mirror of https://github.com/Bunsly/JobSpy
parent
811d4c40b4
commit
338d854b96
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@ -30,6 +30,7 @@ from jobspy import scrape_jobs
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jobs = scrape_jobs(
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jobs = scrape_jobs(
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site_name=["indeed", "linkedin", "zip_recruiter", "glassdoor", "google"],
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site_name=["indeed", "linkedin", "zip_recruiter", "glassdoor", "google"],
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search_term="software engineer",
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search_term="software engineer",
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google_search_term="software engineer jobs near San Francisco, CA since yesterday",
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location="San Francisco, CA",
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location="San Francisco, CA",
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results_wanted=20,
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results_wanted=20,
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hours_old=72, # (only Linkedin/Indeed is hour specific, others round up to days old)
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hours_old=72, # (only Linkedin/Indeed is hour specific, others round up to days old)
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@ -65,6 +66,9 @@ Optional
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| (default is all)
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| (default is all)
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│
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│
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├── search_term (str)
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├── search_term (str)
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├── google_search_term (str)
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| search term for google jobs. This is is only param for filtering google jobs.
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│
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│
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├── location (str)
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├── location (str)
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│
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│
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@ -171,9 +175,9 @@ Indeed specific
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## Supported Countries for Job Searching
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## Supported Countries for Job Searching
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### **LinkedIn / Google**
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### **LinkedIn**
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LinkedIn & Google searches globally & uses only the `location` parameter.
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LinkedIn searches globally & uses only the `location` parameter.
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### **ZipRecruiter**
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### **ZipRecruiter**
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@ -1,6 +1,6 @@
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[tool.poetry]
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[tool.poetry]
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name = "python-jobspy"
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name = "python-jobspy"
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version = "1.1.74"
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version = "1.1.75"
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description = "Job scraper for LinkedIn, Indeed, Glassdoor & ZipRecruiter"
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description = "Job scraper for LinkedIn, Indeed, Glassdoor & ZipRecruiter"
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authors = ["Zachary Hampton <zachary@bunsly.com>", "Cullen Watson <cullen@bunsly.com>"]
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authors = ["Zachary Hampton <zachary@bunsly.com>", "Cullen Watson <cullen@bunsly.com>"]
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homepage = "https://github.com/Bunsly/JobSpy"
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homepage = "https://github.com/Bunsly/JobSpy"
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@ -24,6 +24,7 @@ from .scrapers.exceptions import (
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def scrape_jobs(
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def scrape_jobs(
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site_name: str | list[str] | Site | list[Site] | None = None,
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site_name: str | list[str] | Site | list[Site] | None = None,
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search_term: str | None = None,
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search_term: str | None = None,
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google_search_term: str | None = None,
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location: str | None = None,
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location: str | None = None,
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distance: int | None = 50,
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distance: int | None = 50,
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is_remote: bool = False,
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is_remote: bool = False,
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@ -86,6 +87,7 @@ def scrape_jobs(
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site_type=get_site_type(),
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site_type=get_site_type(),
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country=country_enum,
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country=country_enum,
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search_term=search_term,
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search_term=search_term,
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google_search_term=google_search_term,
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location=location,
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location=location,
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distance=distance,
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distance=distance,
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is_remote=is_remote,
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is_remote=is_remote,
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@ -216,8 +218,8 @@ def scrape_jobs(
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"title",
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"title",
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"company",
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"company",
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"location",
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"location",
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"job_type",
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"date_posted",
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"date_posted",
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"job_type",
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"salary_source",
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"salary_source",
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"interval",
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"interval",
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"min_amount",
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"min_amount",
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@ -248,6 +250,8 @@ def scrape_jobs(
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jobs_df = jobs_df[desired_order]
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jobs_df = jobs_df[desired_order]
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# Step 4: Sort the DataFrame as required
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# Step 4: Sort the DataFrame as required
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return jobs_df.sort_values(by=["site", "date_posted"], ascending=[True, False])
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return jobs_df.sort_values(
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by=["site", "date_posted"], ascending=[True, False]
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).reset_index(drop=True)
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else:
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else:
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return pd.DataFrame()
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return pd.DataFrame()
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@ -28,6 +28,7 @@ class SalarySource(Enum):
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class ScraperInput(BaseModel):
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class ScraperInput(BaseModel):
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site_type: list[Site]
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site_type: list[Site]
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search_term: str | None = None
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search_term: str | None = None
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google_search_term: str | None = None
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location: str | None = None
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location: str | None = None
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country: Country | None = Country.USA
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country: Country | None = Country.USA
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@ -59,13 +59,14 @@ class GoogleJobsScraper(Scraper):
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self.session = create_session(
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self.session = create_session(
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proxies=self.proxies, ca_cert=self.ca_cert, is_tls=False, has_retry=True
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proxies=self.proxies, ca_cert=self.ca_cert, is_tls=False, has_retry=True
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)
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)
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forward_cursor = self._get_initial_cursor()
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forward_cursor, job_list = self._get_initial_cursor_and_jobs()
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if forward_cursor is None:
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if forward_cursor is None:
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logger.error("initial cursor not found")
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logger.warning(
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return JobResponse(jobs=[])
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"initial cursor not found, try changing your query or there was at most 10 results"
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)
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return JobResponse(jobs=job_list)
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page = 1
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page = 1
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job_list: list[JobPost] = []
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while (
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while (
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len(self.seen_urls) < scraper_input.results_wanted + scraper_input.offset
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len(self.seen_urls) < scraper_input.results_wanted + scraper_input.offset
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@ -74,7 +75,11 @@ class GoogleJobsScraper(Scraper):
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logger.info(
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logger.info(
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f"search page: {page} / {math.ceil(scraper_input.results_wanted / self.jobs_per_page)}"
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f"search page: {page} / {math.ceil(scraper_input.results_wanted / self.jobs_per_page)}"
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)
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)
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jobs, forward_cursor = self._get_jobs_next_page(forward_cursor)
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try:
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jobs, forward_cursor = self._get_jobs_next_page(forward_cursor)
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except Exception as e:
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logger.error(f"failed to get jobs on page: {page}, {e}")
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break
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if not jobs:
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if not jobs:
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logger.info(f"found no jobs on page: {page}")
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logger.info(f"found no jobs on page: {page}")
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break
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break
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@ -87,8 +92,8 @@ class GoogleJobsScraper(Scraper):
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]
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]
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)
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)
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def _get_initial_cursor(self):
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def _get_initial_cursor_and_jobs(self) -> Tuple[str, list[JobPost]]:
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"""Gets initial cursor to paginate through job listings"""
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"""Gets initial cursor and jobs to paginate through job listings"""
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query = f"{self.scraper_input.search_term} jobs"
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query = f"{self.scraper_input.search_term} jobs"
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def get_time_range(hours_old):
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def get_time_range(hours_old):
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@ -121,13 +126,22 @@ class GoogleJobsScraper(Scraper):
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if self.scraper_input.is_remote:
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if self.scraper_input.is_remote:
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query += " remote"
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query += " remote"
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if self.scraper_input.google_search_term:
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query = self.scraper_input.google_search_term
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params = {"q": query, "udm": "8"}
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params = {"q": query, "udm": "8"}
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response = self.session.get(self.url, headers=headers_initial, params=params)
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response = self.session.get(self.url, headers=headers_initial, params=params)
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pattern_fc = r'<div jsname="Yust4d"[^>]+data-async-fc="([^"]+)"'
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pattern_fc = r'<div jsname="Yust4d"[^>]+data-async-fc="([^"]+)"'
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match_fc = re.search(pattern_fc, response.text)
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match_fc = re.search(pattern_fc, response.text)
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data_async_fc = match_fc.group(1) if match_fc else None
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data_async_fc = match_fc.group(1) if match_fc else None
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return data_async_fc
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jobs_raw = self._find_job_info_initial_page(response.text)
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jobs = []
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for job_raw in jobs_raw:
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job_post = self._parse_job(job_raw)
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if job_post:
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jobs.append(job_post)
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return data_async_fc, jobs
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def _get_jobs_next_page(self, forward_cursor: str) -> Tuple[list[JobPost], str]:
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def _get_jobs_next_page(self, forward_cursor: str) -> Tuple[list[JobPost], str]:
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params = {"fc": [forward_cursor], "fcv": ["3"], "async": [async_param]}
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params = {"fc": [forward_cursor], "fcv": ["3"], "async": [async_param]}
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@ -147,55 +161,55 @@ class GoogleJobsScraper(Scraper):
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match_fc = re.search(pattern_fc, job_data)
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match_fc = re.search(pattern_fc, job_data)
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data_async_fc = match_fc.group(1) if match_fc else None
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data_async_fc = match_fc.group(1) if match_fc else None
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jobs_on_page = []
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jobs_on_page = []
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for array in parsed:
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for array in parsed:
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_, job_data = array
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_, job_data = array
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if not job_data.startswith("[[["):
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if not job_data.startswith("[[["):
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continue
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continue
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job_d = json.loads(job_data)
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job_d = json.loads(job_data)
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job_info = self._find_job_info(job_d)
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job_info = self._find_job_info(job_d)
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job_post = self._parse_job(job_info)
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job_url = job_info[3][0][0] if job_info[3] and job_info[3][0] else None
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if job_post:
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if job_url in self.seen_urls:
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jobs_on_page.append(job_post)
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continue
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self.seen_urls.add(job_url)
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title = job_info[0]
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company_name = job_info[1]
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location = city = job_info[2]
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state = country = date_posted = None
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if location and "," in location:
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city, state, *country = [*map(lambda x: x.strip(), location.split(","))]
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days_ago_str = job_info[12]
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if type(days_ago_str) == str:
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match = re.search(r"\d+", days_ago_str)
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days_ago = int(match.group()) if match else None
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date_posted = (datetime.now() - timedelta(days=days_ago)).date()
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description = job_info[19]
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job_post = JobPost(
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id=f"go-{job_info[28]}",
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title=title,
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company_name=company_name,
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location=Location(
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city=city, state=state, country=country[0] if country else None
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),
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job_url=job_url,
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job_url_direct=job_url,
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date_posted=date_posted,
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is_remote="remote" in description.lower()
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or "wfh" in description.lower(),
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description=description,
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emails=extract_emails_from_text(description),
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job_type=extract_job_type(description),
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)
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jobs_on_page.append(job_post)
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return jobs_on_page, data_async_fc
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return jobs_on_page, data_async_fc
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def _parse_job(self, job_info: list):
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job_url = job_info[3][0][0] if job_info[3] and job_info[3][0] else None
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if job_url in self.seen_urls:
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return
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self.seen_urls.add(job_url)
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title = job_info[0]
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company_name = job_info[1]
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location = city = job_info[2]
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state = country = date_posted = None
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if location and "," in location:
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city, state, *country = [*map(lambda x: x.strip(), location.split(","))]
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days_ago_str = job_info[12]
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if type(days_ago_str) == str:
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match = re.search(r"\d+", days_ago_str)
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days_ago = int(match.group()) if match else None
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date_posted = (datetime.now() - timedelta(days=days_ago)).date()
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description = job_info[19]
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job_post = JobPost(
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id=f"go-{job_info[28]}",
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title=title,
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company_name=company_name,
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location=Location(
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city=city, state=state, country=country[0] if country else None
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),
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job_url=job_url,
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date_posted=date_posted,
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is_remote="remote" in description.lower() or "wfh" in description.lower(),
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description=description,
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emails=extract_emails_from_text(description),
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job_type=extract_job_type(description),
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)
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return job_post
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@staticmethod
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@staticmethod
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def _find_job_info(jobs_data: list | dict) -> list | None:
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def _find_job_info(jobs_data: list | dict) -> list | None:
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"""Iterates through the JSON data to find the job listings"""
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"""Iterates through the JSON data to find the job listings"""
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@ -213,3 +227,24 @@ class GoogleJobsScraper(Scraper):
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if result:
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if result:
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return result
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return result
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return None
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return None
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@staticmethod
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def _find_job_info_initial_page(html_text: str):
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pattern = (
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f'520084652":('
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+ r"\[(?:[^\[\]]|\[(?:[^\[\]]|\[(?:[^\[\]]|\[[^\[\]]*\])*\])*\])*\])"
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)
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results = []
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matches = re.finditer(pattern, html_text)
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import json
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for match in matches:
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try:
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parsed_data = json.loads(match.group(1))
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results.append(parsed_data)
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except json.JSONDecodeError as e:
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logger.error(f"Failed to parse match: {str(e)}")
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results.append({"raw_match": match.group(0), "error": str(e)})
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return results
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Loading…
Reference in New Issue