fakers 0.5.0

A package that provides ready-made objects of fake-data.

fakers Library

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• Author: Ashish Garg
• Year of release: 2024
• Description: This generates pre-built container objects filled with fabricated data.

Embrace the art of deception with our library—where faking it means making it!

How it helps?

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If you need fake data that is structured in groups of fields, it typically requires additional time and effort. The fakers library solves this problem & saves you that extra time by providing pre-structured grouped fake data.

Version History

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Version	Release	Description
0.5.0	Latest release - (01 Apr, 2024)	Module temperature device available (no support for spark and pandas df)
0.1.0 to 0.4.0	Initial releases	Fake objects related to two modules were released - Retail and Person.

Module description

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Temperature Device

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This module provide you with the sensor device data particularly for temperature sensor. You can generate single or multiple sensors.

Example:

import fakers

sensor = fakers.TemperatureDevice.fake_device()
fake_sensors = fakers.TemperatureDevice.fake_devices_event_pack(4)
fake_sensor_event = fakers.TemperatureDevice.fake_device_event_pack()
fake_sensors_event = fakers.TemperatureDevice.fake_devices_event_pack(5)

Retail

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This contains the fake objects related to the retail data and with relationship. For example:

import fakers

order = fakers.Retail.fake_order()
fifteen_orders = fakers.Retail.fake_orders(15)
user = fakers.Retail.fake_user()
ten_users = fakers.Retail.fake_users(10)
sale = fakers.Retail.fake_sale()
three_sales = fakers.Retail.fake_sales(3)
product = fakers.Retail.fake_product()
two_products = fakers.Retail.fake_products(2)

Person

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This contains the fake objects related to the Person module. For example:

import fakers

fake_person = fakers.Person.fake_person()
fake_persons = fakers.Person.fake_persons(10)
fake_address = fakers.Person.fake_address()
fake_addresses = fakers.Person.fake_addresses(100)

Good to have features

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💡 You can convert these objects directly to Pandas and pyspark dataframes. At present this is enabled only for two modules: Person & Retail.

The functions you use for it are:

1. to_pandas
2. to_spark

Exmaple:

import fakers
import pandas as pd

fake_user = fakers.Retail.fake_user()
fake_user_df = fake_user.to_pandas()