Python Types Intro

Python has support for optional "type hints".

These "type hints" 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:

{!../../../docs_src/python_types/tutorial001.py!}

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.

{!../../../docs_src/python_types/tutorial001.py!}

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":

{!../../../docs_src/python_types/tutorial002.py!}

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:

{!../../../docs_src/python_types/tutorial003.py!}

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):

{!../../../docs_src/python_types/tutorial004.py!}

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

{!../../../docs_src/python_types/tutorial005.py!}

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. See some examples below.

List

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

=== "Python 3.6 and above"

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

``` Python hl_lines="1"
{!> ../../../docs_src/python_types/tutorial006.py!}
```

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:

```Python hl_lines="4"
{!> ../../../docs_src/python_types/tutorial006.py!}
```

=== "Python 3.9 and above"

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:

```Python hl_lines="1"
{!> ../../../docs_src/python_types/tutorial006_py39.py!}
```

!!! 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:

=== "Python 3.6 and above"

```Python hl_lines="1  4"
{!> ../../../docs_src/python_types/tutorial007.py!}
```

=== "Python 3.9 and above"

```Python hl_lines="1"
{!> ../../../docs_src/python_types/tutorial007_py39.py!}
```

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:

=== "Python 3.6 and above"

```Python hl_lines="1  4"
{!> ../../../docs_src/python_types/tutorial008.py!}
```

=== "Python 3.9 and above"

```Python hl_lines="1"
{!> ../../../docs_src/python_types/tutorial008_py39.py!}
```

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 an alternative syntax where you can put the possible types separated by a vertical bar (|).

=== "Python 3.6 and above"

```Python hl_lines="1  4"
{!> ../../../docs_src/python_types/tutorial008b.py!}
```

=== "Python 3.10 and above"

```Python hl_lines="1"
{!> ../../../docs_src/python_types/tutorial008b_py310.py!}
```

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.

{!../../../docs_src/python_types/tutorial009.py!}

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:

=== "Python 3.6 and above"

```Python hl_lines="1  4"
{!> ../../../docs_src/python_types/tutorial009.py!}
```

=== "Python 3.6 and above - alternative"

```Python hl_lines="1  4"
{!> ../../../docs_src/python_types/tutorial009b.py!}
```

=== "Python 3.10 and above"

```Python hl_lines="1"
{!> ../../../docs_src/python_types/tutorial009_py310.py!}
```

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:

{!../../../docs_src/python_types/tutorial009c.py!}

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:

{!../../../docs_src/python_types/tutorial009c_py310.py!}

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:

=== "Python 3.6 and above"

* `List`
* `Tuple`
* `Set`
* `Dict`
* `Union`
* `Optional`
* ...and others.

=== "Python 3.9 and above"

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 3.6, from the `typing` module:

* `Union`
* `Optional`
* ...and others.

=== "Python 3.10 and above"

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 3.6, from the `typing` module:

* `Union`
* `Optional` (the same as with Python 3.6)
* ...and others.

In Python 3.10, as an alternative to using the generics `Union` and `Optional`, you can use the <abbr title='also called "bitwise or operator", but that meaning is not relevant here'>vertical bar (`|`)</abbr> to declare unions of types.

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:

{!../../../docs_src/python_types/tutorial010.py!}

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

{!../../../docs_src/python_types/tutorial010.py!}

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

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:

=== "Python 3.6 and above"

```Python
{!> ../../../docs_src/python_types/tutorial011.py!}
```

=== "Python 3.9 and above"

```Python
{!> ../../../docs_src/python_types/tutorial011_py39.py!}
```

=== "Python 3.10 and above"

```Python
{!> ../../../docs_src/python_types/tutorial011_py310.py!}
```

!!! 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{.internal-link target=_blank}.

!!! 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 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{.internal-link target=_blank}.

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.

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python-types.md

python-types.md

Python Types Intro

Motivation

Edit it

Add types

More motivation

Declaring types

Simple types

Generic types with type parameters

Newer versions of Python

List

Tuple and Set

Dict

Union

Possibly `None`

Using `Union` or `Optional`

Generic types

Classes as types

Pydantic models

Type hints in FastAPI

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python-types.md

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python-types.md

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Python Types Intro

Motivation

Edit it

Add types

More motivation

Declaring types

Simple types

Generic types with type parameters

Newer versions of Python

List

Tuple and Set

Dict

Union

Possibly None

Using Union or Optional

Generic types

Classes as types

Pydantic models

Type hints in FastAPI

Possibly `None`

Using `Union` or `Optional`