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Extra Data Types

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Up to now, you have been using common data types, like:

  • int
  • float
  • str
  • bool

But you can also use more complex data types.

And you will still have the same features as seen up to now:

  • Great editor support.
  • Data conversion from incoming requests.
  • Data conversion for response data.
  • Data validation.
  • Automatic annotation and documentation.

Other data types

Here are some of the additional data types you can use:

  • UUID:
    • A standard "Universally Unique Identifier", common as an ID in many databases and systems.
    • In requests and responses will be represented as a str.
  • datetime.datetime:
    • A Python datetime.datetime.
    • In requests and responses will be represented as a str in ISO 8601 format, like: 2008-09-15T15:53:00+05:00.
  • datetime.date:
    • Python datetime.date.
    • In requests and responses will be represented as a str in ISO 8601 format, like: 2008-09-15.
  • datetime.time:
    • A Python datetime.time.
    • In requests and responses will be represented as a str in ISO 8601 format, like: 14:23:55.003.
  • datetime.timedelta:
    • A Python datetime.timedelta.
    • In requests and responses will be represented as a float of total seconds.
    • Pydantic also allows representing it as a "ISO 8601 time diff encoding", see the docs for more info.
  • frozenset:
    • In requests and responses, treated the same as a set:
      • In requests, a list will be read, eliminating duplicates and converting it to a set.
      • In responses, the set will be converted to a list.
      • The generated schema will specify that the set values are unique (using JSON Schema's uniqueItems).
  • bytes:
    • Standard Python bytes.
    • In requests and responses will be treated as str.
    • The generated schema will specify that it's a str with binary "format".
  • Decimal:
    • Standard Python Decimal.
    • In requests and responses, handled the same as a float.
  • You can check all the valid pydantic data types here: Pydantic data types.

Example

Here's an example path operation with parameters using some of the above types.

from datetime import datetime, time, timedelta
from typing import Optional
from uuid import UUID

from fastapi import Body, FastAPI

app = FastAPI()


@app.put("/items/{item_id}")
async def read_items(
    item_id: UUID,
    start_datetime: Optional[datetime] = Body(None),
    end_datetime: Optional[datetime] = Body(None),
    repeat_at: Optional[time] = Body(None),
    process_after: Optional[timedelta] = Body(None),
):
    start_process = start_datetime + process_after
    duration = end_datetime - start_process
    return {
        "item_id": item_id,
        "start_datetime": start_datetime,
        "end_datetime": end_datetime,
        "repeat_at": repeat_at,
        "process_after": process_after,
        "start_process": start_process,
        "duration": duration,
    }

Note that the parameters inside the function have their natural data type, and you can, for example, perform normal date manipulations, like:

from datetime import datetime, time, timedelta
from typing import Optional
from uuid import UUID

from fastapi import Body, FastAPI

app = FastAPI()


@app.put("/items/{item_id}")
async def read_items(
    item_id: UUID,
    start_datetime: Optional[datetime] = Body(None),
    end_datetime: Optional[datetime] = Body(None),
    repeat_at: Optional[time] = Body(None),
    process_after: Optional[timedelta] = Body(None),
):
    start_process = start_datetime + process_after
    duration = end_datetime - start_process
    return {
        "item_id": item_id,
        "start_datetime": start_datetime,
        "end_datetime": end_datetime,
        "repeat_at": repeat_at,
        "process_after": process_after,
        "start_process": start_process,
        "duration": duration,
    }