pydantic/docs/usage/types.md

Where possible pydantic uses standard library types 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.

If no existing type suits your purpose you can also implement your own pydantic-compatible types with custom properties and validation.

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.

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 this 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, or generators and casts to a list; see typing.List below for sub-type constraints

tuple

allows list, tuple, set, frozenset, or generators and casts to a tuple; see typing.Tuple below for sub-type constraints

dict

dict(v) is used to attempt to convert a dictionary; see typing.Dict below for sub-type constraints

set

allows list, tuple, set, frozenset, or generators and casts to a set; see typing.Set below for sub-type constraints

frozenset

allows list, tuple, set, frozenset, or generators and casts to a frozen set; see typing.FrozenSet below for sub-type constraints

datetime.date

see Datetime Types below for more detail on parsing and validation

datetime.time

see Datetime Types below for more detail on parsing and validation

datetime.datetime

see Datetime Types below 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 include None, thus an Any field is optional

typing.TypeVar

constrains the values allowed based on constraints or bound, see TypeVar

typing.Union

see Unions below for more detail on parsing and validation

typing.Optional

Optional[x] is simply short hand for Union[x, None]; see Unions below for more detail on parsing and validation and Required Fields for details about required fields that can receive None as a value.

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

typing.Dict

see Typing Iterables below for more detail on parsing and validation

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.Sequence

see Typing Iterables below for more detail on parsing and validation

typing.Iterable

this is reserved for iterables that shouldn't be consumed. See Infinite Generators below for more detail on parsing and validation

typing.Type

see Type below for more detail on parsing and validation

typing.Callable

see Callable below 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); see Pydantic Types for other custom IP address types

ipaddress.IPv4Interface

simply uses the type itself for validation by passing the value to IPv4Address(v); see Pydantic Types for other custom IP address types

ipaddress.IPv4Network

simply uses the type itself for validation by passing the value to IPv4Network(v); see Pydantic Types for other custom IP address types

ipaddress.IPv6Address

simply uses the type itself for validation by passing the value to IPv6Address(v); see Pydantic Types for other custom IP address types

ipaddress.IPv6Interface

simply uses the type itself for validation by passing the value to IPv6Interface(v); see Pydantic Types for other custom IP address types

ipaddress.IPv6Network

simply uses the type itself for validation by passing the value to IPv6Network(v); see Pydantic Types for other custom IP address types

enum.Enum

checks that the value is a valid member of the enum; see Enums and Choices for more details

enum.IntEnum

checks that the value is a valid member of the integer enum; see Enums and Choices for more details

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); see Pydantic Types for other more strict path types

uuid.UUID

strings and bytes (converted to strings) are passed to UUID(v), with a fallback to UUID(bytes=v) for bytes and bytearray; see Pydantic Types for other stricter UUID types

ByteSize

converts a bytes string with units to bytes

Typing Iterables

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

{!.tmp_examples/types_iterables.py!}

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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:

{!.tmp_examples/types_infinite_generator.py!}

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!!! 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.

!!! tip 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.

Validating the first value

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

{!.tmp_examples/types_infinite_generator_validate_first.py!}

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Unions

The Union type allows a model attribute to accept different types, e.g.:

!!! warning This script is complete, it should run "as is". However, it may not reflect the desired behavior; see below.

{!.tmp_examples/types_union_incorrect.py!}

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.

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:

{!.tmp_examples/types_union_correct.py!}

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!!! tip 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.

See more details in [Required Fields](models.md#required-fields).

Enums and Choices

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

{!.tmp_examples/types_choices.py!}

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Datetime 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 milliseconds (if > 2e10) since 1 January 1970

  • str, following formats work:

    • YYYY-MM-DD[T]HH:MM[:SS[.ffffff]][Z[±]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]]

• 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)

{!.tmp_examples/types_dt.py!}

Booleans

!!! warning The logic for parsing bool fields has changed as of version v1.0.

Prior to **v1.0**, `bool` parsing never failed, leading to some unexpected results.
The new logic is described below.

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

!!! note If you want stricter boolean logic (e.g. a field which only permits True and False) you can use StrictBool.

Here is a script demonstrating some of these behaviors:

{!.tmp_examples/types_boolean.py!}

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Callable

Fields can also be of type Callable:

{!.tmp_examples/types_callable.py!}

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

Type

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

{!.tmp_examples/types_type.py!}

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

{!.tmp_examples/types_bare_type.py!}

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TypeVar

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

{!.tmp_examples/types_typevar.py!}

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Literal Type

!!! note This is a new feature of the python standard library as of python 3.8; prior to python 3.8, it requires the typing-extensions package.

pydantic supports the use of typing.Literal (or typing_extensions.Literal prior to python 3.8) as a lightweight way to specify that a field may accept only specific literal values:

{!.tmp_examples/types_literal1.py!}

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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:

{!.tmp_examples/types_literal2.py!}

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

{!.tmp_examples/types_literal3.py!}

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Pydantic Types

pydantic also provides a variety of other useful types:

FilePath

like Path, but the path must exist and be a file

DirectoryPath

like Path, but the path must exist and be a directory

EmailStr

requires email-validator to be installed; the input string must be a valid email address, and the output is a simple string

NameEmail

requires email-validator to be installed; the input string must be either a valid email address or in the format Fred Bloggs <fred.bloggs@example.com>, and the output is a NameEmail object which has two properties: name and email. For Fred Bloggs <fred.bloggs@example.com> the name would be "Fred Bloggs"; for fred.bloggs@example.com it would be "fred.bloggs".

PyObject

expects a string and loads the python object importable at that dotted path; e.g. if 'math.cos' was provided, the resulting field value would be the function cos

Color

for parsing HTML and CSS colors; see Color Type

Json

a special type wrapper which loads JSON before parsing; see JSON Type

PaymentCardNumber

for parsing and validating payment cards; see payment cards

AnyUrl

any URL; see URLs

AnyHttpUrl

an HTTP URL; see URLs

HttpUrl

a stricter HTTP URL; see URLs

PostgresDsn

a postgres DSN style URL; see URLs

RedisDsn

a redis DSN style URL; see URLs

stricturl

a type method for arbitrary URL constraints; see URLs

UUID1

requires a valid UUID of type 1; see UUID above

UUID3

requires a valid UUID of type 3; see UUID above

UUID4

requires a valid UUID of type 4; see UUID above

UUID5

requires a valid UUID of type 5; see UUID above

SecretBytes

bytes where the value is kept partially secret; see Secrets

SecretStr

string where the value is kept partially secret; see Secrets

IPvAnyAddress

allows either an IPv4Address or an IPv6Address

IPvAnyInterface

allows either an IPv4Interface or an IPv6Interface

IPvAnyNetwork

allows either an IPv4Network or an IPv6Network

NegativeFloat

allows a float which is negative; uses standard float parsing then checks the value is less than 0; see Constrained Types

NegativeInt

allows an int which is negative; uses standard int parsing then checks the value is less than 0; see Constrained Types

PositiveFloat

allows a float which is positive; uses standard float parsing then checks the value is greater than 0; see Constrained Types

PositiveInt

allows an int which is positive; uses standard int parsing then checks the value is greater than 0; see Constrained Types

conbytes

type method for constraining bytes; see Constrained Types

condecimal

type method for constraining Decimals; see Constrained Types

confloat

type method for constraining floats; see Constrained Types

conint

type method for constraining ints; see Constrained Types

conlist

type method for constraining lists; see Constrained Types

constr

type method for constraining strs; see Constrained Types

URLs

For URI/URL validation the following types are available:

• AnyUrl: any scheme allowed, TLD not required
• AnyHttpUrl: schema http or https, TLD not required
• HttpUrl: schema http or https, TLD required, max length 2083
• PostgresDsn: schema postgres or postgresql, userinfo required, TLD not required
• RedisDsn: schema redis, userinfo required, tld not required
• stricturl, method with the following keyword arguments:
  • strip_whitespace: bool = True
  • min_length: int = 1
  • max_length: int = 2 ** 16
  • tld_required: bool = True
  • allowed_schemes: Optional[Set[str]] = None

The above types (which all inherit from AnyUrl) will attempt to give descriptive errors when invalid URLs are provided:

{!.tmp_examples/types_urls.py!}

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If you require a custom URI/URL type, it can be created in a similar way to the types defined above.

URL Properties

Assuming an input URL of http://samuel:pass@example.com:8000/the/path/?query=here#fragment=is;this=bit, the above types export the following properties:

• scheme: always set - the url schema (http above)

• host: always set - the url host (example.com above)

• host_type: always set - describes the type of host, either:

  • domain: e.g. example.com,
  • int_domain: international domain, see below, e.g. exampl£e.org,
  • ipv4: an IP V4 address, e.g. 127.0.0.1, or
  • ipv6: an IP V6 address, e.g. 2001:db8:ff00:42

• user: optional - the username if included (samuel above)

• password: optional - the password if included (pass above)

• tld: optional - the top level domain (com above), Note: this will be wrong for any two-level domain, e.g. "co.uk". You'll need to implement your own list of TLDs if you require full TLD validation

• port: optional - the port (8000 above)

• path: optional - the path (/the/path/ above)

• query: optional - the URL query (aka GET arguments or "search string") (query=here above)

• fragment: optional - the fragment (fragment=is;this=bit above)

If further validation is required, these properties can be used by validators to enforce specific behaviour:

{!.tmp_examples/types_url_properties.py!}

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International Domains

"International domains" (e.g. a URL where the host or TLD includes non-ascii characters) will be encoded via punycode (see this article for a good description of why this is important):

{!.tmp_examples/types_url_punycode.py!}

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!!! warning #### Underscores in Hostnames

In *pydantic* underscores are allowed in all parts of a domain except the tld.
Technically this might be wrong - in theory the hostname cannot have underscores, but subdomains can.

To explain this; consider the following two cases:

- `exam_ple.co.uk`: the hostname is `exam_ple`, which should not be allowed since it contains an underscore
- `foo_bar.example.com` the hostname is `example`, which should be allowed since the underscore is in the subdomain

Without having an exhaustive list of TLDs, it would be impossible to differentiate between these two. Therefore
underscores are allowed, but you can always do further validation in a validator if desired.

Also, Chrome, Firefox, and Safari all currently accept `http://exam_ple.com` as a URL, so we're in good
(or at least big) company.

Color Type

You can use the Color data type for storing colors as per CSS3 specification. Colors can be defined via:

• name (e.g. "Black", "azure")
• hexadecimal value (e.g. "0x000", "#FFFFFF", "7fffd4")
• RGB/RGBA tuples (e.g. (255, 255, 255), (255, 255, 255, 0.5))
• RGB/RGBA strings (e.g. "rgb(255, 255, 255)", "rgba(255, 255, 255, 0.5)")
• HSL strings (e.g. "hsl(270, 60%, 70%)", "hsl(270, 60%, 70%, .5)")

{!.tmp_examples/types_color.py!}

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Color has the following methods:

original

the original string or tuple passed to Color

as_named

returns a named CSS3 color; fails if the alpha channel is set or no such color exists unless fallback=True is supplied, in which case it falls back to as_hex

as_hex

returns a string in the format #fff or #ffffff; will contain 4 (or 8) hex values if the alpha channel is set, e.g. #7f33cc26

as_rgb

returns a string in the format rgb(<red>, <green>, <blue>), or rgba(<red>, <green>, <blue>, <alpha>) if the alpha channel is set

as_rgb_tuple

returns a 3- or 4-tuple in RGB(a) format. The alpha keyword argument can be used to define whether the alpha channel should be included; options: True - always include, False - never include, None (default) - include if set

as_hsl

string in the format hsl(<hue deg>, <saturation %>, <lightness %>) or hsl(<hue deg>, <saturation %>, <lightness %>, <alpha>) if the alpha channel is set

as_hsl_tuple

returns a 3- or 4-tuple in HSL(a) format. The alpha keyword argument can be used to define whether the alpha channel should be included; options: True - always include, False - never include, None (the default) - include if set

The __str__ method for Color returns self.as_named(fallback=True).

!!! note the as_hsl* refer to hue, saturation, lightness "HSL" as used in html and most of the world, not "HLS" as used in python's colorsys.

Secret Types

You can use the SecretStr and the SecretBytes data types for storing sensitive information that you do not want to be visible in logging or tracebacks. SecretStr and SecretBytes can be initialized idempotently or by using str or bytes literals respectively. The SecretStr and SecretBytes will be formatted as either '**********' or '' on conversion to json.

{!.tmp_examples/types_secret_types.py!}

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Json Type

You can use Json data type to make pydantic first load a raw JSON string. It can also optionally be used to parse the loaded object into another type base on the type Json is parameterised with:

{!.tmp_examples/types_json_type.py!}

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Payment Card Numbers

The PaymentCardNumber type validates payment cards (such as a debit or credit card).

{!.tmp_examples/types_payment_card_number.py!}

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PaymentCardBrand can be one of the following based on the BIN:

• PaymentCardBrand.amex
• PaymentCardBrand.mastercard
• PaymentCardBrand.visa
• PaymentCardBrand.other

The actual validation verifies the card number is:

• a str of only digits
• luhn valid
• the correct length based on the BIN, if Amex, Mastercard or Visa, and between 12 and 19 digits for all other brands

Constrained Types

The value of numerous common types can be restricted using con* type functions:

{!.tmp_examples/types_constrained.py!}

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Where Field refers to the field function.

Strict Types

You can use the StrictStr, StrictInt, StrictFloat, and StrictBool types to prevent coercion from compatible types. These types will only pass validation when the validated value is of the respective type or is a subtype of that type. This behavior is also exposed via the strict field of the ConstrainedStr, ConstrainedFloat and ConstrainedInt classes and can be combined with a multitude of complex validation rules.

The following caveats apply:

• StrictInt (and the strict option of ConstrainedInt) will not accept bool types, even though bool is a subclass of int in Python. Other subclasses will work.
• StrictFloat (and the strict option of ConstrainedFloat) will not accept int.

{!.tmp_examples/types_strict.py!}

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ByteSize

You can use the ByteSize data type to convert byte string representation to raw bytes and print out human readable versions of the bytes as well.

!!! info Note that 1b will be parsed as "1 byte" and not "1 bit".

{!.tmp_examples/types_bytesize.py!}

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Custom Data Types

You can also define your own custom data types. There are several ways to achieve it.

Classes with __get_validators__

You use a custom class with a classmethod __get_validators__. It will be called to get validators to parse and validate the input data.

!!! tip These validators have the same semantics as in Validators, you can declare a parameter config, field, etc.

{!.tmp_examples/types_custom_type.py!}

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Similar validation could be achieved using constr(regex=...) except the value won't be formatted with a space, the schema would just include the full pattern and the returned value would be a vanilla string.

See Schema for more details on how the model's schema is generated.

Arbitrary Types Allowed

You can allow arbitrary types using the arbitrary_types_allowed config in the Model Config.

{!.tmp_examples/types_arbitrary_allowed.py!}

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Generic Classes as Types

!!! warning 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.

{!.tmp_examples/types_generics.py!}

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