In Python, working with nested data structures is a common requirement when dealing with complex and hierarchical data. Nested data allows you to organize related information in a structured manner, such as nested dictionaries or lists. In this article, we will explore various techniques for working with nested data in Python, including accessing nested elements, modifying values, and performing operations on nested structures. By the end, you'll have a solid understanding of how to navigate and manipulate nested data effectively.

  1. Accessing Nested Elements:

    • Example 1: Accessing nested elements in a dictionary

      student = {
      "name": "John", "age": 20, "courses": ["Math", "Science", "History"], "grades": { "Math": 90, "Science": 85, "History": 92 } }

      print(student["name"])

      # Output: John print(student["courses"][0])

      # Output: Math

      print(student["grades"]["Math"])

      # Output: 90
       

    • Example 2: Accessing nested elements in a list of dictionaries

      students = [

      {"name": "John", "age": 20},

      {"name": "Emily", "age": 22},

      {"name": "Michael", "age": 21}

      ]

      print(students[0]["name"])

      # Output: John

      print(students[1]["age"])

      # Output: 22

  2. Modifying Nested Values:

    • Example 1: Modifying a nested value in a dictionary

      student = {

      "name": "John",

      "age": 20,

      "courses": ["Math", "Science", "History"],

      "grades": {

      "Math": 90,

      "Science": 85,

      "History": 92

      }

      }

      student["age"] = 21

      student["grades"]["Math"] = 95

      print(student)

      # Output: Updated dictionary with modified values
       

    • Example 2: Modifying a nested value in a list of dictionaries

      students = [

      {"name": "John", "age": 20},

      {"name": "Emily", "age": 22},

      {"name": "Michael", "age": 21}

      ]

      students[1]["age"] = 23

      print(students)

      # Output: Updated list with modified value

  3. Performing Operations on Nested Structures:

    • Example 1: Calculating the average grade from a nested dictionary
      student = {

      "name": "John",

      "grades": {

      "Math": 90,

      "Science": 85,

      "History": 92

      }

      }

      average_grade = sum(student["grades"].values()) / len(student["grades"])

      print(average_grade)

      # Output: Average grade of the student
    •  
    • Example 2: Finding specific elements in a nested list of dictionaries
      students = [

      {"name": "John", "age": 20, "gender": "Male"},

      {"name": "Emily", "age": 22, "gender": "Female"},

      {"name": "Michael", "age": 21, "gender": "Male"}

      ]

      male_students = [student for student in students if student["gender"] == "Male"]

      print(male_students)

      # Output: List of male students

Conclusion: Working with nested data structures in Python allows you to effectively organize and manipulate hierarchical data. In this article, we explored techniques for accessing nested elements, modifying values, and performing operations on nested structures. By mastering these techniques, you'll be equipped to handle complex data scenarios, such as nested dictionaries and lists, with ease. Whether you're working with JSON data, configuration settings, or any other hierarchical data, understanding how to work with nested structures will empower you to write more efficient and flexible Python code.