Skip to main content

Does type dictionary [typeDict] enforce the types on the attributes in Python?

Regular Dictionary [Ptython Dict] may depend on the TypeDict Class so programmers know what the types of input is.


What is TypedDict?

TypedDict is a way to define the structure of a dictionary with specific key-value type annotations. It’s used for static type checking (e.g., with tools like mypy, pyright, or IDEs) to catch mismatched types during development, but it does NOT enforce types at runtime. Think of it as a "schema" for dictionaries.

Key Features

  1. Static Type Checking:
    Helps tools verify that your dictionary keys have the expected types (e.g., "name" should be a str, "age" should be an int).

    • ✅ Catches errors in IDEs or during static analysis.

    • ❌ No runtime validation (you can still assign wrong types).

  2. Use Cases:
    Useful when working with structured data (e.g., JSON, configuration dictionaries, API responses) where you want to document/enforce types during development.

Syntax Example

Python 3.8+ (using typing):

python
Copy
from typing import TypedDict

class Person(TypedDict):
    name: str
    age: int
    is_student: bool  # Optional key (not required unless `total=False` is set)

Python 3.12+ (simpler syntax):

python
Copy
class Person(TypedDict):
    name: str
    age: int
    is_student: bool

How to Use It

python
Copy
# Valid (passes static checks)
person: Person = {
    "name": "Alice",
    "age": 30,
    "is_student": False
}

# Invalid (static type checkers like mypy will flag this)
invalid_person: Person = {
    "name": "Bob",
    "age": "thirty",  # Error: Expected `int`, got `str`
}

Runtime Behavior

At runtime, Python ignores TypedDict annotations. This code will not raise errors:

python
Copy
person = Person(name=123, age="thirty")  # No runtime error (but mypy will complain)

Runtime Validation Options

If you need runtime type enforcement, use libraries like:

  1. pydantic (validates data at runtime):

    python
    Copy
    from pydantic import BaseModel

class Person(BaseModel):
    name: str
    age: int

# Raises ValidationError at runtime for invalid types
Person(name="Alice", age="30")  # Error: 'age' must be an int

  2. Manual validation (e.g., in __init__ methods).

TypedDict vs. dataclass

  • TypedDict: For dictionaries with fixed keys/value types (no runtime checks).

  • @dataclass: For class-based objects with enforced types (if you add validation logic).

Summary

  • TypedDict defines a type-safe dictionary schema for static analysis tools.

  • It does not enforce types at runtime.

  • Use pydantic or similar libraries for runtime validation.

In Python, type annotations (including structures like TypedDict or class __annotations__) do not enforce types at runtime by default. Here's a breakdown:

  1. Type Hints vs. Enforcement:

    • Type annotations (e.g., name: str) are static hints for tools like mypy, pyright, or IDEs. They do not enforce types during execution.

    • Example: You can assign age: int = "twenty" without runtime errors, even though it violates the type hint.

  2. TypedDict (from typing):

    • Defines a dictionary structure with expected key-value types for static analysis.

    • No runtime validation: A TypedDict allows invalid types at runtime (e.g., Person(name=123, age="twenty") won’t raise errors).

  3. Runtime Enforcement Options:

    • Manual Checks: Add validation in __init__ or setters.

    • Libraries: Use pydantic, dataclasses (with @dataclass and __post_init__), or typeguard to enforce types based on annotations.

    • Example with pydantic:

      python
      Copy
      from pydantic import BaseModel

class Person(BaseModel):
    name: str
    age: int

# Raises ValidationError if types mismatch
person = Person(name="Alice", age="thirty")  # Error: 'age' is not an int

Summary: Type dictionaries (annotations, TypedDict) provide structure for static analysis but do not enforce types at runtime. Use additional tools or manual checks for runtime enforcement.

Comments

Post a Comment

Popular posts from this blog

Simple Linear Regression - and Related Regression Loss Functions

Today's Topics: a. Regression Algorithms  b. Outliers - Explained in Simple Terms c. Common Regression Metrics Explained d. Overfitting and Underfitting e. How are Linear and Non Linear Regression Algorithms used in Neural Networks [Future study topics] Regression Algorithms Regression algorithms are a category of machine learning methods used to predict a continuous numerical value. Linear regression is a simple, powerful, and interpretable algorithm for this type of problem. Quick Example: These are the scores of students vs. the hours they spent studying. Looking at this dataset of student scores and their corresponding study hours, can we determine what score someone might achieve after studying for a random number of hours? Example: From the graph, we can estimate that 4 hours of daily study would result in a score near 80. It is a simple example, but for more complex tasks the underlying concept will be similar. If you understand this graph, you will understand this blog. Sim...
Post a Comment
Read more

What problems can AI Neural Networks solve

How does AI Neural Networks solve Problems? What problems can AI Neural Networks solve? Based on effectiveness and common usage, here's the ranking from best to least suitable for neural networks (Classification Problems, Regression Problems and Optimization Problems.) But first some Math, background and related topics as how the Neural Network Learn by training (Supervised Learning and Unsupervised Learning.)  Background Note - Mathematical Precision vs. Practical AI Solutions. Math can solve all these problems with very accurate results. While Math can theoretically solve classification, regression, and optimization problems with perfect accuracy, such calculations often require impractical amounts of time—hours, days, or even years for complex real-world scenarios. In practice, we rarely need absolute precision; instead, we need actionable results quickly enough to make timely decisions. Neural networks excel at this trade-off, providing "good enough" solutions in seco...
Post a Comment
Read more

Activation Functions in Neural Networks

  A Guide to Activation Functions in Neural Networks 🧠 Question: Without activation function can a neural network with many layers be non-linear? Answer: Provided at the end of this document. Activation functions are a crucial component of neural networks. Their primary purpose is to introduce non-linearity , which allows the network to learn the complex, winding patterns found in real-world data. Without them, a neural network, no matter how deep, would just be a simple linear model. In the diagram below the f is the activation function that receives input and send output to next layers. Commonly used activation functions. 1. Sigmoid Function 2. Tanh (Hyperbolic Tangent) 3. ReLU (Rectified Linear Unit - Like an Electronic Diode) 4. Leaky ReLU & PReLU 5. ELU (Exponential Linear Unit) 6. Softmax 7. GELU, Swish, and SiLU 1. Sigmoid Function                       The classic "S-curve," Sigmoid squashes any input value t...
Post a Comment
Read more