fix: dup line

README.md

@@ -38,7 +38,8 @@ jobs = scrape_jobs(
     location="Dallas, TX",
     results_wanted=20,
     hours_old=72, # (only Linkedin/Indeed is hour specific, others round up to days old)
-    country_indeed='USA'  # only needed for indeed / glassdoor
+    country_indeed='USA',  # only needed for indeed / glassdoor
+    # linkedin_fetch_description=True # get full description and direct job url for linkedin (slower)
 )
 print(f"Found {len(jobs)} jobs")
 print(jobs.head())
@@ -48,7 +49,7 @@ jobs.to_csv("jobs.csv", quoting=csv.QUOTE_NONNUMERIC, escapechar="\\", index=Fal
 ### Output
 
 ```
-SITE           TITLE                             COMPANY_NAME      CITY          STATE  JOB_TYPE  INTERVAL  MIN_AMOUNT  MAX_AMOUNT  JOB_URL                                            DESCRIPTION
+SITE           TITLE                             COMPANY           CITY          STATE  JOB_TYPE  INTERVAL  MIN_AMOUNT  MAX_AMOUNT  JOB_URL                                            DESCRIPTION
 indeed         Software Engineer                 AMERICAN SYSTEMS  Arlington     VA     None      yearly    200000      150000      https://www.indeed.com/viewjob?jk=5e409e577046...  THIS POSITION COMES WITH A 10K SIGNING BONUS!...
 indeed         Senior Software Engineer          TherapyNotes.com  Philadelphia  PA     fulltime  yearly    135000      110000      https://www.indeed.com/viewjob?jk=da39574a40cb...  About Us TherapyNotes is the national leader i...
 linkedin       Software Engineer - Early Career  Lockheed Martin   Sunnyvale     CA     fulltime  yearly    None        None        https://www.linkedin.com/jobs/view/3693012711      Description:By bringing together people that u...
@@ -60,23 +61,24 @@ zip_recruiter Software Developer                 TEKsystems        Phoenix
 ### Parameters for `scrape_jobs()`
 
 ```plaintext
-Required
-├── site_type (List[enum]): linkedin, zip_recruiter, indeed, glassdoor
-└── search_term (str)
 Optional
+├── site_name (list|str): linkedin, zip_recruiter, indeed, glassdoor (default is all four)
+├── search_term (str)
 ├── location (str)
 ├── distance (int): in miles, default 50
-├── job_type (enum): fulltime, parttime, internship, contract
+├── job_type (str): fulltime, parttime, internship, contract
 ├── proxy (str): in format 'http://user:pass@host:port'
 ├── is_remote (bool)
-├── linkedin_fetch_description (bool): fetches full description for LinkedIn (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 the job board site (not supported on Indeed)
-├── linkedin_company_ids (list[int): searches for linkedin jobs with specific company ids
-├── description_format (enum): markdown, html (format type of the job descriptions)
-├── country_indeed (enum): filters the country on Indeed (see below for correct spelling)
-├── offset (num): starts the search from an offset (e.g. 25 will start the search from the 25th result)
-├── hours_old (int): filters jobs by the number of hours since the job was posted (ZipRecruiter and Glassdoor round up to next day. If you use this on Indeed, it will not filter by job_type or is_remote)
+├── results_wanted (int): number of job results to retrieve for each site specified in 'site_name'
+├── easy_apply (bool): filters for jobs that are hosted on the job board site (LinkedIn & Indeed do not allow pairing this with hours_old)
+├── linkedin_fetch_description (bool): fetches full description and direct job url for LinkedIn (slower)
+├── linkedin_company_ids (list[int]): searches for linkedin jobs with specific company ids
+├── description_format (str): markdown, html (Format type of the job descriptions. Default is markdown.)
+├── country_indeed (str): filters the country on Indeed (see below for correct spelling)
+├── offset (int): starts the search from an offset (e.g. 25 will start the search from the 25th result)
+├── hours_old (int): filters jobs by the number of hours since the job was posted (ZipRecruiter and Glassdoor round up to next day. If you use this on Indeed, it will not filter by job_type/is_remote/easy_apply)
+├── verbose (int) {0, 1, 2}: Controls the verbosity of the runtime printouts (0 prints only errors, 1 is errors+warnings, 2 is all logs. Default is 2.)
+├── hyperlinks (bool): Whether to turn `job_url`s into hyperlinks. Default is false.
 ```
 
 ### JobPost Schema
@@ -119,7 +121,7 @@ Indeed specific
 
 ### **LinkedIn**
 
-LinkedIn searches globally & uses only the `location` parameter. You can only fetch 1000 jobs max from the LinkedIn endpoint we are using
+LinkedIn searches globally & uses only the `location` parameter. 
 
 ### **ZipRecruiter**
 
@@ -154,7 +156,7 @@ You can specify the following countries when searching on Indeed (use the exact
 
 ## Notes
 * Indeed is the best scraper currently with no rate limiting.  
-* Glassdoor/Ziprecruiter can only fetch 900/1000 jobs from the endpoints we are using on a given search.  
+* All the job board endpoints are capped at around 1000 jobs on a given search.  
 * LinkedIn is the most restrictive and usually rate limits around the 10th page.
 
 ## Frequently Asked Questions

examples/JobSpy_LongScrape.py

@@ -32,17 +32,18 @@ while len(all_jobs) < results_wanted:
                 search_term="software engineer",
                 # New York, NY
                 # Dallas, TX
-
                 # Los Angeles, CA
                 location="Los Angeles, CA",
-                results_wanted=min(results_in_each_iteration, results_wanted - len(all_jobs)),
+                results_wanted=min(
+                    results_in_each_iteration, results_wanted - len(all_jobs)
+                ),
                 country_indeed="USA",
                 offset=offset,
                 # proxy="http://jobspy:5a4vpWtj8EeJ2hoYzk@ca.smartproxy.com:20001",
             )
 
             # Add the scraped jobs to the list
-            all_jobs.extend(jobs.to_dict('records'))
+            all_jobs.extend(jobs.to_dict("records"))
 
             # Increment the offset for the next page of results
             offset += results_in_each_iteration

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "python-jobspy"
-version = "1.1.49"
+version = "1.1.52"
 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"
@@ -19,13 +19,14 @@ NUMPY = "1.24.2"
 pydantic = "^2.3.0"
 tls-client = "^1.0.1"
 markdownify = "^0.11.6"
+regex = "^2024.4.28"
 
 
 [tool.poetry.group.dev.dependencies]
 pytest = "^7.4.1"
 jupyter = "^1.0.0"
-black = "^24.2.0"
-pre-commit = "^3.6.2"
+black = "*"
+pre-commit = "*"
 
 [build-system]
 requires = ["poetry-core"]

src/jobspy/__init__.py

@@ -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
+from .scrapers.utils import logger, set_logger_level
 from .scrapers.indeed import IndeedScraper
 from .scrapers.ziprecruiter import ZipRecruiterScraper
 from .scrapers.glassdoor import GlassdoorScraper
@@ -36,11 +36,12 @@ def scrape_jobs(
     linkedin_company_ids: list[int] | None = None,
     offset: int | None = 0,
     hours_old: int = None,
+    verbose: int = 2,
     **kwargs,
 ) -> pd.DataFrame:
     """
     Simultaneously scrapes job data from multiple job sites.
-    :return: results_wanted: pandas dataframe containing job data
+    :return: pandas dataframe containing job data
     """
     SCRAPER_MAPPING = {
         Site.LINKEDIN: LinkedInScraper,
@@ -48,6 +49,7 @@ def scrape_jobs(
         Site.ZIP_RECRUITER: ZipRecruiterScraper,
         Site.GLASSDOOR: GlassdoorScraper,
     }
+    set_logger_level(verbose)
 
     def map_str_to_site(site_name: str) -> Site:
         return Site[site_name.upper()]

src/jobspy/scrapers/__init__.py

@@ -1,5 +1,7 @@
 from __future__ import annotations
 
+from abc import ABC, abstractmethod
+
 from ..jobs import (
     Enum,
     BaseModel,
@@ -36,9 +38,10 @@ class ScraperInput(BaseModel):
     hours_old: int | None = None
 
 
-class Scraper:
+class Scraper(ABC):
     def __init__(self, site: Site, proxy: list[str] | None = None):
         self.site = site
         self.proxy = (lambda p: {"http": p, "https": p} if p else None)(proxy)
 
+    @abstractmethod
     def scrape(self, scraper_input: ScraperInput) -> JobResponse: ...

src/jobspy/scrapers/indeed/__init__.py

@@ -90,18 +90,18 @@ class IndeedScraper(Scraper):
         jobs = []
         new_cursor = None
         filters = self._build_filters()
-        location = (
-            self.scraper_input.location
-            or self.scraper_input.country.value[0].split(",")[-1]
-        )
+        search_term = self.scraper_input.search_term.replace('"', '\\"') if self.scraper_input.search_term else ""
         query = self.job_search_query.format(
             what=(
-                f'what: "{self.scraper_input.search_term}"'
-                if self.scraper_input.search_term
+                f'what: "{search_term}"'
+                if search_term
+                else ""
+            ),
+            location=(
+                f'location: {{where: "{self.scraper_input.location}", radius: {self.scraper_input.distance}, radiusUnit: MILES}}'
+                if self.scraper_input.location
                 else ""
             ),
-            location=location,
-            radius=self.scraper_input.distance,
             dateOnIndeed=self.scraper_input.hours_old,
             cursor=f'cursor: "{cursor}"' if cursor else "",
             filters=filters,
@@ -120,7 +120,7 @@ class IndeedScraper(Scraper):
         )
         if response.status_code != 200:
             logger.info(
-                f"Indeed responded with status code: {response.status_code} (submit GitHub issue if this appears to be a beg)"
+                f"Indeed responded with status code: {response.status_code} (submit GitHub issue if this appears to be a bug)"
             )
             return jobs, new_cursor
         data = response.json()
@@ -151,6 +151,15 @@ class IndeedScraper(Scraper):
             """.format(
                 start=self.scraper_input.hours_old
             )
+        elif self.scraper_input.easy_apply:
+            filters_str = """
+            filters: {
+                keyword: {
+                  field: "indeedApplyScope",
+                  keys: ["DESKTOP"]
+                }
+            }
+            """
         elif self.scraper_input.job_type or self.scraper_input.is_remote:
             job_type_key_mapping = {
                 JobType.FULL_TIME: "CF3CP",
@@ -343,7 +352,7 @@ class IndeedScraper(Scraper):
         query GetJobData {{
           jobSearch(
             {what}
-            location: {{ where: "{location}", radius: {radius}, radiusUnit: MILES }}
+            {location}
             includeSponsoredResults: NONE
             limit: 100
             sort: DATE

src/jobspy/scrapers/linkedin/__init__.py

@@ -9,6 +9,8 @@ from __future__ import annotations
 
 import time
 import random
+import regex as re
+import urllib.parse
 from typing import Optional
 from datetime import datetime
 
@@ -51,6 +53,7 @@ class LinkedInScraper(Scraper):
         super().__init__(Site(Site.LINKEDIN), proxy=proxy)
         self.scraper_input = None
         self.country = "worldwide"
+        self.job_url_direct_regex = re.compile(r'(?<=\?url=)[^"]+')
 
     def scrape(self, scraper_input: ScraperInput) -> JobResponse:
         """
@@ -194,16 +197,16 @@ class LinkedInScraper(Scraper):
             if metadata_card
             else None
         )
-        date_posted = description = job_type = None
+        date_posted = None
         if datetime_tag and "datetime" in datetime_tag.attrs:
             datetime_str = datetime_tag["datetime"]
             try:
                 date_posted = datetime.strptime(datetime_str, "%Y-%m-%d")
             except:
                 date_posted = None
-        benefits_tag = job_card.find("span", class_="result-benefits__text")
+        job_details = {}
         if full_descr:
-            description, job_type = self._get_job_description(job_url)
+            job_details = self._get_job_details(job_url)
 
         return JobPost(
             title=title,
@@ -213,18 +216,18 @@ class LinkedInScraper(Scraper):
             date_posted=date_posted,
             job_url=job_url,
             compensation=compensation,
-            job_type=job_type,
-            description=description,
-            emails=extract_emails_from_text(description) if description else None,
+            job_type=job_details.get("job_type"),
+            description=job_details.get("description"),
+            job_url_direct=job_details.get("job_url_direct"),
+            emails=extract_emails_from_text(job_details.get("description")),
+            logo_photo_url=job_details.get("logo_photo_url"),
         )
 
-    def _get_job_description(
-        self, job_page_url: str
-    ) -> tuple[None, None] | tuple[str | None, tuple[str | None, JobType | None]]:
+    def _get_job_details(self, job_page_url: str) -> dict:
         """
-        Retrieves job description by going to the job page url
+        Retrieves job description and other job details by going to the job page url
         :param job_page_url:
-        :return: description or None
+        :return: dict
         """
         try:
             session = create_session(is_tls=False, has_retry=True)
@@ -233,9 +236,9 @@ class LinkedInScraper(Scraper):
             )
             response.raise_for_status()
         except:
-            return None, None
+            return {}
         if response.url == "https://www.linkedin.com/signup":
-            return None, None
+            return {}
 
         soup = BeautifulSoup(response.text, "html.parser")
         div_content = soup.find(
@@ -253,7 +256,14 @@ class LinkedInScraper(Scraper):
             description = div_content.prettify(formatter="html")
             if self.scraper_input.description_format == DescriptionFormat.MARKDOWN:
                 description = markdown_converter(description)
-        return description, self._parse_job_type(soup)
+        return {
+            "description": description,
+            "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(
+                "data-delayed-url"
+            ),
+        }
 
     def _get_location(self, metadata_card: Optional[Tag]) -> Location:
         """
@@ -306,6 +316,23 @@ class LinkedInScraper(Scraper):
 
         return [get_enum_from_job_type(employment_type)] if employment_type else []
 
+    def _parse_job_url_direct(self, soup: BeautifulSoup) -> str | None:
+        """
+        Gets the job url direct from job page
+        :param soup:
+        :return: str
+        """
+        job_url_direct = None
+        job_url_direct_content = soup.find("code", id="applyUrl")
+        if job_url_direct_content:
+            job_url_direct_match = self.job_url_direct_regex.search(
+                job_url_direct_content.decode_contents().strip()
+            )
+            if job_url_direct_match:
+                job_url_direct = urllib.parse.unquote(job_url_direct_match.group())
+
+        return job_url_direct
+
     @staticmethod
     def job_type_code(job_type_enum: JobType) -> str:
         return {

src/jobspy/scrapers/utils.py

@@ -21,6 +21,23 @@ if not logger.handlers:
     logger.addHandler(console_handler)
 
 
+def set_logger_level(verbose: int = 2):
+    """
+    Adjusts the logger's level. This function allows the logging level to be changed at runtime.
+
+    Parameters:
+    - verbose: int {0, 1, 2} (default=2, all logs)
+    """
+    if verbose is None:
+        return
+    level_name = {2: "INFO", 1: "WARNING", 0: "ERROR"}.get(verbose, "INFO")
+    level = getattr(logging, level_name.upper(), None)
+    if level is not None:
+        logger.setLevel(level)
+    else:
+        raise ValueError(f"Invalid log level: {level_name}")
+
+
 def markdown_converter(description_html: str):
     if description_html is None:
         return None