簡単にサンプルデータを生成するPythonのライブラリ「faker」

pip install faker

% pip install faker
Collecting faker
  Downloading Faker-33.1.0-py3-none-any.whl.metadata (15 kB)
Requirement already satisfied: python-dateutil>=2.4 in /Users/ishikawa.satoru/.pyenv/versions/3.11.10/lib/python3.11/site-packages (from faker) (2.9.0.post0)
Requirement already satisfied: typing-extensions in /Users/ishikawa.satoru/.pyenv/versions/3.11.10/lib/python3.11/site-packages (from faker) (4.12.2)
Requirement already satisfied: six>=1.5 in /Users/ishikawa.satoru/.pyenv/versions/3.11.10/lib/python3.11/site-packages (from python-dateutil>=2.4->faker) (1.17.0)
Downloading Faker-33.1.0-py3-none-any.whl (1.9 MB)
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Installing collected packages: faker
Successfully installed faker-33.1.0

from faker import Faker

fake = Faker()

print(fake.name())
print(fake.address())
print(fake.email())

% python faker_sample.py
Tammy Gillespie
021 Louis Drive Apt. 759
Gonzalezbury, DE 58333
jennifer67@example.org

from faker import Faker

fake = Faker('ja_JP') # 日本語のデータを生成

print(fake.name())
print(fake.address())
print(fake.email())

% python faker_sample.py
石川 英樹
宮城県東村山市山中新田41丁目16番13号
rnakagawa@example.net

fake.profile(fields=['name', 'address', 'birthdate', 'job'])

{'job': 'モデル', 'name': '佐々木 春香', 'address': '千葉県武蔵野市所野29丁目12番3号', 'birthdate': datetime.date(1938, 8, 9)}

faker name
faker address
faker -r 3 -s ";" name  # 3つの名前をセミコロン区切りで生成

% faker --help
usage: faker [-h] [--version] [-v] [-o output] [-l LOCALE] [-r REPEAT]
             [-s SEP] [--seed SEED] [-i [INCLUDE ...]]
             [fake] [fake argument ...]

faker version 33.1.0

positional arguments:
  fake                  name of the fake to generate output for (e.g. profile)
  fake argument         optional arguments to pass to the fake (e.g. the
                        profile fake takes an optional list of comma separated
                        field names as the first argument)

options:
  -h, --help            show this help message and exit
  --version             show program's version number and exit
  -v, --verbose         show INFO logging events instead of CRITICAL, which is
                        the default. These logging events provide insight into
                        localization of specific providers.
  -o output             redirect output to a file
  -l LOCALE, --lang LOCALE
                        specify the language for a localized provider (e.g.
                        de_DE)
  -r REPEAT, --repeat REPEAT
                        generate the specified number of outputs
  -s SEP, --sep SEP     use the specified separator after each output
  --seed SEED           specify a seed for the random generator so that
                        results are repeatable. Also compatible with 'repeat'
                        option
  -i [INCLUDE ...], --include [INCLUDE ...]
                        list of additional custom providers to user, given as
                        the import path of the module containing your Provider
                        class (not the provider class itself)

supported locales:

  ar_AA, ar_AE, ar_BH, ar_EG, ar_JO, ar_PS, ar_SA, az_AZ, bg_BG, bn_BD, bs_BA, cs_CZ, da_DK, de, de_AT, de_CH, de_DE, dk_DK, el_CY, el_GR, en, en_AU, en_BD, en_CA, en_GB, en_IE, en_IN, en_MS, en_NZ, en_PH, en_PK, en_TH, en_US, es, es_AR, es_CA, es_CL, es_CO, es_ES, es_MX, et_EE, fa_IR, fi_FI, fil_PH, fr_BE, fr_CA, fr_CH, fr_FR, fr_QC, ga_IE, gu_IN, he_IL, hi_IN, hr_HR, hu_HU, hy_AM, id_ID, it_CH, it_IT, ja_JP, ka_GE, ko_KR, la, lb_LU, lt_LT, lv_LV, mt_MT, ne_NP, nl_BE, nl_NL, no_NO, or_IN, pl_PL, pt_BR, pt_PT, ro_RO, ru_RU, sk_SK, sl_SI, sq_AL, sv_SE, sw, ta_IN, th, th_TH, tl_PH, tr_TR, tw_GH, uk_UA, uz_UZ, vi_VN, yo_NG, zh_CN, zh_TW, zu_ZA

  Faker can take a locale as an optional argument, to return localized data. If
  no locale argument is specified, the factory falls back to the user's OS
  locale as long as it is supported by at least one of the providers.
     - for this user, the default locale is ja_JP.

  If the optional argument locale and/or user's default locale is not available
  for the specified provider, the factory falls back to faker's default locale,
  which is en_US.

examples:

  $ faker address
  968 Bahringer Garden Apt. 722
  Kristinaland, NJ 09890

  $ faker -l de_DE address
  Samira-Niemeier-Allee 56
  94812 Biedenkopf

  $ faker profile ssn,birthdate
  {'ssn': u'628-10-1085', 'birthdate': '2008-03-29'}

  $ faker -r=3 -s=";" name
  Willam Kertzmann;
  Josiah Maggio;
  Gayla Schmitt;

from faker import Faker
from datetime import datetime

fake = Faker()

def generate_company_data(num_companies):
    companies = []
    for _ in range(num_companies):
        company = {
            'name': fake.company(),
            'catch_phrase': fake.catch_phrase(),
            'address': fake.address(),
            'website': fake.url(),
            'employee_count': fake.random_int(min=10, max=1000)
        }
        companies.append(company)
    return companies

company_data = generate_company_data(5)
for company in company_data:
    print(company)

{'name': 'Webb-Collins', 'catch_phrase': 'Inverse content-based groupware', 'address': 'Unit 7676 Box 2382\nDPO AP 78501', 'website': 'https://www.smith-morton.com/', 'employee_count': 39}
{'name': 'Moore, Robertson and Davis', 'catch_phrase': 'Self-enabling interactive product', 'address': '39730 Mark Oval\nLake Dariusport, CA 96309', 'website': 'http://wise.com/', 'employee_count': 627}
{'name': 'Singh-Mccann', 'catch_phrase': 'Virtual uniform capability', 'address': '6705 Maxwell Trail Suite 563\nEast Sherri, CO 02637', 'website': 'http://brown.com/', 'employee_count': 750}
{'name': 'Mckinney, Mathews and Armstrong', 'catch_phrase': 'Polarized context-sensitive collaboration', 'address': '88155 Juarez Bridge\nNew Jennifer, FM 25694', 'website': 'http://reynolds.org/', 'employee_count': 977}
{'name': 'Anderson-Roy', 'catch_phrase': 'Expanded regional capacity', 'address': '49299 Katrina Station\nEast Taramouth, WV 44486', 'website': 'http://www.jackson-young.com/', 'employee_count': 280}

from faker import Faker
from datetime import datetime

fake = Faker()

def generate_transactions(num_transactions):
    transactions = []
    start_date = datetime(2023, 1, 1)
    end_date = datetime(2023, 12, 31)
    for _ in range(num_transactions):
        transaction = {
            'date': f"{fake.date_between(start_date=start_date, end_date=end_date)}",
            'amount': round(fake.random.uniform(10, 1000), 2),
            'description': fake.bs(),
            'category': fake.random_element(elements=('Food', 'Transport', 'Entertainment', 'Utilities', 'Shopping'))
        }
        transactions.append(transaction)
    return sorted(transactions, key=lambda x: x['date'])

fake_transactions = generate_transactions(5)
for transaction in fake_transactions:
    print(transaction)

{'date': '2023-01-07', 'amount': 465.16, 'description': 'grow one-to-one e-services', 'category': 'Transport'}
{'date': '2023-02-01', 'amount': 787.54, 'description': 'integrate scalable methodologies', 'category': 'Food'}
{'date': '2023-02-10', 'amount': 110.6, 'description': 'iterate strategic mindshare', 'category': 'Food'}
{'date': '2023-03-22', 'amount': 220.29, 'description': 'reinvent proactive mindshare', 'category': 'Shopping'}
{'date': '2023-05-29', 'amount': 244.21, 'description': 'whiteboard B2C infrastructures', 'category': 'Entertainment'}

from faker import Faker
from datetime import datetime

fake = Faker()

def generate_product_catalog(num_products):
    catalog = []
    for _ in range(num_products):
        product = {
            'name': fake.catch_phrase(),
            'description': fake.text(max_nb_chars=200),
            'price': round(fake.random.uniform(10, 500), 2),
            'category': fake.random_element(elements=('Electronics', 'Clothing', 'Home & Garden', 'Books', 'Toys')),
            'sku': fake.ean(length=13)
        }
        catalog.append(product)
    return catalog

product_catalog = generate_product_catalog(5)
for product in product_catalog:
    print(product)

{'name': 'Streamlined scalable contingency', 'description': 'Improve benefit morning lose enough nor station. Respond tax example sister cost. Agency generation ready anything.', 'price': 199.07, 'category': 'Clothing', 'sku': '9128152314040'}
{'name': 'Reverse-engineered static groupware', 'description': 'Heavy class finish. Best agent believe work. Big throughout vote suggest on true owner.\nMatter to economy tax front table character. Challenge television contain set pass.', 'price': 202.08, 'category': 'Electronics', 'sku': '2656143964600'}
{'name': 'De-engineered human-resource projection', 'description': 'Apply care set plan. Meet certain determine stay.\nPattern really step why number thing gas sound. Inside point rich method quite window. Key do benefit.', 'price': 335.43, 'category': 'Books', 'sku': '4708567544178'}
{'name': 'Seamless directional open architecture', 'description': 'Who bad hit treatment president money. Say yourself society perform when modern production. Say cup push everything protect capital information attack.', 'price': 466.55, 'category': 'Books', 'sku': '2993136285954'}
{'name': 'Compatible next generation definition', 'description': 'Policy establish wall usually avoid. Answer forward might Mrs. Identify research production interview without the. Yeah television laugh treat federal employee.', 'price': 296.92, 'category': 'Clothing', 'sku': '9997702672066'}

import csv
from faker import Faker

fake = Faker('ja_JP')

with open('sample_data.csv', 'w', newline='', encoding='utf-8') as f:
    writer = csv.writer(f)
    writer.writerow(['名前', '住所', '電話番号'])
    for _ in range(5):
        writer.writerow([fake.name(), fake.address(), fake.phone_number()])

名前,住所,電話番号
田中 智也,山口県三宅島三宅村明石町25丁目25番16号 アーバン長間554,070-7566-1557
森 翔太,岩手県川崎市幸区浅草19丁目10番19号,090-2866-8956
福田 裕樹,佐賀県川崎市高津区西神田18丁目18番3号 富久町シティ851,080-1853-7056
山田 裕太,大阪府君津市明石町5丁目12番15号 パレス丸の内349,73-7863-4597
岡田 太郎,千葉県利島村台場20丁目9番14号,080-1785-6615

writer = csv.writer(f, delimiter='\t')

from faker import Faker

fake = Faker('ja_JP')

with open('sample_data.jsonl', 'w', encoding='utf-8') as f:
    for _ in range(5):
        user = {
            'name': fake.name(),
            'address': fake.address(),
            'email': fake.email(),
            'dob': f'{fake.date_of_birth(minimum_age=18, maximum_age=80)}',
            'phone': fake.phone_number()
        }
        print(user, file=f)

{'name': '小川 陽一', 'address': '兵庫県山武郡九十九里町南郷屋39丁目26番7号 芝公園クレスト912', 'email': 'ryohei29@example.org', 'dob': '1955-07-01', 'phone': '080-8424-0382'}
{'name': '渡辺 千代', 'address': '京都府新宿区押上1丁目27番1号 東上野クレスト629', 'email': 'yamaguchinanami@example.org', 'dob': '1977-04-25', 'phone': '080-0229-1025'}
{'name': '佐藤 太郎', 'address': '京都府足立区九段南15丁目20番1号', 'email': 'mikiyoshida@example.org', 'dob': '1977-08-03', 'phone': '07-1853-2477'}
{'name': '中村 裕美子', 'address': '大分県君津市京橋4丁目21番17号 シティ竜泉650', 'email': 'haruka74@example.org', 'dob': '1983-07-21', 'phone': '24-5977-9758'}
{'name': '遠藤 洋介', 'address': '高知県神津島村中宮祠18丁目18番7号', 'email': 'tsubasayamamoto@example.org', 'dob': '1952-05-31', 'phone': '06-8649-2618'}