Sampler

Sampler is a random data generator written in Python supporting a variety of fields and nested data structures.

Sampler is available through pip, to install it simply:

pip install sampler

Basic Usage

The desired data schema is defined in Python by subclassing the Sampler class and adding Field instances. Note that you can nest as many Sampler instances as you like to form complex data structures.

from sampler import *

class Person(Sampler):
    id = IncrementField()
    name = NameField()
    gender = ListField(['male', 'female'])
  
    job = Sampler(
        company = WeightedListField([
          ('Acme', 0.8),
          ('Super Corp', 0.2)
        ]),
      salary = GaussianField(10000, 5000)
    )
  
Person().generate()

The above snippet will generate exemplary:

{
  'id': 1,
  'name': 'Lloyd Jerde'
  'gender': 'male',
  'job': {
    'company': 'Acme',
    'salary': 15935.377898698585
  }
}

By default generate() only creates a single instance, but you can pass a number to return a list of instances instead, e.g. Person.generate(10) which is the same as Person.count(10).generate().

If you don't know the number of instances you'd like to create in advance you can also pass two numbers to count(min, max) which will randomize the batch size.

from sampler import *

class Order(Sampler):
    id = IncrementField()

    items = Sampler(
        description = ListField([
          'Spaceship Superior',
          'Candy Deluxe',
          'Fluffy Ball'
        ]),
        quantity = ListField(range(1, 15))
    ).count(1, 5)

Order().generate(3)

This code will generate 3 orders with between 1 and 5 items each.