def getting_started_example():
generic = Generic()
#generic = Generic(locales.EN)
print('Month =', generic.datetime.month())
print('Datetime =',
generic.datetime.datetime(start=1900, end=2035,
timezone=None)) # Type: datetime.datetime.
print('IMEI =', generic.code.imei())
print('Fruit =', generic.food.fruit())
print('RNA =', generic.science.rna_sequence())
print('Word =', generic.text.word())
with generic.text.override_locale(locales.FR):
print('Word =', generic.text.word())
print('Word =', generic.text.word())
generic = Generic('en')
generic.add_provider(USASpecProvider)
print('SSN =', generic.usa_provider.ssn())
#print('CPF =', generic.usa_provider.cpf()) # AttributeError: 'USASpecProvider' object has no attribute 'cpf'.
generic = Generic('pt-br')
#generic = Generic(locales.PT_BR)
generic.add_provider(BrazilSpecProvider)
#print('SSN =', generic.brazil_provider.ssn()) # AttributeError: 'BrazilSpecProvider' object has no attribute 'ssn'.
print('CPF =', generic.brazil_provider.cpf())
#--------------------
numbers = Numbers()
print('Numbers =', numbers.between()) # Type: int.
print('Numbers =', numbers.between(10, 10000000000000000)) # Type: int.
#--------------------
person = Person(locales.KO)
print('Full name =', person.full_name(gender=Gender.FEMALE))
print('Full name =', person.full_name(gender=Gender.MALE, reverse=True))
with person.override_locale(locales.RU):
print('Full name =', person.full_name())
print('Full name =', person.full_name())
print('Telephone =', person.telephone())
print('Telephone =', person.telephone(mask='(###)-###-####'))
print('Identifier =', person.identifier())
print('Identifier =', person.identifier(mask='######-#######'))
#--------------------
de = Address('de')
ru = Address('ru')
print('Region =', de.region())
print('Federal subject =', ru.federal_subject())
print('Address =', de.address())
print('Address =', ru.address())
ko = Address('ko')
print('Address =', ko.province(), ko.city(), ko.address())
print('Zip code =', ko.zip_code())
#--------------------
business = Business('ko')
#print('Price =', business.price(minimum=1.0, maximum=1000000000.0)) # Type: str.
#print('Price =', business.price(minimum=1.0, maximum=1000000000.0)[:-2]) # Type: str.
print('Price =',
business.price(minimum=1.0, maximum=1000000000.0)[:-5]) # Type: str.
#--------------------
payment = Payment()
print('Credit card =',
payment.credit_card_number(card_type=None)) # Type: str.
##################################################
##################################################
### Generate a DataFrame of user information
##################################################
# Generate 10,000 rows of the following:
# user_id, first_name, last_name, email, password, address,
# birth_date, credit_card_num, credit_card_exp, security_answer,
# account_balance
user_df = pd.DataFrame([[x, person.name(), person.surname(), person.gender(),
person.email(), hashed_passwd(person.password()),
address.address(), person.age(),
payment.credit_card_number(),
payment.credit_card_expiration_date(), text.word(),
account_balance(), np.random.randint(1, 11)]
for x in range(10000)])
user_df.columns = ["user_id", "first_name", "last_name",
"gender", "email", "password_hashed", "address",
"age", "credit_card_num", "credit_card_exp",
"security_answer", "account_balance",
"marketing_level"]
# Generate sales, based on a noisy linear model
user_df['sales'] = generate_sales(user_df)
user_df['sales'] = user_df['sales'] - user_df['sales'].min()
user_df['sales'] /= 40
