Extra Data Types

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.Python 3.10+Python 3.9+Python 3.8+Python 3.10+ non-AnnotatedPython 3.8+ non-Annotatedfrom datetime import datetime, time, timedelta from typing import Annotated 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: Annotated[datetime | None, Body()] = None, end_datetime: Annotated[datetime | None, Body()] = None, repeat_at: Annotated[time | None, Body()] = None, process_after: Annotated[timedelta | None, 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:Python 3.10+Python 3.9+Python 3.8+Python 3.10+ non-AnnotatedPython 3.8+ non-Annotatedfrom datetime import datetime, time, timedelta from typing import Annotated 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: Annotated[datetime | None, Body()] = None, end_datetime: Annotated[datetime | None, Body()] = None, repeat_at: Annotated[time | None, Body()] = None, process_after: Annotated[timedelta | None, 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, }