codeIn [Spark]

Non-primitive data types in Python are more complex structures that can store multiple values or a combination of different types of data. They are derived from primitive data types and provide more functionality for handling collections and more complex data structures. Let’s explore each non-primitive data type in detail:

List #

Description:

• An ordered collection of items.
• Items in a list can be of different types.
• Lists are mutable, meaning their elements can be changed after the list is created.

Example:

codefruits = ["apple", "banana", "cherry"]
print(fruits)  # Output: ['apple', 'banana', 'cherry']

# Accessing elements
print(fruits[0])  # Output: apple

# Modifying elements
fruits[1] = "blueberry"
print(fruits)  # Output: ['apple', 'blueberry', 'cherry']

# Adding elements
fruits.append("date")
print(fruits)  # Output: ['apple', 'blueberry', 'cherry', 'date']

# Removing elements
fruits.remove("apple")
print(fruits)  # Output: ['blueberry', 'cherry', 'date']

Tuple #

Description:

• An ordered collection of items, similar to a list.
• Tuples are immutable, meaning once they are created, their elements cannot be changed.
• Often used for fixed collections of items.

Example:

codecoordinates = (10.0, 20.0)
print(coordinates)  # Output: (10.0, 20.0)

# Accessing elements
print(coordinates[1])  # Output: 20.0

# Tuples cannot be modified
# coordinates[1] = 25.0  # This will raise a TypeError

Set #

Description:

• An unordered collection of unique items.
• Sets are mutable, but they do not allow duplicate elements.
• Useful for membership testing and eliminating duplicate entries.

Example:

codeunique_numbers = {1, 2, 3, 4, 5}
print(unique_numbers)  # Output: {1, 2, 3, 4, 5}

# Adding elements
unique_numbers.add(6)
print(unique_numbers)  # Output: {1, 2, 3, 4, 5, 6}

# Removing elements
unique_numbers.remove(3)
print(unique_numbers)  # Output: {1, 2, 4, 5, 6}

# Sets do not allow duplicates
unique_numbers.add(1)
print(unique_numbers)  # Output: {1, 2, 4, 5, 6}

Dictionary #

Description:

• A collection of key-value pairs.
• Each key is unique and maps to a value.
• Dictionaries are mutable, allowing for dynamic changes in keys and values.

Example:

codestudent = {"name": "Alice", "age": 22, "is_student": True}
print(student)  # Output: {'name': 'Alice', 'age': 22, 'is_student': True}

# Accessing values
print(student["name"])  # Output: Alice

# Modifying values
student["age"] = 23
print(student)  # Output: {'name': 'Alice', 'age': 23, 'is_student': True}

# Adding new key-value pairs
student["major"] = "Computer Science"
print(student)  # Output: {'name': 'Alice', 'age': 23, 'is_student': True, 'major': 'Computer Science'}

# Removing key-value pairs
del student["is_student"]
print(student)  # Output: {'name': 'Alice', 'age': 23, 'major': 'Computer Science'}

Summary #

Non-primitive data types in Python include:

• List: An ordered, mutable collection of items.
• Tuple: An ordered, immutable collection of items.
• Set: An unordered collection of unique items.
• Dictionary: A collection of key-value pairs.

These data types provide powerful ways to handle and organize data, offering flexibility and efficiency in various programming tasks.