Data Types in Python with Examples

Python data types classify data items. They determine the type of value a variable stores. This helps you write effective and error-free programs.

What are Data Types in Python?

A Python data type defines the type of value a variable can hold. It indicates what operations you can perform on that data. Python automatically understands the data type when you assign a value. You do not need to declare it explicitly.

Here are the main categories of built-in data types in Python:

• Numeric Types: Used for numbers.
• Sequence Types: Ordered collections of items.
• Mapping Type: Stores data as key-value pairs.
• Set Types: Unordered collections of unique elements.
• Boolean Type: Represents truth values.
• None Type: Signifies the absence of a value.

You can check the type of any variable using the built-in type() function. For example:

number = 10
print(type(number)) # Output: <class 'int'>

1. Numeric Data Types

Numeric data types handle numerical values. Python has three primary numeric types:

• Integers (int): Whole numbers, positive or negative, without decimals. They have unlimited precision.
  
  Example: age = 25, count = -100
• Floating-point numbers (float): Numbers with decimal points or in exponential form. They represent real numbers.
  
  Example: price = 19.99, temperature = 37.5, big_number = 1.23e5 (123000.0)
• Complex numbers (complex): Numbers with a real and an imaginary part, expressed as a + bj.Example: z = 2 + 3j

2. Sequence Data Types

Sequence data types store ordered collections of elements. They support indexing and slicing.

• Strings (str): Ordered sequences of characters enclosed in single, double, or triple quotes. Strings are immutable; you cannot change their content after creation.
  
  Example: name = "Alice", message = 'Hello World'
  Accessing characters: name[0] gives ‘A’.
  Slicing: message[0:5] gives ‘Hello’.
• Lists (list): Ordered, mutable collections of items enclosed in square brackets []. Lists can hold elements of different data types. You can modify, add, or remove elements.
  
  Example:
  fruits = ["apple", "banana", "cherry"], mixed_list = [1, "hello", True]
  Modifying a list: fruits.append("grape")
  Accessing elements: fruits[0] gives ‘apple’.
• Tuples (tuple): Ordered, immutable collections of items enclosed in parentheses (). Tuples are similar to lists but cannot be modified after creation. They are faster than lists for iteration.
  
  Example: coordinates = (10, 20), rgb_color = (255, 0, 128)
  Accessing elements: coordinates[0] gives 10.

3. Mapping Data Type

• Dictionaries (dict): Unordered collections of key-value pairs enclosed in curly braces {}. Each key must be unique and immutable. Values can be of any data type. Dictionaries are mutable
  
  Example: person = {"name": "Bob", "age": 30}
  Accessing values: person["name"] gives ‘Bob’.
  Adding or changing entries: person["city"] = "New York"

4. Set Data Types

Set data types are unordered collections of unique elements. They do not allow duplicate items.

• Sets (set): Mutable, unordered collections of unique elements enclosed in curly braces {}. You can add or remove elements.
  
  Example: unique_numbers = {1, 2, 3, 2} (stores as {1, 2, 3})
  Adding an element: unique_numbers.add(4)
• Frozensets (frozenset): Immutable versions of sets. Once created, you cannot change their content.
  
  Example: immutable_set = frozenset([1, 2, 3])

5. Boolean Type

• Booleans (bool): Represent truth values: True or False. They are used in logical operations and control flow.
  
  Example: is_active = True, has_permission = False
True evaluates to 1 and False to 0 in numeric contexts.

6. None Type

• NoneType (None): Represents the absence of a value. It is a special data type with a single value, None.Example: result = None

Mutable vs. Immutable Data Types

Understanding mutability is crucial in Python.

• Mutable Data Types: You can change the value of these objects after they are created without creating a new object in memory.Examples: Lists, Dictionaries, Sets, Bytearray
  Benefit: Memory efficient for frequent modifications.
• Immutable Data Types: You cannot change the value of these objects after they are created. Any operation that appears to modify an immutable object actually creates a new object.
  
  Examples: Integers, Floats, Complex numbers, Booleans, Strings, Tuples, Frozensets, Bytes
  Benefit: Ensures data integrity and can be faster in some scenarios.

Common Uses of Python Data Types

• Integers and Floats: Perform mathematical calculations, represent quantities, or store measurements.
• Strings: Handle text data, parse user input, or format messages.
• Lists: Store collections of items that might change, like items in a shopping cart or a list of user names.
• Tuples: Store fixed collections of data where immutability is important, such as coordinates or database records.
• Dictionaries: Store data that requires quick lookups by a unique key, like user profiles or configuration settings.
• Sets: Store unique items, perform membership tests, or eliminate duplicates from a collection.
• Booleans: Control program flow with conditional statements (if, else) and loops.
• None: Indicate that a variable has no value or a function returns nothing.