Ana works in the medical records department at a large medical office. Her job includes scanning and uploading medical records i
1 answer:
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Answer:
Check the explanation
Explanation:
Ans 1) storing the data in one single row comes with its own barriers and that is if you delete one of the record in the spreadsheet, all the equivalent records which you didn't wanted to delete would be deleted. For take for instance, if you wish to delete the record of author "James Taylor" then all the books he has written would also be deleted from the spreadsheet.
Ans 2) In 1NF, all row is expected to have only 1 tuple, but here in this spreadsheet the title field for Author "James Taylor" has 2 books, so it would be separated into single rows.
Ans 3) The table in the attached image below doesn't delete with Delete anomaly, i.e., for author "May Norton" if you delete this record, then the only book written by her would be deleted.
So, you have to convert this table into 2NF.
Make separate tables for Author and Book
Author(Author Name, Author Country)
Books(Author Name, Book title, Publisher, Publisher Location, Year, price)
This cannot be normalized any further.
Solution :
class Employee:
#Define the
#constructor.
def ____(
, ID_number,
, email):
#Set the values of
#the data members of the class.
= name
_number = ID_number
= salary
self.email_address = email
#Define the function
#make_employee_dict().
def make_employee_dict(list_names, list_ID, list_salary, list_email):
#Define the dictionary
#to store the results.
employee_dict = {}
#Store the length
#of the list.
list_len = len(list_ID)
#Run the loop to
#traverse the list.
for i in range(list_len):
#Access the lists to
#get the required details.
name = list_names[i]
id_num = list_ID[i]
salary = list_salary[i]
email = list_email[i]
#Define the employee
#object and store
#it in the dictionary.
employee_dict[id_num] = Employee(name, id_num, salary, email)
#Return the
#resultant dictionary.
return employee_dict