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faker-datasets

Faker provider that loads data from your datasets

    0.1.0

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Build Faker providers based on datasets

faker-datasets offers a building block for seeding the data generation with existing data.

You can create simple providers picking a random entry from a tailored dataset or assemble complex ones where you generate new combinations from more datasets, all this while keeping an eye on speed and memory consumption.

Let's see how to.

Crash course

We'll use the wonderful Countries State Cities DB maintained by Darshan Gada. Download the cities and the countries datasets.

Basic random picker

Cities generates a city by randomly picking an entry in the cities dataset. Here the dataset is named cities, the dataset file is cities.json (adjust to the actual path of the file saved earlier) and the picker, the method to get a random city, is named city.

How we define it in file cities_provider.py:

from faker_datasets import Provider, add_dataset

@add_dataset("cities", "cities.json", picker="city")
class Cities(Provider):
    pass

How we could use it to generate 10 cities:

from faker import Faker
from cities_provider import Cities

fake = Faker()
fake.add_provider(Cities)

for _ in range(10):
    # Use of the picker named in @add_dateset
    city = fake.city()
    print("{name} is in {country_name}".format(**city))

One of the many possible outputs:

Poiana Cristei is in Romania
Codosera La is in Spain
Jeremoabo is in Brazil
Rodrígo M. Quevedo is in Mexico
Cary is in United States
Locking is in United Kingdom
Mezinovskiy is in Russia
Nesoddtangen is in Norway
Zalesnoye is in Ukraine
Cefa is in Romania

Because the data generation is a pseudo-random process, every execution outputs different results. If you want reproducible outputs, you have to seed the Faker generator as documented here.

Customize the random picker

CitiesEx is functionally identical to Cities but shows how to define the picker by yourself. Here picker= is gone from the parameters of @add_dataset but a new city method is defined.

from faker_datasets import Provider, add_dataset, with_datasets

@add_dataset("cities", "cities.json")
class CitiesEx(Provider):

    @with_datasets("cities")
    def city(self, cities):
        return self.__pick__(cities)

Note how the city method is decorated with @with_datasets("cities") and how, consequently, it receives the said dataset as parameter. The call to __pick__ just selects a random entry from cities.

Matching a criterium

CitiesFromCountry exploits the custom picker to return only cities from a given country. A first implementation could just discard cities from any other country, getting slower with increasing bad luck.

from faker_datasets import Provider, add_dataset, with_datasets

@add_dataset("cities", "cities.json")
class CitiesFromCountry(Provider):

    @with_datasets("cities")
    def city(self, cities, country_name):
        while True:
            city = self.__pick__(cities)
            if city["country_name"] == country_name:
                return city

It's better to limit to the number of attempts though otherwise if country_name is misspelled the picker would enter in an infinite loop.

from faker_datasets import Provider, add_dataset, with_datasets

@add_dataset("cities", "cities.json")
class CitiesFromCountry(Provider):

    @with_datasets("cities")
    def city(self, cities, country_name, max_attempts=10000):
        while max_attempts:
            city = self.__pick__(cities)
            if city["country_name"] == country_name:
                return city
            max_attempts -= 1
        raise ValueError("Run out of attempts")

Or, with same results, use the match= and max_attempts= parameters of __pick__.

from faker_datasets import Provider, add_dataset, with_datasets

@add_dataset("cities", "cities.json")
class CitiesFromCountry(Provider):

    @with_datasets("cities")
    def city(self, cities, country_name):
        # match tells to __picker__ whether the city is good or not
        match = lambda city: city["country_name"] == country_name
        return self.__pick__(cities, match=match, max_attempts=10000)

If you know ahead which country you are interested in, say Afghanistan, you can use the @with_match picker decorator. It produces a new index of only matching entries and the picking speed is again constant and independent from bad luck.

from faker_datasets import Provider, add_dataset, with_datasets, with_match

@add_dataset("cities", "cities.json")
class CitiesFromCountry(Provider):

    @with_datasets("cities")
    @with_match(lambda city: city["country_name"] == "Afghanistan")
    def afghan_city(self, cities):
        return self.__pick__(cities)

At such conditions though it's maybe better to massage your dataset and leave only the entries matching your criteria.

Using multiple datasets

CitiesAndCountries fuses two datasets for more advanced matches. Note how @add_dataset makes multiple datasets available to the provider and @with_datasets passes them to the given picker.

from faker_datasets import Provider, add_dataset, with_datasets, with_match

@add_dataset("cities", "cities.json")
@add_dataset("countries", "countries.json")
class CitiesAndCountries(Provider):

    @with_datasets("cities", "countries")
    def city_by_region(self, cities, countries, region):
        def match(city):
            # Given a city, find its country info in the countries dataset
            country = next(country for country in countries if country["name"] == city["country_name"])
            # Check that the country is in the region of interest
            return country["region"] == region
        return self.__pick__(cities, match=match, max_attempts=10000)

The picker performs the data mix and match so that the region request is satisfied or an error is signaled.

Summary

You use @add_dataset to attach a dataset to your provider, if you specify a picker= parameter you'll get for free a random picker of entries. The more datasets you need, the more @add_dataset you can use.

If you have special needs you can define the pickers for yourself, each using what datasets are most appropriate among those made available with @add_dataset. You can add as many pickers as you need.

A picker can use match= and max_attempts= to make the generation respect some useful criteria.

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