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Python Types Intro

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Python has support for optional "type hints" (also called "type annotations").

These "type hints" or annotations are a special syntax that allow declaring the type of a variable.

By declaring types for your variables, editors and tools can give you better support.

This is just a quick tutorial / refresher about Python type hints. It covers only the minimum necessary to use them with FastAPI... which is actually very little.

FastAPI is all based on these type hints, they give it many advantages and benefits.

But even if you never use FastAPI, you would benefit from learning a bit about them.

Note

If you are a Python expert, and you already know everything about type hints, skip to the next chapter.

Motivation

Let's start with a simple example:

def get_full_name(first_name, last_name):
    full_name = first_name.title() + " " + last_name.title()
    return full_name


print(get_full_name("john", "doe"))

Calling this program outputs:

John Doe

The function does the following:

  • Takes a first_name and last_name.
  • Converts the first letter of each one to upper case with title().
  • Concatenates them with a space in the middle.
def get_full_name(first_name, last_name):
    full_name = first_name.title() + " " + last_name.title()
    return full_name


print(get_full_name("john", "doe"))

Edit it

It's a very simple program.

But now imagine that you were writing it from scratch.

At some point you would have started the definition of the function, you had the parameters ready...

But then you have to call "that method that converts the first letter to upper case".

Was it upper? Was it uppercase? first_uppercase? capitalize?

Then, you try with the old programmer's friend, editor autocompletion.

You type the first parameter of the function, first_name, then a dot (.) and then hit Ctrl+Space to trigger the completion.

But, sadly, you get nothing useful:

Add types

Let's modify a single line from the previous version.

We will change exactly this fragment, the parameters of the function, from:

    first_name, last_name

to:

    first_name: str, last_name: str

That's it.

Those are the "type hints":

def get_full_name(first_name: str, last_name: str):
    full_name = first_name.title() + " " + last_name.title()
    return full_name


print(get_full_name("john", "doe"))

That is not the same as declaring default values like would be with:

    first_name="john", last_name="doe"

It's a different thing.

We are using colons (:), not equals (=).

And adding type hints normally doesn't change what happens from what would happen without them.

But now, imagine you are again in the middle of creating that function, but with type hints.

At the same point, you try to trigger the autocomplete with Ctrl+Space and you see:

With that, you can scroll, seeing the options, until you find the one that "rings a bell":

More motivation

Check this function, it already has type hints:

def get_name_with_age(name: str, age: int):
    name_with_age = name + " is this old: " + age
    return name_with_age

Because the editor knows the types of the variables, you don't only get completion, you also get error checks:

Now you know that you have to fix it, convert age to a string with str(age):

def get_name_with_age(name: str, age: int):
    name_with_age = name + " is this old: " + str(age)
    return name_with_age

Declaring types

You just saw the main place to declare type hints. As function parameters.

This is also the main place you would use them with FastAPI.

Simple types

You can declare all the standard Python types, not only str.

You can use, for example:

  • int
  • float
  • bool
  • bytes
def get_items(item_a: str, item_b: int, item_c: float, item_d: bool, item_e: bytes):
    return item_a, item_b, item_c, item_d, item_d, item_e

Generic types with type parameters

There are some data structures that can contain other values, like dict, list, set and tuple. And the internal values can have their own type too.

These types that have internal types are called "generic" types. And it's possible to declare them, even with their internal types.

To declare those types and the internal types, you can use the standard Python module typing. It exists specifically to support these type hints.

Newer versions of Python

The syntax using typing is compatible with all versions, from Python 3.6 to the latest ones, including Python 3.9, Python 3.10, etc.

As Python advances, newer versions come with improved support for these type annotations and in many cases you won't even need to import and use the typing module to declare the type annotations.

If you can choose a more recent version of Python for your project, you will be able to take advantage of that extra simplicity.

In all the docs there are examples compatible with each version of Python (when there's a difference).

For example "Python 3.6+" means it's compatible with Python 3.6 or above (including 3.7, 3.8, 3.9, 3.10, etc). And "Python 3.9+" means it's compatible with Python 3.9 or above (including 3.10, etc).

If you can use the latest versions of Python, use the examples for the latest version, those will have the best and simplest syntax, for example, "Python 3.10+".

List

For example, let's define a variable to be a list of str.

Declare the variable, with the same colon (:) syntax.

As the type, put list.

As the list is a type that contains some internal types, you put them in square brackets:

def process_items(items: list[str]):
    for item in items:
        print(item)

From typing, import List (with a capital L):

from typing import List


def process_items(items: List[str]):
    for item in items:
        print(item)

Declare the variable, with the same colon (:) syntax.

As the type, put the List that you imported from typing.

As the list is a type that contains some internal types, you put them in square brackets:

from typing import List


def process_items(items: List[str]):
    for item in items:
        print(item)

Info

Those internal types in the square brackets are called "type parameters".

In this case, str is the type parameter passed to List (or list in Python 3.9 and above).

That means: "the variable items is a list, and each of the items in this list is a str".

Tip

If you use Python 3.9 or above, you don't have to import List from typing, you can use the same regular list type instead.

By doing that, your editor can provide support even while processing items from the list:

Without types, that's almost impossible to achieve.

Notice that the variable item is one of the elements in the list items.

And still, the editor knows it is a str, and provides support for that.

Tuple and Set

You would do the same to declare tuples and sets:

def process_items(items_t: tuple[int, int, str], items_s: set[bytes]):
    return items_t, items_s

from typing import Set, Tuple


def process_items(items_t: Tuple[int, int, str], items_s: Set[bytes]):
    return items_t, items_s

This means:

  • The variable items_t is a tuple with 3 items, an int, another int, and a str.
  • The variable items_s is a set, and each of its items is of type bytes.

Dict

To define a dict, you pass 2 type parameters, separated by commas.

The first type parameter is for the keys of the dict.

The second type parameter is for the values of the dict:

def process_items(prices: dict[str, float]):
    for item_name, item_price in prices.items():
        print(item_name)
        print(item_price)

from typing import Dict


def process_items(prices: Dict[str, float]):
    for item_name, item_price in prices.items():
        print(item_name)
        print(item_price)

This means:

  • The variable prices is a dict:
    • The keys of this dict are of type str (let's say, the name of each item).
    • The values of this dict are of type float (let's say, the price of each item).

Union

You can declare that a variable can be any of several types, for example, an int or a str.

In Python 3.6 and above (including Python 3.10) you can use the Union type from typing and put inside the square brackets the possible types to accept.

In Python 3.10 there's also a new syntax where you can put the possible types separated by a vertical bar (|).

def process_item(item: int | str):
    print(item)

from typing import Union


def process_item(item: Union[int, str]):
    print(item)

In both cases this means that item could be an int or a str.

Possibly None

You can declare that a value could have a type, like str, but that it could also be None.

In Python 3.6 and above (including Python 3.10) you can declare it by importing and using Optional from the typing module.

from typing import Optional


def say_hi(name: Optional[str] = None):
    if name is not None:
        print(f"Hey {name}!")
    else:
        print("Hello World")

Using Optional[str] instead of just str will let the editor help you detecting errors where you could be assuming that a value is always a str, when it could actually be None too.

Optional[Something] is actually a shortcut for Union[Something, None], they are equivalent.

This also means that in Python 3.10, you can use Something | None:

def say_hi(name: str | None = None):
    if name is not None:
        print(f"Hey {name}!")
    else:
        print("Hello World")

from typing import Optional


def say_hi(name: Optional[str] = None):
    if name is not None:
        print(f"Hey {name}!")
    else:
        print("Hello World")

from typing import Union


def say_hi(name: Union[str, None] = None):
    if name is not None:
        print(f"Hey {name}!")
    else:
        print("Hello World")

Using Union or Optional

If you are using a Python version below 3.10, here's a tip from my very subjective point of view:

  • 🚨 Avoid using Optional[SomeType]
  • Instead ✨ use Union[SomeType, None] ✨.

Both are equivalent and underneath they are the same, but I would recommend Union instead of Optional because the word "optional" would seem to imply that the value is optional, and it actually means "it can be None", even if it's not optional and is still required.

I think Union[SomeType, None] is more explicit about what it means.

It's just about the words and names. But those words can affect how you and your teammates think about the code.

As an example, let's take this function:

from typing import Optional


def say_hi(name: Optional[str]):
    print(f"Hey {name}!")

The parameter name is defined as Optional[str], but it is not optional, you cannot call the function without the parameter:

say_hi()  # Oh, no, this throws an error! 😱

The name parameter is still required (not optional) because it doesn't have a default value. Still, name accepts None as the value:

say_hi(name=None)  # This works, None is valid 🎉

The good news is, once you are on Python 3.10 you won't have to worry about that, as you will be able to simply use | to define unions of types:

def say_hi(name: str | None):
    print(f"Hey {name}!")

And then you won't have to worry about names like Optional and Union. 😎

Generic types

These types that take type parameters in square brackets are called Generic types or Generics, for example:

You can use the same builtin types as generics (with square brackets and types inside):

  • list
  • tuple
  • set
  • dict

And the same as with Python 3.8, from the typing module:

  • Union
  • Optional (the same as with Python 3.8)
  • ...and others.

In Python 3.10, as an alternative to using the generics Union and Optional, you can use the vertical bar (|) to declare unions of types, that's a lot better and simpler.

You can use the same builtin types as generics (with square brackets and types inside):

  • list
  • tuple
  • set
  • dict

And the same as with Python 3.8, from the typing module:

  • Union
  • Optional
  • ...and others.
  • List
  • Tuple
  • Set
  • Dict
  • Union
  • Optional
  • ...and others.

Classes as types

You can also declare a class as the type of a variable.

Let's say you have a class Person, with a name:

class Person:
    def __init__(self, name: str):
        self.name = name


def get_person_name(one_person: Person):
    return one_person.name

Then you can declare a variable to be of type Person:

class Person:
    def __init__(self, name: str):
        self.name = name


def get_person_name(one_person: Person):
    return one_person.name

And then, again, you get all the editor support:

Notice that this means "one_person is an instance of the class Person".

It doesn't mean "one_person is the class called Person".

Pydantic models

Pydantic is a Python library to perform data validation.

You declare the "shape" of the data as classes with attributes.

And each attribute has a type.

Then you create an instance of that class with some values and it will validate the values, convert them to the appropriate type (if that's the case) and give you an object with all the data.

And you get all the editor support with that resulting object.

An example from the official Pydantic docs:

from datetime import datetime

from pydantic import BaseModel


class User(BaseModel):
    id: int
    name: str = "John Doe"
    signup_ts: datetime | None = None
    friends: list[int] = []


external_data = {
    "id": "123",
    "signup_ts": "2017-06-01 12:22",
    "friends": [1, "2", b"3"],
}
user = User(**external_data)
print(user)
# > User id=123 name='John Doe' signup_ts=datetime.datetime(2017, 6, 1, 12, 22) friends=[1, 2, 3]
print(user.id)
# > 123

from datetime import datetime
from typing import Union

from pydantic import BaseModel


class User(BaseModel):
    id: int
    name: str = "John Doe"
    signup_ts: Union[datetime, None] = None
    friends: list[int] = []


external_data = {
    "id": "123",
    "signup_ts": "2017-06-01 12:22",
    "friends": [1, "2", b"3"],
}
user = User(**external_data)
print(user)
# > User id=123 name='John Doe' signup_ts=datetime.datetime(2017, 6, 1, 12, 22) friends=[1, 2, 3]
print(user.id)
# > 123

from datetime import datetime
from typing import List, Union

from pydantic import BaseModel


class User(BaseModel):
    id: int
    name: str = "John Doe"
    signup_ts: Union[datetime, None] = None
    friends: List[int] = []


external_data = {
    "id": "123",
    "signup_ts": "2017-06-01 12:22",
    "friends": [1, "2", b"3"],
}
user = User(**external_data)
print(user)
# > User id=123 name='John Doe' signup_ts=datetime.datetime(2017, 6, 1, 12, 22) friends=[1, 2, 3]
print(user.id)
# > 123

Info

To learn more about Pydantic, check its docs.

FastAPI is all based on Pydantic.

You will see a lot more of all this in practice in the Tutorial - User Guide.

Tip

Pydantic has a special behavior when you use Optional or Union[Something, None] without a default value, you can read more about it in the Pydantic docs about Required Optional fields.

Type Hints with Metadata Annotations

Python also has a feature that allows putting additional metadata in these type hints using Annotated.

In Python 3.9, Annotated is part of the standard library, so you can import it from typing.

from typing import Annotated


def say_hello(name: Annotated[str, "this is just metadata"]) -> str:
    return f"Hello {name}"

In versions below Python 3.9, you import Annotated from typing_extensions.

It will already be installed with FastAPI.

from typing_extensions import Annotated


def say_hello(name: Annotated[str, "this is just metadata"]) -> str:
    return f"Hello {name}"

Python itself doesn't do anything with this Annotated. And for editors and other tools, the type is still str.

But you can use this space in Annotated to provide FastAPI with additional metadata about how you want your application to behave.

The important thing to remember is that the first type parameter you pass to Annotated is the actual type. The rest, is just metadata for other tools.

For now, you just need to know that Annotated exists, and that it's standard Python. 😎

Later you will see how powerful it can be.

Tip

The fact that this is standard Python means that you will still get the best possible developer experience in your editor, with the tools you use to analyze and refactor your code, etc. ✨

And also that your code will be very compatible with many other Python tools and libraries. 🚀

Type hints in FastAPI

FastAPI takes advantage of these type hints to do several things.

With FastAPI you declare parameters with type hints and you get:

  • Editor support.
  • Type checks.

...and FastAPI uses the same declarations to:

  • Define requirements: from request path parameters, query parameters, headers, bodies, dependencies, etc.
  • Convert data: from the request to the required type.
  • Validate data: coming from each request:
    • Generating automatic errors returned to the client when the data is invalid.
  • Document the API using OpenAPI:
    • which is then used by the automatic interactive documentation user interfaces.

This might all sound abstract. Don't worry. You'll see all this in action in the Tutorial - User Guide.

The important thing is that by using standard Python types, in a single place (instead of adding more classes, decorators, etc), FastAPI will do a lot of the work for you.

Info

If you already went through all the tutorial and came back to see more about types, a good resource is the "cheat sheet" from mypy.