Data Types in Python

What are Data types ?

Data Types in Python , are the foundation of any programming language. Data type is a classification that specifies which type of value a variable can hold, determining how Python interprets and manipulates data. It ensures that the right operations are performed.

Purpose of Data Types

The core purpose of data types in programming is to tell the computer what type of information a variable or a value can represents. This could be a number (int, float ) , text (string), a true/false value ( Boolean ), or something more complex. By knowing the data type, the computer understands what operations can be performed on that data.

Why are data types important ?

• Memory management: Data types help the computer allocate the right amount of memory to store data efficiently.
• Program efficiency: By using the correct data types, you can optimize your program’s performance.
• Error prevention: They help prevent errors by ensuring that operations are performed on compatible data.
• Code readability: Data types make code easier to understand by clearly indicating the kind of data being used.
  

Python’s Built- in Core Data Types

Python provides a robust set of built- in core data types that enables efficient data manipulation and storage. These data types are the foundation of Python programming and can be categorized into several groups.

Numeric Data Types

Numeric data types are essential in Python programming for performing mathematical operation , comparison, and other numerical computation:

• Integers (int) – example 1,2,3,4, etc.
• Floating – Point Number (float) – 3.45,12.8, etc.
• Complex Numbers (complex) – 4+i9, 3+i8, etc.

Text Data Types

In Python, the primary text data type is:

String (str)

Strings data type in python are sequence of characters. They can be enclosed in:

• Single quotes (”): ‘hello’
• Double quotes (“”): “hello”
• Triple quotes (”’ or “”””): ”’hello”’ or “””hello”””

Sequence Data Types

Sequence data type in python are used to store multiple values in a single variable. These data types are characterized by their ability to store ordered collection of items. Different types sequence data types are mentioned below:

Lists (list)

• Ordered collections of items
• Mutable (can be modified after creation)
• Defined using square brackets []
• Examples: [1, 2, 3], [“a”, “b”, “c”], [True, False, True]

Tuples (tuple)

• Ordered, immutable collections of items
• Cannot be modified after creation
• Defined using parentheses ()
• Examples: (1, 2, 3), (“a”, “b”, “c”), (True, False, True)

Range (range)

• Sequences of numbers
• Often used in loops or for iterating over a sequence of numbers
• Defined using the range() function
• Examples: range(5), range(1, 10, 2), range(10, 0, -1)

Mapping Data Types

Mapping data types, are versatile data structures that store data as key-value pairs. In Python, mapping data types are typically unordered, meaning that the order of the key-value pairs is not preserved.
Mapping data types can be modified after creation, allowing you to add, remove, or update key-value pairs. In Python, the primary mapping data type is the dictionary . Here’s an overview:

Dictionary

• Unordered collection of key-value pairs
• Mutable, allowing additions, removals, and updates
• Keys must be unique and immutable .
• Example : person = {“name”: “John”, “age”: 30, “city”: “New York”}, etc.

Set Data Types

A set is a collection of unique objects, known as elements or members, that can be anything (numbers, letters, people, etc.). Sets are a fundamental concept in mathematics and are used to define and describe relationship between the objects. In Python, a set is an unordered collection of unique elements. Here are the key features and operations of sets in Python:

1. Unordered: Sets do not maintain the order in which elements were added.
2. Unique elements: Sets only store unique elements, eliminating duplicates.
3. Mutable: Sets can be modified after creation.

Boolean Data Types

In Python, Boolean data types are used to represent logical values that can have only two possible values: True or False. Boolean values can be used in logical operations – AND, OR, and NOT.

Characteristics of Boolean Data Types

• Boolean (bool): True or False.
• The Boolean data type is represented by the bool keyword.

None Data Type

The None data type in Python represents the absence of a value or a null object reference. It is a singleton object, meaning there is only one None object in the Python interpreter. In Python, the None data type is represented by None keyword.

Characteristics of None Data Type:

1. Singleton object: There is only one None object in the Python interpreter.
2. Null object reference: None represents the absence of a value or a null object reference.
3. Immutable: None is an immutable object, meaning its value cannot be changed.

Mutable Objects

Mutable objects are those that can be changed after they are created. Examples of mutable objects in Python include:

1. Lists: Lists are ordered collections of items that can be changed after they are created.
2. Dictionaries: Dictionaries are unordered collections of key-value pairs that can be changed after they are created.
3. Sets: Sets are unordered collections of unique items that can be changed after they are created.

Immutable Objects

Immutable objects are those that cannot be changed after they are created. Examples of immutable objects in Python include:

1. Integers: Integers are whole numbers that cannot be changed after they are created.
2. Floats: Floats are decimal numbers that cannot be changed after they are created.
3. Strings: Strings are sequences of characters that cannot be changed after they are created.
4. Tuples: Tuples are ordered, immutable collections of items.