3.3. Pydantic Types Stdlib

Where possible pydantic uses standard library to define fields, thus smoothing the learning curve. For many useful applications, however, no standard library type exists, so pydantic implements many commonly used types.

3.3.1. Standard Library Types

pydantic supports many common types from the python standard library. If you need stricter processing see Strict Types; if you need to constrain the values allowed (e.g. to require a positive int) see Constrained Types.

None - allows only None value

bool: see Booleans below for details on how bools are validated and what values are permitted
int: *pydantic uses int(v) to coerce types to an int; see Data Conversion warning on loss of information during data conversion
float: similarly, float(v) is used to coerce values to floats
str: strings are accepted as-is, int float and Decimal are coerced using str(v), bytes and bytearray are converted using v.decode(), enums inheriting from str are converted using v.value, and all other types cause an error
bytes: bytes are accepted as-is, bytearray is converted using bytes(v), str are converted using v.encode(), and int, float, and Decimal are coerced using str(v).encode()
list: allows list, tuple, set, frozenset, deque, or generators and casts to a list
tuple: allows list, tuple, set, frozenset, deque, or generators and casts to a tuple
dict: dict(v) is used to attempt to convert a dictionary
set: allows list, tuple, set, frozenset, deque, or generators and casts to a set
frozenset: allows list, tuple, set, frozenset, deque, or generators and casts to a frozen set
deque: allows list, tuple, set, frozenset, deque, or generators and casts to a deque
datetime.date: see Datetime Types for more detail on parsing and validation
datetime.time: see Datetime Types for more detail on parsing and validation
datetime.datetime: see Datetime Types for more detail on parsing and validation
datetime.timedelta: see Datetime Types below for more detail on parsing and validation
typing.Any: allows any value including None, thus an Any field is optional
typing.Annotated: allows wrapping another type with arbitrary metadata, as per PEP 593. The Annotated hint may contain a single call to the Field function, but otherwise the additional metadata is ignored and the root type is used.
typing.TypeVar: constrains the values allowed based on constraints or bound
typing.Union: see Unions for more detail on parsing and validation
typing.Optional: Optional[x] is simply short hand for Union[x, None]
typing.List: see Typing Iterables> below for more detail on parsing and validation
typing.Tuple: see Typing Iterables> below for more detail on parsing and validation
subclass of typing.NamedTuple: Same as tuple but instantiates with the given namedtuple and validates fields since they are annotated
subclass of collections.namedtuple: Same as subclass of typing.NamedTuple but all fields will have type Any since they are not annotated
typing.Dict: see Typing Iterables below for more detail on parsing and validation
subclass of typing.TypedDict: Same as dict but pydantic will validate the dictionary since keys are annotated
typing.Set: see Typing Iterables below for more detail on parsing and validation
typing.FrozenSet: see Typing Iterables below for more detail on parsing and validation
typing.Deque: see Typing Iterables below for more detail on parsing and validation
typing.Sequence: see Typing Iterables below for more detail on parsing and validation
typing.Iterable: this is reserved for iterables that shouldn't be consumed
typing.Type: see Type for more detail on parsing and validation
typing.Callable: see Callable for more detail on parsing and validation
typing.Pattern: will cause the input value to be passed to re.compile(v) to create a regex pattern
ipaddress.IPv4Address: simply uses the type itself for validation by passing the value to IPv4Address(v)
ipaddress.IPv4Interface: simply uses the type itself for validation by passing the value to IPv4Address(v)
ipaddress.IPv4Network: simply uses the type itself for validation by passing the value to IPv4Network(v)
ipaddress.IPv6Address: simply uses the type itself for validation by passing the value to IPv6Address(v)
ipaddress.IPv6Interface: simply uses the type itself for validation by passing the value to IPv6Interface(v)
ipaddress.IPv6Network: simply uses the type itself for validation by passing the value to IPv6Network(v)
enum.Enum: checks that the value is a valid Enum instance
subclass of enum.Enum: checks that the value is a valid member of the enum
enum.IntEnum: checks that the value is a valid IntEnum instance
subclass of enum.IntEnum: checks that the value is a valid member of the integer enum
decimal.Decimal: pydantic attempts to convert the value to a string, then passes the string to Decimal(v)
pathlib.Path: simply uses the type itself for validation by passing the value to Path(v)
uuid.UUID: strings and bytes (converted to strings) are passed to UUID(v), with a fallback to UUID(bytes=v) for bytes and bytearray;
ByteSize: converts a bytes string with units to bytes

3.3.2. Datetime Types

datetime fields
date fields
time fields
timedelta fields
https://docs.python.org/library/datetime.html#available-types

Pydantic supports the following datetime types

datetime fields can be:
- datetime, existing datetime object
- int or float, assumed as Unix time, i.e. seconds (if >= -2e10 or <= 2e10) or milliseconds (if < -2e10``or > ``2e10) since 1 January 1970
- str, following formats work:
  - YYYY-MM-DD[T]HH:MM[:SS[.ffffff]][Z or [±]HH[:]MM]]]
  - int or float as a string (assumed as Unix time)
date fields can be:
- date, existing date object
- int or float, see datetime
- str, following formats work:
  - YYYY-MM-DD
  - int or float, see datetime
time fields can be:
- time, existing time object
- str, following formats work:
  - HH:MM[:SS[.ffffff]][Z or [±]HH[:]MM]]]
timedelta fields can be:
- timedelta, existing timedelta object
- int or float, assumed as seconds
- str, following formats work:
  - [-][DD ][HH:MM]SS[.ffffff]
  - [±]P[DD]DT[HH]H[MM]M[SS]S (ISO 8601 format for timedelta)

3.3.3. Booleans

A standard bool field will raise a ValidationError if the value is not one of the following:

A valid boolean (i.e. True or False),
The integers 0 or 1,
a str which when converted to lower case is one of '0', 'off', 'f', 'false', 'n', 'no', '1', 'on', 't', 'true', 'y', 'yes'
a bytes which is valid (per the previous rule) when decoded to str

If you want stricter boolean logic (e.g. a field which only permits True and False) you can use `StrictBool <#strict-types>`_.

Here is a script demonstrating some of these behaviors:

3.3.4. Callable

Fields can also be of type Callable:

Callable fields only perform a simple check that the argument is callable; no validation of arguments, their types, or the return type is performed.

3.3.5. Type

pydantic supports the use of type[T] to specify that a field may only accept classes (not instances) that are subclasses of T.

You may also use Type to specify that any class is allowed.

3.3.6. TypeVar

TypeVar is supported either unconstrained, constrained or with a bound.

3.3.7. Literal Type

pydantic supports the use of typing.Literal as a lightweight way to specify that a field may accept only specific literal values:

One benefit of this field type is that it can be used to check for equality with one or more specific values without needing to declare custom validators:

With proper ordering in an annotated Union, you can use this to parse types of decreasing specificity:

3.3.8. Annotated Types

3.3.9. NamedTuple

3.3.10. TypedDict

3.3.11. Typing Iterables

pydantic uses standard library typing types as defined in PEP 484 to define complex objects.

3.3.12. Infinite Generators

If you have a generator you can use Sequence as described above. In that case, the generator will be consumed and stored on the model as a list and its values will be validated with the sub-type of Sequence (e.g. int in Sequence[int]).

But if you have a generator that you don't want to be consumed, e.g. an infinite generator or a remote data loader, you can define its type with Iterable:

Warning Iterable fields only perform a simple check that the argument is iterable and won't be consumed. No validation of their values is performed as it cannot be done without consuming the iterable.

If you want to validate the values of an infinite generator you can create a separate model and use it while consuming the generator, reporting the validation errors as appropriate.

pydantic can't validate the values automatically for you because it would require consuming the infinite generator.

3.3.13. Validating the first value

You can create a Validator to validate the first value in an infinite generator and still not consume it entirely.

3.3.14. Unions

The Union type allows a model attribute to accept different types

You may get unexpected coercion with Union; see below. Know that you can also make the check slower but stricter by using Smart Union. However, as can be seen above, pydantic will attempt to 'match' any of the types defined under Union and will use the first one that matches. In the above example the id of user_03 was defined as a uuid.UUID class (which is defined under the attribute's Union annotation) but as the uuid.UUID can be marshalled into an int it chose to match against the int type and disregarded the other types.

typing.Union also ignores order when defined, so Union[int, float] == Union[float, int] which can lead to unexpected behaviour when combined with matching based on the Union type order inside other type definitions, such as List and Dict types (because python treats these definitions as singletons). For example, Dict[str, Union[int, float]] == Dict[str, Union[float, int]] with the order based on the first time it was defined. Please note that this can also be affected by third party libraries and their internal type definitions and the import orders. As such, it is recommended that, when defining Union annotations, the most specific type is included first and followed by less specific types.

In the above example, the UUID class should precede the int and str classes to preclude the unexpected representation as such. The type Optional[x] is a shorthand for Union[x, None]. Optional[x] can also be used to specify a required field that can take None as a value.

3.3.15. Discriminated Unions (a.k.a. Tagged Unions)

When Union is used with multiple submodels, you sometimes know exactly which submodel needs to be checked and validated and want to enforce this. To do that you can set the same field - let's call it my_discriminator - in each of the submodels with a discriminated value, which is one (or many) Literal value(s). For your Union, you can set the discriminator in its value: Field(discriminator='my_discriminator').

Setting a discriminated union has many benefits:

validation is faster since it is only attempted against one model
only one explicit error is raised in case of failure
the generated JSON schema implements the associated OpenAPI specification

Using the Annotated Fields syntax can be handy to regroup the Union and discriminator information.

3.3.16. Nested Discriminated Unions

Only one discriminator can be set for a field but sometimes you want to combine multiple discriminators. In this case you can always create "intermediate" models with _root__ and add your discriminator.

3.3.17. Enums and Choices

pydantic uses python's standard enum classes to define choices.

3.3.18. Generic Classes as Types

This is an advanced technique that you might not need in the beginning. In most of the cases you will probably be fine with standard pydantic models.

You can use Generic Classes as field types and perform custom validation based on the "type parameters" (or sub-types) with _get_validators__.

If the Generic class that you are using as a sub-type has a classmethod _get_validators__ you don't need to use arbitrary_types_allowed for it to work.

Because you can declare validators that receive the current field, you can extract the sub_fields (from the generic class type parameters) and validate data with them.