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

Concept Flow - Pydantic model definition

Define class inheriting BaseModel

↓

Add fields with types

↓

Create instance with data

↓

Pydantic validates data

↓

Valid data

↓

Instance created

You define a class with fields and types, then create an instance. Pydantic checks the data and either creates the object or raises an error.

Execution Sample

FastAPI

from pydantic import BaseModel

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

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

Defines a User model with id and name, then creates a valid User instance.

Execution Table

Step	Action	Input Data	Validation Result	Output
1	Define User model	N/A	N/A	User class ready with id:int, name:str
2	Create User instance	{id: 1, name: 'Alice'}	Valid	User(id=1, name='Alice')
3	Create User instance	{id: 'one', name: 'Bob'}	Invalid	ValidationError raised: id must be int

💡 Execution stops when instance is created or validation error is raised.

Variable Tracker

Variable	Start	After Step 2	After Step 3
user	undefined	User(id=1, name='Alice')	ValidationError (no instance)

Key Moments - 2 Insights

Why does Pydantic raise an error when id is a string instead of int?

Because the model defines id as int, Pydantic checks the type at instance creation (see execution_table step 3) and raises a ValidationError if the type does not match.

Can I create a User without providing all fields?

No, unless you provide default values in the model. Otherwise, Pydantic requires all fields defined without defaults to be present (see execution_table step 2).

Visual Quiz - 3 Questions

Look at the execution table, what is the output when creating User with id=1 and name='Alice'?

A User(id=1, name='Alice')

B ValidationError

C None

D User(id='1', name='Alice')

💡 Hint

Check execution_table row 2 Output column

At which step does Pydantic raise a ValidationError?

A Step 1

B Step 2

C Step 3

D No error raised

💡 Hint

Look at execution_table rows and Validation Result column

If you remove the type annotation for 'id', what happens when creating User with id='one'?

A ValidationError still raised

B User instance created with id='one'

C Syntax error

D Runtime error unrelated to validation

💡 Hint

Pydantic validates only annotated fields; see how type affects validation in execution_table

Concept Snapshot

Pydantic model definition:
- Create a class inheriting BaseModel
- Define fields with Python types
- Instantiate with data dict
- Pydantic validates types automatically
- Raises ValidationError if data invalid
- Used in FastAPI for request/response models

Full Transcript

Pydantic model definition means creating a Python class that inherits from BaseModel. You add fields with types like int or str. When you create an instance of this class with data, Pydantic checks if the data matches the types. If it does, the instance is created. If not, Pydantic raises a ValidationError. For example, if you define a User model with id as int and name as str, and you try to create a User with id='one' (a string), Pydantic will raise an error because it expects an int. This helps catch mistakes early and ensures your data is correct. This process is key in FastAPI to handle data validation automatically.

Practice

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

easy

A. To specify the expected data structure and validate incoming data automatically

B. To create database tables automatically

C. To style the API responses with CSS

D. To handle user authentication

Solution

1. Step 1: Understand Pydantic model role

   Pydantic models define the shape and types of data expected in requests or responses.

2. Step 2: Identify validation feature

   They automatically check if incoming data matches the defined types and structure.

3. Final Answer:

   To specify the expected data structure and validate incoming data automatically -> Option A

4. Quick Check:

   Data validation = C [OK]

Hint: Pydantic models shape and check data automatically [OK]

Common Mistakes:

• Confusing Pydantic models with database models
• Thinking Pydantic handles styling or authentication
• Assuming Pydantic models run code logic

2. Which of the following is the correct way to define a Pydantic model with a required string field name and an optional integer field age?

easy

A. class User(BaseModel): name: str; age: int = None

B. class User(BaseModel): name: str; age: Optional[int] = None

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

D. class User(BaseModel): name: str = None; age: int

Solution

1. Step 1: Check required and optional fields syntax

   Required fields have type only, optional fields use Optional[type] with default None.

2. Step 2: Identify correct syntax

   class User(BaseModel): name: str; age: Optional[int] = None uses name: str (required) and age: Optional[int] = None (optional), which is correct.

3. Final Answer:

   class User(BaseModel): name: str; age: Optional[int] = None -> Option B

4. Quick Check:

   Optional fields use Optional[type] = None [OK]

Hint: Use Optional[type] = None for optional fields [OK]

Common Mistakes:

• Using plain type with default None without Optional
• Assigning type with equals sign incorrectly
• Setting required fields with default None

3. Given this Pydantic model:

from pydantic import BaseModel

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

item = Item(id='5', name='Book', price=12.99)

What will be the type of item.id after creation?

medium

A. TypeError raised

B. str with value '5'

C. float with value 5.0

D. int with value 5

Solution

1. Step 1: Understand Pydantic type coercion

   Pydantic automatically converts compatible types to the declared type if possible.

2. Step 2: Analyze input and conversion

   The string '5' can be converted to int 5, so item.id becomes int 5.

3. Final Answer:

   int with value 5 -> Option D

4. Quick Check:

   Pydantic converts compatible types automatically [OK]

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

Common Mistakes:

• Assuming input type must match exactly without conversion
• Expecting a TypeError for string input
• Thinking the value stays as string

4. What is wrong with this Pydantic model definition?

from pydantic import BaseModel

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

medium

A. Missing import for Optional

B. Class must inherit from object, not BaseModel

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

D. Field names cannot be lowercase

Solution

1. Step 1: Check field types and default values

   The field price expects a float but is given a string default 'free'.

2. Step 2: Understand Pydantic validation

   Pydantic will raise a validation error because 'free' cannot be converted to float.

3. Final Answer:

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

4. Quick Check:

   Default value type must match field type [OK]

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

Common Mistakes:

• Assigning default values of wrong type
• Confusing inheritance requirements
• Thinking field names have naming restrictions

5. You want to create a Pydantic model User with a required username (string), an optional email (string), and a roles list of strings that defaults to an empty list. Which is the correct way to define it?

hard

A. class User(BaseModel): username: str; email: Optional[str] = None; roles: list[str] = Field(default_factory=list)

B. class User(BaseModel): username: str; email: str = None; roles: list[str] = []

C. class User(BaseModel): username: str; email: Optional[str]; roles: list[str] = None

D. class User(BaseModel): username: str; email: Optional[str] = None; roles: list[str] = []

Solution

1. Step 1: Identify optional email field syntax

   Email is optional, so use Optional[str] = None to allow missing value.

2. Step 2: Handle mutable default for roles list

   Using roles: list[str] = [] is unsafe because lists are mutable defaults shared across instances. Use Field(default_factory=list) to create a new list each time.

3. Final Answer:

   class User(BaseModel): username: str; email: Optional[str] = None; roles: list[str] = Field(default_factory=list) -> Option A

4. Quick Check:

   Use Field(default_factory=list) for mutable defaults [OK]

Hint: Use Field(default_factory=list) for empty list defaults [OK]

Common Mistakes:

• Using mutable empty list as default directly
• Not marking optional fields with Optional and default None
• Assigning None to non-optional list fields