JobPost schema

The Pydantic data model for a single job posting, and how its fields map to DataFrame columns in the scrape_jobs() output.

Each job returned by scrape_jobs() is internally represented as a JobPost Pydantic model. When building the output DataFrame, the model fields are flattened and nested objects (such as location and compensation) are expanded into individual columns.

JobPost fields

id · str | None

Platform-specific job identifier. May be None for some job boards.

title · str · required

Job title as listed by the employer.

company_name · str | None

Name of the hiring company. Maps to the company column in the DataFrame.

job_url · str · required

Canonical URL of the job listing on the job board.

job_url_direct · str | None

Direct URL to the employer’s own job application page, when available. For LinkedIn, this requires linkedin_fetch_description=True.

location · Location | None

Structured location of the job. Flattened to a display string in the DataFrame (e.g., "San Francisco, CA, USA").

Show Location properties

country · Country | str | None

Country of the job, either as a Country enum value or a raw string.

city · str | None

City where the job is located.

state · str | None

State or province where the job is located.

description · str | None

Full job description. The format is controlled by the description_format parameter passed to scrape_jobs() — one of "markdown", "html", or "plain".

company_url · str | None

URL of the company’s profile page on the job board.

company_url_direct · str | None

URL of the company’s own website, when available.

job_type · list[JobType] | None

Employment type(s) associated with the posting (e.g., FULL_TIME, CONTRACT). In the DataFrame, this is serialized as a comma-separated string of the primary values (e.g., "fulltime"). See JobType.

compensation · Compensation | None

Salary or compensation data. In the DataFrame, this object is expanded into four separate columns: interval, min_amount, max_amount, and currency.

Show Compensation properties

interval · CompensationInterval | None

Pay frequency. One of YEARLY, MONTHLY, WEEKLY, DAILY, HOURLY. See CompensationInterval.

min_amount · float | None

Minimum salary or pay amount for the stated interval.

max_amount · float | None

Maximum salary or pay amount for the stated interval.

currency · str | None · default: "USD"

ISO 4217 currency code (e.g., "USD", "GBP", "EUR"). Defaults to "USD".

date_posted · date | None

The date the job was posted, as a Python datetime.date object.

emails · list[str] | None

Email addresses extracted from the job description. In the DataFrame, serialized as a comma-separated string.

is_remote · bool | None

Whether the job is remote. True for remote, False for on-site, None if not specified.

listing_type · str | None

The type of listing (e.g., organic vs. sponsored), when available from the job board.

job_level · str | None

Seniority or experience level (e.g., "Entry level", "Mid-Senior level"). LinkedIn-specific.

company_industry · str | None

The industry the company operates in. Available from LinkedIn and Indeed.

company_addresses · str | None

Physical address(es) of the company. Indeed-specific.

company_num_employees · str | None

Company headcount range as a string (e.g., "1001-5000 employees"). Indeed-specific.

company_revenue · str | None

Company annual revenue as a string (e.g., "$1B to $5B"). Indeed-specific.

company_description · str | None

Short description of the company from its job board profile. Indeed-specific.

company_logo · str | None

URL of the company’s logo image. Indeed-specific.

banner_photo_url · str | None

URL of the company’s banner photo. Indeed-specific.

job_function · str | None

Functional area of the role (e.g., "Engineering", "Product Management"). LinkedIn-specific.

skills · list[str] | None

Skills and technologies listed in the posting. In the DataFrame, serialized as a comma-separated string. Naukri-specific.

experience_range · str | None

Required years of experience as a range string (e.g., "2-5 Yrs"). Naukri-specific.

company_rating · float | None

Aggregate company rating from AmbitionBox. Naukri-specific.

company_reviews_count · int | None

Number of company reviews on AmbitionBox. Naukri-specific.

vacancy_count · int | None

Number of open positions for this listing. Naukri-specific.

work_from_home_type · str | None

Work arrangement type (e.g., "Hybrid", "Remote", "Work from Office"). Naukri-specific.

DataFrame column mapping

The table below maps each JobPost model field to its corresponding column name in the scrape_jobs() output DataFrame.

Model field	DataFrame column	Notes
id	id
(added by scraper)	site	Source job board name
job_url	job_url
job_url_direct	job_url_direct
title	title
company_name	company	Renamed in DataFrame
location	location	Flattened to display string
date_posted	date_posted
job_type	job_type	Comma-separated string
(from compensation)	salary_source	"direct_data" or "description"
compensation.interval	interval
compensation.min_amount	min_amount
compensation.max_amount	max_amount
compensation.currency	currency
is_remote	is_remote
job_level	job_level	LinkedIn only
job_function	job_function	LinkedIn only
listing_type	listing_type
emails	emails	Comma-separated string
description	description
company_industry	company_industry	LinkedIn & Indeed
company_url	company_url
company_logo	company_logo	Indeed only
company_url_direct	company_url_direct
company_addresses	company_addresses	Indeed only
company_num_employees	company_num_employees	Indeed only
company_revenue	company_revenue	Indeed only
company_description	company_description	Indeed only
skills	skills	Naukri only, comma-separated
experience_range	experience_range	Naukri only
company_rating	company_rating	Naukri only
company_reviews_count	company_reviews_count	Naukri only
vacancy_count	vacancy_count	Naukri only
work_from_home_type	work_from_home_type	Naukri only

Example

from jobspy import scrape_jobs

jobs = scrape_jobs(
    site_name=["indeed", "linkedin"],
    search_term="data engineer",
    results_wanted=10,
)

# Access individual columns
print(jobs[["title", "company", "location", "min_amount", "max_amount", "currency"]])

# Filter remote jobs with salary data
remote_with_salary = jobs[
    (jobs["is_remote"] == True) & (jobs["min_amount"].notna())
]