Salary parse (#163)

Correct initial page offset calculation
Separate page variable from request counter
Fix job offset starting value
Increment offset by number of jobs returned instead of expected value

README.md

@@ -11,10 +11,7 @@ work with us.*
 
 - Scrapes job postings from **LinkedIn**, **Indeed**, **Glassdoor**, & **ZipRecruiter** simultaneously
 - Aggregates the job postings in a Pandas DataFrame
-- Proxy support
-
-[Video Guide for JobSpy](https://www.youtube.com/watch?v=RuP1HrAZnxs&pp=ygUgam9icyBzY3JhcGVyIGJvdCBsaW5rZWRpbiBpbmRlZWQ%3D) -
-Updated for release v1.1.3
+- Proxies support
 
 ![jobspy](https://github.com/cullenwatson/JobSpy/assets/78247585/ec7ef355-05f6-4fd3-8161-a817e31c5c57)
 
@@ -38,17 +35,21 @@ 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)
+    # proxies=["208.195.175.46:65095", "208.195.175.45:65095", "localhost"],
+    
 )
 print(f"Found {len(jobs)} jobs")
 print(jobs.head())
-jobs.to_csv("jobs.csv", quoting=csv.QUOTE_NONNUMERIC, escapechar="\\", index=False) # to_xlsx
+jobs.to_csv("jobs.csv", quoting=csv.QUOTE_NONNUMERIC, escapechar="\\", index=False) # to_excel
 ```
 
 ### 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...
@@ -60,25 +61,71 @@ 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
-├── proxy (str): in format 'http://user:pass@host:port'
+│
+├── distance (int): 
+|    in miles, default 50
+│
+├── job_type (str): 
+|    fulltime, parttime, internship, contract
+│
+├── proxies (list): 
+|    in format ['user:pass@host:port', 'localhost']
+|    each job board will round robin through the proxies
+│
 ├── 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
+│
+├── description_format (str): 
+|    markdown, html (Format type of the job descriptions. Default is markdown.)
+│
+├── 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.)
+│
+├── 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.)
+
+├── linkedin_fetch_description (bool): 
+|    fetches full description and direct job url for LinkedIn (Increases requests by O(n))
+│
+├── linkedin_company_ids (list[int]): 
+|    searches for linkedin jobs with specific company ids
+|
+├── country_indeed (str): 
+|    filters the country on Indeed & Glassdoor (see below for correct spelling)
 ```
 
+```
+├── Indeed limitations:
+|    Only one from this list can be used in a search:
+|    - hours_old
+|    - job_type & is_remote
+|    - easy_apply
+│
+└── LinkedIn limitations:
+|    Only one from this list can be used in a search:
+|    - hours_old
+|    - easy_apply
+```
+
+
 ### JobPost Schema
 
 ```plaintext
@@ -93,13 +140,14 @@ 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)
 
 Indeed specific
@@ -119,7 +167,7 @@ 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**
 
@@ -154,8 +202,8 @@ 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.  
-* LinkedIn is the most restrictive and usually rate limits around the 10th page.
+* 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 with one ip. Proxies are a must basically.
 
 ## Frequently Asked Questions
 
@@ -170,7 +218,7 @@ persist, [submit an issue](https://github.com/Bunsly/JobSpy/issues).
 **Q: Received a response code 429?**  
 **A:** This indicates that you have been blocked by the job board site for sending too many requests. All of the job board sites are aggressive with blocking. We recommend:
 
-- Waiting some time between scrapes (site-dependent).
-- Trying a VPN or proxy to change your IP address.
+- Wait some time between scrapes (site-dependent).
+- Try using the proxies param to change your IP address.
 
 ---

examples/JobSpy_AllSites.py

@@ -1,30 +0,0 @@
-from jobspy import scrape_jobs
-import pandas as pd
-
-jobs: pd.DataFrame = scrape_jobs(
-    site_name=["indeed", "linkedin", "zip_recruiter", "glassdoor"],
-    search_term="software engineer",
-    location="Dallas, TX",
-    results_wanted=25,  # be wary the higher it is, the more likey you'll get blocked (rotating proxy can help tho)
-    country_indeed="USA",
-    # 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
-
-# 1: output to console
-print(jobs)
-
-# 2: output to .csv
-jobs.to_csv("./jobs.csv", index=False)
-print("outputted to jobs.csv")
-
-# 3: output to .xlsx
-# jobs.to_xlsx('jobs.xlsx', index=False)
-
-# 4: display in Jupyter Notebook (1. pip install jupyter 2. jupyter notebook)
-# display(jobs)

examples/JobSpy_Demo.ipynb

@@ -1,167 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "00a94b47-f47b-420f-ba7e-714ef219c006",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from jobspy import scrape_jobs\n",
-    "import pandas as pd\n",
-    "from IPython.display import display, HTML"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9f773e6c-d9fc-42cc-b0ef-63b739e78435",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.set_option('display.max_columns', None)\n",
-    "pd.set_option('display.max_rows', None)\n",
-    "pd.set_option('display.width', None)\n",
-    "pd.set_option('display.max_colwidth', 50)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1253c1f8-9437-492e-9dd3-e7fe51099420",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# example 1 (no hyperlinks, USA)\n",
-    "jobs = scrape_jobs(\n",
-    "    site_name=[\"linkedin\"],\n",
-    "    location='san francisco',\n",
-    "    search_term=\"engineer\",\n",
-    "    results_wanted=5,\n",
-    "\n",
-    "    # use if you want to use a proxy\n",
-    "    # proxy=\"socks5://jobspy:5a4vpWtj4EeJ2hoYzk@us.smartproxy.com:10001\",\n",
-    "    proxy=\"http://jobspy:5a4vpWtj4EeJ2hoYzk@us.smartproxy.com:10001\",\n",
-    "    #proxy=\"https://jobspy:5a4vpWtj4EeJ2hoYzk@us.smartproxy.com:10001\",\n",
-    ")\n",
-    "display(jobs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6a581b2d-f7da-4fac-868d-9efe143ee20a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# example 2 - remote USA & hyperlinks\n",
-    "jobs = scrape_jobs(\n",
-    "    site_name=[\"linkedin\", \"zip_recruiter\", \"indeed\"],\n",
-    "    # location='san francisco',\n",
-    "    search_term=\"software engineer\",\n",
-    "    country_indeed=\"USA\",\n",
-    "    hyperlinks=True,\n",
-    "    is_remote=True,\n",
-    "    results_wanted=5, \n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fe8289bc-5b64-4202-9a64-7c117c83fd9a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# use if hyperlinks=True\n",
-    "html = jobs.to_html(escape=False)\n",
-    "# change max-width: 200px to show more or less of the content\n",
-    "truncate_width = f'<style>.dataframe td {{ max-width: 200px; overflow: hidden; text-overflow: ellipsis; white-space: nowrap; }}</style>{html}'\n",
-    "display(HTML(truncate_width))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "951c2fe1-52ff-407d-8bb1-068049b36777",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# example 3 - with hyperlinks, international - linkedin (no zip_recruiter)\n",
-    "jobs = scrape_jobs(\n",
-    "    site_name=[\"linkedin\"],\n",
-    "    location='berlin',\n",
-    "    search_term=\"engineer\",\n",
-    "    hyperlinks=True,\n",
-    "    results_wanted=5,\n",
-    "    easy_apply=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1e37a521-caef-441c-8fc2-2eb5b2e7da62",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# use if hyperlinks=True\n",
-    "html = jobs.to_html(escape=False)\n",
-    "# change max-width: 200px to show more or less of the content\n",
-    "truncate_width = f'<style>.dataframe td {{ max-width: 200px; overflow: hidden; text-overflow: ellipsis; white-space: nowrap; }}</style>{html}'\n",
-    "display(HTML(truncate_width))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0650e608-0b58-4bf5-ae86-68348035b16a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# example 4 - international indeed (no zip_recruiter)\n",
-    "jobs = scrape_jobs(\n",
-    "    site_name=[\"indeed\"],\n",
-    "    search_term=\"engineer\",\n",
-    "    country_indeed = \"China\",\n",
-    "    hyperlinks=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "40913ac8-3f8a-4d7e-ac47-afb88316432b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# use if hyperlinks=True\n",
-    "html = jobs.to_html(escape=False)\n",
-    "# change max-width: 200px to show more or less of the content\n",
-    "truncate_width = f'<style>.dataframe td {{ max-width: 200px; overflow: hidden; text-overflow: ellipsis; white-space: nowrap; }}</style>{html}'\n",
-    "display(HTML(truncate_width))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

examples/JobSpy_LongScrape.py

@@ -1,77 +0,0 @@
-from jobspy import scrape_jobs
-import pandas as pd
-import os
-import time
-
-# creates csv a new filename if the jobs.csv already exists.
-csv_filename = "jobs.csv"
-counter = 1
-while os.path.exists(csv_filename):
-    csv_filename = f"jobs_{counter}.csv"
-    counter += 1
-
-# results wanted and offset
-results_wanted = 1000
-offset = 0
-
-all_jobs = []
-
-# max retries
-max_retries = 3
-
-# nuumber of results at each iteration
-results_in_each_iteration = 30
-
-while len(all_jobs) < results_wanted:
-    retry_count = 0
-    while retry_count < max_retries:
-        print("Doing from", offset, "to", offset + results_in_each_iteration, "jobs")
-        try:
-            jobs = scrape_jobs(
-                site_name=["indeed"],
-                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)),
-                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'))
-
-            # Increment the offset for the next page of results
-            offset += results_in_each_iteration
-
-            # Add a delay to avoid rate limiting (you can adjust the delay time as needed)
-            print(f"Scraped {len(all_jobs)} jobs")
-            print("Sleeping secs", 100 * (retry_count + 1))
-            time.sleep(100 * (retry_count + 1))  # Sleep for 2 seconds between requests
-
-            break  # Break out of the retry loop if successful
-        except Exception as e:
-            print(f"Error: {e}")
-            retry_count += 1
-            print("Sleeping secs before retry", 100 * (retry_count + 1))
-            time.sleep(100 * (retry_count + 1))
-            if retry_count >= max_retries:
-                print("Max retries reached. Exiting.")
-                break
-
-# DataFrame from the collected job data
-jobs_df = pd.DataFrame(all_jobs)
-
-# Formatting
-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)
-
-print(jobs_df)
-
-jobs_df.to_csv(csv_filename, index=False)
-print(f"Outputted to {csv_filename}")

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "python-jobspy"
-version = "1.1.50"
+version = "1.1.57"
 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, extract_salary
 from .scrapers.indeed import IndeedScraper
 from .scrapers.ziprecruiter import ZipRecruiterScraper
 from .scrapers.glassdoor import GlassdoorScraper
@@ -30,17 +30,18 @@ def scrape_jobs(
     results_wanted: int = 15,
     country_indeed: str = "usa",
     hyperlinks: bool = False,
-    proxy: str | None = None,
+    proxies: list[str] | str | None = None,
     description_format: str = "markdown",
     linkedin_fetch_description: bool | None = False,
     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()]
@@ -94,7 +96,7 @@ def scrape_jobs(
 
     def scrape_site(site: Site) -> Tuple[str, JobResponse]:
         scraper_class = SCRAPER_MAPPING[site]
-        scraper = scraper_class(proxy=proxy)
+        scraper = scraper_class(proxies=proxies)
         scraped_data: JobResponse = scraper.scrape(scraper_input)
         cap_name = site.value.capitalize()
         site_name = "ZipRecruiter" if cap_name == "Zip_recruiter" else cap_name
@@ -116,6 +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():
@@ -148,11 +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)
@@ -166,6 +194,7 @@ def scrape_jobs(
 
         # Desired column order
         desired_order = [
+            "id",
             "site",
             "job_url_hyper" if hyperlinks else "job_url",
             "job_url_direct",
@@ -179,6 +208,7 @@ def scrape_jobs(
             "max_amount",
             "currency",
             "is_remote",
+            "job_function",
             "emails",
             "description",
             "company_url",

src/jobspy/jobs/__init__.py

@@ -226,6 +226,7 @@ class DescriptionFormat(Enum):
 
 
 class JobPost(BaseModel):
+    id: str | None = None
     title: str
     company_name: str | None
     job_url: str
@@ -253,6 +254,9 @@ class JobPost(BaseModel):
     logo_photo_url: str | None = None
     banner_photo_url: str | None = None
 
+    # linkedin only atm
+    job_function: str | None = None
+
 
 class JobResponse(BaseModel):
     jobs: list[JobPost] = []

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:
-    def __init__(self, site: Site, proxy: list[str] | None = None):
+class Scraper(ABC):
+    def __init__(self, site: Site, proxies: list[str] | None = None):
+        self.proxies = proxies
         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/glassdoor/__init__.py

@@ -34,12 +34,12 @@ from ...jobs import (
 
 
 class GlassdoorScraper(Scraper):
-    def __init__(self, proxy: Optional[str] = None):
+    def __init__(self, proxies: list[str] | str | None = None):
         """
         Initializes GlassdoorScraper with the Glassdoor job search url
         """
         site = Site(Site.GLASSDOOR)
-        super().__init__(site, proxy=proxy)
+        super().__init__(site, proxies=proxies)
 
         self.base_url = None
         self.country = None
@@ -59,7 +59,7 @@ class GlassdoorScraper(Scraper):
         self.scraper_input.results_wanted = min(900, scraper_input.results_wanted)
         self.base_url = self.scraper_input.country.get_glassdoor_url()
 
-        self.session = create_session(self.proxy, is_tls=True, has_retry=True)
+        self.session = create_session(proxies=self.proxies, is_tls=True, has_retry=True)
         token = self._get_csrf_token()
         self.headers["gd-csrf-token"] = token if token else self.fallback_token
 
@@ -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,
@@ -190,6 +190,7 @@ class GlassdoorScraper(Scraper):
             description = None
         company_url = f"{self.base_url}Overview/W-EI_IE{company_id}.htm"
         return JobPost(
+            id=str(job_id),
             title=title,
             company_url=company_url if company_id else None,
             company_name=company_name,
@@ -244,7 +245,6 @@ class GlassdoorScraper(Scraper):
         if not location or is_remote:
             return "11047", "STATE"  # remote options
         url = f"{self.base_url}/findPopularLocationAjax.htm?maxLocationsToReturn=10&term={location}"
-        session = create_session(self.proxy, has_retry=True)
         res = self.session.get(url, headers=self.headers)
         if res.status_code != 200:
             if res.status_code == 429:

src/jobspy/scrapers/indeed/__init__.py

@@ -12,14 +12,13 @@ from typing import Tuple
 from datetime import datetime
 from concurrent.futures import ThreadPoolExecutor, Future
 
-import requests
-
 from .. import Scraper, ScraperInput, Site
 from ..utils import (
     extract_emails_from_text,
     get_enum_from_job_type,
     markdown_converter,
     logger,
+    create_session,
 )
 from ...jobs import (
     JobPost,
@@ -33,10 +32,13 @@ from ...jobs import (
 
 
 class IndeedScraper(Scraper):
-    def __init__(self, proxy: str | None = None):
+    def __init__(self, proxies: list[str] | str | None = None):
         """
         Initializes IndeedScraper with the Indeed API url
         """
+        super().__init__(Site.INDEED, proxies=proxies)
+
+        self.session = create_session(proxies=self.proxies, is_tls=False)
         self.scraper_input = None
         self.jobs_per_page = 100
         self.num_workers = 10
@@ -45,8 +47,6 @@ class IndeedScraper(Scraper):
         self.api_country_code = None
         self.base_url = None
         self.api_url = "https://apis.indeed.com/graphql"
-        site = Site(Site.INDEED)
-        super().__init__(site, proxy=proxy)
 
     def scrape(self, scraper_input: ScraperInput) -> JobResponse:
         """
@@ -90,12 +90,13 @@ class IndeedScraper(Scraper):
         jobs = []
         new_cursor = None
         filters = self._build_filters()
+        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
-                else ""
-            ),
+            what=(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
@@ -110,16 +111,15 @@ class IndeedScraper(Scraper):
         }
         api_headers = self.api_headers.copy()
         api_headers["indeed-co"] = self.api_country_code
-        response = requests.post(
+        response = self.session.post(
             self.api_url,
             headers=api_headers,
             json=payload,
-            proxies=self.proxy,
             timeout=10,
         )
         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()
@@ -150,6 +150,15 @@ 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",
@@ -203,6 +212,7 @@ class IndeedScraper(Scraper):
         employer_details = employer.get("employerDetails", {}) if employer else {}
         rel_url = job["employer"]["relativeCompanyPageUrl"] if job["employer"] else None
         return JobPost(
+            id=str(job["key"]),
             title=job["title"],
             description=description,
             company_name=job["employer"].get("name") if job.get("employer") else None,
@@ -287,8 +297,8 @@ 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"],
         )
 

src/jobspy/scrapers/linkedin/__init__.py

@@ -9,17 +9,17 @@ from __future__ import annotations
 
 import time
 import random
+import regex as re
 from typing import Optional
 from datetime import datetime
 
-from threading import Lock
 from bs4.element import Tag
 from bs4 import BeautifulSoup
-from urllib.parse import urlparse, urlunparse
+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,
@@ -44,13 +44,22 @@ class LinkedInScraper(Scraper):
     band_delay = 4
     jobs_per_page = 25
 
-    def __init__(self, proxy: Optional[str] = None):
+    def __init__(self, proxies: list[str] | str | None = None):
         """
         Initializes LinkedInScraper with the LinkedIn job search url
         """
-        super().__init__(Site(Site.LINKEDIN), proxy=proxy)
+        super().__init__(Site.LINKEDIN, proxies=proxies)
+        self.session = create_session(
+            proxies=self.proxies,
+            is_tls=False,
+            has_retry=True,
+            delay=5,
+            clear_cookies=True,
+        )
+        self.session.headers.update(self.headers)
         self.scraper_input = None
         self.country = "worldwide"
+        self.job_url_direct_regex = re.compile(r'(?<=\?url=)[^"]+')
 
     def scrape(self, scraper_input: ScraperInput) -> JobResponse:
         """
@@ -60,9 +69,9 @@ class LinkedInScraper(Scraper):
         """
         self.scraper_input = scraper_input
         job_list: list[JobPost] = []
-        seen_urls = set()
-        url_lock = Lock()
-        page = scraper_input.offset // 25 + 25 if scraper_input.offset else 0
+        seen_ids = set()
+        page = scraper_input.offset // 10 * 10 if scraper_input.offset else 0
+        request_count = 0
         seconds_old = (
             scraper_input.hours_old * 3600 if scraper_input.hours_old else None
         )
@@ -70,8 +79,8 @@ class LinkedInScraper(Scraper):
             lambda: len(job_list) < scraper_input.results_wanted and page < 1000
         )
         while continue_search():
-            logger.info(f"LinkedIn search page: {page // 25 + 1}")
-            session = create_session(is_tls=False, has_retry=True, delay=5)
+            request_count += 1
+            logger.info(f"LinkedIn search page: {request_count}")
             params = {
                 "keywords": scraper_input.search_term,
                 "location": scraper_input.location,
@@ -83,7 +92,7 @@ class LinkedInScraper(Scraper):
                     else None
                 ),
                 "pageNum": 0,
-                "start": page + scraper_input.offset,
+                "start": page,
                 "f_AL": "true" if scraper_input.easy_apply else None,
                 "f_C": (
                     ",".join(map(str, scraper_input.linkedin_company_ids))
@@ -96,12 +105,9 @@ class LinkedInScraper(Scraper):
 
             params = {k: v for k, v in params.items() if v is not None}
             try:
-                response = session.get(
+                response = self.session.get(
                     f"{self.base_url}/jobs-guest/jobs/api/seeMoreJobPostings/search?",
                     params=params,
-                    allow_redirects=True,
-                    proxies=self.proxy,
-                    headers=self.headers,
                     timeout=10,
                 )
                 if response.status_code not in range(200, 400):
@@ -127,36 +133,34 @@ 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}"
 
-                with url_lock:
-                    if job_url in seen_urls:
+                    if job_id in seen_ids:
                         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))
+                    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))
-                page += self.jobs_per_page
+                page += len(job_list)
 
         job_list = job_list[: scraper_input.results_wanted]
         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")
 
@@ -194,48 +198,49 @@ 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_id)
 
         return JobPost(
+            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_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"),
+            job_function=job_details.get("job_function"),
         )
 
-    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_id: 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)
-            response = session.get(
-                job_page_url, headers=self.headers, timeout=5, proxies=self.proxy
+            response = self.session.get(
+                f"{self.base_url}/jobs-guest/jobs/api/jobPosting/{job_id}", timeout=5
             )
             response.raise_for_status()
         except:
-            return None, None
-        if response.url == "https://www.linkedin.com/signup":
-            return None, None
+            return {}
+        if "linkedin.com/signup" in response.url:
+            return {}
 
         soup = BeautifulSoup(response.text, "html.parser")
         div_content = soup.find(
@@ -243,17 +248,31 @@ 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:
                 description = markdown_converter(description)
-        return description, self._parse_job_type(soup)
+
+        h3_tag = soup.find(
+            "h3", text=lambda text: text and "Job function" in text.strip()
+        )
+
+        job_function = None
+        if h3_tag:
+            job_function_span = h3_tag.find_next(
+                "span", class_="description__job-criteria-text"
+            )
+            if job_function_span:
+                job_function = job_function_span.text.strip()
+        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"
+            ),
+            "job_function": job_function,
+        }
 
     def _get_location(self, metadata_card: Optional[Tag]) -> Location:
         """
@@ -306,6 +325,23 @@ 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 = 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

@@ -2,6 +2,8 @@ from __future__ import annotations
 
 import re
 import logging
+from itertools import cycle
+
 import requests
 import tls_client
 import numpy as np
@@ -21,6 +23,122 @@ if not logger.handlers:
     logger.addHandler(console_handler)
 
 
+class RotatingProxySession:
+    def __init__(self, proxies=None):
+        if isinstance(proxies, str):
+            self.proxy_cycle = cycle([self.format_proxy(proxies)])
+        elif isinstance(proxies, list):
+            self.proxy_cycle = (
+                cycle([self.format_proxy(proxy) for proxy in proxies])
+                if proxies
+                else None
+            )
+        else:
+            self.proxy_cycle = None
+
+    @staticmethod
+    def format_proxy(proxy):
+        """Utility method to format a proxy string into a dictionary."""
+        if proxy.startswith("http://") or proxy.startswith("https://"):
+            return {"http": proxy, "https": proxy}
+        return {"http": f"http://{proxy}", "https": f"http://{proxy}"}
+
+
+class RequestsRotating(RotatingProxySession, requests.Session):
+
+    def __init__(self, proxies=None, has_retry=False, delay=1, clear_cookies=False):
+        RotatingProxySession.__init__(self, proxies=proxies)
+        requests.Session.__init__(self)
+        self.clear_cookies = clear_cookies
+        self.allow_redirects = True
+        self.setup_session(has_retry, delay)
+
+    def setup_session(self, has_retry, delay):
+        if has_retry:
+            retries = Retry(
+                total=3,
+                connect=3,
+                status=3,
+                status_forcelist=[500, 502, 503, 504, 429],
+                backoff_factor=delay,
+            )
+            adapter = HTTPAdapter(max_retries=retries)
+            self.mount("http://", adapter)
+            self.mount("https://", adapter)
+
+    def request(self, method, url, **kwargs):
+        if self.clear_cookies:
+            self.cookies.clear()
+
+        if self.proxy_cycle:
+            next_proxy = next(self.proxy_cycle)
+            if next_proxy["http"] != "http://localhost":
+                self.proxies = next_proxy
+            else:
+                self.proxies = {}
+        return requests.Session.request(self, method, url, **kwargs)
+
+
+class TLSRotating(RotatingProxySession, tls_client.Session):
+
+    def __init__(self, proxies=None):
+        RotatingProxySession.__init__(self, proxies=proxies)
+        tls_client.Session.__init__(self, random_tls_extension_order=True)
+
+    def execute_request(self, *args, **kwargs):
+        if self.proxy_cycle:
+            next_proxy = next(self.proxy_cycle)
+            if next_proxy["http"] != "http://localhost":
+                self.proxies = next_proxy
+            else:
+                self.proxies = {}
+        response = tls_client.Session.execute_request(self, *args, **kwargs)
+        response.ok = response.status_code in range(200, 400)
+        return response
+
+
+def create_session(
+    *,
+    proxies: dict | str | None = None,
+    is_tls: bool = True,
+    has_retry: bool = False,
+    delay: int = 1,
+    clear_cookies: bool = False,
+) -> requests.Session:
+    """
+    Creates a requests session with optional tls, proxy, and retry settings.
+    :return: A session object
+    """
+    if is_tls:
+        session = TLSRotating(proxies=proxies)
+    else:
+        session = RequestsRotating(
+            proxies=proxies,
+            has_retry=has_retry,
+            delay=delay,
+            clear_cookies=clear_cookies,
+        )
+
+    return session
+
+
+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
@@ -35,39 +153,6 @@ def extract_emails_from_text(text: str) -> list[str] | None:
     return email_regex.findall(text)
 
 
-def create_session(
-    proxy: dict | None = None,
-    is_tls: bool = True,
-    has_retry: bool = False,
-    delay: int = 1,
-) -> requests.Session:
-    """
-    Creates a requests session with optional tls, proxy, and retry settings.
-    :return: A session object
-    """
-    if is_tls:
-        session = tls_client.Session(random_tls_extension_order=True)
-        session.proxies = proxy
-    else:
-        session = requests.Session()
-        session.allow_redirects = True
-        if proxy:
-            session.proxies.update(proxy)
-        if has_retry:
-            retries = Retry(
-                total=3,
-                connect=3,
-                status=3,
-                status_forcelist=[500, 502, 503, 504, 429],
-                backoff_factor=delay,
-            )
-            adapter = HTTPAdapter(max_retries=retries)
-
-            session.mount("http://", adapter)
-            session.mount("https://", adapter)
-    return session
-
-
 def get_enum_from_job_type(job_type_str: str) -> JobType | None:
     """
     Given a string, returns the corresponding JobType enum member if a match is found.
@@ -94,3 +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

src/jobspy/scrapers/ziprecruiter/__init__.py

@@ -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,
@@ -36,14 +41,15 @@ class ZipRecruiterScraper(Scraper):
     base_url = "https://www.ziprecruiter.com"
     api_url = "https://api.ziprecruiter.com"
 
-    def __init__(self, proxy: Optional[str] = None):
+    def __init__(self, proxies: list[str] | str | None = None):
         """
         Initializes ZipRecruiterScraper with the ZipRecruiter job search url
         """
+        super().__init__(Site.ZIP_RECRUITER, proxies=proxies)
+
         self.scraper_input = None
-        self.session = create_session(proxy)
+        self.session = create_session(proxies=proxies)
         self._get_cookies()
-        super().__init__(Site.ZIP_RECRUITER, proxy=proxy)
 
         self.delay = 5
         self.jobs_per_page = 20
@@ -150,7 +156,10 @@ 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,
             company_name=company,
             location=location,
@@ -163,10 +172,42 @@ 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,
         )
 
+    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"