def extract_offers_per_cities(offers_file, colnames, min_creation_date, data_folder='data'):
"""Extract the interesting cities in terms of number of offers for each job group.
Args:
offers_file: path of cvs file with offers.
colnames: the names of the columns in the offer file.
min_creation_date: the date from which we consider the offers.
"""
required_fields = {
_CITY_CODE_FIELD, _JOB_GROUP_CODE_FIELD, _LATITUDE_CODE_FIELD,
_LONGITUDE_CODE_FIELD, _CITY_NAME_CODE_FIELD}
offers_rows = job_offers.iterate(offers_file, colnames, required_fields)
city_data = _list_hiring_cities(offers_rows, min_creation_date, data_folder)
# Computing the threshold per job group.
job_group_threshold = collections.defaultdict(float)
for job_group, offers in city_data.offers_per_job_group.items():
job_group_threshold[job_group] = math.pow(offers, 0.6) / 40
job_group_to_kept_cities = collections.defaultdict(list)
for job_group, city_ids in city_data.job_group_to_city_ids.items():
kept_cities = []
for city_id, offer_count in city_ids.items():
if offer_count > job_group_threshold[job_group]:
kept_cities.append({'city': city_data.city_info[city_id], 'offers': offer_count})
job_group_to_kept_cities[job_group] = sorted(
kept_cities, key=lambda k: k['offers'], reverse=True)
return [
{'_id': job_group_id, 'hiringCities': job_group_weighted_cities}
for job_group_id, job_group_weighted_cities in job_group_to_kept_cities.items()]
def trim_job_offers_csv(in_csv,
colnames_txt,
out_csv,
min_creation_date='',
fields=_DEFAULT_FIELDS):
"""Trim job offers CSV.
Args:
in_csv: the path of the CSV file containing all job offers in the Pôle
Emploi format (using latin-1 encoding, | separators, etc).
colnames_txt: the TXT file containing the list of column names.
out_csv: the path where to store the output CSV file.
fields: the list of fields to keep, separated by commas.
"""
fieldnames = fields.split(',')
all_job_offers = job_offers.iterate(in_csv,
colnames_txt,
required_fields=set(fieldnames +
['creation_date']))
with open(out_csv, 'w') as out_file:
writer = csv.DictWriter(out_file, fieldnames=fieldnames)
writer.writeheader()
for job_offer in all_job_offers:
if job_offer.creation_date < min_creation_date:
continue
writer.writerow(
{field: getattr(job_offer, field)
for field in fieldnames})
def test_missing_required_fields(self):
"""Test missing required field."""
offers = job_offers.iterate(path.join(self.testdata_folder,
'job_offers.csv'),
path.join(self.testdata_folder,
'column_names.txt'),
required_fields=set(['foobar']))
self.assertRaises(ValueError, next, offers)
def test_basic(self):
"""Test basic usage."""
offers = list(
job_offers.iterate(
path.join(self.testdata_folder, 'job_offers.csv'),
path.join(self.testdata_folder, 'column_names.txt')))
# Golden values.
self.assertEqual(8, len(offers))
self.assertEqual('000053Q', offers[0].id_offre)
self.assertEqual('Contrat travail', offers[1].contract_nature_name)
def csv2dicts(job_offers_csv, colnames_txt, last_year='2015'):
"""Import the changes of # of job offers per job group and dept in MongoDB.
Args:
job_offers_csv: Path of the csv containing the data.
colnames_txt: Path to a file containing the name of the CSV's columns.
last_year: The year to consider to compute the metrics.
Returns:
Evolution data as a LocalJobStats JSON-proto compatible dict.
"""
counter = _EvolutionCounter(int(last_year))
for job_offer in job_offers.iterate(
job_offers_csv, colnames_txt, _REQUIRED_FIELDS):
counter.collect(job_offer)
return list(counter.get_proto_dicts())
def csv2dicts(job_offers_csv, colnames_txt):
"""Import the requirement from job offers grouped by Job Group in MongoDB.
Args:
job_offers_csv: Path of the csv containing the data.
colnames_txt: Path to a file containing the name of the CSV's columns.
Returns:
Requirements as a JobRequirements JSON-proto compatible dict.
"""
job_groups = collections.defaultdict(_RequirementsCollector)
for job_offer in job_offers.iterate(
job_offers_csv, colnames_txt, _REQUIRED_FIELDS):
job_groups[job_offer.rome_profession_card_code].collect(job_offer)
return [
dict(job_groups[job_group_id].get_proto_dict(), _id=job_group_id)
for job_group_id in sorted(job_groups)]
