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.
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
andlast_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!}
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:
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":
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!}
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.
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!}
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.
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.
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.
You would do the same to declare tuple
s and set
s:
=== "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 atuple
with 3 items, anint
, anotherint
, and astr
. - The variable
items_s
is aset
, and each of its items is of typebytes
.
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 adict
:- The keys of this
dict
are of typestr
(let's say, the name of each item). - The values of this
dict
are of typefloat
(let's say, the price of each item).
- The keys of this
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
.
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!}
```
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
. 😎
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 brakets 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 brakets 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.
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 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.
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
.