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Attempt 2 outlined in blog post: https://www.tonic.ai/post/how-to-generate-simple-test-data-with-faker/
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| import random, csv | |
| from datetime import timedelta, datetime | |
| from faker import Faker | |
| from faker.providers import person | |
| from faker.providers import internet | |
| from faker.providers import ssn | |
| from faker.providers import address | |
| from faker.providers import job | |
| from faker.providers import date_time | |
| fake = Faker() | |
| fake.add_provider(person) | |
| fake.add_provider(internet) | |
| fake.add_provider(ssn) | |
| fake.add_provider(address) | |
| fake.add_provider(job) | |
| fake.add_provider(date_time) | |
| def first_name_and_gender(): | |
| g = 'M' if random.randint(0,1) == 0 else 'F' | |
| n = fake.first_name_male() if g=='M' else fake.first_name_female() | |
| return {'gender':g,'first_name':n} | |
| def birth_and_start_date(): | |
| sd = fake.date_between(start_date="-20y", end_date="now") | |
| delta = timedelta(days=365*random.randint(18,40)) | |
| bd = sd-delta | |
| return {'birth_date':bd.strftime('%m/%d/%Y'), 'start_date': sd.strftime('%m/%d/%Y')} | |
| def birth_and_start_date_on_windows(): | |
| bd = datetime(1960, 1, 1) + timedelta(seconds=random.randint(0,1261600000)) #40 year time delta | |
| earliest_start_date = bd + timedelta(seconds=random.randint(0,567720000)) #earliest start date is 18 years after birth | |
| latest_start_date = datetime.now() | |
| delta = latest_start_date-earliest_start_date | |
| delta_in_seconds = delta.days*24*60*60+delta.seconds | |
| random_second = random.randint(0,delta_in_seconds) | |
| return {'birth_date':bd.strftime('%m/%d/%Y'), 'start_date': (bd+timedelta(seconds=random_second)).strftime('%m/%d/%Y')} | |
| def title_office_org(): | |
| #generate a map of real office to fake office | |
| offices = ['New York','Austin','Seattle','Chicago'] | |
| #codify the hierarchical structure | |
| allowed_orgs_per_office = {'New York':['Sales'],'Austin':['Devops','Platform','Product','Internal Tools'],'Chicago':['Devops'], 'Seattle':['Internal Tools','Product']} | |
| allowed_titles_per_org = { | |
| 'Devops':['Engineer','Senior Engineer','Manager'], | |
| 'Sales':['Associate'], | |
| 'Platform':['Engineer'], | |
| 'Product':['Manager','VP'], | |
| 'Internal Tools':['Engineer','Senior Engineer','VP','Manager'] | |
| } | |
| office = random.choice(offices) | |
| org = random.choice(allowed_orgs_per_office[office]) | |
| title = random.choice(allowed_titles_per_org[org]) | |
| return {'office':office, 'title':title,'org': org} | |
| def salary_and_bonus(): | |
| salary = round(random.randint(90000,120000)/1000)*1000 | |
| bonus_ratio = random.uniform(0.15,0.2) | |
| bonus = round(salary*bonus_ratio/500)*500 | |
| return {'salary':salary,'bonus':bonus} | |
| def title_office_org_salary_bonus(): | |
| position = title_office_org() | |
| title_and_salary_range = {'Engineer':[90,120],'Senior Engineer':[110,140],'Manager':[130,150],'Associate':[60,80],'VP':[150,250]} | |
| salary_range = title_and_salary_range[position['title']] | |
| salary = round(random.randint(1000*salary_range[0],1000*salary_range[1])/1000)*1000 | |
| bonus_ratio = random.uniform(0.15,0.2) | |
| bonus = round(salary*bonus_ratio/500)*500 | |
| position.update({'salary':salary,'bonus':bonus}) | |
| return position | |
| d = dict() | |
| d['first_name_and_gender'] = first_name_and_gender | |
| d['last_name'] = lambda: {'last_name':fake.last_name()} | |
| d['personal_email'] = lambda: {'email':fake.email()} | |
| d['ssn'] = lambda: {'ssn':fake.ssn()} | |
| d['birth_and_start_date'] = birth_and_start_date | |
| d['title_office_org_salary_bonus'] = title_office_org_salary_bonus | |
| d['accrued_holidays'] = lambda: {'accrued_holiday':random.randint(0,20)} | |
| numRows = 100000 | |
| for _ in range(numRows): | |
| deep_list = [list(d[k]().values()) for k in d.keys()] | |
| row = [item for sublist in deep_list for item in sublist] | |
| print(row) |
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