Using Python Faker generate different data for 5000 rows

Disclaimer: this answer is added much after the question and adds some new info not directly answering the question.

Now there is a fast new library Mimesis - Fake Data Generator.

• Upside: It is stated it works times faster than faker (see below my test of data similar to one in question).
• Downside: works from 3.6 version of Python only.

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'

The same with developed earlier faker:

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

Below it my recent timing of Mimesis vs. Faker based on code provided in answer from forzer0eight:

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 times: user 3.51 s, sys: 2.86 ms, total: 3.51 s Wall time: 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 times: user 178 ms, sys: 1.7 ms, total: 180 ms Wall time: 179 ms

Below is resulting data for comparison:

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 [email protected] Deborah Garcia  1218    Oklahoma
1   2568 Gonzales Field\nRichardhaven, NC 79149 West Rachel 1985-02-03 00:33:00 [email protected]  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  [email protected] Ernest  1673    Georgia
1   517 Williams Hill   Malden  2008-01-26 18:12:01.654995  [email protected]  Jonathan    1845    North Dakota

Following scripts can remarkably enhance the pandas performance.

    from faker import Faker
    import pandas as pd
    import random
    fake = Faker()
    def create_rows(num=1):
        output = [{"name":fake.name(),
                   "address":fake.address(),
                   "name":fake.name(),
                   "email":fake.email(),
                   "bs":fake.bs(),
                   "address":fake.address(),
                   "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

It takes 5.55s.

    %%time
    df = pd.DataFrame(create_rows(5000))

    Wall time: 5.55 s

I placed the fake stuff array inside my for loop to achieve the desired result:

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