lock file (#173)

Changed Numpy to the most recent version so the package can properly install

README.md

@@ -110,6 +110,9 @@ Optional
 |
 ├── country_indeed (str): 
 |    filters the country on Indeed & Glassdoor (see below for correct spelling)
+|
+├── enforce_annual_salary (bool): 
+|    converts wages to annual salary
 ```
 
 ```
@@ -130,42 +133,42 @@ Optional
 
 ```plaintext
 JobPost
-├── title (str)
+├── title
-├── company (str)
+├── company
-├── company_url (str)
+├── company_url
-├── job_url (str)
+├── job_url
-├── location (object)
+├── location
-│   ├── country (str)
+│   ├── country
-│   ├── city (str)
+│   ├── city
-│   ├── state (str)
+│   ├── state
-├── description (str)
+├── description
-├── job_type (str): fulltime, parttime, internship, contract
+├── job_type: fulltime, parttime, internship, contract
-├── job_function (str)
+├── job_function
-├── compensation (object)
+│   ├── interval: yearly, monthly, weekly, daily, hourly
-│   ├── interval (str): yearly, monthly, weekly, daily, hourly
+│   ├── min_amount
-│   ├── min_amount (int)
+│   ├── max_amount
-│   ├── max_amount (int)
+│   ├── currency
-│   └── currency (enum)
+│   └── salary_source: direct_data, description (parsed from posting)
-├── date_posted (date)
+├── date_posted
-├── emails (str)
+├── emails
-└── is_remote (bool)
+└── is_remote
 
 Linkedin specific
-└── job_level (str)
+└── job_level
 
 Linkedin & Indeed specific
-└── company_industry (str)
+└── company_industry
 
 Indeed specific
-├── company_country (str)
+├── company_country
-└── company_addresses (str)
+├── company_addresses
-└── company_employees_label (str)
+├── company_employees_label
-└── company_revenue_label (str)
+├── company_revenue_label
-└── company_description (str)
+├── company_description
-└── ceo_name (str)
+├── ceo_name
-└── ceo_photo_url (str)
+├── ceo_photo_url
-└── logo_photo_url (str)
+├── logo_photo_url
-└── banner_photo_url (str)
+└── banner_photo_url
 ```
 
 ## Supported Countries for Job Searching

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "python-jobspy"
-version = "1.1.59"
+version = "1.1.60"
 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"
@@ -15,7 +15,7 @@ python = "^3.10"
 requests = "^2.31.0"
 beautifulsoup4 = "^4.12.2"
 pandas = "^2.1.0"
-NUMPY = "1.24.2"
+NUMPY = "1.26.3"
 pydantic = "^2.3.0"
 tls-client = "^1.0.1"
 markdownify = "^0.11.6"

src/jobspy/__init__.py

@@ -10,7 +10,7 @@ from .scrapers.indeed import IndeedScraper
 from .scrapers.ziprecruiter import ZipRecruiterScraper
 from .scrapers.glassdoor import GlassdoorScraper
 from .scrapers.linkedin import LinkedInScraper
-from .scrapers import ScraperInput, Site, JobResponse, Country
+from .scrapers import SalarySource, ScraperInput, Site, JobResponse, Country
 from .scrapers.exceptions import (
     LinkedInException,
     IndeedException,
@@ -36,6 +36,7 @@ def scrape_jobs(
     linkedin_company_ids: list[int] | None = None,
     offset: int | None = 0,
     hours_old: int = None,
+    enforce_annual_salary: bool = False,
     verbose: int = 2,
     **kwargs,
 ) -> pd.DataFrame:
@@ -165,7 +166,8 @@ 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["salary_source"] = SalarySource.DIRECT_DATA.value
+                if enforce_annual_salary and (
                     job_data["interval"]
                     and job_data["interval"] != "yearly"
                     and job_data["min_amount"]
@@ -180,8 +182,15 @@ def scrape_jobs(
                         job_data["min_amount"],
                         job_data["max_amount"],
                         job_data["currency"],
-                    ) = extract_salary(job_data["description"])
+                    ) = extract_salary(
+                        job_data["description"],
+                        enforce_annual_salary=enforce_annual_salary,
+                    )
+                    job_data["salary_source"] = SalarySource.DESCRIPTION.value
 
+            job_data["salary_source"] = (
+                job_data["salary_source"] if job_data["min_amount"] else None
+            )
             job_df = pd.DataFrame([job_data])
             jobs_dfs.append(job_df)
 
@@ -203,6 +212,7 @@ def scrape_jobs(
             "location",
             "job_type",
             "date_posted",
+            "salary_source",
             "interval",
             "min_amount",
             "max_amount",

src/jobspy/scrapers/__init__.py

@@ -18,6 +18,9 @@ class Site(Enum):
     ZIP_RECRUITER = "zip_recruiter"
     GLASSDOOR = "glassdoor"
 
+class SalarySource(Enum):
+    DIRECT_DATA = "direct_data"
+    DESCRIPTION = "description"
 
 class ScraperInput(BaseModel):
     site_type: list[Site]

src/jobspy/scrapers/utils.py

@@ -10,7 +10,7 @@ import numpy as np
 from markdownify import markdownify as md
 from requests.adapters import HTTPAdapter, Retry
 
-from ..jobs import JobType
+from ..jobs import CompensationInterval, JobType
 
 logger = logging.getLogger("JobSpy")
 logger.propagate = False
@@ -193,6 +193,7 @@ def extract_salary(
     upper_limit=700000,
     hourly_threshold=350,
     monthly_threshold=30000,
+    enforce_annual_salary=False,
 ):
     if not salary_str:
         return None, None, None, None
@@ -220,20 +221,30 @@ def extract_salary(
 
         # Convert to annual if less than the hourly threshold
         if min_salary < hourly_threshold:
-            min_salary = convert_hourly_to_annual(min_salary)
+            interval = CompensationInterval.HOURLY.value
+            annual_min_salary = convert_hourly_to_annual(min_salary)
             if max_salary < hourly_threshold:
-                max_salary = convert_hourly_to_annual(max_salary)
+                annual_max_salary = convert_hourly_to_annual(max_salary)
 
         elif min_salary < monthly_threshold:
-            min_salary = convert_monthly_to_annual(min_salary)
+            interval = CompensationInterval.MONTHLY.value
+            annual_min_salary = convert_monthly_to_annual(min_salary)
             if max_salary < monthly_threshold:
-                max_salary = convert_monthly_to_annual(max_salary)
+                annual_max_salary = convert_monthly_to_annual(max_salary)
+
+        else:
+            interval = CompensationInterval.YEARLY.value
+            annual_min_salary = min_salary
+            annual_max_salary = max_salary
 
         # Ensure salary range is within specified limits
         if (
-            lower_limit <= min_salary <= upper_limit
+            lower_limit <= annual_min_salary <= upper_limit
-            and lower_limit <= max_salary <= upper_limit
+            and lower_limit <= annual_max_salary <= upper_limit
-            and min_salary < max_salary
+            and annual_min_salary < annual_max_salary
         ):
-            return "yearly", min_salary, max_salary, "USD"
+            if enforce_annual_salary:
+                return interval, annual_min_salary, annual_max_salary, "USD"
+            else:
+                return interval, min_salary, max_salary, "USD"
     return None, None, None, None