Using the Faker Class
2.0.4 and below, the
Faker object is just a shortcut for the class method
Factory.create, and that method creates a
Generator object with access to the wide
selection of provider methods. Because of how everything was set up, it was difficult to do
certain things without going through the
Generator internals and without
potentially breaking a lot of things that will be difficult for users to fix when they upgrade.
The solution was to introduce a new
Faker proxy class that will, for the most part, behave
just like the old
Faker shortcut but with support for multiple locales while providing the
option to subclass and a very simple upgrade path should old code be affected. For the purposes
of this document, the terms new
Faker and old
Faker will be used where the former refers
to the new proxy class, and the latter refers to the
Any codebase that uses the
Faker.seed() method will be affected, because while both old and
Faker.seed() points to
Generator.seed(), in new
Faker, invocation of the method
Faker object instance has been disabled, and attempting to do so will raise a
TypeError as shown below.
TypeError: Calling `.seed()` on instances is deprecated. 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.
Suppose that the affected code looks something like this:
from faker import Faker fake = Faker() fake.seed(0) # This will raise a TypeError
Just replace all seed() method calls from instances with
Faker.seed() as shown below. This
is all that is needed to start using the new
Faker class and its features, even if additional
arguments are passed to
Faker, because the arguments expected by new
Faker and old
Faker are the same.
from faker import Faker fake = Faker() Faker.seed(0)
A conservative approach is to redefine
Faker as the old shortcut shown below. This will skip
using the new proxy class, but the code will still be able to use any new provider methods moving
forward while being unaffected by new bugs. Of course, that also means there will be no multiple
locale support and no option to subclass.
from faker.factory import Factory Faker = Factory.create fake = Faker() fake.seed(0)
Proxy Class Implementation Details
Faker instance is just a proxy object that has references to
one for each unique locale specified at instantiation. Those
Generator objects are just
"instances" of old
Faker. If there is only one internal
Generator object, the new
Faker instance is running in single locale mode. If there is more than one, then it is
running in multiple locale mode.
In single locale mode, a new
Faker instance can easily be forced to behave like an instance
created using old
Faker, because a similar interface can be exposed on the new
instance, and then proxy calls to methods, properties, and attributes to the sole
object in a 1:1 fashion. In fact, that is how it is implemented and how backwards compatibility
was preserved (save for
In multiple locale mode, however, that 1:1 mapping is no longer present, and how calls are proxied
depends on whether the attribute is a provider method or some attribute present in
objects. It is possible to provide sane default implementations that will map neatly like what
we did for
seed_instance, but the rest like add_provider and the random getter and setter
are more dependent on specific use cases or are potentially dangerous.
In those cases, it is better for users to create their own subclass with their implementation or to
directly call those methods from the internal
Generator objects themselves. Multiple locale mode
will be discussed in more detail in its dedicated section.
Proxy Class Attribute Name Resolution
The proxy class has a fairly involved attribute name resolution behavior that runs in this order:
- If the attribute name is
seed, raise a TypeError. This prevents the class method
seedfrom being called from an instance.
- If #1 does not apply, check if the attribute name matches an attribute present in the proxy class instance. If there is one, return the matching attribute.
- If #2 failed, check if the instance is in single locale mode. If yes, proxy the call to the
Generatorobject, and attempt to return a matching attribute.
- If #3 does not apply, the instance is henceforth known to be in multiple locale mode. Proceed
by checking if the attribute name matches a
Generatorattribute. If it does, raise a NotImplementedError.
- If #4 does not apply, check if the attribute name matches a cache pattern regex. If it does not, raise an AttributeError, since it should already have been handled by #2 if one does exist.
- If everything else has failed or does not apply, assume that the attribute name might be
referring to a provider method and perform factory/generator selection, and proxy the call
to the selected
Factory/generator selection will be discussed in more detail under multiple locale mode's dedicated section.
Depending on the
locale value passed, a new
Faker instance will either operate in single
locale mode or multiple locale mode. The value of
locale can be one of the following:
- Any empty value like
None(automatically defaults to
- A valid locale string, underscored or hyphenated
- A list, tuple, or set with valid locale strings, underscored or hyphenated
- An OrderedDict with key-value pairs of valid locale strings (underscored or hyphenated) and weights
The first two are options already expected by old
Faker, so it is pretty much the same for new
Faker. Using any of those two options will always result in a new
Faker instance that is
in single locale mode. In that mode, there is really no need to retrieve a reference to the
Generator object because of the 1:1 proxying behavior discussed earlier.
The potential pitfalls lie in multiple locale mode and when there is a need to access the internal
Generator objects individually. Since locale strings can be written underscored (
en-US), this can lead to confusion and errors, so locale strings have to be normalized
to provide consistent results without duplicates.
During instantiation, new
Faker will normalize locale strings to the underscore format, and it
will also store them as such. In other words, the locale string
en_US will be treated the same
en-US, and when both are specified, the last to be processed will be treated as a duplicate
and will be discarded. The same normalization is also performed when accessing the internal
Generator object via key index.
For example, the code below will create a new
Faker instance that is in single locale mode
even if four locales were specified.
from faker import Faker fake = Faker(['en-US', 'en_US', 'en_US', 'en-US']) # Will return ['en_US'] fake.locales # Get reference to en_US generator us1 = fake['en_US'] # Get reference to en-US generator us2 = fake['en-US'] # Will return True us1 == us2
Multiple Locale Mode
To enable multiple locale mode, the value of
locale argument must be a list, tuple, set, or
OrderedDict with more than one valid locale, post-normalization. For example:
from collections import OrderedDict from faker import Faker locale_list = ['en-US', 'ja-JP', 'en_US'] fake1 = Faker(locale_list) # Will return ['en_US', 'ja_JP'] fake1.locales locale_odict = OrderedDict([ ('en-US', 1), ('ja-JP', 2), ('en_US', 2), ]) fake2 = Faker(locale_odict) # Will return ['en_US', 'ja_JP'] fake2.locales
In this mode, calling a prospective provider method from the new
Faker instance will run
factory/selection logic in this order:
- Check if a cached mapping already exists for the provider method. If yes, use that mapping, and skip to #3.
- If #1 does not apply, check which
Generatorobjects support the provider method. Cache the results of the mapping, along with corresponding weights if they were provided during instantiation.
- If no generator supports the provider method, an AttributeError will be raised just as it
would have been raised using old
- If there is only one generator that supports the provider method, return the only generator.
- If there is more than one applicable generator, and no weights were provided, randomly select
a generator using a uniform distribution, i.e.
- If there is more than one applicable generator, and weights were provided, randomly select a generator using a distribution defined by the provided weights.
Other than being able to customize probabilities based on locales and minimizing performance
penalties, the factory selection logic guarantees that invoking a provider method will not fail,
for as long as at least there is at least one internal
Generator object supports it.
There are times when it is much easier to show than it is to explain in words, so here is
a cheatsheet for new
Faker in multiple locale mode.
from collections import OrderedDict from faker import Faker locales = OrderedDict([ ('en-US', 1), ('en-PH', 2), ('ja_JP', 3), ]) fake = Faker(locales) # Get the list of locales specified during instantiation fake.locales # Get the list of internal generators of this `Faker` instance fake.factories # Get the internal generator for 'en_US' locale fake['en_US'] # Get the internal generator for 'en_PH' locale fake['en_PH'] # Get the internal generator for 'ja_JP' locale fake['ja_JP'] # Will raise a KeyError as 'en_GB' was not included fake['en_GB'] # Set the seed value of the shared `random.Random` object # across all internal generators that will ever be created Faker.seed(0) # Creates and seeds a unique `random.Random` object for # each internal generator of this `Faker` instance fake.seed_instance(0) # Creates and seeds a unique `random.Random` object for # the en_US internal generator of this `Faker` instance fake.seed_locale('en_US', 0) # Generate a name based on the provided weights # en_US - 16.67% of the time (1 / (1 + 2 + 3)) # en_PH - 33.33% of the time (2 / (1 + 2 + 3)) # ja_JP - 50.00% of the time (3 / (1 + 2 + 3)) fake.name() # Generate a name under the en_US locale fake['en-US'].name() # Generate a zipcode based on the provided weights # Note: en_PH does not support the zipcode provider method # en_US - 25% of the time (1 / (1 + 3)) # ja_JP - 75% of the time (3 / (1 + 3)) fake.zipcode() # Generate a zipcode under the ja_JP locale fake['ja_JP'].zipcode() # Will raise an AttributeError fake['en_PH'].zipcode() # Generate a Luzon province name # Note: only en_PH out of the three supports this provider method fake.luzon_province() # Generate a Luzon province name fake['en_PH'].luzon_province() # Will raise an AttributeError fake['ja_JP'].luzon_province()
New in version
v4.2.0 is the
.unique attribute on the
Accessing provider methods through this attribute guarantees that
the returned values are unique for the lifetime of the
import faker fake = faker.Faker() numbers = set(fake.unique.random_int() for i in range(1000)) assert len(numbers) == 1000
To clear already seen values, simply call
fake.unique.clear(), which will
allow previous values generated to be returned again.
Different argument signatures for provider methods do not share a uniqueness pool.
import faker fake = faker.Faker() numbers = set(fake.unique.random_int(min=1, max=10) for i in range(10)) other_numbers = set(fake.unique.random_int(min=1, max=5) for i in range(5)) assert other_numbers.issubset(numbers)
If the range of possible values generated by a provider function is small
.unique attribute has been used, it's possible that no
suitable unique value will be found after a certain number of iterations.
To avoid infinite loops, at that point, a
import faker fake = faker.Faker() for i in range(3): fake.unique.boolean() # UniquenessException!
As a final caveat, only hashable arguments and return values can be used
.unique attribute, as it is backed internally by a set for
fast membership testing.
import faker fake = faker.Faker() fake.unique.profile() # TypeError: unhashable type: 'dict'