real python pydantic

Monica 123 Published: 09/21/2024

real python pydantic

I apologize for the misunderstanding earlier. Here's a detailed explanation of Pydantic and how it is used in Python:

Pydantic is a Python library that helps you to validate, sanitize and generate data structures. It is heavily inspired by the popular Java library called Hibernate Validator.

In Python, validation of data is very important as it ensures that the data being processed or stored meets certain criteria, such as format, length, range, etc. This is where Pydantic comes in. It provides a simple and efficient way to validate your data.

Pydantic is particularly useful for validating JSON data, which is commonly used when working with web APIs. It allows you to define a model of the data that you expect to receive, and then it validates the incoming data against that model.

Here's an example of how you can use Pydantic:

from pydantic import BaseModel

class User(BaseModel):


name: str


age: int


user = User(name="John", age=30)


print(user.dict())  # Output: {'name': 'John', 'age': 30}

In this example, we're defining a User model with two fields: name and age. The name field is expected to be a string, while the age field is expected to be an integer.

If you try to create a User object with invalid data, such as a non-string value for name or a non-integer value for age, Pydantic will raise a validation error:

user = User(name=123, age="hello")
Output: ValueError: 1 validation error for User
name should be string

print(user.dict())

In this case, the output is an error message indicating that there was one validation error. The name field is expected to be a string, but it received a non-string value.

Pydantic provides several features to help you define your data models and validate your data:

Type checking: Pydantic allows you to specify the types of the fields in your model using Python type hints (e.g., str, int, etc.). Validation rules: You can use validation rules such as max_length and min_value to ensure that the input data meets certain criteria. Generation: Pydantic allows you to generate a dictionary or JSON from your model, which is useful for serializing your data. Parsing: It also allows you to parse JSON into your model, making it easy to work with JSON data in Python. Error handling: Pydantic provides various error messages and exceptions that you can use to handle errors when validating your data.

In summary, Pydantic is a powerful library for working with structured data in Python. It provides a simple way to define models of your data, validate the input data against those models, generate JSON from your models, parse JSON into your models, and handle errors when things go wrong.

What is marshmallow Python vs pydantic?

I see you're looking to learn about some popular libraries in the Python world! Marshmallow and Pydantic are two distinct libraries that serve different purposes, despite both being used for data serialization and validation.

Marshmallow (https://marshmallow.readthedocs.io/en/latest/) is a widely-used library for serializing and deserializing complex objects. It provides a simple, flexible way to convert Python objects into JSON, YAML, or other formats, and vice versa. Marshmallow's core feature is its ability to handle nested objects and collections (like lists or dictionaries) seamlessly.

Marshmallow's strengths lie in its ease of use, flexibility, and broad format support. You can customize the serialization process by defining schema classes for each type of object you want to serialize. This allows you to control exactly how your data gets converted into a specific format. Marshmallow also supports advanced features like validation (e.g., checking if an attribute is present or has a certain value), which makes it suitable for use cases where you need to ensure the integrity of your data.

On the other hand, Pydantic (https://docs.pydantic.com/) is a more recent library that emerged as an alternative to Marshmallow. It's designed to simplify the process of creating robust and predictable serializable models using Python 3.7+ features like type hints and the dataclasses module.

Pydantic excels in providing strong typing and validation for your data, which is crucial when working with structured data (like JSON or XML). Its core feature is the ability to define robust and self-documenting schemas for complex objects using Python's built-in type hinting system. Pydantic also provides advanced features like recursive validation (for nested objects), automatic conversion of primitive types, and support for custom converters.

While both libraries share some similarities in their functionality, they have distinct philosophies:

Marshmallow focuses on flexibility and ease of use, aiming to provide a straightforward way to serialize and deserialize complex data structures. Its strength lies in its adaptability and broad format support. Pydantic prioritizes strong typing and validation, leveraging Python's built-in type hinting system to create robust and predictable serializable models. It excels at providing self-documenting schemas for complex objects.

In summary:

Use Marshmallow when you need a flexible and easy-to-use library for handling diverse data formats. Use Pydantic when you prioritize strong typing and validation, and want to define robust and predictable serializable models using Python's type hinting system.

Now that you've learned about these two powerful libraries, which one do you think you'll use first?

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