FastAPI How to Convert Pydantic Model to Dict Easily

In FastAPI, you can convert a Pydantic model to a dictionary by calling the .dict() method on the model instance, like model_instance.dict().

Examples

InputUser(name='Alice', age=30)

Output{"name": "Alice", "age": 30}

InputUser(name='Bob', age=None)

Output{"name": "Bob", "age": null}

InputUser(name='Eve', age=25, extra='ignored')

Output{"name": "Eve", "age": 25}

How to Think About It

To convert a Pydantic model to a dictionary, you simply use the built-in .dict() method that Pydantic provides. This method returns all the model's data as a plain Python dictionary, which is easy to use or send as JSON in FastAPI responses.

Algorithm

1

Create or receive a Pydantic model instance.

2

Call the <code>.dict()</code> method on the model instance.

3

Use or return the resulting dictionary.

Code

python

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class User(BaseModel):
    name: str
    age: int | None = None

@app.get("/user_dict")
def get_user_dict():
    user = User(name="Alice", age=30)
    user_dict = user.dict()
    print(user_dict)  # {'name': 'Alice', 'age': 30}
    return user_dict

Output

{'name': 'Alice', 'age': 30}

Dry Run

Let's trace converting a User model instance with name 'Alice' and age 30 to a dictionary.

1

Create User instance

user = User(name='Alice', age=30)

2

Call .dict() method

user_dict = user.dict() # returns {'name': 'Alice', 'age': 30}

3

Use the dictionary

print(user_dict) outputs {'name': 'Alice', 'age': 30}

Step	Action	Value
1	Create User instance	User(name='Alice', age=30)
2	Call .dict()	{'name': 'Alice', 'age': 30}
3	Print dictionary	{'name': 'Alice', 'age': 30}

Why This Works

Step 1: Pydantic Model Holds Data

The Pydantic model stores data in attributes like name and age.

Step 2: Use .dict() to Extract Data

Calling .dict() converts the model's data into a plain Python dictionary.

Step 3: Dictionary is Easy to Use

The dictionary can be used directly in FastAPI responses or for other processing.

Alternative Approaches

json() method

python

user_json = user.json()  # returns JSON string like '{"name": "Alice", "age": 30}'

Returns a JSON string instead of a dictionary; useful if you want a JSON text output.

dict(exclude_unset=True)

python

user_dict = user.dict(exclude_unset=True)  # excludes fields not set explicitly

Useful to get only fields that were set, ignoring defaults.

Complexity: O(n) time, O(n) space

Time Complexity

The .dict() method iterates over all fields in the model, so it runs in linear time relative to the number of fields.

Space Complexity

It creates a new dictionary holding all field data, so space usage is linear with the number of fields.

Which Approach is Fastest?

Using .dict() is the fastest and safest way to convert a Pydantic model to a dictionary compared to manual methods.

Approach	Time	Space	Best For
.dict()	O(n)	O(n)	Safe, full dict conversion
.json()	O(n)	O(n)	JSON string output
dict(exclude_unset=True)	O(n)	O(n)	Partial dict with set fields only