Student Name: Id Or Ssn: Fill & Download for Free

Super Key : Super key is a set of one or more attributes whose values identify tuple in the relation uniquely.Candidate Key : Candidate key can be defined as a minimal subset of super key. In some cases , candidate key can not alone since there is alone one attribute is the minimal subset. Example,Employee(id, ssn, name, addrress)Here Candidate key is (id + ssn) because we can easily identify the tuple using either id or ssn . Althrough, minimal subset of super key is either id or ssn. but both of them can be considered as candidate key.Primary Key : Primary key is a one of the candidate key.Now explain these keys relate through an example :Student(Id, Name, Dept, Result)HereSuper Key : {Id, Id+Name, Id+Name+Dept}Candidate Key : Id because id alone is the minimal subset of super key.Primary Key : Id because Id is one of the candidate key and uniquely identify the tuples in relation.

Super Key : Super key is a set of one or more attributes whose values identify tuple in the relation uniquely.Candidate Key : Candidate key can be defined as a minimal subset of super key. In some cases , candidate key can not alone since there is alone one attribute is the minimal subset. Example,Employee(id, ssn, name, addrress)Here Candidate key is (id, ssn) because we can easily identify the tuple using either id or ssn . Althrough, minimal subset of super key is either id or ssn. but both of them can be considered as candidate key.Primary Key : Primary key is a one of the candidate key.Now explain these keys relate through an example :Student(Id, Name, Dept, Result)HereSuper Key : {Id, Id+name, Id+name+dept}Candidate Key : Id because id alone is the minimal subset of super key.Primary Key : Id because Id is one of the candidate key and uniquely identify the tuples in relation.

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 LibraryFaker is a Python package that generates fake data for you.Installation: Help LinkOpen Anaconda prompt command to install:conda install -c conda-forge faker  Import package 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 commandsfake.name() fake.address() fake.email() fake.text() fake.country() from faker import Faker fake = Faker() print (fake.email()) print(fake.country()) print(fake.name()) print(fake.text()) print(fake.latitude(), fake.longitude()) print(fake.url()) OUTPUT:(Different every time)[email protected] Belgium Shane Hunter Commodi vel libero placeat quibusdam odio odio consequatur. Ducimus libero quae optio non quidem. Facilis quas impedit quo. 26.5687745 -124.802165 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.from faker import Faker import json # To create a json file  from random import randint # For student id fake = Faker() def input_data(x):  # dictionary  student_data ={}  for i in range(0, x):  student_data[i]={}  student_data[i]['id']= randint(1, 100)  student_data[i]['name']= fake.name()  student_data[i]['address']= fake.address()  student_data[i]['latitude']= str(fake.latitude())  student_data[i]['longitude']= str(fake.longitude())  print(student_data)  # dictionary dumped as json in a json file  with open('students.json', 'w') as fp:  json.dump(student_data, fp) def main():  # Enter number of students  number_of_students = 10 # For the above task make this 100  input_data(number_of_students) main() # The folder or location where this python code # is save there a students.json will be created  # having 10 students data. OUTPUT{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.from faker import Faker fake = Faker('hi_IN') #'hi_IN' changed the language for i in range(0, 10):  print('Name->', fake.name(), 'Country->', fake.country()) List on languages we can use to get Faker DataApplication 3: Create fake profileimport faker from Faker fake = Faker() print(fake.profile()) OUTPUT{'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': '[email protected]', '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.Examplefrom faker import Faker fake = Faker() fake.seed(1) print(fake.name()) print(fake.address()) print(fake.email()) OUTPUTRyan Gallagher 7631 Johnson Village Suite 690 Adamsbury, NC 50008 [email protected] 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.import faker from Faker fake = Faker() # Print random sentences print(fake.sentence()) # List has words that we want in our sentence word_list = ["GFG", "Geeksforgeeks", "shaurya", "says", "Gfg", "GEEKS"] # Let's print 5 sentences that have words from our word_list  for i in range(0, 5):  # You need to use ext_word_list = listnameyoucreated   print(fake.sentence(ext_word_list = word_list)) OUTPUT# This is the random sentence that is generated using # fake.sentence() Error architecto inventore aut.  # These are the 5 sentence that contains words from  # word_list we provided Shaurya shaurya GEEKS Geeksforgeeks. Gfg shaurya Geeksforgeeks GFG Gfg GFG. Geeksforgeeks Gfg says Geeksforgeeks GEEKS Gfg Gfg GFG. Geeksforgeeks shaurya GFG Geeksforgeeks Gfg GEEKS. Gfg Geeksforgeeks says GFG GEEKS says. Summary of what we learned from Faker1. Fake data generation like name, address, email, text, sentence, etc2. Creating a JSON file of fake data.3. Different language fake data printed.4. Creating a Profile5. Seeding i.e printing particular fake data6. Generating a sentence that contains the words we provided.I hope someone who reads this article gains something new. #HAPPYCODINGEdit: 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