Pydantic v2 - Core Concepts Review

This document explains the main Pydantic concepts used in the exercises from the Cosmic Data module (nº9).

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What is Pydantic?

Pydantic is a Python library used for:

• Data validation
• Automatic type conversion
• Parsing external data
• Safer and cleaner models

It allows developers to define strict data structures using Python type hints.

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BaseModel

BaseModel is the foundation of every Pydantic model.

When a class inherits from BaseModel, Pydantic automatically validates the data.

Example

from pydantic import BaseModel

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

Creating an object

user = User(name="Sara", age=20)

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Automatic Type Conversion

Pydantic automatically converts compatible types.

Example

user = User(name="Sara", age="20")

Even though "20" is a string, Pydantic converts it into an integer.

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ValidationError

If invalid data is provided, Pydantic raises a ValidationError.

Example

User(name="Sara", age="abc")

Output:

ValidationError

Because "abc" cannot be converted into an integer.

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Field

Field() is used to add validation constraints and metadata to attributes.

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Numeric Constraints

Example

from pydantic import Field

age: int = Field(ge=18, le=80)

Meaning

• ge = greater or equal
• le = less or equal

This means age must be between 18 and 80.

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String Constraints

Example

name: str = Field(min_length=3, max_length=50)

The string: - must contain at least 3 characters - cannot exceed 50 characters

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Float Constraints

Example

power_level: float = Field(ge=0.0, le=100.0)

Useful for percentages and ranges.

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Default Values

Pydantic supports default values.

Example

is_active: bool = True

If no value is provided, the field automatically becomes True.

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Optional Fields

Optional fields may contain a value or None.

Example

from typing import Optional

notes: Optional[str]

Equivalent to:

str | None

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Enum

Enums restrict a field to predefined values.

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Example

from enum import Enum

class Rank(str, Enum):
    CAPTAIN = "captain"
    COMMANDER = "commander"

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Why use Enum?

Enums prevent invalid values.

Valid

rank="captain"

Invalid

rank="banana"

This would raise a validation error.

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model_validator

@model_validator is used for custom validation logic.

It is useful when validation depends on multiple fields.

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Pydantic v2 Syntax

from pydantic import model_validator

@model_validator(mode='after')

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mode='after'

This means:

1. Pydantic validates all fields first
2. Then your custom validation logic runs

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Example

@model_validator(mode='after')
def validate_contact(self):

    if self.contact_type == "physical" and not self.is_verified:
        raise ValueError("Physical contacts must be verified")

    return self

This type of validation cannot be done with Field() alone.

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return self

Inside a model validator, return self is required.

It returns the validated model instance.

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Nested Models

Nested models are models inside other models.

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Example

class CrewMember(BaseModel):
    name: str

class SpaceMission(BaseModel):
    crew: list[CrewMember]

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What happens automatically?

Pydantic: - validates the list - validates every CrewMember - converts data automatically

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Dictionary Unpacking (**data)

**data converts a dictionary into keyword arguments.

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Example

data = {
    "name": "Sara",
    "age": 20
}

User(**data)

Equivalent to:

User(name="Sara", age=20)

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Datetime Validation

Pydantic automatically parses datetime values.

Example

from datetime import datetime

created_at: datetime

It can convert: - datetime objects - valid datetime strings - timestamps

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Field vs model_validator

Field

Used for single-field validation.

Example

age: int = Field(ge=18)

Validates only the age field.

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model_validator

Used for validation involving multiple fields.

Example

if duration_days > 365 and crew_size < 5:
    raise ValueError("Long missions require larger crews")

This depends on multiple attributes.

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Main Advantages of Pydantic

• Cleaner code
• Automatic validation
• Better error messages
• Automatic type conversion
• Easier API and data handling
• Strong typing support

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Summary

Concept	Purpose
BaseModel	Creates validated models
Field	Adds constraints to fields
Enum	Restricts possible values
ValidationError	Raised when validation fails
model_validator	Custom validation logic
Optional	Allows None values
Nested Models	Models inside other models
datetime	Automatic datetime parsing

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