lock file (#173)

pull/176/head v1.1.60
Cullen Watson 2024-07-17 21:21:22 -05:00 committed by GitHub
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@ -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)
├── company (str)
├── company_url (str)
├── job_url (str)
├── location (object)
│ ├── country (str)
│ ├── city (str)
│ ├── 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)
└── is_remote (bool)
├── title
├── company
├── company_url
├── job_url
├── location
│ ├── country
│ ├── city
│ ├── state
├── description
├── job_type: fulltime, parttime, internship, contract
├── job_function
│ ├── interval: yearly, monthly, weekly, daily, hourly
│ ├── min_amount
│ ├── max_amount
│ ├── currency
│ └── salary_source: direct_data, description (parsed from posting)
├── date_posted
├── emails
└── is_remote
Linkedin specific
└── job_level (str)
└── job_level
Linkedin & Indeed specific
└── company_industry (str)
└── company_industry
Indeed specific
├── company_country (str)
├── company_addresses (str)
├── company_employees_label (str)
├── company_revenue_label (str)
├── company_description (str)
├── ceo_name (str)
├── ceo_photo_url (str)
├── logo_photo_url (str)
└── banner_photo_url (str)
├── company_country
├── company_addresses
├── company_employees_label
├── company_revenue_label
├── company_description
├── ceo_name
├── ceo_photo_url
├── logo_photo_url
└── banner_photo_url
```
## Supported Countries for Job Searching

1228
poetry.lock generated

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@ -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"

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@ -36,7 +36,7 @@ def scrape_jobs(
linkedin_company_ids: list[int] | None = None,
offset: int | None = 0,
hours_old: int = None,
enforce_annual_salary: bool = True,
enforce_annual_salary: bool = False,
verbose: int = 2,
**kwargs,
) -> pd.DataFrame:
@ -182,10 +182,15 @@ def scrape_jobs(
job_data["min_amount"],
job_data["max_amount"],
job_data["currency"],
) = extract_salary(job_data["description"], enforce_annual_salary=enforce_annual_salary)
) = 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)
@ -207,6 +212,7 @@ def scrape_jobs(
"location",
"job_type",
"date_posted",
"salary_source",
"interval",
"min_amount",
"max_amount",