A data type is a classification that specifies the kind of value a variable can store and determines the operations that can be performed on that value. In Python, data types are dynamically assigned based on the value assigned to a variable.

Presents
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Python

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data type
A data type is a classification that specifies the kind of value a variable can store and determines the
operations that can be performed on that value. In Python, data types are dynamically assigned based
on the value assigned to a variable.
Example:
x = 10 # x is of type int
y = "Hello" # y is of type str
z = 3.14 # z is of type float
In simple terms, data types help define what type of data a variable can hold, such as numbers, text, or
boolean values.

importance of Data
types
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• Efficient Memory Usage:
Allocates memory based on the type and size of the data.
• Error Prevention:
Prevents invalid operations (e.g., adding a string to an integer).
• Supports Structured Programming:
Enables organizing data and defining its behavior.

Classification of Python Data
Types
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• Numeric
• String
• Boolean
• List
• Tuple
• Dictionary
• Set
• None Type
• Type conversion

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numeric Data Type
• int: Whole numbers.
• float: Decimal numbers.
• complex: Numbers with real and imaginary
parts.
Example :
1.# Integer
x = 42
2.# Float
y = 3.14159
3.# Complex
z = 2 + 3j
print(x, y, z) # Output: 42 3.14159 (2+3j)

string Data type
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Definition: Sequence of characters.
Key Features:
• Immutable.
• Supports slicing and indexing.
Examples:
text = "Hello, World!"
print(text[0]) # Output: H
print(text[-1]) # Output: !
print(text[0:5]) # Output: Hello
print(len(text)) # Output: 13

Boolean data type
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Definition: Represents True or False.
Applications: Decision-making in conditional
statements.
Examples:
is_valid = 5 > 3
print(is_valid) # Output: True

list data type
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• Definition: Ordered, mutable collection.
• Features:
• Supports multiple data types.
• Allows duplicates.
Examples:
fruits = ["apple", "banana", "cherry"]
fruits.append("mango")
print(fruits) # Output: ['apple', 'banana', 'cherry', 'mango']

tuple data type
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• Definition: Ordered, immutable
collection.
Difference from List:
• Faster than lists.
• Ideal for read-only data.
Examples:
coordinates = (10, 20, 30)
print(coordinates[1]) # Output: 20

dictionary
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• Definition: Collection of key-value pairs.
Features:
• Mutable.
• Keys must be unique.
Examples:
person = {"name": "Alice", "age": 25, "city": "New York"}
person["age"] = 26
print(person) # Output: {'name': 'Alice', 'age': 26, 'city': 'New
York'}

set
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• Definition: Unordered collection of unique
items.
Key Features:
• No duplicates.
• Mutable.
Examples:
numbers = {1, 2, 3, 4, 5}
numbers.add(6)
numbers.add(3) # Duplicate ignored
print(numbers) # Output: {1, 2, 3, 4, 5, 6}

none type
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• Definition: Represents the absence of a value.
Applications:
• Default return value for functions without return.
Examples:
result = None
if result is None:
print("No value assigned.") # Output: No value assigned.

type conversion
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• Implicit Conversion: Python automatically converts types.
• Explicit Conversion: Manually converting types using functions like int(), float(),
etc.
Examples:
# Implicit
a = 5
b = 2.5
c = a + b # a is implicitly converted to float
print(c) # Output: 7.5
# Explicit
x = "123"
y = int(x) # Explicit conversion to int
print(y + 1) # Output: 124

A data type is a classification that specifies the kind of value a variable can store and determines the operations that can be performed on that value. In Python, data types are dynamically assigned based on the value assigned to a variable.

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A data type is a classification that specifies the kind of value a variable can store and determines the operations that can be performed on that value. In Python, data types are dynamically assigned based on the value assigned to a variable.