philiprehberger-data-factory
v0.3.1176 downloadsMIT
Lightweight test data generation with realistic fake values.
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pip install philiprehberger-data-factoryREADME
philiprehberger-data-factory
Lightweight test data generation with realistic fake values.
Installation
pip install philiprehberger-data-factory
Usage
Generate fake values
from philiprehberger_data_factory import fake
fake.name() # "Alice Johnson"
fake.email() # "bob.smith@example.com"
fake.integer() # 472
fake.boolean() # True
fake.text() # "alpha bravo charlie delta echo"
fake.date() # "2023-07-14"
fake.uuid() # "a3b2c1d4-..."
fake.phone() # "+1-555-123-4567"
fake.address() # "742 Oak Street, Springfield"
Factory
Define a schema and generate records in bulk:
from philiprehberger_data_factory import Factory
user_factory = Factory({
"name": "name",
"email": "email",
"age": "integer",
"bio": "text",
"joined": "date",
"id": "uuid",
"phone": "phone",
"address": "address",
})
user = user_factory.build()
# {'name': 'Grace Wilson', 'email': 'henry.moore@mail.com', ...}
users = user_factory.build_batch(100)
# [{'name': ..., 'email': ..., ...}, ...]
Bulk generation with overrides
Use batch() to generate multiple records with optional per-item or shared overrides:
from philiprehberger_data_factory import Factory
factory = Factory({"name": "name", "role": "text"})
# Shared overrides applied to every record
admins = factory.batch(5, overrides={"role": "admin"})
# Per-item overrides
records = factory.batch(3, overrides=[
{"role": "admin"},
{"role": "editor"},
{"role": "viewer"},
])
Relationship / foreign-key support
Link factories so generated objects have consistent foreign-key references:
from philiprehberger_data_factory import Factory
user_factory = Factory({"id": "uuid", "name": "name"})
user = user_factory.build()
post_factory = Factory({"title": "text", "body": "text"})
post_factory.related(user_factory, field="user_id", source_field="id")
post = post_factory.build()
# post["user_id"] matches user["id"]
Statistical distribution profiles
Generate numeric fields following statistical distributions:
from philiprehberger_data_factory import Factory
factory = Factory({"name": "name"})
factory.field("age", distribution="normal", mean=30, std=10)
factory.field("score", distribution="uniform", min=0, max=100)
factory.field("wait_time", distribution="exponential", scale=5.0)
record = factory.build()
# {'name': 'Alice Johnson', 'age': 28.4, 'score': 73.2, 'wait_time': 3.1}
Weighted choices
from philiprehberger_data_factory import fake
tier = fake.weighted_choice({"gold": 0.1, "silver": 0.3, "bronze": 0.6})
Custom providers
Pass a callable instead of a provider string:
from philiprehberger_data_factory import fake, Factory
factory = Factory({
"name": "name",
"role": lambda: fake.choice(["admin", "editor", "viewer"]),
})
Reproducible output
from philiprehberger_data_factory import fake
fake.seed(42)
fake.name() # always the same name for seed 42
API
| Function / Class | Description |
|---|---|
fake.name() | Random full name |
fake.email() | Random email address |
fake.integer(min, max) | Random integer (default 0-1000) |
fake.decimal(min, max, precision) | Random float (default 0-1000, 2 decimals) |
fake.boolean() | Random True or False |
fake.choice(items) | Random element from a list |
fake.text(words) | Random words (default 5) |
fake.date(start, end) | Random ISO date string |
fake.uuid() | Random UUID4 string |
fake.phone() | Random phone number in +1-XXX-XXX-XXXX format |
fake.address() | Random street address with city |
fake.weighted_choice(options) | Weighted random selection from a dict of options to weights |
fake.normal(mean, std) | Random float from a normal distribution |
fake.exponential(scale) | Random float from an exponential distribution |
fake.seed(n) | Set random seed for reproducibility |
Factory(schema) | Create a factory from a schema dict |
factory.build(**overrides) | Generate one record with optional field overrides |
factory.build_batch(n) | Generate n records |
factory.batch(n, overrides) | Generate n records with shared or per-item overrides |
factory.field(name, distribution, **params) | Add a field with a statistical distribution (normal, uniform, exponential) |
factory.related(other, field, source_field) | Link to another factory for foreign-key consistency |
Development
pip install -e .
python -m pytest tests/ -v
Support
If you find this project useful: