Primitive, User-Defined, and Abstract Types
Comprehending data types is essential to computer science, especially when creating and putting into practice effective data structures and algorithms. While algorithms are sequential processes for completing tasks, data structures are methodical approaches to organising and retrieving data. The functions of primitive, user-defined, and abstract data types will be explained in this response along with Python examples.
Primitive Data Types
The simplest and most fundamental data kinds are those that are directly supported by a computer’s hardware or programming language. They are the fundamental units of more intricate data structures, representing discrete, atomic values.
Although all data in Python are eventually objects, the essential or “primitive” types in real-world programming scenarios include types like int (integers), float (floating-point numbers), bool (boolean values), and str (strings, which are character sequences). It point out that in order to clearly demonstrate the fundamental elements of data structures, books frequently employ simple constructs or approximations of primitive types (such as arrays of integers present in languages like C language or C++). In order to analyse the complexity of algorithms, it is essential to comprehend the operations on these primitives.
Primitive Data Types in Python
Example:
# Integer (int) - Represents whole numbers
quantity = 10
# Floating-point number (float) - Represents real numbers
temperature = 25.5
# String (str) - Represents sequences of characters
message = "Hello, world!"
# Boolean (bool) - Represents truth values (True or False)
is_active = True
print(quantity)
print(temperature)
print(message)
print(is_active)Output:
10
25.5
Hello, world!
TrueThese fundamental Python types serve as the basis for all other data representations and are directly useable.
User-Defined Data Types
In order to model more complicated real-world entities or concepts that primitive kinds alone are unable to appropriately represent, programmers build user-defined data types. In object-oriented programming (OOP), which is extensively used to construct data structures, classes provide these user-defined kinds.
Classes are blueprints for creating instances of the class. Both the behaviours (referred to as methods or member functions) that manipulate the data and the data itself (referred to as attributes, fields, instance variables, or data members) are contained within it. Data and the functions that work with it are kept together because to this encapsulation, which encourages software that is reliable and reusable. With Python implementations for the discussed data structures and algorithms, the object-oriented approach is a consistent point of view seen in many data structures texts.
User-Defined Data Type (Class) in Python
Example:
class Student: # 'Student' is a user-defined data type (class)
# The constructor method initializes the attributes of a Student object
def _init_(self, name, student_id, major, gpa):
self.name = name
self.student_id = student_id
self.major = major
self.gpa = gpa
# A method to display student information (behavior)
def display_info(self):
return f"Name: {self.name}, ID: {self.student_id}, Major: {self.major}, GPA: {self.gpa:.2f}"
# A method to update the student's major
def update_major(self, new_major):
self.major = new_major
print(f"{self.name}'s major updated to {new_major}.")
# Creating instances (objects) of the user-defined 'Student' type
student1 = Student("Alice Johnson", "S1001", "Computer Science", 3.85)
student2 = Student("Bob Williams", "S1002", "Electrical Engineering", 3.50)
print(student1.display_info())
student1.update_major("Software Engineering")
print(student1.display_info())
print(student2.display_info())Output:
Name: Alice Johnson, ID: S1001, Major: Computer Science, GPA: 3.85
Alice Johnson's major updated to Software Engineering.
Name: Alice Johnson, ID: S1001, Major: Software Engineering, GPA: 3.85
Name: Bob Williams, ID: S1002, Major: Electrical Engineering, GPA: 3.50Take a look at a student’s record. A Python class can be used to generate a user-defined data type called Student.
The Student class is a user-defined data type in this instance. Both student1 and student2 are examples of this kind, particular objects that have the common methods defined in the Student class but each contain their own distinct data.
Abstract Data Types (ADTs)
A conceptual specification or mathematical description of a data structure is called an Abstract Data Type (ADT). Although it does not outline how these operations are to be carried out, it does describe what can be done to a set of data, the kinds of data that can be stored, and the parameters of those actions. The process of separating “what” from “how” is called abstraction, and it reduces complicated systems to their most basic components and exposed interfaces.
The collective collection of behaviours that an interface supports is described by the ADT. For example, a stack is an ADT that is defined by operations such as peek (seeing the top element without removing it), pop (removing the top element), and push (adding an element to the top). Although pop eliminates the last item added (LIFO principle), the ADT does not specify whether an array, linked list, or other tangible data structure should be used to build the stack. This promotes code modularity and flexibility by enabling several implementations to follow the same ADT description. Stacks, Queues, Trees, Graphs, Linked Lists, Arrays, Heaps, and Priority Queues are examples of common ADTs.
Linking ADTs to Code (ADT Implementation): An ADT is not a concrete idea that can be directly represented in code written in a programming language. Instead, user-defined classes in object-oriented languages like Python are commonly used to build tangible data structures, which are used to implement ADTs. A user-defined class gives an ADT’s “what” a precise “how.”
Let’s take the Stack ADT as an illustration. It can do push, pop, and is empty actions. A Python list, which is a built-in sequence type, can be used as the underlying storage mechanism to implement this ADT.
Example:
# The Stack ADT defines these operations:
# push(element): Adds an element to the top.
# pop(): Removes and returns the top element.
# peek(): Returns the top element without removing it.
# is_empty(): Returns True if the stack is empty.
# Implementation of the Stack ADT using a Python list (a user-defined class)
class Stack: # This 'Stack' class is a user-defined data type implementing the Stack ADT
def _init_(self):
self._items = [] # A private list to hold stack elements
def push(self, item):
"""Adds an item to the top of the stack."""
self._items.append(item)
def pop(self):
"""Removes and returns the top item from the stack. Returns None if empty."""
if not self.is_empty():
return self._items.pop()
else:
print("Stack is empty, cannot pop.")
return None
def peek(self):
"""Returns the top item without removing it. Returns None if empty."""
if not self.is_empty():
return self._items[-1]
else:
print("Stack is empty, no item to peek.")
return None
def is_empty(self):
"""Returns True if the stack is empty, False otherwise."""
return len(self._items) == 0
def size(self):
"""Returns the number of items in the stack."""
return len(self._items)
# Using the Stack implementation
my_stack = Stack() # 'my_stack' is an object of the user-defined 'Stack' type
my_stack.push("Book A")
my_stack.push("Book B")
print(f"Current stack size: {my_stack.size()}")
print(f"Top element: {my_stack.peek()}")
print(f"Popped: {my_stack.pop()}")
print(f"Stack is empty? {my_stack.is_empty()}")
my_stack.push("Book C")
print(f"Popped: {my_stack.pop()}")
print(f"Popped: {my_stack.pop()}")
print(f"Stack is empty? {my_stack.is_empty()}")
my_stack.pop() # Attempt to pop from an empty stackOutput:
Current stack size: 2
Top element: Book B
Popped: Book B
Stack is empty? False
Popped: Book C
Popped: Book A
Stack is empty? True
Stack is empty, cannot pop.A user-defined data type that offers a tangible implementation of the Stack Abstract Data Type is the Stack class in this extensive example. It uses an internal Python list for storage while adhering to the ADT’s contract (its push, pop, peek, and is empty operations). The relationship between an ADT defining what a data structure performs and a user-defined class defining how it does it is evident from this.
The atomic values are known as primitive data types, user-defined data types (classes) are bespoke blueprints for building sophisticated data objects, and abstract data types are conceptual models that determine the interface and behaviour of data structures regardless of how they are implemented. When combined, they provide the essential structure for arranging and modifying data in computer programming.