A nested data structure is a data structure that contains other data structures within it. In Python, lists, tuples, dictionaries, and sets can be nested inside each other to store complex, hierarchical data.

Why Use Nested Data Structures?

Helps represent real-world hierarchical data
Improves organization and readability of data
Allows efficient data storage and retrieval

---

1. Types of Nested Data Structures

Python allows nesting different data structures inside each other. The most common combinations are:
✅ Nested Lists
✅ Nested Tuples
✅ Nested Dictionaries
✅ Mixing Lists, Tuples, and Dictionaries

---

2. Nested Lists

A nested list is a list inside another list. It is useful for storing matrix-like data, hierarchical lists, or multi-dimensional structures.

Example: 2D Matrix Representation

matrix = [
    [1, 2, 3],
    [4, 5, 6],
    [7, 8, 9]
]

print(matrix[1][2])  # Accessing element at row 1, column 2 (6)

Output:

6

Looping Through a Nested List

pythonCopyEditfor row in matrix:
    for num in row:
        print(num, end=" ")

Output:

1 2 3 4 5 6 7 8 9

---

3. Nested Tuples

A nested tuple is a tuple inside another tuple. Since tuples are immutable, nested tuples are useful when you want to store structured data that should not change.

Example: Storing Student Records

students = (
    ("Alice", 22, "A"),
    ("Bob", 21, "B"),
    ("Charlie", 23, "A")
)

print(students[0][1])  # Accessing Alice's age (22)

Output:

22

Looping Through a Nested Tuple

for student in students:
    print(f"Name: {student[0]}, Age: {student[1]}, Grade: {student[2]}")

Output:

Name: Alice, Age: 22, Grade: A
Name: Bob, Age: 21, Grade: B
Name: Charlie, Age: 23, Grade: A

---

4. Nested Dictionaries

A nested dictionary is a dictionary inside another dictionary. It is useful for storing hierarchical data, such as employee records, user profiles, or JSON-like structures.

Example: Employee Records

employees = {
    "emp1": {"name": "Alice", "age": 25, "department": "HR"},
    "emp2": {"name": "Bob", "age": 30, "department": "IT"},
    "emp3": {"name": "Charlie", "age": 28, "department": "Finance"}
}

print(employees["emp2"]["department"])  # Accessing Bob's department (IT)

Output:

IT

Looping Through a Nested Dictionary

for emp_id, details in employees.items():
    print(f"ID: {emp_id}")
    for key, value in details.items():
        print(f"  {key}: {value}")

Output:

ID: emp1
  name: Alice
  age: 25
  department: HR
ID: emp2
  name: Bob
  age: 30
  department: IT
ID: emp3
  name: Charlie
  age: 28
  department: Finance

---

5. Mixing Lists, Tuples, and Dictionaries

Python allows mixing different data structures inside each other for more complex storage.

Example: Student Data with Mixed Structures

students = [
    {"name": "Alice", "grades": (85, 90, 88), "subjects": ["Math", "Science"]},
    {"name": "Bob", "grades": (78, 82, 80), "subjects": ["English", "History"]}
]

print(students[1]["subjects"][0])  # Accessing Bob's first subject (English)

Output:

English

Looping Through Mixed Structures

for student in students:
    print(f"Name: {student['name']}")
    print(f"Grades: {student['grades']}")
    print("Subjects:", ", ".join(student["subjects"]))

Output:

Name: Alice
Grades: (85, 90, 88)
Subjects: Math, Science
Name: Bob
Grades: (78, 82, 80)
Subjects: English, History

---

6. Modifying Nested Data Structures

Modifying a Nested List

matrix[1][2] = 99  # Changing 6 to 99
print(matrix)

Modifying a Nested Dictionary

employees["emp2"]["age"] = 31  # Changing Bob's age to 31
print(employees["emp2"])

---

7. Accessing Nested Data Safely

If you try to access a non-existent key, Python will raise a KeyError. Use .get() to avoid errors.

Example: Using .get() to Prevent Errors

print(employees.get("emp4", "Employee not found"))

Output:

Employee not found

---

8. Converting Between Nested Structures

Converting a Nested Dictionary to JSON

Python dictionaries can be converted to JSON using json.dumps().

import json
json_data = json.dumps(employees, indent=4)
print(json_data)

Converting a Nested List to a Dictionary

data = [["name", "Alice"], ["age", 25]]
dictionary = dict(data)
print(dictionary)

Output:

{'name': 'Alice', 'age': 25}

---

9. When to Use Nested Data Structures?

Use nested data structures when:

• You need to store complex data efficiently
• You are working with hierarchical or structured data
• You need JSON-like storage for APIs

Avoid nesting too deeply when:

• Data access becomes slow and complex
• It reduces code readability