTypeEnum) {
jobview: jobView(
listingId: $jl
contextHolder: {queryString: $queryString, pageTypeEnum: $pageTypeEnum}
) {
job {
description
__typename
}
__typename
}
}
"""
}
]
response = requests.post(url, json=body, headers=GlassdoorScraper.headers())
if response.status_code != 200:
return None
data = response.json()[0]
desc = data['data']['jobview']['job']['description']
soup = BeautifulSoup(desc, 'html.parser')
description = soup.get_text(separator='\n')
return description
@staticmethod
def parse_compensation(data: dict) -> Optional[Compensation]:
pay_period = data.get("payPeriod")

7
src/jobspy/scrapers/indeed/__init__.py
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@ -78,7 +78,7 @@ class IndeedScraper(Scraper):
if sc_values:
params["sc"] = "0kf:" + "".join(sc_values) + ";"
try:
session = create_session(self.proxy, is_tls=True)
session = create_session(self.proxy)
response = session.get(
f"{self.url}/jobs",
headers=self.get_headers(),
@ -140,7 +140,8 @@ class IndeedScraper(Scraper):
date_posted = datetime.fromtimestamp(timestamp_seconds)
date_posted = date_posted.strftime("%Y-%m-%d")
description = self.get_description(job_url)
description = self.get_description(job_url) if scraper_input.full_description else None
with io.StringIO(job["snippet"]) as f:
soup_io = BeautifulSoup(f, "html.parser")
li_elements = soup_io.find_all("li")
@ -246,7 +247,7 @@ class IndeedScraper(Scraper):
return None
soup = BeautifulSoup(job_description, "html.parser")
text_content = " ".join(soup.get_text(separator=" ").split()).strip()
text_content = "\n".join(soup.stripped_strings)
return text_content

21
src/jobspy/scrapers/linkedin/__init__.py
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@ -111,7 +111,7 @@ class LinkedInScraper(Scraper):
# Call process_job directly without threading
try:
job_post = self.process_job(job_card, job_url)
job_post = self.process_job(job_card, job_url, scraper_input.full_description)
if job_post:
job_list.append(job_post)
except Exception as e:
@ -123,7 +123,7 @@ class LinkedInScraper(Scraper):
job_list = job_list[: scraper_input.results_wanted]
return JobResponse(jobs=job_list)
def process_job(self, job_card: Tag, job_url: str) -> Optional[JobPost]:
def process_job(self, job_card: Tag, job_url: str, full_descr: bool) -> Optional[JobPost]:
salary_tag = job_card.find('span', class_='job-search-card__salary-info')
compensation = None
@ -160,7 +160,7 @@ class LinkedInScraper(Scraper):
if metadata_card
else None
)
date_posted = None
date_posted = description = job_type = None
if datetime_tag and "datetime" in datetime_tag.attrs:
datetime_str = datetime_tag["datetime"]
try:
@ -169,9 +169,8 @@ class LinkedInScraper(Scraper):
date_posted = None
benefits_tag = job_card.find("span", class_="result-benefits__text")
benefits = " ".join(benefits_tag.get_text().split()) if benefits_tag else None
# removed to speed up scraping
# description, job_type = self.get_job_description(job_url)
if full_descr:
description, job_type = self.get_job_description(job_url)
return JobPost(
title=title,
@ -182,10 +181,10 @@ class LinkedInScraper(Scraper):
job_url=job_url,
compensation=compensation,
benefits=benefits,
# job_type=job_type,
# description=description,
# emails=extract_emails_from_text(description) if description else None,
# num_urgent_words=count_urgent_words(description) if description else None,
job_type=job_type,
description=description,
emails=extract_emails_from_text(description) if description else None,
num_urgent_words=count_urgent_words(description) if description else None,
)
def get_job_description(
@ -214,7 +213,7 @@ class LinkedInScraper(Scraper):
description = None
if div_content:
description = " ".join(div_content.get_text().split()).strip()
description = "\n".join(line.strip() for line in div_content.get_text(separator="\n").splitlines() if line.strip())
def get_job_type(
soup_job_type: BeautifulSoup,

2
src/jobspy/scrapers/ziprecruiter/__init__.py
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@ -109,7 +109,7 @@ class ZipRecruiterScraper(Scraper):
description = BeautifulSoup(
job.get("job_description", "").strip(), "html.parser"
).get_text()
).get_text(separator="\n")
company = job["hiring_company"].get("name") if "hiring_company" in job else None
country_value = "usa" if job.get("job_country") == "US" else "canada"