from __future__ import annotations
import pandas as pd
from typing import Tuple
from concurrent.futures import ThreadPoolExecutor, as_completed
from .jobs import JobType, Location
from .scrapers.utils import set_logger_level, extract_salary, create_logger
from .scrapers.indeed import IndeedScraper
from .scrapers.ziprecruiter import ZipRecruiterScraper
from .scrapers.glassdoor import GlassdoorScraper
from .scrapers.google import GoogleJobsScraper
from .scrapers.linkedin import LinkedInScraper
from .scrapers.bayt import BaytScraper
from .scrapers import SalarySource, ScraperInput, Site, JobResponse, Country
from .scrapers.exceptions import (
LinkedInException,
IndeedException,
ZipRecruiterException,
GlassdoorException,
GoogleJobsException,
)
def scrape_jobs(
site_name: str | list[str] | Site | list[Site] | None = None,
search_term: str | None = None,
google_search_term: str | None = None,
location: str | None = None,
distance: int | None = 50,
is_remote: bool = False,
job_type: str | None = None,
easy_apply: bool | None = None,
results_wanted: int = 15,
country_indeed: str = "usa",
hyperlinks: bool = False,
proxies: list[str] | str | None = None,
ca_cert: 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,
enforce_annual_salary: bool = False,
verbose: int = 2,
**kwargs,
) -> pd.DataFrame:
"""
Simultaneously scrapes job data from multiple job sites.
:return: pandas dataframe containing job data
"""
SCRAPER_MAPPING = {
Site.LINKEDIN: LinkedInScraper,
Site.INDEED: IndeedScraper,
Site.ZIP_RECRUITER: ZipRecruiterScraper,
Site.GLASSDOOR: GlassdoorScraper,
Site.GOOGLE: GoogleJobsScraper,
Site.BAYT: BaytScraper,
}
set_logger_level(verbose)
def map_str_to_site(site_name: str) -> Site:
return Site[site_name.upper()]
def get_enum_from_value(value_str):
for job_type in JobType:
if value_str in job_type.value:
return job_type
raise Exception(f"Invalid job type: {value_str}")
job_type = get_enum_from_value(job_type) if job_type else None
def get_site_type():
site_types = list(Site)
if isinstance(site_name, str):
site_types = [map_str_to_site(site_name)]
elif isinstance(site_name, Site):
site_types = [site_name]
elif isinstance(site_name, list):
site_types = [
map_str_to_site(site) if isinstance(site, str) else site
for site in site_name
]
return site_types
country_enum = Country.from_string(country_indeed)
scraper_input = ScraperInput(
site_type=get_site_type(),
country=country_enum,
search_term=search_term,
google_search_term=google_search_term,
location=location,
distance=distance,
is_remote=is_remote,
job_type=job_type,
easy_apply=easy_apply,
description_format=description_format,
linkedin_fetch_description=linkedin_fetch_description,
results_wanted=results_wanted,
linkedin_company_ids=linkedin_company_ids,
offset=offset,
hours_old=hours_old,
)
def scrape_site(site: Site) -> Tuple[str, JobResponse]:
scraper_class = SCRAPER_MAPPING[site]
scraper = scraper_class(proxies=proxies, ca_cert=ca_cert)
scraped_data: JobResponse = scraper.scrape(scraper_input)
cap_name = site.value.capitalize()
site_name = "ZipRecruiter" if cap_name == "Zip_recruiter" else cap_name
create_logger(site_name).info(f"finished scraping")
return site.value, scraped_data
site_to_jobs_dict = {}
def worker(site):
site_val, scraped_info = scrape_site(site)
return site_val, scraped_info
with ThreadPoolExecutor() as executor:
future_to_site = {
executor.submit(worker, site): site for site in scraper_input.site_type
}
for future in as_completed(future_to_site):
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():
for job in job_response.jobs:
job_data = job.dict()
job_url = job_data["job_url"]
job_data["job_url_hyper"] = f'<a href="{job_url}">{job_url}</a>'
job_data["site"] = site
job_data["company"] = job_data["company_name"]
job_data["job_type"] = (
", ".join(job_type.value[0] for job_type in job_data["job_type"])
if job_data["job_type"]
else None
)
job_data["emails"] = (
", ".join(job_data["emails"]) if job_data["emails"] else None
)
if job_data["location"]:
job_data["location"] = Location(
**job_data["location"]
).display_location()
compensation_obj = job_data.get("compensation")
if compensation_obj and isinstance(compensation_obj, dict):
job_data["interval"] = (
compensation_obj.get("interval").value
if compensation_obj.get("interval")
else None
)
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")
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"]
and job_data["max_amount"]
):
convert_to_annual(job_data)
else:
if country_enum == Country.USA:
(
job_data["interval"],
job_data["min_amount"],
job_data["max_amount"],
job_data["currency"],
) = 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 "min_amount" in job_data and job_data["min_amount"]
else None
)
job_df = pd.DataFrame([job_data])
jobs_dfs.append(job_df)
if jobs_dfs:
# Step 1: Filter out all-NA columns from each DataFrame before concatenation
filtered_dfs = [df.dropna(axis=1, how="all") for df in jobs_dfs]
# Step 2: Concatenate the filtered DataFrames
jobs_df = pd.concat(filtered_dfs, ignore_index=True)
# Desired column order
desired_order = [
"id",
"site",
"job_url_hyper" if hyperlinks else "job_url",
"job_url_direct",
"title",
"company",
"location",
"date_posted",
"job_type",
"salary_source",
"interval",
"min_amount",
"max_amount",
"currency",
"is_remote",
"job_level",
"job_function",
"listing_type",
"emails",
"description",
"company_industry",
"company_url",
"company_logo",
"company_url_direct",
"company_addresses",
"company_num_employees",
"company_revenue",
"company_description",
]
# Step 3: Ensure all desired columns are present, adding missing ones as empty
for column in desired_order:
if column not in jobs_df.columns:
jobs_df[column] = None # Add missing columns as empty
# Reorder the DataFrame according to the desired order
jobs_df = jobs_df[desired_order]
# Step 4: Sort the DataFrame as required
return jobs_df.sort_values(
by=["site", "date_posted"], ascending=[True, False]
).reset_index(drop=True)
else:
return pd.DataFrame()