class Datasource(CommonCrawlDatasource):
name = module_name(__name__)
query = "gumtree.com.au/s-ad/*"
query_filters = [
"~url:.*/(account-manager|account-relationship-management|accounting|accounts-officer-clerk|accounts-payable|accounts-receivable-credit-control|admin|administration-office-support|administrative-assistant|advertising-arts-media|aged-disability-support|agronomy-farm-services|air-conditioning-refrigeration|analysis-reporting|architecture|art-director|assembly-process-work|automotive-engineering|automotive-trades|bakers-pastry-chefs|banking-financial-services|banking-retail-branch|bar-beverage-staff|bookkeeping-small-practice-accounting|building-services-engineering|building-trades|business-services-corporate-advisory|butcher|call-centre-customer-service|carpentry-cabinet-making|chef-cook|child-welfare-youth-family-services|childcare-after-school-care|civil-structural-engineering|cleaner-housekeeper|coaching-instruction|commercial-sales-leasing-property-mgmt|community-services-development|construction|consulting-generalist-hr|contract-management|corporate-commercial-law|courier-driver-postal-service|customer-service-call-centre|customer-service-customer-facing|defence|dental-dentist|design-architecture|developer-programmer|digital-search-marketing|education-teaching|electrician|employment-services|engineering|event-management|facilities-management-body-corporate|farm-management|farming-veterinary|financial-accounting-reporting|financial-manager-controller|financial-planning|fitter-turner-machinist|florist|foreman-supervisor|freight-cargo-forwarding|front-office-guest-services|funds-management|gardening-landscaping|general-practitioner-gp-|generalist|government-defence|government|graphic-design|hair-beauty-services|healthcare-administration|healthcare-nursing|horticulture|hospitality-tourism|information-communication-technology|interaction-web-design|interior-design|it-support-help-desk|kitchen-sandwich-hand|labourer|legal-secretary|legal|locksmith|machine-operators|machine-plant-operator|maintenance-handyman|maintenance|management|manufacturing-transport-logistics|marketing-assistants|marketing-communications|marketing-communications|marketing-manager|mechanical-engineering|media-planning-strategy-buying|merchandiser|mining-engineering-maintenance|mining-operations|mining-resources-energy|mortgage-broker|nanny-babysitter|new-business-development|nursing|oil-gas-engineering-maintenance|oil-gas-operations|other-jobs|other|pa-ea-secretary|painter-sign-writer|paralegal-law-clerk|payroll-accounting|performing-arts|personal-trainer|pharmacy|physiotherapy-ot-rehabilitation|plumber|police-corrections-officer|printing-publishing-services|production-planning-scheduling|project-management|property-law|public-relations-corporate-affairs|purchasing-procurement-inventory|real-estate-property|receptionist|recruitment-agency|recruitment-hr|recruitment-internal|relationship-account-management|removalist|residential-leasing-property-management|residential-sales|retail-assistant|retail-management|retail|road-transport|sales-call-centre|sales-coordinator|sales-customer-facing|sales-management|sales-representative-consultant|sales|security-services|sports-management|sports-recreation|systems-business-analyst|tailor-dressmaker|taxation|teaching|technician|tour-guide|trade-marketing|trades-services|training-development|travel-agent-consultant|tutoring|vet-animal-welfare|waiting-staff|warehousing-storage-distribution|web-development-production|workplace-training-assessment|writing-journalist|welder-boilermaker)/"
]
def extract(self, html: bytes, uri, view_date):
text = html.decode("utf-8")
obj = parse_js_obj(text, JS_STR_APP)
if obj is None:
return []
else:
data = obj["vip"]["item"]
# adType: OFFER is job ad, WANTED is ask for work
if data["isJobsCategory"] and data["adType"] == "OFFER":
return [{"data": data, "uri": uri, "view_date": view_date}]
else:
return []
def normalise(self, data, uri, view_date):
metadata = {row["value"]: row["name"] for row in data["mainAttributes"]}
salary_raw = metadata.get("Salary Detail")
salary_data = get_salary_data(salary_raw)
return {
"title": data["title"],
"description": html2plain(data["description"]),
"uri": uri,
"view_date": datetime_from_iso_utc(view_date),
"org": None,
**salary_data,
"location_raw": data["mapAddress"],
**AU_GEOCODER.geocode(data["mapAddress"]),
}
class Datasource(CommonCrawlDatasource):
name = module_name(__name__)
def extract(self, html: bytes, base_url: str, view_date):
data = extruct.extract(html, base_url, syntaxes=["json-ld"])["json-ld"]
job_posts = [datum for datum in data if datum["@type"] == "JobPosting"]
return [{
"data": post,
"uri": base_url,
"view_date": view_date
} for post in job_posts]
def normalise(self, data, uri, view_date):
if "description" in data:
description = html2plain(data["description"])
else:
description = None
org = data.get("hiringOrganization")
if isinstance(org, dict):
org = org["name"]
return {
"title": data["title"],
"description": description,
"uri": uri,
"view_date": datetime_from_iso_utc(view_date),
"org": org,
}
class Datasource(CommonCrawlDatasource):
name = module_name(__name__)
def extract(self, html: bytes, base_url: str, view_date):
data = extruct.extract(html, base_url, syntaxes=["microdata"])["microdata"]
job_posts = [
datum["properties"]
for datum in data
if datum["type"] == "http://schema.org/JobPosting"
]
return [
{"data": post, "uri": base_url, "view_date": view_date}
for post in job_posts
]
def normalise(self, data, uri, view_date):
org = data["hiringOrganization"]
if isinstance(org, dict):
org = org.get("name")
if not isinstance(org, str):
org = None
return {
"title": data["title"],
"description": html2plain(data.get("description", "")),
"uri": uri,
"view_date": datetime_from_iso_utc(view_date),
"org": org,
}
class Datasource(CommonCrawlDatasource):
name = module_name(__name__)
query = "probonoaustralia.com.au/jobs/*"
def extract(self, html: Union[bytes, str], uri, view_date):
soup = bs4.BeautifulSoup(html, "html5lib")
infos = soup.select(".org-basic-info > div > p.org-add")
data = {}
for info in infos:
key = info.select_one("b")
if not key:
logging.warning("Missing key in %s; %s", uri, info)
continue
schema_key = key.get_text().strip()
value = "".join(
str(s.get_text() if isinstance(s, bs4.element.Tag) else s).strip()
for s in key.next_siblings
)
data[schema_key] = value
description = str(soup.select_one("#about-role") or "")
hiringOrganization_description = str(
soup.select_one("#about-organisation") or ""
)
header = soup.select_one("h1")
if not header:
logging.warning("Missing header: %s", uri)
title = None
else:
title = header.get_text().strip()
return [
{
"title": title,
"description": description,
"organisation_description": hiringOrganization_description,
"metadata": data,
"uri": uri,
"view_date": view_date,
}
]
def normalise(
self, title, description, organisation_description, metadata, uri, view_date
):
salary_text = metadata.get("Salary :")
salary_data = get_salary_data(salary_text)
location_raw = metadata["Location :"]
return {
"title": title,
"description": html2plain(description),
"uri": uri,
"view_date": datetime_from_iso_utc(view_date),
"org": metadata.get("Organisation :"),
**salary_data,
"location_raw": location_raw,
**AU_GEOCODER.geocode(fix_probono_location(location_raw)),
}
class Datasource(JSONLinkedDatasource):
name = module_name(__name__)
query = "ethicaljobs.com.au/members/*"
def normalise(self, data, uri, view_date):
ans = super().normalise(data, uri, view_date)
# Salary not in metadata
location_raw = location_jsonld(data)
return {
**ans,
"location_raw": location_raw,
**AU_GEOCODER.geocode(location_raw),
}
class Datasource(JSONLinkedDatasource):
name = module_name(__name__)
query = "www.cgcrecruitment.com/job/*"
def normalise(self, data, uri, view_date):
ans = super().normalise(data, uri, view_date)
salary_raw = data["baseSalary"]["value"].get("value")
salary = get_salary_data(salary_raw)
location_raw = location_jsonld(data)
return {
**ans,
**salary,
"location_raw": location_raw,
**AU_GEOCODER.geocode(location_raw),
}
class Datasource(MicrodataDatasource):
name = module_name(__name__)
query = "jobs.csiro.au/job/*"
def normalise(self, data, uri, view_date):
# Description is dometimes a list, e.g. CC-MAIN-2019-18
if isinstance(data.get("description"), list):
data["description"] = "\n".join(data["description"])
ans = super().normalise(data, uri, view_date)
# jobLocation *can* be an array
location_raw = str(data.get("jobLocation") or "")
return {
**ans,
"location_raw": location_raw,
**AU_GEOCODER.geocode(location_raw),
}
class Datasource(CommonCrawlDatasource):
name = module_name(__name__)
query = "iworkfor.nsw.gov.au/job/*"
def extract(self, html: Union[bytes, str], uri, view_date):
soup = bs4.BeautifulSoup(html, "html5lib")
body = soup.select_one("tbody")
# Some pages are missing a body; e.g. CC-MAIN-2018-17
if not body:
return []
infos = body.select("tr")
data = {}
for info in infos:
key = info.select_one("th")
value = info.select_one("td")
if key and value:
data[key.get_text().strip()] = value.get_text().strip()
title_tag = soup.select_one(".job-detail-title")
if not title_tag:
logging.warning("Missing title tag in %s, %s", uri, view_date)
title = None
else:
title = title_tag.get_text().strip()
description = str(soup.select_one(".job-detail-des") or "")
return [
{
"title": title,
"description": description,
"metadata": data,
"uri": uri,
"view_date": view_date,
}
]
def normalise(self, title, description, metadata, uri, view_date):
salary = get_salary_data(metadata.get("Total Remuneration Package:") or "")
location_raw = metadata["Job Location:"]
return {
"title": title,
"description": html2plain(description),
"uri": uri,
"view_date": datetime_from_iso_utc(view_date),
"org": metadata["Organisation/Entity:"],
**salary,
"location_raw": location_raw,
**AU_GEOCODER.geocode(fixup_iworkfornsw_loc(location_raw)),
}
class Datasource(CommonCrawlDatasource):
name = module_name(__name__)
query = "careers.vic.gov.au/job/*"
def extract(self, html: Union[bytes, str], uri,
view_date) -> List[Dict[Any, Any]]:
soup = bs4.BeautifulSoup(html, "html5lib")
data = {}
for info in soup.select(".txt-info"):
key = info.select_one(".txt-bold")
if not key:
continue
key_text = key.get_text().strip()
value = "".join(str(s).strip() for s in key.next_siblings)
data[key_text] = value
title_tag = soup.select_one(".txt-title")
if not title_tag:
logging.warning("Missing title tag in %s", uri)
title = None
else:
title = str(title_tag.get_text())
description = str(soup.select_one(".txt-pre-line") or "")
return [{
"title": title,
"description": description,
"metadata": data,
"uri": uri,
"view_date": view_date,
}]
def normalise(self, title, description, metadata, uri, view_date):
salary_data = get_salary_data(
metadata.get("Salary:") or metadata["Salary Range:"])
location_raw = metadata.get("Location:") or metadata["Work location:"]
return {
"title": title,
"description": html2plain(description),
"uri": uri,
"view_date": datetime_from_iso_utc(view_date),
"org": metadata.get("Organisation:"),
**salary_data,
"location_raw": location_raw,
**AU_GEOCODER.geocode(fixup_careers_vic_location(location_raw)),
}
class Datasource(KaggleDatasource):
# License: CC0: Public Domain
dataset = "promptcloud/latest-seek-australia-job-dataset"
sources = {
"seekau_2019q3":
"marketing_sample_for_seek_au-jobs_listing__20190901_20191231__10k_data.json"
}
name = module_name(__name__)
raw_extension = ".json"
def extract_one(self, path: Path) -> Generator[Dict[Any, Any], None, None]:
with open(path, "r") as f:
for line in f:
yield json.loads(line)
def normalise(self, *args, **data) -> Dict[str, Any]:
location_text = ", ".join([
data["city"],
data["state"],
data.get("country") or data["inferred_country"],
])
salary_text = data.get("salary_offered")
return {
"title":
data["job_title"],
"description":
data["job_description"],
"uri":
data["url"],
"view_date":
datetime.datetime.strptime(data["crawl_timestamp"],
"%Y-%m-%d %H:%M:%S +0000"),
"org":
data["company_name"],
**get_salary_data(salary_text),
"location_raw":
location_text,
**AU_GEOCODER.geocode(location_text),
}
class Datasource(KaggleDatasource):
# License: CC0: Public Domain
dataset = "santokalayil/data-scientist-jobs-in-australia-october-25-2019"
sources = {
"indeedau_datascience_202010":
"datascientist_jobs_in_australia_Oct_25_2019.csv"
}
raw_extension = ".csv"
name = module_name(__name__)
def extract_one(self, path: Path) -> Generator[Dict[Any, Any], None, None]:
with open(path, "r", encoding="latin-1") as f:
for row in csv.DictReader(f):
yield {k: v for k, v in row.items() if k}
def normalise(self, *args, **data) -> Dict[str, Any]:
return {
"title": data["title"],
"description": data["summary"],
"view_date": datetime.datetime(2019, 10, 25),
"org": data["company"],
"location_raw": data["location"],
**AU_GEOCODER.geocode(data["location"]),
}
class Datasource(KaggleDatasource):
# License: CC BY-SA 4.0
dataset = "PromptCloudHQ/australian-job-listings-data-from-seek-job-board"
sources = {"seekau": "seek_australia_sample.csv"}
raw_extension = ".csv"
name = module_name(__name__)
def extract_one(self, path: Path) -> Generator[Dict[Any, Any], None, None]:
with open(path, "r", encoding="latin-1") as f:
for row in csv.DictReader(f):
yield row
def normalise(self, *args, **data) -> Dict[str, Any]:
parts = [
x for x in [data.get("city"),
data.get("state"),
data.get("geo")] if x
]
location_text = ", ".join(parts)
return {
"title":
data["job_title"],
"description":
data["job_description"],
"uri":
data["pageurl"],
"view_date":
datetime.datetime.strptime(data["crawl_timestamp"],
"%Y-%m-%d %H:%M:%S +0000"),
"org":
data["company_name"],
**get_salary_data(data.get("salary_offered")),
"location_raw":
location_text,
**AU_GEOCODER.geocode(location_text),
}
class Datasource(CommonCrawlDatasource):
name = module_name(__name__)
query = "seek.com.au/job/*"
query_filters = ["!~url:.*/apply/*"]
def extract(self, html: bytes, uri, view_date):
text = html.decode("utf-8")
obj = parse_js_obj(text, JS_STR_REDUX)
if obj is None:
return []
else:
return [{
"data": obj["jobdetails"]["result"],
"uri": uri,
"view_date": view_date,
}]
def normalise(self, data, uri, view_date):
salary_text = data["salary"]
location = data["locationHierarchy"]
location_text = ", ".join([
location["suburb"],
location["city"],
location["state"],
location["nation"],
])
return {
"title": data["title"],
"description": html2plain(data["mobileAdTemplate"]),
"uri": uri,
"view_date": datetime_from_iso_utc(view_date),
"org": data["advertiser"]["description"],
**get_salary_data(salary_text),
"location_raw": location_text,
**AU_GEOCODER.geocode(location_text),
}
class Datasource(KaggleDatasource):
# License: CC BY-NC-SA 4.0
dataset = "PromptCloudHQ/australian-jobs-on-gumtreecomau"
sources = {"gumtreeau": "gumtree_com_au-sample.csv"}
raw_extension = ".csv"
name = module_name(__name__)
def extract_one(self, path: Path) -> Generator[Dict[Any, Any], None, None]:
with open(path, "r") as f:
for row in csv.DictReader(f):
yield row
def normalise(self, *args, **data) -> Dict[str, Any]:
return {
"title": data["job_title"],
"description": data["job_description"],
"uri": data["page_url"],
# Not quite true; this is date added
"view_date": datetime.datetime.strptime(data["date_added"],
"%d/%m/%Y"),
**get_salary_data(data["salary"]),
"location_raw": data["location"],
**AU_GEOCODER.geocode(data["location"]),
}