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Ankit malik
Ankit malik

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What is Pydantic in python

#python #pydantic

Introduction

In Python, data validation and settings management are common challenges, especially when working with APIs, configuration files, or user inputs. While Python is a dynamically typed language, developers often need ways to ensure that data provided should be of expected types. This is where Pydantic comes into picture.

Pydantic is a data validation and parsing library that uses Python type annotations to validate structured data. Built on top of Python 3.6+ type hints, Pydantic provides automatic data validation and conversion, making it especially popular in FastAPI and other modern Python frameworks.

Why Use Pydantic

Here are some reasons why Pydantic is a go-to tool for developers:

  • Data Validation: Automatically validate input data using standard Python types.
  • Error Reporting: Provides clear, structured error messages when validation fails.
  • Performance: Pydantic is built using dataclasses and is extremely fast.
  • Type Coercion: Automatically converts compatible types (e.g., string to integer).
  • Integration: Plays well with modern Python frameworks like FastAPI, which uses it extensively for request and response validation.
  • Ease of Use: Leverages Python type hints, reducing the need for verbose validation logic.

Code Example

Here’s a simple example to demonstrate Pydantic in action:

from pydantic import BaseModel, ValidationError
from typing import List

class User(BaseModel):
    id: int
    name: str
    is_active: bool = True
    tags: List[str] = []

# Valid input
user_data = {
    'id': '123',  # Will be coerced to int
    'name': 'Alice',
    'tags': ['python', 'developer']
}

user = User(**user_data)
print(user)
print(user.model_dump())

# Invalid input
try:
    invalid_data = {'id': 'abc', 'name': 234}
    User(**invalid_data)
except ValidationError as e:
    print(e)
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Output:

id=123 name='Alice' is_active=True tags=['python', 'developer']
{'id': 123, 'name': 'Alice', 'is_active': True, 'tags': ['python', 'developer']}
2 validation errors for User
id
  Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='abc', input_type=str]
    For further information visit https://errors.pydantic.dev/2.11/v/int_parsing
name
  Input should be a valid string [type=string_type, input_value=234, input_type=int]
    For further information visit https://errors.pydantic.dev/2.11/v/string_type
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While both Pydantic and TypedDict from the typing module allow type annotations for dictionaries, they serve different purposes:

Feature Pydantic TypedDict
Validation Yes No
Default Values Yes Yes (in Python 3.9+)
Type Coercion Yes (e.g., str → int) No
Runtime Behavior Full-featured at runtime Mainly for static analysis
Error Reporting Detailed errors via exceptions None
Performance Slightly slower due to checks Faster (no runtime overhead)

Summary: Use TypedDict when you only need type hints for static checking (e.g., with mypy). Use Pydantic when you need runtime validation, coercion, and structured error handling.

Conclusion

Pydantic is a powerful and user-friendly library that brings the type-safe validation into dynamic Python code. It helps developers write cleaner, more maintainable code by enforcing data structure constraints and providing detailed feedback on invalid inputs. Whether you're building a REST API, working with external data sources, or managing configurations, Pydantic can be an essential part of your Python toolkit.

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