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JobSpy
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Multiple job types for Indeed, urgent keywords column (#56) 

* enh(indeed): mult job types

* feat(jobs):  urgent kws

* fix(indeed): use new session obj per request

* fix: emails as comma separated in output

* fix: put num urgent words in output

* chore: readme
pull/58/head
Cullen Watson 2023-10-10 11:23:04 -05:00 committed by GitHub
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31
README.md
View File

@ -33,37 +33,19 @@ _Python version >= [3.10](https://www.python.org/downloads/release/python-3100/)
```python
from jobspy import scrape_jobs
import pandas as pd
jobs: pd.DataFrame = scrape_jobs(
jobs = scrape_jobs(
site_name=["indeed", "linkedin", "zip_recruiter"],
search_term="software engineer",
location="Dallas, TX",
results_wanted=10,
country_indeed='USA' # only needed for indeed
# use if you want to use a proxy
# proxy="http://jobspy:5a4vpWtj8EeJ2hoYzk@ca.smartproxy.com:20001",
# offset=25 # use if you want to start at a specific offset
)
print(f"Found {len(jobs)} jobs")
print(jobs.head())
jobs.to_csv("jobs.csv", index=False)
# formatting for pandas
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', 50) # set to 0 to see full job url / desc
# 1 output to console
print(jobs)
# 2 display in Jupyter Notebook (1. pip install jupyter 2. jupyter notebook)
# display(jobs)
# 3 output to .csv
# jobs.to_csv('jobs.csv', index=False)
# 4 output to .xlsx
# output to .xlsx
# jobs.to_xlsx('jobs.xlsx', index=False)
```
@ -117,6 +99,9 @@ JobPost
│ ├── max_amount (int)
│ └── currency (enum)
└── date_posted (date)
└── emails (str)
└── num_urgent_words (int)
└── is_remote (bool) - just for Indeed at the momen
```
### Exceptions

14
examples/JobSpy_Demo.py
View File

@ -6,23 +6,23 @@ jobs: pd.DataFrame = scrape_jobs(
search_term="software engineer",
location="Dallas, TX",
results_wanted=50, # be wary the higher it is, the more likey you'll get blocked (rotating proxy should work tho)
country_indeed='USA',
country_indeed="USA",
offset=25 # start jobs from an offset (use if search failed and want to continue)
# proxy="http://jobspy:5a4vpWtj8EeJ2hoYzk@ca.smartproxy.com:20001",
)
# formatting for pandas
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', 50) # set to 0 to see full job url / desc
pd.set_option("display.max_columns", None)
pd.set_option("display.max_rows", None)
pd.set_option("display.width", None)
pd.set_option("display.max_colwidth", 50) # set to 0 to see full job url / desc
# 1: output to console
print(jobs)
# 2: output to .csv
jobs.to_csv('./jobs.csv', index=False)
print('outputted to jobs.csv')
jobs.to_csv("./jobs.csv", index=False)
print("outputted to jobs.csv")
# 3: output to .xlsx
# jobs.to_xlsx('jobs.xlsx', index=False)

2
pyproject.toml
View File

@ -1,6 +1,6 @@
[tool.poetry]
name = "python-jobspy"
version = "1.1.12"
version = "1.1.13"
description = "Job scraper for LinkedIn, Indeed & ZipRecruiter"
authors = ["Zachary Hampton <zachary@zacharysproducts.com>", "Cullen Watson <cullen@cullen.ai>"]
homepage = "https://github.com/cullenwatson/JobSpy"

35
src/jobspy/__init__.py
View File

@ -1,7 +1,7 @@
import pandas as pd
import concurrent.futures
from concurrent.futures import ThreadPoolExecutor
from typing import List, Tuple, Optional
from typing import Tuple, Optional
from .jobs import JobType, Location
from .scrapers.indeed import IndeedScraper
@ -26,7 +26,7 @@ def _map_str_to_site(site_name: str) -> Site:
def scrape_jobs(
site_name: str | List[str] | Site | List[Site],
site_name: str | list[str] | Site | list[Site],
search_term: str,
location: str = "",
distance: int = None,
@ -37,7 +37,7 @@ def scrape_jobs(
country_indeed: str = "usa",
hyperlinks: bool = False,
proxy: Optional[str] = None,
offset: Optional[int] = 0
offset: Optional[int] = 0,
) -> pd.DataFrame:
"""
Simultaneously scrapes job data from multiple job sites.
@ -72,7 +72,7 @@ def scrape_jobs(
job_type=job_type,
easy_apply=easy_apply,
results_wanted=results_wanted,
offset=offset
offset=offset,
)
def scrape_site(site: Site) -> Tuple[str, JobResponse]:
@ -98,8 +98,8 @@ def scrape_jobs(
site_to_jobs_dict = {}
def worker(site):
site_value, scraped_data = scrape_site(site)
return site_value, scraped_data
site_val, scraped_info = scrape_site(site)
return site_val, scraped_info
with ThreadPoolExecutor() as executor:
future_to_site = {
@ -110,7 +110,7 @@ def scrape_jobs(
site_value, scraped_data = future.result()
site_to_jobs_dict[site_value] = scraped_data
jobs_dfs: List[pd.DataFrame] = []
jobs_dfs: list[pd.DataFrame] = []
for site, job_response in site_to_jobs_dict.items():
for job in job_response.jobs:
@ -120,12 +120,14 @@ def scrape_jobs(
] = f'<a href="{job_data["job_url"]}">{job_data["job_url"]}</a>'
job_data["site"] = site
job_data["company"] = job_data["company_name"]
if job_data["job_type"]:
# Take the first value from the job type tuple
job_data["job_type"] = job_data["job_type"].value[0]
else:
job_data["job_type"] = None
job_data["job_type"] = (
", ".join(job_type.value[0] for job_type in job_data["job_type"])
if job_data["job_type"]
else None
)
job_data["emails"] = (
", ".join(job_data["emails"]) if job_data["emails"] else None
)
job_data["location"] = Location(**job_data["location"]).display_location()
compensation_obj = job_data.get("compensation")
@ -149,7 +151,7 @@ def scrape_jobs(
if jobs_dfs:
jobs_df = pd.concat(jobs_dfs, ignore_index=True)
desired_order: List[str] = [
desired_order: list[str] = [
"job_url_hyper" if hyperlinks else "job_url",
"site",
"title",
@ -158,12 +160,13 @@ def scrape_jobs(
"job_type",
"date_posted",
"interval",
"benefits",
"min_amount",
"max_amount",
"currency",
"is_remote",
"num_urgent_words",
"benefits",
"emails",
"job_url_hyper" if hyperlinks else "job_url",
"description",
]
jobs_formatted_df = jobs_df[desired_order]

15
src/jobspy/jobs/__init__.py
View File

@ -182,12 +182,15 @@ class JobPost(BaseModel):
job_url: str
location: Optional[Location]
description: Optional[str] = None
job_type: Optional[JobType] = None
compensation: Optional[Compensation] = None
date_posted: Optional[date] = None
benefits: Optional[str] = None
emails: Optional[list[str]] = None
description: str | None = None
job_type: list[JobType] | None = None
compensation: Compensation | None = None
date_posted: date | None = None
benefits: str | None = None
emails: list[str] | None = None
num_urgent_words: int | None = None
is_remote: bool | None = None
# company_industry: str | None = None
class JobResponse(BaseModel):

91
src/jobspy/scrapers/indeed/__init__.py
View File

@ -9,15 +9,14 @@ import math
import io
import json
from datetime import datetime
from typing import Optional
import tls_client
import urllib.parse
from bs4 import BeautifulSoup
from bs4.element import Tag
from concurrent.futures import ThreadPoolExecutor, Future
from ..exceptions import IndeedException
from ..utils import count_urgent_words, extract_emails_from_text, create_session
from ...jobs import (
JobPost,
Compensation,
@ -28,15 +27,9 @@ from ...jobs import (
)
from .. import Scraper, ScraperInput, Site
def extract_emails_from_text(text: str) -> Optional[list[str]]:
if not text:
return None
email_regex = re.compile(r"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}")
return email_regex.findall(text)
class IndeedScraper(Scraper):
def __init__(self, proxy: Optional[str] = None):
def __init__(self, proxy: str | None = None):
"""
Initializes IndeedScraper with the Indeed job search url
"""
@ -49,20 +42,18 @@ class IndeedScraper(Scraper):
self.seen_urls = set()
def scrape_page(
self, scraper_input: ScraperInput, page: int, session: tls_client.Session
self, scraper_input: ScraperInput, page: int
) -> tuple[list[JobPost], int]:
"""
Scrapes a page of Indeed for jobs with scraper_input criteria
:param scraper_input:
:param page:
:param session:
:return: jobs found on page, total number of jobs found for search
"""
self.country = scraper_input.country
domain = self.country.domain_value
self.url = f"https://{domain}.indeed.com"
job_list: list[JobPost] = []
session = create_session(self.proxy)
params = {
"q": scraper_input.search_term,
@ -84,9 +75,9 @@ class IndeedScraper(Scraper):
try:
response = session.get(
f"{self.url}/jobs",
headers=self.get_headers(),
params=params,
allow_redirects=True,
proxy=self.proxy,
timeout_seconds=10,
)
if response.status_code not in range(200, 400):
@ -114,7 +105,7 @@ class IndeedScraper(Scraper):
):
raise IndeedException("No jobs found.")
def process_job(job) -> Optional[JobPost]:
def process_job(job) -> JobPost | None:
job_url = f'{self.url}/jobs/viewjob?jk={job["jobkey"]}'
job_url_client = f'{self.url}/viewjob?jk={job["jobkey"]}'
if job_url in self.seen_urls:
@ -143,8 +134,7 @@ class IndeedScraper(Scraper):
date_posted = datetime.fromtimestamp(timestamp_seconds)
date_posted = date_posted.strftime("%Y-%m-%d")
description = self.get_description(job_url, session)
emails = extract_emails_from_text(description)
description = self.get_description(job_url)
with io.StringIO(job["snippet"]) as f:
soup_io = BeautifulSoup(f, "html.parser")
li_elements = soup_io.find_all("li")
@ -160,11 +150,15 @@ class IndeedScraper(Scraper):
state=job.get("jobLocationState"),
country=self.country,
),
emails=extract_emails_from_text(description),
job_type=job_type,
compensation=compensation,
date_posted=date_posted,
job_url=job_url_client,
emails=extract_emails_from_text(description) if description else None,
num_urgent_words=count_urgent_words(description)
if description
else None,
is_remote=self.is_remote_job(job),
)
return job_post
@ -184,20 +178,16 @@ class IndeedScraper(Scraper):
:param scraper_input:
:return: job_response
"""
session = tls_client.Session(
client_identifier="chrome112", random_tls_extension_order=True
)
pages_to_process = (
math.ceil(scraper_input.results_wanted / self.jobs_per_page) - 1
)
#: get first page to initialize session
job_list, total_results = self.scrape_page(scraper_input, 0, session)
job_list, total_results = self.scrape_page(scraper_input, 0)
with ThreadPoolExecutor(max_workers=1) as executor:
futures: list[Future] = [
executor.submit(self.scrape_page, scraper_input, page, session)
executor.submit(self.scrape_page, scraper_input, page)
for page in range(1, pages_to_process + 1)
]
@ -215,21 +205,24 @@ class IndeedScraper(Scraper):
)
return job_response
def get_description(self, job_page_url: str, session: tls_client.Session) -> Optional[str]:
def get_description(self, job_page_url: str) -> str | None:
"""
Retrieves job description by going to the job page url
:param job_page_url:
:param session:
:return: description
"""
parsed_url = urllib.parse.urlparse(job_page_url)
params = urllib.parse.parse_qs(parsed_url.query)
jk_value = params.get("jk", [None])[0]
formatted_url = f"{self.url}/viewjob?jk={jk_value}&spa=1"
session = create_session(self.proxy)
try:
response = session.get(
formatted_url, allow_redirects=True, timeout_seconds=5, proxy=self.proxy
formatted_url,
headers=self.get_headers(),
allow_redirects=True,
timeout_seconds=5,
)
except Exception as e:
return None
@ -246,20 +239,23 @@ class IndeedScraper(Scraper):
return text_content
@staticmethod
def get_job_type(job: dict) -> Optional[JobType]:
def get_job_type(job: dict) -> list[JobType] | None:
"""
Parses the job to get JobTypeIndeed
Parses the job to get list of job types
:param job:
:return:
"""
job_types: list[JobType] = []
for taxonomy in job["taxonomyAttributes"]:
if taxonomy["label"] == "job-types":
if len(taxonomy["attributes"]) > 0:
label = taxonomy["attributes"][0].get("label")
for i in range(len(taxonomy["attributes"])):
label = taxonomy["attributes"][i].get("label")
if label:
job_type_str = label.replace("-", "").replace(" ", "").lower()
return IndeedScraper.get_enum_from_job_type(job_type_str)
return None
job_types.append(
IndeedScraper.get_enum_from_job_type(job_type_str)
)
return job_types
@staticmethod
def get_enum_from_job_type(job_type_str):
@ -280,7 +276,7 @@ class IndeedScraper(Scraper):
:return: jobs
"""
def find_mosaic_script() -> Optional[Tag]:
def find_mosaic_script() -> Tag | None:
"""
Finds jobcards script tag
:return: script_tag
@ -330,3 +326,30 @@ class IndeedScraper(Scraper):
data = json.loads(json_str)
total_num_jobs = int(data["searchTitleBarModel"]["totalNumResults"])
return total_num_jobs
@staticmethod
def get_headers():
return {
"authority": "www.indeed.com",
"accept": "*/*",
"accept-language": "en-US,en;q=0.9",
"referer": "https://www.indeed.com/viewjob?jk=fe6182337d72c7b1&tk=1hcbfcmd0k62t802&from=serp&vjs=3&advn=8132938064490989&adid=408692607&ad=-6NYlbfkN0A3Osc99MJFDKjquSk4WOGT28ALb_ad4QMtrHreCb9ICg6MiSVy9oDAp3evvOrI7Q-O9qOtQTg1EPbthP9xWtBN2cOuVeHQijxHjHpJC65TjDtftH3AXeINjBvAyDrE8DrRaAXl8LD3Fs1e_xuDHQIssdZ2Mlzcav8m5jHrA0fA64ZaqJV77myldaNlM7-qyQpy4AsJQfvg9iR2MY7qeC5_FnjIgjKIy_lNi9OPMOjGRWXA94CuvC7zC6WeiJmBQCHISl8IOBxf7EdJZlYdtzgae3593TFxbkd6LUwbijAfjax39aAuuCXy3s9C4YgcEP3TwEFGQoTpYu9Pmle-Ae1tHGPgsjxwXkgMm7Cz5mBBdJioglRCj9pssn-1u1blHZM4uL1nK9p1Y6HoFgPUU9xvKQTHjKGdH8d4y4ETyCMoNF4hAIyUaysCKdJKitC8PXoYaWhDqFtSMR4Jys8UPqUV&xkcb=SoDD-_M3JLQfWnQTDh0LbzkdCdPP&xpse=SoBa6_I3JLW9FlWZlB0PbzkdCdPP&sjdu=i6xVERweJM_pVUvgf-MzuaunBTY7G71J5eEX6t4DrDs5EMPQdODrX7Nn-WIPMezoqr5wA_l7Of-3CtoiUawcHw",
"sec-ch-ua": '"Google Chrome";v="119", "Chromium";v="119", "Not?A_Brand";v="24"',
"sec-ch-ua-mobile": "?0",
"sec-ch-ua-platform": '"Windows"',
"sec-fetch-dest": "empty",
"sec-fetch-mode": "cors",
"sec-fetch-site": "same-origin",
"user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36",
}
@staticmethod
def is_remote_job(job: dict) -> bool:
"""
:param job:
:return: bool
"""
for taxonomy in job.get("taxonomyAttributes", []):
if taxonomy["label"] == "remote" and len(taxonomy["attributes"]) > 0:
return True
return False

47
src/jobspy/scrapers/linkedin/__init__.py
View File

@ -17,6 +17,7 @@ from bs4.element import Tag
from threading import Lock
from .. import Scraper, ScraperInput, Site
from ..utils import count_urgent_words, extract_emails_from_text
from ..exceptions import LinkedInException
from ...jobs import (
JobPost,
@ -26,13 +27,6 @@ from ...jobs import (
)
def extract_emails_from_text(text: str) -> Optional[list[str]]:
if not text:
return None
email_regex = re.compile(r"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}")
return email_regex.findall(text)
class LinkedInScraper(Scraper):
MAX_RETRIES = 3
DELAY = 10
@ -99,13 +93,15 @@ class LinkedInScraper(Scraper):
break
except requests.HTTPError as e:
if hasattr(e, 'response') and e.response is not None:
if hasattr(e, "response") and e.response is not None:
if e.response.status_code == 429:
time.sleep(self.DELAY)
retries += 1
continue
else:
raise LinkedInException(f"bad response status code: {e.response.status_code}")
raise LinkedInException(
f"bad response status code: {e.response.status_code}"
)
else:
raise
except ProxyError as e:
@ -114,7 +110,9 @@ class LinkedInScraper(Scraper):
raise LinkedInException(str(e))
else:
# Raise an exception if the maximum number of retries is reached
raise LinkedInException("Max retries reached, failed to get a valid response")
raise LinkedInException(
"Max retries reached, failed to get a valid response"
)
soup = BeautifulSoup(response.text, "html.parser")
@ -141,7 +139,9 @@ class LinkedInScraper(Scraper):
if job_post:
job_list.append(job_post)
except Exception as e:
raise LinkedInException("Exception occurred while processing jobs")
raise LinkedInException(
"Exception occurred while processing jobs"
)
page += 25
job_list = job_list[: scraper_input.results_wanted]
@ -158,7 +158,11 @@ class LinkedInScraper(Scraper):
metadata_card = job_card.find("div", class_="base-search-card__metadata")
location = self.get_location(metadata_card)
datetime_tag = metadata_card.find("time", class_="job-search-card__listdate") if metadata_card else None
datetime_tag = (
metadata_card.find("time", class_="job-search-card__listdate")
if metadata_card
else None
)
date_posted = None
if datetime_tag and "datetime" in datetime_tag.attrs:
datetime_str = datetime_tag["datetime"]
@ -178,13 +182,16 @@ class LinkedInScraper(Scraper):
location=location,
date_posted=date_posted,
job_url=job_url,
# job_type=[JobType.FULL_TIME],
job_type=job_type,
benefits=benefits,
emails=extract_emails_from_text(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(self, job_page_url: str) -> tuple[None, None] | tuple[
str | None, tuple[str | None, JobType | None]]:
def get_job_description(
self, job_page_url: str
) -> tuple[None, None] | tuple[str | None, tuple[str | None, JobType | None]]:
"""
Retrieves job description by going to the job page url
:param job_page_url:
@ -207,7 +214,7 @@ class LinkedInScraper(Scraper):
def get_job_type(
soup_job_type: BeautifulSoup,
) -> JobType | None:
) -> list[JobType] | None:
"""
Gets the job type from job page
:param soup_job_type:
@ -238,7 +245,7 @@ class LinkedInScraper(Scraper):
def get_enum_from_value(value_str):
for job_type in JobType:
if value_str in job_type.value:
return job_type
return [job_type]
return None
def get_location(self, metadata_card: Optional[Tag]) -> Location:
@ -263,9 +270,3 @@ class LinkedInScraper(Scraper):
)
return location
def extract_emails_from_text(text: str) -> Optional[list[str]]:
if not text:
return None
email_regex = re.compile(r"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}")
return email_regex.findall(text)

229
src/jobspy/scrapers/ziprecruiter/__init__.py
View File

@ -11,7 +11,6 @@ from datetime import datetime, date
from typing import Optional, Tuple, Any
from urllib.parse import urlparse, parse_qs, urlunparse
import tls_client
import requests
from bs4 import BeautifulSoup
from bs4.element import Tag
@ -19,6 +18,7 @@ from concurrent.futures import ThreadPoolExecutor, Future
from .. import Scraper, ScraperInput, Site
from ..exceptions import ZipRecruiterException
from ..utils import count_urgent_words, extract_emails_from_text, create_session
from ...jobs import (
JobPost,
Compensation,
@ -29,12 +29,6 @@ from ...jobs import (
Country,
)
def extract_emails_from_text(text: str) -> Optional[list[str]]:
if not text:
return None
email_regex = re.compile(r"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}")
return email_regex.findall(text)
class ZipRecruiterScraper(Scraper):
def __init__(self, proxy: Optional[str] = None):
@ -47,9 +41,6 @@ class ZipRecruiterScraper(Scraper):
self.jobs_per_page = 20
self.seen_urls = set()
self.session = tls_client.Session(
client_identifier="chrome112", random_tls_extension_order=True
)
def find_jobs_in_page(
self, scraper_input: ScraperInput, page: int
@ -60,14 +51,13 @@ class ZipRecruiterScraper(Scraper):
:param page:
:return: jobs found on page
"""
job_list: list[JobPost] = []
session = create_session(self.proxy)
try:
response = self.session.get(
response = session.get(
f"{self.url}/jobs-search",
headers=ZipRecruiterScraper.headers(),
params=ZipRecruiterScraper.add_params(scraper_input, page),
headers=self.headers(),
params=self.add_params(scraper_input, page),
allow_redirects=True,
proxy=self.proxy,
timeout_seconds=10,
)
if response.status_code != 200:
@ -121,7 +111,11 @@ class ZipRecruiterScraper(Scraper):
:param scraper_input:
:return: job_response
"""
start_page = (scraper_input.offset // self.jobs_per_page) + 1 if scraper_input.offset else 1
start_page = (
(scraper_input.offset // self.jobs_per_page) + 1
if scraper_input.offset
else 1
)
#: get first page to initialize session
job_list: list[JobPost] = self.find_jobs_in_page(scraper_input, start_page)
pages_to_process = max(
@ -142,91 +136,10 @@ class ZipRecruiterScraper(Scraper):
job_list = job_list[: scraper_input.results_wanted]
return JobResponse(jobs=job_list)
def process_job_html_1(self, job: Tag) -> Optional[JobPost]:
"""
Parses a job from the job content tag
:param job: BeautifulSoup Tag for one job post
:return JobPost
TODO this method isnt finished due to not encountering this type of html often
"""
job_url = self.cleanurl(job.find("a", {"class": "job_link"})["href"])
if job_url in self.seen_urls:
return None
title = job.find("h2", {"class": "title"}).text
company = job.find("a", {"class": "company_name"}).text.strip()
description, updated_job_url = self.get_description(job_url)
# job_url = updated_job_url if updated_job_url else job_url
if description is None:
description = job.find("p", {"class": "job_snippet"}).text.strip()
job_type_element = job.find("li", {"class": "perk_item perk_type"})
job_type = None
if job_type_element:
job_type_text = (
job_type_element.text.strip().lower().replace("_", "").replace(" ", "")
)
job_type = ZipRecruiterScraper.get_job_type_enum(job_type_text)
date_posted = ZipRecruiterScraper.get_date_posted(job)
job_post = JobPost(
title=title,
description=description,
company_name=company,
location=ZipRecruiterScraper.get_location(job),
job_type=job_type,
compensation=ZipRecruiterScraper.get_compensation(job),
date_posted=date_posted,
job_url=job_url,
emails=extract_emails_from_text(description),
)
return job_post
def process_job_html_2(self, job: Tag) -> Optional[JobPost]:
"""
Parses a job from the job content tag for a second variat of HTML that ZR uses
:param job: BeautifulSoup Tag for one job post
:return JobPost
"""
job_url = self.cleanurl(job.find("a", class_="job_link")["href"])
title = job.find("h2", class_="title").text
company = job.find("a", class_="company_name").text.strip()
description, updated_job_url = self.get_description(job_url)
# job_url = updated_job_url if updated_job_url else job_url
if description is None:
description = job.find("p", class_="job_snippet").get_text().strip()
job_type_text = job.find("li", class_="perk_item perk_type")
job_type = None
if job_type_text:
job_type_text = (
job_type_text.get_text()
.strip()
.lower()
.replace("-", "")
.replace(" ", "")
)
job_type = ZipRecruiterScraper.get_job_type_enum(job_type_text)
date_posted = ZipRecruiterScraper.get_date_posted(job)
job_post = JobPost(
title=title,
description=description,
company_name=company,
location=ZipRecruiterScraper.get_location(job),
job_type=job_type,
compensation=ZipRecruiterScraper.get_compensation(job),
date_posted=date_posted,
job_url=job_url,
)
return job_post
def process_job_javascript(self, job: dict) -> JobPost:
"""the most common type of jobs page on ZR"""
title = job.get("Title")
job_url = self.cleanurl(job.get("JobURL"))
job_url = job.get("JobURL")
description, updated_job_url = self.get_description(job_url)
# job_url = updated_job_url if updated_job_url else job_url
@ -280,38 +193,126 @@ class ZipRecruiterScraper(Scraper):
return JobPost(
title=title,
description=description,
company_name=company,
location=location,
job_type=job_type,
compensation=compensation,
date_posted=date_posted,
job_url=job_url,
description=description,
emails=extract_emails_from_text(description) if description else None,
num_urgent_words=count_urgent_words(description) if description else None,
)
def process_job_html_2(self, job: Tag) -> Optional[JobPost]:
"""
second most common type of jobs page on ZR after process_job_javascript()
Parses a job from the job content tag for a second variat of HTML that ZR uses
:param job: BeautifulSoup Tag for one job post
:return JobPost
"""
job_url = job.find("a", class_="job_link")["href"]
title = job.find("h2", class_="title").text
company = job.find("a", class_="company_name").text.strip()
description, updated_job_url = self.get_description(job_url)
# job_url = updated_job_url if updated_job_url else job_url
if description is None:
description = job.find("p", class_="job_snippet").get_text().strip()
job_type_text = job.find("li", class_="perk_item perk_type")
job_type = None
if job_type_text:
job_type_text = (
job_type_text.get_text()
.strip()
.lower()
.replace("-", "")
.replace(" ", "")
)
job_type = ZipRecruiterScraper.get_job_type_enum(job_type_text)
date_posted = ZipRecruiterScraper.get_date_posted(job)
job_post = JobPost(
title=title,
company_name=company,
location=ZipRecruiterScraper.get_location(job),
job_type=job_type,
compensation=ZipRecruiterScraper.get_compensation(job),
date_posted=date_posted,
job_url=job_url,
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 process_job_html_1(self, job: Tag) -> Optional[JobPost]:
"""
TODO this method isnt finished due to not encountering this type of html often
least common type of jobs page on ZR (rarely found)
Parses a job from the job content tag
:param job: BeautifulSoup Tag for one job post
:return JobPost
"""
job_url = job.find("a", {"class": "job_link"})["href"]
# job_url = self.cleanurl(job.find("a", {"class": "job_link"})["href"])
if job_url in self.seen_urls:
return None
title = job.find("h2", {"class": "title"}).text
company = job.find("a", {"class": "company_name"}).text.strip()
description, _ = self.get_description(job_url)
# job_url = updated_job_url if updated_job_url else job_url
# get description from jobs listing page if get_description from the specific job page fails
if description is None:
description = job.find("p", {"class": "job_snippet"}).text.strip()
job_type_element = job.find("li", {"class": "perk_item perk_type"})
job_type = None
if job_type_element:
job_type_text = (
job_type_element.text.strip().lower().replace("_", "").replace(" ", "")
)
job_type = ZipRecruiterScraper.get_job_type_enum(job_type_text)
date_posted = ZipRecruiterScraper.get_date_posted(job)
job_post = JobPost(
title=title,
description=description,
company_name=company,
location=ZipRecruiterScraper.get_location(job),
job_type=job_type,
compensation=ZipRecruiterScraper.get_compensation(job),
date_posted=date_posted,
job_url=job_url,
emails=extract_emails_from_text(description),
num_urgent_words=count_urgent_words(description),
)
return job_post
@staticmethod
def get_job_type_enum(job_type_str: str) -> Optional[JobType]:
def get_job_type_enum(job_type_str: str) -> list[JobType] | None:
for job_type in JobType:
if job_type_str in job_type.value:
a = True
return job_type
return [job_type]
return None
def get_description(self, job_page_url: str) -> Tuple[Optional[str], Optional[str]]:
def get_description(self, job_page_url: str) -> Tuple[str | None, str | None]:
"""
Retrieves job description by going to the job page url
:param job_page_url:
:param session:
:return: description or None, response url
"""
try:
response = requests.get(
session = create_session(self.proxy)
response = session.get(
job_page_url,
headers=ZipRecruiterScraper.headers(),
headers=self.headers(),
allow_redirects=True,
timeout=5,
proxies=self.proxy,
timeout_seconds=5,
)
if response.status_code not in range(200, 400):
return None, None
@ -467,8 +468,8 @@ class ZipRecruiterScraper(Scraper):
"User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/78.0.3904.97 Safari/537.36"
}
@staticmethod
def cleanurl(url):
parsed_url = urlparse(url)
return urlunparse((parsed_url.scheme, parsed_url.netloc, parsed_url.path, parsed_url.params, '', ''))
# @staticmethod
# def cleanurl(url) -> str:
# parsed_url = urlparse(url)
#
# return urlunparse((parsed_url.scheme, parsed_url.netloc, parsed_url.path, parsed_url.params, '', ''))

4
src/tests/test_all.py
View File

@ -9,4 +9,6 @@ def test_all():
results_wanted=5,
)
assert isinstance(result, pd.DataFrame) and not result.empty, "Result should be a non-empty DataFrame"
assert (
isinstance(result, pd.DataFrame) and not result.empty
), "Result should be a non-empty DataFrame"

4
src/tests/test_indeed.py
View File

@ -7,4 +7,6 @@ def test_indeed():
site_name="indeed",
search_term="software engineer",
)
assert isinstance(result, pd.DataFrame) and not result.empty, "Result should be a non-empty DataFrame"
assert (
isinstance(result, pd.DataFrame) and not result.empty
), "Result should be a non-empty DataFrame"

4
src/tests/test_linkedin.py
View File

@ -7,4 +7,6 @@ def test_linkedin():
site_name="linkedin",
search_term="software engineer",
)
assert isinstance(result, pd.DataFrame) and not result.empty, "Result should be a non-empty DataFrame"
assert (
isinstance(result, pd.DataFrame) and not result.empty
), "Result should be a non-empty DataFrame"

4
src/tests/test_ziprecruiter.py
View File

@ -8,4 +8,6 @@ def test_ziprecruiter():
search_term="software engineer",
)
assert isinstance(result, pd.DataFrame) and not result.empty, "Result should be a non-empty DataFrame"
assert (
isinstance(result, pd.DataFrame) and not result.empty
), "Result should be a non-empty DataFrame"