python-3.x - 使用Python Faker生成5000行的不同数据

Question

我想使用Python Faker库生成500行数据，但是我使用下面提供的代码重复获取数据。您能指出我要去哪里了吗？我相信这与for循环有关。提前致谢:

from faker import Factory
import pandas as pd
import random

def create_fake_stuff(fake):


df = pd.DataFrame(columns=('name'
    , 'email'
    , 'bs'
    , 'address'
    , 'city'
    , 'state'
    , 'date_time'
    , 'paragraph'
    , 'Conrad'
    ,'randomdata'))

stuff = [fake.name()
    , fake.email()
    , fake.bs()
    , fake.address()
    , fake.city()
    , fake.state()
    , fake.date_time()
    , fake.paragraph()
    , fake.catch_phrase()
    , random.randint(1000,2000)]

for i in range(10):
        df.loc[i] = [item for item in stuff]
print(df)

if __name__ == '__main__':
    fake = Factory.create()
    create_fake_stuff(fake)

Answer 1

免责声明:此答案在问题后添加了很多，并添加了一些不直接回答问题的新信息。

现在有一个快速的新库Mimesis - Fake Data Generator。

优势:据说它的工作时间比faker快(请参阅下面我对有问题的数据的测试)。

缺点:仅适用于3.6版本的Python。

pip install mimesis

>>> from mimesis import Person
>>> from mimesis.enums import Gender
>>> person = Person('en')

>>> person.full_name(gender=Gender.FEMALE)
'Antonetta Garrison'
>>> personru = Person('ru')
>>> personru.full_name()
'Рената Черкасова'

与早期开发的faker相同:
pip install faker

>>> from faker import Faker
>>> fake_ru=Faker('ja_JP')
>>> fake_ru=Faker('ru_RU')
>>> fake_jp=Faker('ja_JP')
>>> print (fake_ru.name())
Субботина Елена Наумовна
>>> print (fake_jp.name())
大垣 花子

在其下面，基于forzer0eight的答复中提供的代码，我最近对Mimesis vs. Faker的计时:

from faker import Faker
import pandas as pd
import random
fake = Faker()
def create_rows_faker(num=1):
    output = [{"name":fake.name(),
                   "address":fake.address(),
                   "name":fake.name(),
                   "email":fake.email(),
                   #"bs":fake.bs(),
                   "city":fake.city(),
                   "state":fake.state(),
                   "date_time":fake.date_time(),
                   #"paragraph":fake.paragraph(),
                   #"Conrad":fake.catch_phrase(),
                   "randomdata":random.randint(1000,2000)} for x in range(num)]
    return output

%%time
df_faker = pd.DataFrame(create_rows_faker(5000))

CPU时间:用户3.51 s，系统:2.86 ms，总计:3.51 s
壁挂时间:3.51 s

from mimesis import Person
from mimesis import Address
from mimesis.enums import Gender
from mimesis import Datetime
person = Person('en')
import pandas as pd
import random
person = Person()
addess = Address()
datetime = Datetime()
def create_rows_mimesis(num=1):
    output = [{"name":person.full_name(gender=Gender.FEMALE),
                   "address":addess.address(),
                   "name":person.name(),
                   "email":person.email(),
                   #"bs":person.bs(),
                   "city":addess.city(),
                   "state":addess.state(),
                   "date_time":datetime.datetime(),
                   #"paragraph":person.paragraph(),
                   #"Conrad":person.catch_phrase(),
                   "randomdata":random.randint(1000,2000)} for x in range(num)]
    return output

%%time
df_mimesis = pd.DataFrame(create_rows_mimesis(5000))

CPU时间:用户178毫秒，系统时间:1.7毫秒，总计:180毫秒
挂墙时间:179毫秒

以下是用于比较的结果数据:

df_faker.head(2)
address city    date_time   email   name    randomdata  state
0   3818 Goodwin Haven\nBrocktown, GA 06168 Valdezport  2004-10-18 20:35:52 joseph81@gomez-beltran.info Deborah Garcia  1218    Oklahoma
1   2568 Gonzales Field\nRichardhaven, NC 79149 West Rachel 1985-02-03 00:33:00 lbeck@wang.com  Barbara Pineda  1536    Tennessee

df_mimesis.head(2)
address city    date_time   email   name    randomdata  state
0   351 Nobles Viaduct  Cedar Falls 2013-08-22 08:20:25.288883  chemotherapeutics1964@gmail.com Ernest  1673    Georgia
1   517 Williams Hill   Malden  2008-01-26 18:12:01.654995  biochemical1972@yandex.com  Jonathan    1845    North Dakota