pydantic/docs/usage/exporting_models.md

As well as accessing model attributes directly via their names (e.g. model.foobar), models can be converted and exported in a number of ways:

model.dict(...)

This is the primary way of converting a model to a dictionary. Sub-models will be recursively converted to dictionaries.

Arguments:

• include: fields to include in the returned dictionary; see below
• exclude: fields to exclude from the returned dictionary; see below
• by_alias: whether field aliases should be used as keys in the returned dictionary; default False
• exclude_unset: whether fields which were not explicitly set when creating the model should be excluded from the returned dictionary; default False. Prior to v1.0, exclude_unset was known as skip_defaults; use of skip_defaults is now deprecated
• exclude_defaults: whether fields which are equal to their default values (whether set or otherwise) should be excluded from the returned dictionary; default False
• exclude_none: whether fields which are equal to None should be excluded from the returned dictionary; default False

Example:

{!.tmp_examples/exporting_models_dict.py!}

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dict(model) and iteration

pydantic models can also be converted to dictionaries using dict(model), and you can also iterate over a model's field using for field_name, value in model:. With this approach the raw field values are returned, so sub-models will not be converted to dictionaries.

Example:

{!.tmp_examples/exporting_models_iterate.py!}

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model.copy(...)

copy() allows models to be duplicated, which is particularly useful for immutable models.

Arguments:

• include: fields to include in the returned dictionary; see below
• exclude: fields to exclude from the returned dictionary; see below
• update: a dictionary of values to change when creating the copied model
• deep: whether to make a deep copy of the new model; default False

Example:

{!.tmp_examples/exporting_models_copy.py!}

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model.json(...)

The .json() method will serialise a model to JSON. Typically, .json() in turn calls .dict() and serialises its result. (For models with a custom root type, after calling .dict(), only the value for the __root__ key is serialised)

Arguments:

• include: fields to include in the returned dictionary; see below
• exclude: fields to exclude from the returned dictionary; see below
• by_alias: whether field aliases should be used as keys in the returned dictionary; default False
• exclude_unset: whether fields which were not set when creating the model and have their default values should be excluded from the returned dictionary; default False. Prior to v1.0, exclude_unset was known as skip_defaults; use of skip_defaults is now deprecated
• exclude_defaults: whether fields which are equal to their default values (whether set or otherwise) should be excluded from the returned dictionary; default False
• exclude_none: whether fields which are equal to None should be excluded from the returned dictionary; default False
• encoder: a custom encoder function passed to the default argument of json.dumps(); defaults to a custom encoder designed to take care of all common types
• **dumps_kwargs: any other keyword arguments are passed to json.dumps(), e.g. indent.

pydantic can serialise many commonly used types to JSON (e.g. datetime, date or UUID) which would normally fail with a simple json.dumps(foobar).

{!.tmp_examples/exporting_models_json.py!}

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json_encoders

Serialisation can be customised on a model using the json_encoders config property; the keys should be types, and the values should be functions which serialise that type (see the example below):

{!.tmp_examples/exporting_models_json_encoders.py!}

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By default, timedelta is encoded as a simple float of total seconds. The timedelta_isoformat is provided as an optional alternative which implements ISO 8601 time diff encoding.

Serialising subclasses

!!! note New in version v1.5.

Subclasses of common types were not automatically serialised to JSON before **v1.5**.

Subclasses of common types are automatically encoded like their super-classes:

{!.tmp_examples/exporting_models_json_subclass.py!}

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Custom JSON (de)serialisation

To improve the performance of encoding and decoding JSON, alternative JSON implementations (e.g. ujson) can be used via the json_loads and json_dumps properties of Config.

{!.tmp_examples/exporting_models_ujson.py!}

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ujson generally cannot be used to dump JSON since it doesn't support encoding of objects like datetimes and does not accept a default fallback function argument. To do this, you may use another library like orjson.

{!.tmp_examples/exporting_models_orjson.py!}

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Note that orjson takes care of datetime encoding natively, making it faster than json.dumps but meaning you cannot always customise the encoding using Config.json_encoders.

pickle.dumps(model)

Using the same plumbing as copy(), pydantic models support efficient pickling and unpickling.

{!.tmp_examples/exporting_models_pickle.py!}

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Advanced include and exclude

The dict, json, and copy methods support include and exclude arguments which can either be sets or dictionaries. This allows nested selection of which fields to export:

{!.tmp_examples/exporting_models_exclude1.py!}

The ellipsis (...) indicates that we want to exclude or include an entire key, just as if we included it in a set. Of course, the same can be done at any depth level.

Special care must be taken when including or excluding fields from a list or tuple of submodels or dictionaries. In this scenario, dict and related methods expect integer keys for element-wise inclusion or exclusion. To exclude a field from every member of a list or tuple, the dictionary key '__all__' can be used as follows:

{!.tmp_examples/exporting_models_exclude2.py!}

The same holds for the json and copy methods.