Introduction

In Python, dictionaries are one of the most flexible and widely used data structures. However, they lack explicit structure, which can lead to bugs and hard-to-read code, especially in larger codebases. To address this, Python's typing module introduced TypedDict in PEP 589. TypedDict allows you to define the expected structure of dictionary objects — including the type of each key-value pair — while still using the familiar dictionary syntax.
It's similar to structs with Tags in Golang.

Benefits

Using TypedDict provides several key benefits:

Type Safety: It helps catch bugs during development by enforcing value types.

Readability: It makes the expected structure of dictionaries clear and explicit.

Better Tooling Support: Static analysis tools can provide autocomplete, linting, and validation.

Improved Documentation: Acts as a form of self-documentation for function parameters or data models.

Use Cases

Here are common use cases where TypedDict proves valuable:

API Responses: Ensuring API response data conforms to an expected schema.

Configuration Files: Defining the structure of configuration options.

Data Models in Applications: Replacing lightweight classes or when class overhead is unnecessary.

Interfacing with JSON: Typing JSON-like nested data structures.

Sample Code 

from typing import TypedDict

# Define the TypedDict
class Employee(TypedDict):
    name: str
    id: int
    department: str
    is_active: bool

# Function using the TypedDict
def print_employee_info(emp: Employee) -> None:
    print(f"Name: {emp['name']}")
    print(f"ID: {emp['id']}")
    print(f"Department: {emp['department']}")
    print(f"Active: {'Yes' if emp['is_active'] else 'No'}")

# Example usage
employee_data = {
    'name': 'Alice Johnson',
    'id': 1024,
    'department': 'Engineering',
    'is_active': True
}

print_employee_info(employee_data)

Output:

Name: Alice Johnson
ID: 1024
Department: Engineering
Active: Yes

Conclusion

TypedDict bridges the gap between Python's dynamic nature and the benefits of static typing. It allows developers to write safer, more maintainable, and self-explanatory code, especially when working with structured dictionary data. As type checking gains traction in Python, TypedDict becomes an essential tool for any developer working with complex or nested dictionary structures.