Data Types and Data Structure
Two basic yet separate ideas in computer science that are essential to comprehending how information is managed within programs are data types and data structures. They have diverse functions and function at various levels of abstraction, despite the fact that they are frequently discussed simultaneously.
Data Types
The types of values that can be stored in a variable and the actions that can be applied to those values are specified by the data type. It basically instructs the machine on how to understand a piece of data through classification. For instance, integers (int), floating-point numbers (float), and strings (str) are examples of typical primitive data types and data structure in Python.
Several categories of data types are mentioned
Primitive data types: The fundamental building blocks are known as primitive data types, and they include characters, floats, and integers.
User-defined data types: Programmers typically combine primitive kinds to construct user-defined data types, which represent more complicated entities.
Abstract Data Types: ADTs, or abstract data types, are at a higher degree of abstraction. A data types and data structure ADT is a mathematical model that describes the kinds of data that are stored, the operations that can be performed on them, and the kinds of parameters that may be used in those actions. Its public interface is the part of an ADT that describes what each operation does, but not how it accomplishes it. An ADT that defines operations like push and pop, for example, is called a stack. It does not specify whether an array or a linked list is used for implementation.
This is a basic Python example that shows how primitive data types and data structure work.
Example:
# Integer data type
age = 30
print(f"Age: {age}, Type: {type(age)}")
# Floating-point data type
price = 19.99
print(f"Price: {price}, Type: {type(price)}")
# String data type
name = "Alice"
print(f"Name: {name}, Type: {type(name)}") Output:
Age: 30, Type: <class 'int'>
Price: 19.99, Type: <class 'float'>
Name: Alice, Type: <class 'str'>The variables age, price, and name in this example each hold distinct data types, each of which has a set of acceptable operations (e.g., concatenation for strings, arithmetic for numbers).
Data Structures
The methodical arrangement and storage of data in computer memory for effective access, management, and modification is known as a data structure. It concerns the organisation and relationships between data sets in order to maximise processes such as retrieval, insertion, and deletion. Because data structures help programs and programmers operate more efficiently, they are regarded as the foundation of computer science.
Generally speaking, data structures fall into two major categories.
Linear data structures
Elements are kept in sequential order in linear data structures. Arrays, linked lists, stacks, and queues are a few examples.
- Arrays are memory-saving and code-length-reducing containers that can house a set number of identically typed elements. Internally, Python’s built-in list, tuple, and str classes use a low-level array idea and are regarded as “sequence” types.
- According to the Last In-First Out (LIFO) principle, the final piece added is the first to be deleted from a stack. Among the operations are pop (deletion) and push (addition).
- FIFO (First In-First Out) is the principle that queues adhere to.
Non-linear data structures
Elements are not kept in a sequential fashion in non-linear data structures. There are heaps, graphs, and trees as examples.
- In hierarchical structures called trees, objects are arranged according to parent-child relationships. One kind of tree is the binary search tree.
- Advanced data structures called graphs, which are made up of nodes (vertices) and edges, are used to arrange objects in a network.
In Python, a list is a popular array-based sequence that serves as an example of a data structure.
Example:
# A Python list, representing a collection of integers (a data structure)
student_scores = [15, 26, 43, 45]
print(f"Original scores: {student_scores}")
# Operations on the data structure:
# Adding an element (insertion)
student_scores.append(90)
print(f"Scores after adding: {student_scores}")
# Searching for an element
search_score = 95
if search_score in student_scores:
print(f"{search_score} found in scores.")
else: # Added an else block for when the score is not found
print(f"{search_score} not found in scores.")
# Accessing elements (retrieval)
if student_scores:
print(f"First score: {student_scores[0]}")
else:
print("The list is empty.")
# Deleting an element
score_to_remove = 43
if score_to_remove in student_scores:
student_scores.remove(score_to_remove)
print(f"Scores after removing {score_to_remove}: {student_scores}")
else:
print(f"{score_to_remove} not found in scores, cannot remove.")Output:
Original scores: [15, 26, 43, 45]
Scores after adding: [15, 26, 43, 45, 90]
95 not found in scores.
First score: 15
Scores after removing 43: [15, 26, 45, 90]This code shows how we may gather, arrange, and carry out different actions on many bits of data using a list as a data structure.
The Difference Between Data Types vs Data Structures
Their scope and goal are where they differ most.
- Data types are concerned with the nature and properties of individual data points, such as whether they are text, decimal, or whole numbers. They describe a single piece of information.
- The organisation, relationship, and storage of various data elements to enable effective manipulation and retrieval is the main focus of data structures. They explain the arrangement of an assortment of objects.
Consider basic data types as separate ingredients in a recipe (for example, “sugar” is a float or int type). For example, a “cake” recipe specifies how sugar, flour, and eggs must be blended and piled, while a “bowl” contains several ingredients. A data structure is similar to the complete recipe or a particular container for those ingredients. An array data structure, for instance, may include numerous integer data types. Data structures are frequently constructed from data types.
Python many built-in features, which include complicated “data structures” (such lists, tuples, sets, and dictionaries) as native types, blur these lines because it is a high-level language. However, a thorough understanding of computer science necessitates understanding the implementation of these underlying structures and their complexity characteristics. It is crucial to comprehend the fundamental data structures, their intricacies, and trade-offs in order to determine which one is best for a particular programming request and to anticipate potential failures in algorithms or data structures.
To put it briefly, data structures are about the relationships and organisation of collections of values to accomplish effective data management in software development, whereas data types are about the characteristics of individual values.