What are the essential Python libraries to know?
Profile photo for Shaurya Uppal
Shaurya Uppal
Data Scientist · Updated 4y ·

One interesting library I would like to share is Python faker. This is a very useful library many people don't know about it so I thought to share its application :

Python Faker Library

Faker is a Python package that generates fake data for you.

Installation: Help Link
Open Anaconda prompt command to install:

  1. conda install -c conda-forge faker  

Import package

  1.  from faker import Faker 

Faker has the ability to print/get a lot of different fake data, for instance, it can print fake name, address, email, text, etc.

Important most commonly used faker commands

  1. fake.name() 
  2. fake.address() 
  3. fake.email() 
  4. fake.text() 
  5. fake.country() 
  6. from faker import Faker 
  7. fake = Faker() 
  8. print (fake.email()) 
  9. print(fake.country()) 
  10. print(fake.name()) 
  11. print(fake.text()) 
  12. print(fake.latitude(), fake.longitude()) 
  13. print(fake.url()) 

OUTPUT:(Different every time)

  1. vwilson@hotmail.com 
  2. Belgium 
  3. Shane Hunter 
  4. Commodi vel libero placeat quibusdam odio odio consequatur. Ducimus libero quae optio non quidem. Facilis quas impedit quo. 
  5. 26.5687745 -124.802165 
  6. http://www.turner.com/ 

Application 1 : Create a json of 100 students with name students.json that contains student name, address, location coordinates and student roll number.

  1. from faker import Faker 
  2. import json  # To create a json file   
  3. from random import randint  # For student id 
  4. fake = Faker() 
  5. def input_data(x): 
  6.  # dictionary 
  7.  student_data ={} 
  8.  for i in range(0, x): 
  9.  student_data[i]={} 
  10.  student_data[i]['id']= randint(1, 100) 
  11.  student_data[i]['name']= fake.name() 
  12.  student_data[i]['address']= fake.address() 
  13.  student_data[i]['latitude']= str(fake.latitude()) 
  14.  student_data[i]['longitude']= str(fake.longitude()) 
  15.  print(student_data) 
  16.  # dictionary dumped as json in a json file 
  17.  with open('students.json', 'w') as fp: 
  18.  json.dump(student_data, fp) 
  19. def main(): 
  20.  # Enter number of students 
  21.  number_of_students = 10  # For the above task make this 100 
  22.  input_data(number_of_students) 
  23. main() 
  24. # The folder or location where this python code 
  25. # is save there a students.json will be created  
  26. # having 10 students data. 

OUTPUT

  1. {0: {'id': 20, 'name': 'Benjamin Washington', 'address': 'USCGC Garrison\nFPO AP 48025-9793', 'latitude': '-68.975800', 'longitude': '153.009590'}, 1: {'id': 2, 'name': 'Christopher Howell', 'address': '7778 Sarah Center Apt. 663\nLawrenceport, WY 78084', 'latitude': '-21.8141675', 'longitude': '-122.830387'}, 2: {'id': 67, 'name': 'Fernando Fuentes', 'address': '7756 Bradford Plain Suite 997\nEast Chelseaburgh, KY 75776', 'latitude': '-82.791227', 'longitude': '-42.964122'}, 3: {'id': 86, 'name': 'Patrick Torres', 'address': 'Unit 5217 Box 7477\nDPO AE 82354-0160', 'latitude': '34.949096', 'longitude': '121.715387'}, 4: {'id': 11, 'name': 'James Hines', 'address': '4567 Donald Grove\nWilliamhaven, MO 85891', 'latitude': '86.7208035', 'longitude': '-48.103935'}, 5: {'id': 33, 'name': 'James Miller', 'address': 'PSC 2613, Box 7165\nAPO AP 29256-6576', 'latitude': '-35.4630595', 'longitude': '-50.415667'}, 6: {'id': 76, 'name': 'Randall Fuller', 'address': '7731 Garcia Pike\nNew Eric, KS 20545', 'latitude': '12.198124', 'longitude': '126.720134'}, 7: {'id': 49, 'name': 'Ivan Franco', 'address': '801 Chambers Light\nWest Daniel, IA 17114-4374', 'latitude': '-58.2576055', 'longitude': '171.773233'}, 8: {'id': 75, 'name': 'Amy Smith', 'address': '995 Luna Stream Apt. 297\nThompsonchester, NY 82115', 'latitude': '80.4262245', 'longitude': '115.142004'}, 9: {'id': 38, 'name': 'Danielle Thomas', 'address': '7309 Chris Ferry Suite 674\nColebury, MA 39673-2967', 'latitude': '-73.340443', 'longitude': '-176.964241'}} 

Application 2: Print 10 fake names and countries in Hindi language.

  1. from faker import Faker 
  2. fake = Faker('hi_IN')  #'hi_IN' changed the language 
  3. for i in range(0, 10): 
  4.  print('Name->', fake.name(), 'Country->', fake.country()) 

List on languages we can use to get Faker Data

Application 3: Create fake profile

  1. import faker from Faker 
  2. fake = Faker() 
  3. print(fake.profile()) 

OUTPUT

  1. {'job': 'Town planner', 'company': 'Martinez-Clark', 'ssn': '559-93-0521', 'residence': '46820 Johnny Circles\nStokesside, IL 87065-2470', 'current_location': (Decimal('83.5271055'), Decimal('43.705455')), 'blood_group': 'A+', 'website': ['https://www.taylor.com/'], 'username': 'hsmith', 'name': 'Christopher Davis', 'sex': 'M', 'address': '335 Mcdaniel Fork Suite 589\nTeresabury, AZ 85283', 'mail': 'kenneth48@yahoo.com', 'birthdate': '1981-03-29'} 

Application 4: Seeding the Generator getting particular fake data again.
Seeding gives use the same fake data result that was generated at first instance at that seed number.
Example

  1. from faker import Faker 
  2. fake = Faker() 
  3. fake.seed(1) 
  4. print(fake.name()) 
  5. print(fake.address()) 
  6. print(fake.email()) 

OUTPUT

  1. Ryan Gallagher 
  2. 7631 Johnson Village Suite 690 
  3. Adamsbury, NC 50008 
  4. bparks@johnson.info 

NOTE: Even if I run the program, again and again, I would get the same result. As soon as I remove that fake.seed(1) line, we see randomness in data generation.

Application 5: Print data from the list you want.

  1. import faker from Faker 
  2. fake = Faker() 
  3. # Print random sentences 
  4. print(fake.sentence()) 
  5. # List has words that we want in our sentence 
  6. word_list = ["GFG", "Geeksforgeeks", "shaurya", "says", "Gfg", "GEEKS"] 
  7. # Let's print 5 sentences that have words from our word_list  
  8. for i in range(0, 5): 
  9.  # You need to use ext_word_list = listnameyoucreated  
  10.  print(fake.sentence(ext_word_list = word_list)) 

OUTPUT

  1. # This is the random sentence that is generated using 
  2. # fake.sentence() 
  3. Error architecto inventore aut. 
  4.  
  5. # These are the 5 sentence that contains words from  
  6. # word_list we provided 
  7. Shaurya shaurya GEEKS Geeksforgeeks. 
  8. Gfg shaurya Geeksforgeeks GFG Gfg GFG. 
  9. Geeksforgeeks Gfg says Geeksforgeeks GEEKS Gfg Gfg GFG. 
  10. Geeksforgeeks shaurya GFG Geeksforgeeks Gfg GEEKS. 
  11. Gfg Geeksforgeeks says GFG GEEKS says. 

Summary of what we learned from Faker
1. Fake data generation like name, address, email, text, sentence, etc
2. Creating a JSON file of fake data.
3. Different language fake data printed.
4. Creating a Profile
5. Seeding i.e printing particular fake data
6. Generating a sentence that contains the words we provided.

I hope someone who reads this article gains something new. #HAPPYCODING

Edit: Reached 300 so I thought let me share the reason why I researched faker python and which question did I solve using it. Uploading that question and its answer Find Friends. In short, the task was to find no. of employees nearby using longitude and latitude. Data to be used was in JSON format which was to be generated fakely. (full question there in the comment section in that gist link).

Source: Python Faker Library - GeeksforGeeks

50.1K views ·
View upvotes
 ·
View 7 shares
 ·
1 of 22 answers
View 21 other answers to this question
About · Careers · Privacy · Terms · Contact · Languages · Your Ad Choices · Press ·
© Quora, Inc. 2025