To answer the question:
now how do I make sure the generated id is unique in the dataset?
You have to use: unique.random_int
So, your code will be like this as you see below:
from faker import Faker
import random
import pandas as pd
Faker.seed(0)
random.seed(0)
fake = Faker("en_US")
fixed_digits = 6
concatid = 'ID'
idcode,name, city, country, job, age = [[] for k in range(0,6)]
for row in range(0,100):
idcode.append(concatid + str(fake.unique.random_int(min=111111, max=999999)))
name.append(fake.name())
city.append(fake.city())
country.append(fake.country())
job.append(fake.job())
age.append(random.randint(20,100))
d = {"ID Code":idcode, "Name":name, "Age":age, "City":city, "Country":country, "Job":job}
df = pd.DataFrame(d)
df.head()
Answer from Geek Logbook on Stack OverflowFaker
faker.readthedocs.io › en › master › providers › faker.providers.python.html
faker.providers.python — Faker 40.12.0 documentation
pystr_format(string_format: str = '?#-###{{random_int}}{{random_letter}}', letters: str = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ') → str¶ ... >>> Faker.seed(0) >>> for _ in range(5): ... fake.pystr_format() ...
Faker
faker.readthedocs.io › en › master › providers › baseprovider.html
faker.providers. - BaseProvider - Faker's documentation!
If digits is None (default), its value will be set to a random integer from 1 to 9. If fix_len is False (default), all integers that do not exceed the number of digits can be generated. If fix_len is True, only integers with the exact number of digits can be generated.
Videos
18:34
Generate Dummy Data using Faker Library For Projects | #faker #python ...
17:59
How to Generate Random and Fake Data in Python - Create Mock Datasets!
19:22
Generating Professional Sample Data with Faker in Python - YouTube
How To Easily Create Data With Python Faker Library
CodingNomads
codingnomads.com › how-to-use-python-faker-random-data-generator
How to Use Python Faker: Random Data Generator
In this case, n is a random number between 0 and the number of users, less one. Want to know my favorite Python Faker function? It's gotta be bs() which stands for [BEEEEP] and generates some cool-sounding titles for each composition. "Orchestrate Bricks-and-Clicks Portals" is but one example of ...
DEV Community
dev.to › dev_neil_a › python-how-to-create-sample-data-using-faker-co4
Python How-To: Create Sample Data Using Faker - DEV Community
October 20, 2024 - Prior to using faker, it will need to be installed as it is a third-party library that is not part of the base Python library collection. To do this, use the pip command in the terminal as follows: ... The following example will generate three addresses and peoples names, each of which will be a dictionary that will be added to a list. # --- 1. Import the required libraries: from faker import Faker from random import randint # --- 2.
ZetCode
zetcode.com › python › faker
Python Faker - generating fake data in Python with Faker module
January 29, 2024 - The example creates six fake jobs. $ ./jobs.py Town planner Paediatric nurse Geographical information systems officer Water quality scientist Engineer, maintenance Designer, ceramics/pottery · The Faker supports localized data to some extent. The locale is passed to the constructor method. Note that the locales are finished to various levels. ... #!/usr/bin/python from faker import Faker faker = Faker('cz_CZ') for i in range(3): name = faker.name() address = faker.address() phone = faker.phone_number() print(f'{name}, {address}, {phone}')
HotExamples
python.hotexamples.com › examples › faker › Faker › random_int › python-faker-random_int-method-examples.html
Python Faker.random_int Examples, faker.Faker.random_int Python Examples - HotExamples
rand_pub = randint(1, 10) publish_query = Publisher.objects.get(id=rand_pub) rand_genre = randint(1, 8) genre_query = Genre.objects.get(id=rand_genre) book = Book( name=fake.text(max_nb_chars=40), active=True, unit_price=fake.pydecimal( left_digits=2, right_digits=2, positive=True), ...
Linux Hint
linuxhint.com › python-faker-generate-dummy-data
How to Use Python Faker to Generate Dummy Data – Linux Hint
Different types of random numbers can be generated by using the faker library. Create a Python file with the following script that will generate three types of random numbers. The random_int() function will generate a random integer number. The random_number(digit=5) function will generate ...
Top answer 1 of 4
12
You can use python providers as well.
class MyModel(factory.django.DjangoModelFactory)
number_field = factory.Faker('pyint', min_value=0, max_value=1000)
class Meta:
model = SomeModel
Documentation
2 of 4
9
I was able to figure out this problem by using a lazy attribute (factory.LazyAttribute). From the docs:
Most factory attributes can be added using static values that are evaluated when the factory is defined, but some attributes (such as fields whose value is computed from other elements) will need values assigned each time an instance is generated.
class FabricFactory(DjangoModelFactory):
class Meta:
model = Fabric
title = factory.Faker('name')
description = factory.Faker('catch_phrase')
price = factory.LazyAttribute(random.randrange(MIN_PRICE, MAX_PRICE + 1))
Blue Book
lyz-code.github.io › blue-book › coding › python › faker
Faker - The Blue Book
from random import SystemRandom @pytest.fixture(scope="session", autouse=True) def faker_seed() -> int: """Create a random seed for the Faker library.""" return SystemRandom().randint(0, 999999)
GitHub
github.com › joke2k › faker
GitHub - joke2k/faker: Faker is a Python package that generates fake data for you. · GitHub
Factory Boy already ships with integration with Faker. Simply use the factory.Faker method of factory_boy: import factory from myapp.models import Book class BookFactory(factory.Factory): class Meta: model = Book title = factory.Faker('sentence', nb_words=4) author_name = factory.Faker('name') The .random property on the generator returns the instance of random.Random used to generate the values:
Starred by 19.2K users
Forked by 2.1K users
Languages Python
PyPI
pypi.org › project › Faker
Faker · PyPI
When using Faker for unit testing, you will often want to generate the same data set. For convenience, the generator also provides a seed() method, which seeds the shared random number generator.
» pip install Faker
Faker
faker.readthedocs.io › en › master › providers › faker.providers.misc.html
faker.providers.misc — Faker 40.11.1 documentation
boolean(chance_of_getting_true: int = 50) → bool¶ · Generate a random boolean value based on chance_of_getting_true. ... >>> Faker.seed(0) >>> for _ in range(5): ... fake.boolean(chance_of_getting_true=25) ...
Faker
faker.readthedocs.io › en › latest › providers › faker.providers.python.html
faker.providers.python — Faker 40.11.1 documentation
pystr_format(string_format: str = '?#-###{{random_int}}{{random_letter}}', letters: str = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ') → str¶ ... >>> Faker.seed(0) >>> for _ in range(5): ... fake.pystr_format() ...
Faker
faker.readthedocs.io › en › stable › providers › faker.providers.python.html
faker.providers.python — Faker 40.4.0 documentation
pystr_format(string_format: str = '?#-###{{random_int}}{{random_letter}}', letters: str = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ') → str¶ ... >>> Faker.seed(0) >>> for _ in range(5): ... fake.pystr_format() ...
Faker
faker.readthedocs.io › en › master › fakerclass.html
Using the Faker Class — Faker 40.12.0 documentation
Use the class method `Faker.seed()` instead. The rationale can be found in the relevant PR, but the goal is to deal with a non-explicit legacy behavior involving a shared random.Random instance that we believe can only become more confusing once new Faker is added.
Faker
faker.readthedocs.io
Welcome to Faker’s documentation! — Faker 40.12.0 documentation
When using Faker for unit testing, you will often want to generate the same data set. For convenience, the generator also provides a seed() method, which seeds the shared random number generator.