Pydantic model basics in FastAPI - Step-by-Step Execution

Concept Flow - Pydantic model basics

Define Pydantic Model Class

↓

Create Model Instance with Data

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Pydantic Validates Data

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Instance Created

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Access Model Attributes

This flow shows how you define a Pydantic model, create an instance with data, Pydantic validates it, and then you can use the model's attributes if valid.

Execution Sample

FastAPI

from pydantic import BaseModel

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

user = User(id=1, name="Alice")
print(user.name)

Defines a User model, creates an instance with valid data, and prints the user's name.

Execution Table

Step	Action	Input Data	Validation Result	Output/State
1	Define User model	id:int, name:str	N/A	User class ready
2	Create User instance	{id: 1, name: 'Alice'}	Valid	User(id=1, name='Alice') created
3	Access user.name	N/A	N/A	'Alice' printed
4	Create User instance	{id: 'abc', name: 'Bob'}	Invalid	ValidationError raised

💡 Execution stops on ValidationError if input data is invalid

Variable Tracker

Variable	Start	After Step 2	After Step 4
user	undefined	User(id=1, name='Alice')	ValidationError (no instance)

Key Moments - 2 Insights

Why does creating User(id='abc', name='Bob') cause an error?

Because 'abc' is a string but 'id' expects an integer. See execution_table step 4 where ValidationError is raised.

Can I access attributes before creating an instance?

No, attributes belong to instances. Step 3 shows accessing user.name after instance creation.

Visual Quiz - 3 Questions

Look at the execution table, what is the output when user.name is accessed at step 3?

A 'Alice'

B 1

C User instance

D ValidationError

💡 Hint

Check the Output/State column at step 3 in the execution_table

At which step does the ValidationError occur?

A Step 3

B Step 4

C Step 2

D No error

💡 Hint

Look for 'ValidationError' in the Validation Result column

If you change 'id' to a float like 1.5 in step 2, what happens?

A Instance created with id=1.5

B id converted to int automatically

C ValidationError raised

D No effect, works as string

💡 Hint

Pydantic expects int for id, see how invalid types cause ValidationError in step 4

Concept Snapshot

Pydantic models define data shapes with types.
Create instances by passing data.
Pydantic checks types and raises errors if invalid.
Access attributes on valid instances.
Used in FastAPI for request/response data validation.

Full Transcript

This visual execution shows how Pydantic models work in FastAPI. First, you define a model class with typed fields. Then you create an instance by passing data. Pydantic validates the data types. If data is valid, an instance is created and you can access its attributes. If data is invalid, Pydantic raises a ValidationError stopping execution. For example, creating User(id=1, name='Alice') works fine, but User(id='abc', name='Bob') causes an error because 'abc' is not an integer. This helps keep data clean and predictable in your app.