Categories of Data Types in Python


 


Python is a powerful and versatile programming language that supports a rich set of data types. In this lecture, we will explore the various categories of data types available in Python and provide a brief introduction to each of them.

Categories of Data Types in Python

Python data types can be broadly categorized into:

  1. Numeric Types

  2. Sequence Types

  3. Set Types

  4. Mapping Types (Dictionary)

Each of these categories plays a crucial role in Python programming, and we will explore them in detail.

1. Numeric Data Types

Numeric data types store numerical values and support mathematical operations. Python provides three main numeric data types:

  • Integer (int): Represents whole numbers. Example:

    x = 10  # Integer

  • Float (float): Represents decimal or floating-point numbers. Example:

    y = 19.25  # Float

  • Boolean (bool): Represents True or False. Example:

    is_active = True  # Boolean

  • Complex (complex): Represents complex numbers in the form a + bj. Example:

    z = 2 + 3j  # Complex number

2. Sequence Data Types

Sequence data types store collections of values. These values can be indexed and accessed efficiently.

a) List

A list is an ordered collection of values that can be modified (mutable). Example:

numbers = [2, 4, 6, 8, 10]  # List
numbers[0] = 12  # Lists are mutable{codeBox}

b) Tuple

A tuple is similar to a list but immutable, meaning its values cannot be changed after assignment. Example:

tuple_values = (2, 4, 6, 8, 10)  # Tuple

c) String

A string is a sequence of characters enclosed in single or double quotes. Example:

message = "Hello, Python!"  # String

d) Bytes and Byte Arrays

  • Bytes: Immutable sequences of byte values (0-255). Example:

    byte_data = bytes([65, 66, 67])  # Immutable

  • Byte Arrays: Similar to bytes but mutable. Example:

    byte_array_data = bytearray([65, 66, 67])  # Mutable

3. Set Data Types

Sets store unique, unordered values and do not support indexing.

a) Set

A set is a collection of unique elements, and it does not allow duplicate values. Example:

unique_numbers = {2, 4, 6, 8, 10}  # Set

b) Frozen Set

A frozen set is an immutable version of a set. Example:

immutable_set = frozenset({2, 4, 6, 8, 10})  # Frozen set

4. Mapping Data Type (Dictionary)

A dictionary stores key-value pairs, making data retrieval efficient.

student_info = {"name": "John", "roll": 126, "department": "CS"}  # Dictionary

In Python, mutable and immutable refer to whether an object's value can be changed after it is created.

Mutable Objects

  • Mutable objects can be changed after creation.
  • You can modify their contents without changing their identity (i.e., their memory address remains the same).
  • Examples of mutable objects:
    • Lists (list)
    • Dictionaries (dict)
    • Sets (set)
    • Bytearray (bytearray)

Example of a Mutable Object (List)

my_list = [1, 2, 3]
print(my_list)  # Output: [1, 2, 3]

my_list[0] = 10  # Modifying the first element
print(my_list)  # Output: [10, 2, 3]

Immutable Objects
  • Immutable objects cannot be changed after creation.
  • If you try to modify them, Python creates a new object instead of changing the existing one.
  • Examples of immutable objects:
    • Integers (int)
    • Floats (float)
    • Strings (str)
    • Tuples (tuple)
    • Booleans (bool)
    • Frozen sets (frozenset)
    • Bytes (bytes)
Example of an Immutable Object (Tuple)
my_tuple = (1, 2, 3)
print(my_tuple)  # Output: (1, 2, 3)

my_tuple[0] = 10  # ❌ This will cause an error

🚨 This will raise an error:
 
TypeError: 'tuple' object does not support item assignment
 
Since tuples are immutable, you cannot modify their elements.

However, while integers (int) and strings (str) might seem like they can be changed, they are actually immutable because any modification creates a new object instead of changing the existing one.

Example with Integers (int)

x = 10
print(id(x))  # Example output: 140728537274128 (memory address of x)

x = x + 5  # This creates a new object
print(id(x))  # Example output: 140728537274288 (new memory address)
 

 ðŸš€ Explanation:
When x = x + 5 is executed, Python does not modify the original 10. Instead, it creates a new integer 15 and assigns it to x. The old value 10 is discarded if it is no longer referenced.

Example with Strings (str)

s = "Hello"
print(id(s))  # Example output: 140728537272432

s = s + " World"  # Creates a new string object
print(id(s))  # Example output: 140728537273216 (new memory address)
 

 ðŸš€ Explanation:
Strings are immutable, so modifying s actually creates a new string "Hello World", and s now refers to this new object. The original "Hello" remains unchanged.

Mutable vs Immutable Behavior

🔹 Mutable Example (list):

 lst = [1, 2, 3]
print(id(lst))  # Example: 140728537333184

lst.append(4)  # Modify the list
print(id(lst))  # Same memory address: 140728537333184

✅ The list is modified without changing its memory address.

🔹 Immutable Example (str):

 text = "Python"
print(id(text))  # Example: 140728537272432

text += " Rocks"  # Creates a new string
print(id(text))  # Different memory address: 140728537273216

❌ Strings cannot be modified in place; Python creates a new object instead.

 ✔️ Even though we can "change" int and str, Python actually creates new objects rather than modifying the existing ones.
✔️ Mutable objects (like lists) modify the same object in memory, while immutable objects (like ints and strs) always create new objects when modified.

Summary

In this blog, we introduced the various data types in Python. Each of these data types has its unique characteristics and is used for specific purposes. In the upcoming lectures, we will explore each data type in more detail.

Stay tuned for the next lecture, where we will dive deeper into numeric data types in Python!


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