def person(
cls,
*,
locale=Locales.EN,
qualification=None,
age=None,
blood_type=None,
email=None,
first_name=None,
last_name=None,
gender=None,
height=None,
id=None,
language=None,
nationality=None,
occupation=None,
phone=None,
title=None,
university=None,
weight=None,
work_experience=None,
):
'''
Create an Person Data Entity object.
All individual fields are automatically randomly generated based on locale. If provided, the corresponding values are overriden.
Note:
All individual fields are randomly generated. Don't expect correct correlation e.g. correct postal code for the generated city.
Keyword Arguments:
locale: Approprite Random.locale.<local_name> object. Default is Random.locale.EN
qualification: Educational Qualification
age: Age
blood_type: Blood type
email: Email address
first_name: First name
last_name: Last name
gender: Gender
height: Height
id: Identifier
language: Language
nationality: Nationality
occupation: Occupation
phone: Phone number
title: Title
university: University
weight: Weight
work_experience: Work Experience
'''
person = Person(locale=locale)
from arjuna.engine.data.entity.person import Person as ArjPerson
first_name = first_name is not None and first_name or person.first_name(
)
last_name = last_name is not None and last_name or person.last_name()
return ArjPerson(
qualification=qualification is not None and qualification
or person.academic_degree(),
age=age is not None and age or person.age(),
blood_type=blood_type is not None and blood_type
or person.blood_type(),
email=email is not None and email or person.email(),
first_name=first_name,
last_name=last_name,
name=first_name + " " + last_name,
gender=gender is not None and gender or person.gender(),
height=height is not None and height or person.height(),
id=id is not None and id or person.identifier(),
language=language is not None and language or person.language(),
nationality=nationality is not None and nationality
or person.nationality(),
occupation=occupation is not None and occupation
or person.occupation(),
phone=phone is not None and phone or person.telephone(),
title=title is not None and title or person.title(),
university=university is not None and university
or person.university(),
weight=weight is not None and weight or person.weight(),
work_experience=work_experience is not None and work_experience
or person.work_experience(),
)
from collections import OrderedDict
from csv import DictWriter
import os
from mimesis import Person
COUNT = 100
LOCAL = 'en-gb'
HERE = os.path.dirname(__file__)
person = Person(LOCAL)
records = []
for _ in range(COUNT):
record = OrderedDict(
name=person.full_name(),
email=person.email(),
age=person.age(minimum=18, maximum=45),
height=person.height(),
blood_type=person.blood_type()
)
records.append(record)
with open(os.path.join(HERE, './fakedata.csv'), 'w') as f:
writer = DictWriter(f, fieldnames=records[0].keys())
writer.writeheader()
writer.writerows(records)
