List and set validation in FastAPI - Step-by-Step Execution

Concept Flow - List and set validation

Receive input data

↓

Check if data is list or set

Yes↓

Validate each item type

↓

Accept data

↓

End

The flow checks if input is a list or set, then validates each item type, accepting or rejecting accordingly.

Execution Sample

FastAPI

from fastapi import FastAPI
from pydantic import BaseModel
from typing import List, Set

class DataModel(BaseModel):
    items_list: List[int]
    items_set: Set[str]

Defines a FastAPI model validating a list of integers and a set of strings.

Execution Table

Step	Input Data	Validation Check	Result	Action
1	{"items_list": [1, 2, 3], "items_set": ["a", "b"]}	Is items_list a list? Yes	Pass	Check each item is int
2	[1, 2, 3]	Are all items int? Yes	Pass	items_list valid
3	["a", "b"]	Is items_set a set? No	Fail	Return validation error for items_set
4	["a", "b"]	Are all items str? Yes	Pass	items_set valid
5	{"items_list": [1, "two", 3], "items_set": ["a", "b"]}	Is items_list a list? Yes	Pass	Check each item is int
6	[1, "two", 3]	Are all items int? No	Fail	Return validation error for items_list
7	{"items_list": [1, 2, 3], "items_set": ["a", 2]}	Is items_set a set? No	Fail	Return validation error for items_set
8	["a", 2]	Are all items str? No	Fail	Return validation error for items_set

💡 Validation stops when any item fails type check, returning error.

Variable Tracker

Variable	Start	After Step 2	After Step 4	After Step 6	After Step 8
items_list	None	[1, 2, 3]	[1, 2, 3]	[1, "two", 3]	[1, 2, 3]
items_set	None	["a", "b"]	["a", "b"]	["a", "b"]	["a", 2]

Key Moments - 3 Insights

Why does validation fail when one item in the list is the wrong type?

Because FastAPI checks each item in the list individually (see steps 2 and 6). If any item is not the expected type, validation fails immediately.

Can the input accept a list where items are strings if the model expects integers?

No, the model strictly requires integers in the list (step 2). Strings cause validation errors (step 6).

Why is a set used for items_set instead of a list?

A set ensures unique items and FastAPI validates it as a set type (steps 3 and 7). This affects how duplicates are handled.

Visual Quiz - 3 Questions

Look at the execution table, what is the result of validating items_list at step 6?

A Pass

B Skipped

C Fail

D Error ignored

💡 Hint

Check the 'Result' column at step 6 in the execution_table.

At which step does validation confirm all items in items_set are strings?

A Step 3

B Step 4

C Step 7

D Step 8

💡 Hint

Look for the step where items_set items are checked for type in the execution_table.

If items_list had a float instead of an int, how would the validation result change at step 2?

A It would fail because float is not int

B It would pass because float is a number

C It would skip validation

D It would convert float to int automatically

💡 Hint

Refer to how strict type checking is shown in steps 2 and 6 of the execution_table.

Concept Snapshot

FastAPI validates lists and sets by checking the container type first.
Then it validates each item inside for the correct type.
If any item fails, validation returns an error.
Use List[type] for ordered collections, Set[type] for unique items.
This ensures input data matches expected structure and types.

Full Transcript

This visual execution trace shows how FastAPI validates list and set inputs using Pydantic models. First, it checks if the input data is the correct container type (list or set). Then it validates each item inside for the expected type, such as integers for a list or strings for a set. If all items pass, the data is accepted. If any item fails type validation, FastAPI returns an error immediately. This ensures the API receives data in the correct format and type, preventing bugs and errors later. The trace includes examples of valid and invalid inputs, showing exactly when and why validation passes or fails.