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

Concept Flow - Pydantic model basics

Define Pydantic Model Class

↓

Create Model Instance with Data

↓

Pydantic Validates Data

↓

Instance Created

↓

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.

Practice

1. What is the main purpose of a Pydantic model in FastAPI?

easy

A. To validate and organize data automatically

B. To create HTML templates

C. To manage database connections

D. To handle user authentication

Solution

1. Step 1: Understand Pydantic model role

   Pydantic models are designed to check and organize data by validating types and structure. This matches "To validate and organize data automatically".

2. Final Answer:

   To validate and organize data automatically -> Option A

3. Quick Check:

   Pydantic = Data validation and organization [OK]

Hint: Pydantic models are for data validation, not UI or DB [OK]

Common Mistakes:

• Confusing Pydantic with template engines
• Thinking Pydantic manages databases
• Assuming Pydantic handles authentication

2. Which of the following is the correct way to define a simple Pydantic model with a required string field name?

easy

A. class User:
name = str

B. def User():
name: str

C. class User(BaseModel):
name: str

D. class User(BaseModel):
name = 'str'

Solution

1. Step 1: Recall Pydantic model syntax

   A Pydantic model must inherit from BaseModel and define fields with type annotations like class User(BaseModel): name: str. Other options miss inheritance or use assignment instead of type annotation.

2. Final Answer:

   class User(BaseModel):\n name: str -> Option C

3. Quick Check:

   BaseModel + type annotation = correct model [OK]

Hint: Use BaseModel and type annotations for fields [OK]

Common Mistakes:

• Not inheriting from BaseModel
• Using assignment instead of type annotation
• Defining model as a function

3. Given this Pydantic model and code:

from pydantic import BaseModel

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

item = Item(id='10', name='Book')
print(item.id, type(item.id))

What will be the output?

medium

A. 10

B. '10'

C. Error: id must be int

D. 10

Solution

1. Step 1: Understand Pydantic type coercion

   Pydantic automatically converts compatible types like string '10' to int 10, so print(item.id, type(item.id)) outputs 10 <class 'int'>.

2. Final Answer:

   10 <class 'int'> -> Option D

3. Quick Check:

   Pydantic converts compatible types automatically [OK]

Hint: Pydantic converts strings to int if possible [OK]

Common Mistakes:

• Expecting a type error for string input
• Assuming no conversion happens
• Confusing output type with input type

4. What is wrong with this Pydantic model definition?

from pydantic import BaseModel

class Product(BaseModel):
    price: float = 'free'

medium

A. Class name should be lowercase

B. Default value 'free' is a string, but price expects a float

C. Missing import for BaseModel

D. Field price must be an integer

Solution

1. Step 1: Check field type and default value

   price: float = 'free' is invalid because the string 'free' cannot be converted to float, causing a validation error.

2. Final Answer:

   Default value 'free' is a string, but price expects a float -> Option B

3. Quick Check:

   Field type and default must match [OK]

Hint: Default values must match field types exactly [OK]

Common Mistakes:

• Using wrong default value types
• Ignoring type mismatch errors
• Thinking class names must be lowercase

5. You want to create a Pydantic model User with an optional age field that defaults to 18 if not provided. Which is the correct way?

hard

A. class User(BaseModel):
age: int = 18

B. class User(BaseModel):
age: Optional[int]

C. class User(BaseModel):
age: int = None

D. class User(BaseModel):
age: int

Solution

1. Step 1: Understand optional field with default

   To make age optional with default 18, use age: int = 18. age: Optional[int] lacks default (required but allows None), age: int = None is invalid (None not int), age: int requires a value.

2. Final Answer:

   class User(BaseModel):\n age: int = 18 -> Option A

3. Quick Check:

   Default value makes field optional [OK]

Hint: Assign default value to make field optional [OK]

Common Mistakes:

• Using Optional without default
• Assigning None to non-optional int
• Forgetting to assign default value