Hello, readers. This article talks about the Python Faker Module along with its use and implementation.
So, let us begin! 🙂
Python Faker module – Crisp Overview
Python offers us various modules to build/construct applications with a variety of automation as the feature. Be it IoT or data science, Python provides us with various functions to implement the necessary pieces of the story to it.
With the Faker module, we can generate dummy data in various formats to be used. So, a question may strike your mind, why do we need to generate fake data?
Let us help you understand that 🙂
In the domain of data science, we deal with various prediction problems wherein we are supposed to predict certain scenarios on the basis of some patterns or historical data. Now, before we go and perform predictions using modeling, it becomes important for us to actually study and get a feel of the data being used.
While we perform pre-processing on the data values, we often come across missing values or a scenario where we want a good amount of area to be substituted with some dummy data for training and analysis of the modeling algorithm to be used.
This is when the Faker module comes into the picture. It helps us generate dummy data/fake data for use.
1. Generation of fake data using Faker module
At first, we would need to install the Faker module onto our workstation as shown below.
Then, we would need to import the module into our environment using the import statement. Prior to creating some fake data, we would need to associate an object with the Faker module for us to utilize the functions at ease.
Once we have the object associated, we can generate various fields such as name, age, email, address, country, text, etc with the functions offered by the Faker library.
Here, we generate random fake data using the Faker module. We make use of name(), text(), and email() functions to generate fake data in terms of name, text sentence, and email values.
from faker import Faker faker_obj = Faker() print(faker_obj.name()) print(faker_obj.text()) print(faker_obj.email())
Mr. Kevin Lee MD Find boy under should special environment health. Nature century near own Republican by skin left. firstname.lastname@example.org
2. Generating fake data in a variety of languages using Faker module
Apart from building the fake data at random and in a standardized form, we can even generate random data in a variety of languages.
Let us try to generate some fake data in the Japanese language now.
from faker import Faker faker_obj = Faker('ja_JP') print(faker_obj.name())
3. Building fake tabular data
Having created fake data in various forms such as name, country, and text, it is now time to generate some fake data in a table format. We can relate this data with the Python DataFrame format.
Have a look at the below code.
import pandas as p from faker import Faker faker_obj = Faker() info= [faker_obj.profile() for i in range(2)] df = p.DataFrame(info) print(df)
job company ssn \ 0 Video editor Ho Inc 582-29-5414 1 Programme researcher, broadcasting/film/video Schneider Inc 574-29-9054 residence \ 0 03691 Hall Causeway Apt. 233\nPetertown, ND 66563 1 7001 Julie Extension Apt. 257\nWoodmouth, AZ 6... current_location blood_group website \ 0 (-52.029649, -115.595130) A+ [https://morales.org/] 1 (88.6719445, 122.925423) B+ [http://www.bridges.com/] username name sex \ 0 kstephens Susan Wang F 1 elliottedward Monica Williams F address mail \ 0 508 Cox Place Apt. 692\nLorettaside, SD 66115 email@example.com 1 36217 Benjamin Extensions\nCodyville, MS 95229 firstname.lastname@example.org birthdate 0 1914-11-15 1 2015-11-22
By this, we have come to the end of this topic. Feel free to comment below, in case you come across any questions.
For more such posts related to Python programming, Stay tuned with us.
Till then, Happy Learning 🙂