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8 Commits

Author SHA1 Message Date
Cullen Watson
6330c14879 minor fix 2024-07-15 21:19:01 -05:00
Ali Bakhshi Ilani
48631ea271 Add company industry and job level to linkedin scraper (#166) 2024-07-15 21:07:39 -05:00
Cullen Watson
edffe18e65 enh: listing source (#168) 2024-07-15 20:30:04 -05:00
Lluís Salord Quetglas
0988230a24 FEAT: Add Glassdoor logo data if available (#167) 2024-07-15 20:25:18 -05:00
Cullen Watson
d000a81eb3 Salary parse (#163) 2024-06-09 17:45:38 -05:00
Cullen Watson
ccb0c17660 enh: ziprecruiter full description (#162) 2024-06-09 16:21:01 -05:00
Cullen Watson
df339610fa docs: readme 2024-05-29 19:32:32 -05:00
Cullen Watson
c501006bd8 docs: readme 2024-05-28 16:04:26 -05:00
9 changed files with 250 additions and 61 deletions

View File

@@ -37,7 +37,7 @@ jobs = scrape_jobs(
hours_old=72, # (only Linkedin/Indeed is hour specific, others round up to days old)
country_indeed='USA', # only needed for indeed / glassdoor
# linkedin_fetch_description=True # get full description and direct job url for linkedin (slower)
# linkedin_fetch_description=True # get full description , direct job url , company industry and job level (seniority level) for linkedin (slower)
# proxies=["208.195.175.46:65095", "208.195.175.45:65095", "localhost"],
)
@@ -76,7 +76,7 @@ Optional
├── job_type (str):
| fulltime, parttime, internship, contract
├── proxies ():
├── proxies (list):
| in format ['user:pass@host:port', 'localhost']
| each job board will round robin through the proxies
@@ -140,19 +140,25 @@ JobPost
│ ├── state (str)
├── description (str)
├── job_type (str): fulltime, parttime, internship, contract
├── job_function (str)
├── compensation (object)
│ ├── interval (str): yearly, monthly, weekly, daily, hourly
│ ├── min_amount (int)
│ ├── max_amount (int)
│ └── currency (enum)
── date_posted (date)
── emails (str)
── date_posted (date)
── emails (str)
└── is_remote (bool)
Linkedin specific
└── job_level (str)
Linkedin & Indeed specific
└── company_industry (str)
Indeed specific
├── company_country (str)
└── company_addresses (str)
└── company_industry (str)
└── company_employees_label (str)
└── company_revenue_label (str)
└── company_description (str)

View File

@@ -1,6 +1,6 @@
[tool.poetry]
name = "python-jobspy"
version = "1.1.55"
version = "1.1.58"
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"

View File

@@ -5,7 +5,7 @@ from typing import Tuple
from concurrent.futures import ThreadPoolExecutor, as_completed
from .jobs import JobType, Location
from .scrapers.utils import logger, set_logger_level
from .scrapers.utils import logger, set_logger_level, extract_salary
from .scrapers.indeed import IndeedScraper
from .scrapers.ziprecruiter import ZipRecruiterScraper
from .scrapers.glassdoor import GlassdoorScraper
@@ -118,6 +118,21 @@ def scrape_jobs(
site_value, scraped_data = future.result()
site_to_jobs_dict[site_value] = scraped_data
def convert_to_annual(job_data: dict):
if job_data["interval"] == "hourly":
job_data["min_amount"] *= 2080
job_data["max_amount"] *= 2080
if job_data["interval"] == "monthly":
job_data["min_amount"] *= 12
job_data["max_amount"] *= 12
if job_data["interval"] == "weekly":
job_data["min_amount"] *= 52
job_data["max_amount"] *= 52
if job_data["interval"] == "daily":
job_data["min_amount"] *= 260
job_data["max_amount"] *= 260
job_data["interval"] = "yearly"
jobs_dfs: list[pd.DataFrame] = []
for site, job_response in site_to_jobs_dict.items():
@@ -150,11 +165,22 @@ def scrape_jobs(
job_data["min_amount"] = compensation_obj.get("min_amount")
job_data["max_amount"] = compensation_obj.get("max_amount")
job_data["currency"] = compensation_obj.get("currency", "USD")
if (
job_data["interval"]
and job_data["interval"] != "yearly"
and job_data["min_amount"]
and job_data["max_amount"]
):
convert_to_annual(job_data)
else:
job_data["interval"] = None
job_data["min_amount"] = None
job_data["max_amount"] = None
job_data["currency"] = None
if country_enum == Country.USA:
(
job_data["interval"],
job_data["min_amount"],
job_data["max_amount"],
job_data["currency"],
) = extract_salary(job_data["description"])
job_df = pd.DataFrame([job_data])
jobs_dfs.append(job_df)
@@ -182,13 +208,15 @@ def scrape_jobs(
"max_amount",
"currency",
"is_remote",
"job_level",
"job_function",
"company_industry",
"listing_type",
"emails",
"description",
"company_url",
"company_url_direct",
"company_addresses",
"company_industry",
"company_num_employees",
"company_revenue",
"company_description",

View File

@@ -242,10 +242,16 @@ class JobPost(BaseModel):
date_posted: date | None = None
emails: list[str] | None = None
is_remote: bool | None = None
listing_type: str | None = None
# linkedin specific
job_level: str | None = None
# linkedin and indeed specific
company_industry: str | None = None
# indeed specific
company_addresses: str | None = None
company_industry: str | None = None
company_num_employees: str | None = None
company_revenue: str | None = None
company_description: str | None = None

View File

@@ -69,7 +69,7 @@ class GlassdoorScraper(Scraper):
if location_type is None:
logger.error("Glassdoor: location not parsed")
return JobResponse(jobs=[])
all_jobs: list[JobPost] = []
job_list: list[JobPost] = []
cursor = None
range_start = 1 + (scraper_input.offset // self.jobs_per_page)
@@ -81,14 +81,14 @@ class GlassdoorScraper(Scraper):
jobs, cursor = self._fetch_jobs_page(
scraper_input, location_id, location_type, page, cursor
)
all_jobs.extend(jobs)
if not jobs or len(all_jobs) >= scraper_input.results_wanted:
all_jobs = all_jobs[: scraper_input.results_wanted]
job_list.extend(jobs)
if not jobs or len(job_list) >= scraper_input.results_wanted:
job_list = job_list[: scraper_input.results_wanted]
break
except Exception as e:
logger.error(f"Glassdoor: {str(e)}")
break
return JobResponse(jobs=all_jobs)
return JobResponse(jobs=job_list)
def _fetch_jobs_page(
self,
@@ -189,6 +189,15 @@ class GlassdoorScraper(Scraper):
except:
description = None
company_url = f"{self.base_url}Overview/W-EI_IE{company_id}.htm"
company_logo = (
job_data["jobview"].get("overview", {}).get("squareLogoUrl", None)
)
listing_type = (
job_data["jobview"]
.get("header", {})
.get("adOrderSponsorshipLevel", "")
.lower()
)
return JobPost(
id=str(job_id),
title=title,
@@ -201,6 +210,8 @@ class GlassdoorScraper(Scraper):
is_remote=is_remote,
description=description,
emails=extract_emails_from_text(description) if description else None,
logo_photo_url=company_logo,
listing_type=listing_type,
)
def _fetch_job_description(self, job_id):

View File

@@ -176,7 +176,7 @@ class IndeedScraper(Scraper):
keys.append("DSQF7")
if keys:
keys_str = '", "'.join(keys) # Prepare your keys string
keys_str = '", "'.join(keys)
filters_str = f"""
filters: {{
composite: {{
@@ -244,6 +244,7 @@ class IndeedScraper(Scraper):
.replace("Iv1", "")
.replace("_", " ")
.title()
.strip()
if employer_details.get("industry")
else None
),
@@ -297,8 +298,8 @@ class IndeedScraper(Scraper):
max_range = comp["range"].get("max")
return Compensation(
interval=interval,
min_amount=round(min_range, 2) if min_range is not None else None,
max_amount=round(max_range, 2) if max_range is not None else None,
min_amount=int(min_range) if min_range is not None else None,
max_amount=int(max_range) if max_range is not None else None,
currency=job["compensation"]["currencyCode"],
)
@@ -353,7 +354,6 @@ class IndeedScraper(Scraper):
jobSearch(
{what}
{location}
includeSponsoredResults: NONE
limit: 100
sort: DATE
{cursor}
@@ -365,6 +365,9 @@ class IndeedScraper(Scraper):
results {{
trackingKey
job {{
source {{
name
}}
key
title
datePublished

View File

@@ -19,7 +19,7 @@ from urllib.parse import urlparse, urlunparse, unquote
from .. import Scraper, ScraperInput, Site
from ..exceptions import LinkedInException
from ..utils import create_session
from ..utils import create_session, remove_attributes
from ...jobs import (
JobPost,
Location,
@@ -69,7 +69,7 @@ class LinkedInScraper(Scraper):
"""
self.scraper_input = scraper_input
job_list: list[JobPost] = []
seen_urls = set()
seen_ids = set()
page = scraper_input.offset // 10 * 10 if scraper_input.offset else 0
request_count = 0
seconds_old = (
@@ -133,25 +133,24 @@ class LinkedInScraper(Scraper):
return JobResponse(jobs=job_list)
for job_card in job_cards:
job_url = None
href_tag = job_card.find("a", class_="base-card__full-link")
if href_tag and "href" in href_tag.attrs:
href = href_tag.attrs["href"].split("?")[0]
job_id = href.split("-")[-1]
job_url = f"{self.base_url}/jobs/view/{job_id}"
if job_url in seen_urls:
continue
seen_urls.add(job_url)
try:
fetch_desc = scraper_input.linkedin_fetch_description
job_post = self._process_job(job_card, job_url, fetch_desc)
if job_post:
job_list.append(job_post)
if not continue_search():
break
except Exception as e:
raise LinkedInException(str(e))
if job_id in seen_ids:
continue
seen_ids.add(job_id)
try:
fetch_desc = scraper_input.linkedin_fetch_description
job_post = self._process_job(job_card, job_id, fetch_desc)
if job_post:
job_list.append(job_post)
if not continue_search():
break
except Exception as e:
raise LinkedInException(str(e))
if continue_search():
time.sleep(random.uniform(self.delay, self.delay + self.band_delay))
@@ -161,7 +160,7 @@ class LinkedInScraper(Scraper):
return JobResponse(jobs=job_list)
def _process_job(
self, job_card: Tag, job_url: str, full_descr: bool
self, job_card: Tag, job_id: str, full_descr: bool
) -> Optional[JobPost]:
salary_tag = job_card.find("span", class_="job-search-card__salary-info")
@@ -208,18 +207,20 @@ class LinkedInScraper(Scraper):
date_posted = None
job_details = {}
if full_descr:
job_details = self._get_job_details(job_url)
job_details = self._get_job_details(job_id)
return JobPost(
id=self._get_id(job_url),
id=job_id,
title=title,
company_name=company,
company_url=company_url,
location=location,
date_posted=date_posted,
job_url=job_url,
job_url=f"{self.base_url}/jobs/view/{job_id}",
compensation=compensation,
job_type=job_details.get("job_type"),
job_level=job_details.get("job_level", "").lower(),
company_industry=job_details.get("company_industry"),
description=job_details.get("description"),
job_url_direct=job_details.get("job_url_direct"),
emails=extract_emails_from_text(job_details.get("description")),
@@ -227,24 +228,16 @@ class LinkedInScraper(Scraper):
job_function=job_details.get("job_function"),
)
def _get_id(self, url: str):
"""
Extracts the job id from the job url
:param url:
:return: str
"""
if not url:
return None
return url.split("/")[-1]
def _get_job_details(self, job_page_url: str) -> dict:
def _get_job_details(self, job_id: str) -> dict:
"""
Retrieves job description and other job details by going to the job page url
:param job_page_url:
:return: dict
"""
try:
response = self.session.get(job_page_url, timeout=5)
response = self.session.get(
f"{self.base_url}/jobs-guest/jobs/api/jobPosting/{job_id}", timeout=5
)
response.raise_for_status()
except:
return {}
@@ -257,12 +250,6 @@ class LinkedInScraper(Scraper):
)
description = None
if div_content is not None:
def remove_attributes(tag):
for attr in list(tag.attrs):
del tag[attr]
return tag
div_content = remove_attributes(div_content)
description = div_content.prettify(formatter="html")
if self.scraper_input.description_format == DescriptionFormat.MARKDOWN:
@@ -281,6 +268,8 @@ class LinkedInScraper(Scraper):
job_function = job_function_span.text.strip()
return {
"description": description,
"job_level": self._parse_job_level(soup),
"company_industry": self._parse_company_industry(soup),
"job_type": self._parse_job_type(soup),
"job_url_direct": self._parse_job_url_direct(soup),
"logo_photo_url": soup.find("img", {"class": "artdeco-entity-image"}).get(
@@ -340,6 +329,52 @@ class LinkedInScraper(Scraper):
return [get_enum_from_job_type(employment_type)] if employment_type else []
@staticmethod
def _parse_job_level(soup_job_level: BeautifulSoup) -> str | None:
"""
Gets the job level from job page
:param soup_job_level:
:return: str
"""
h3_tag = soup_job_level.find(
"h3",
class_="description__job-criteria-subheader",
string=lambda text: "Seniority level" in text,
)
job_level = None
if h3_tag:
job_level_span = h3_tag.find_next_sibling(
"span",
class_="description__job-criteria-text description__job-criteria-text--criteria",
)
if job_level_span:
job_level = job_level_span.get_text(strip=True)
return job_level
@staticmethod
def _parse_company_industry(soup_industry: BeautifulSoup) -> str | None:
"""
Gets the company industry from job page
:param soup_industry:
:return: str
"""
h3_tag = soup_industry.find(
"h3",
class_="description__job-criteria-subheader",
string=lambda text: "Industries" in text,
)
industry = None
if h3_tag:
industry_span = h3_tag.find_next_sibling(
"span",
class_="description__job-criteria-text description__job-criteria-text--criteria",
)
if industry_span:
industry = industry_span.get_text(strip=True)
return industry
def _parse_job_url_direct(self, soup: BeautifulSoup) -> str | None:
"""
Gets the job url direct from job page

View File

@@ -93,6 +93,7 @@ class TLSRotating(RotatingProxySession, tls_client.Session):
else:
self.proxies = {}
response = tls_client.Session.execute_request(self, *args, **kwargs)
response.ok = response.status_code in range(200, 400)
return response
@@ -178,3 +179,61 @@ def currency_parser(cur_str):
num = float(cur_str)
return np.round(num, 2)
def remove_attributes(tag):
for attr in list(tag.attrs):
del tag[attr]
return tag
def extract_salary(
salary_str,
lower_limit=1000,
upper_limit=700000,
hourly_threshold=350,
monthly_threshold=30000,
):
if not salary_str:
return None, None, None, None
min_max_pattern = r"\$(\d+(?:,\d+)?(?:\.\d+)?)([kK]?)\s*[-—–]\s*(?:\$)?(\d+(?:,\d+)?(?:\.\d+)?)([kK]?)"
def to_int(s):
return int(float(s.replace(",", "")))
def convert_hourly_to_annual(hourly_wage):
return hourly_wage * 2080
def convert_monthly_to_annual(monthly_wage):
return monthly_wage * 12
match = re.search(min_max_pattern, salary_str)
if match:
min_salary = to_int(match.group(1))
max_salary = to_int(match.group(3))
# Handle 'k' suffix for min and max salaries independently
if "k" in match.group(2).lower() or "k" in match.group(4).lower():
min_salary *= 1000
max_salary *= 1000
# Convert to annual if less than the hourly threshold
if min_salary < hourly_threshold:
min_salary = convert_hourly_to_annual(min_salary)
if max_salary < hourly_threshold:
max_salary = convert_hourly_to_annual(max_salary)
elif min_salary < monthly_threshold:
min_salary = convert_monthly_to_annual(min_salary)
if max_salary < monthly_threshold:
max_salary = convert_monthly_to_annual(max_salary)
# Ensure salary range is within specified limits
if (
lower_limit <= min_salary <= upper_limit
and lower_limit <= max_salary <= upper_limit
and min_salary < max_salary
):
return "yearly", min_salary, max_salary, "USD"
return None, None, None, None

View File

@@ -7,19 +7,24 @@ This module contains routines to scrape ZipRecruiter.
from __future__ import annotations
import json
import math
import re
import time
from datetime import datetime
from typing import Optional, Tuple, Any
from concurrent.futures import ThreadPoolExecutor
from bs4 import BeautifulSoup
from .. import Scraper, ScraperInput, Site
from ..utils import (
logger,
extract_emails_from_text,
create_session,
markdown_converter,
remove_attributes,
)
from ...jobs import (
JobPost,
@@ -130,6 +135,7 @@ class ZipRecruiterScraper(Scraper):
self.seen_urls.add(job_url)
description = job.get("job_description", "").strip()
listing_type = job.get("buyer_type", "")
description = (
markdown_converter(description)
if self.scraper_input.description_format == DescriptionFormat.MARKDOWN
@@ -151,6 +157,8 @@ class ZipRecruiterScraper(Scraper):
comp_min = int(job["compensation_min"]) if "compensation_min" in job else None
comp_max = int(job["compensation_max"]) if "compensation_max" in job else None
comp_currency = job.get("compensation_currency")
description_full, job_url_direct = self._get_descr(job_url)
return JobPost(
id=str(job["listing_key"]),
title=title,
@@ -165,10 +173,43 @@ class ZipRecruiterScraper(Scraper):
),
date_posted=date_posted,
job_url=job_url,
description=description,
description=description_full if description_full else description,
emails=extract_emails_from_text(description) if description else None,
job_url_direct=job_url_direct,
listing_type=listing_type,
)
def _get_descr(self, job_url):
res = self.session.get(job_url, headers=self.headers, allow_redirects=True)
description_full = job_url_direct = None
if res.ok:
soup = BeautifulSoup(res.text, "html.parser")
job_descr_div = soup.find("div", class_="job_description")
company_descr_section = soup.find("section", class_="company_description")
job_description_clean = (
remove_attributes(job_descr_div).prettify(formatter="html")
if job_descr_div
else ""
)
company_description_clean = (
remove_attributes(company_descr_section).prettify(formatter="html")
if company_descr_section
else ""
)
description_full = job_description_clean + company_description_clean
script_tag = soup.find("script", type="application/json")
if script_tag:
job_json = json.loads(script_tag.string)
job_url_val = job_json["model"]["saveJobURL"]
m = re.search(r"job_url=(.+)", job_url_val)
if m:
job_url_direct = m.group(1)
if self.scraper_input.description_format == DescriptionFormat.MARKDOWN:
description_full = markdown_converter(description_full)
return description_full, job_url_direct
def _get_cookies(self):
data = "event_type=session&logged_in=false&number_of_retry=1&property=model%3AiPhone&property=os%3AiOS&property=locale%3Aen_us&property=app_build_number%3A4734&property=app_version%3A91.0&property=manufacturer%3AApple&property=timestamp%3A2024-01-12T12%3A04%3A42-06%3A00&property=screen_height%3A852&property=os_version%3A16.6.1&property=source%3Ainstall&property=screen_width%3A393&property=device_model%3AiPhone%2014%20Pro&property=brand%3AApple"
url = f"{self.api_url}/jobs-app/event"