dacite 0.0.14

Simple creation of data classes from dictionaries.

This module simplifies creation of data classes (PEP 557) from dictionaries.

Installation

To install dacite, simply use pip (or pipenv):

$ pip install dacite

Requirements

Minimum Python version supported by dacite is 3.6.

Data classes will be available in Python 3.7 as a part of the standard library, but you can use dataclasses module now - it’s available as an external package from PyPI. It will be installed automatically as a dacite dependence.

Quick start

from dataclasses import dataclass
from dacite import from_dict


@dataclass
class User:
    name: str
    age: int
    is_active: bool


data = {
    'name': 'john',
    'age': 30,
    'is_active': True,
}

user = from_dict(data_class=User, data=data)

assert user == User(name='john', age=30, is_active=True)

Features

Dacite supports following features:

• nested structures

• (basic) types checking

• optional fields (i.e. typing.Optional)

• unions

• collections

• values casting and transformation

• remapping of fields names

Motivation

Passing plain dictionaries as a data container between your functions or methods isn’t a good practice. Of course you can always create your custom class instead, but this solution is an overkill if you only want to merge a few fields within a single object.

Fortunately Python has a good solution to this problem - data classes. Thanks to @dataclass decorator you can easily create a new custom type with a list of given fields in a declarative manner. Data classes support type hints by design.

However, even if you are using data classes, you have to create their instances somehow. In many such cases, your input is a dictionary - it can be a payload from a HTTP request or a raw data from a database. If you want to convert those dictionaries into data classes, dacite is your best friend.

This library was originally created to simplify creation of type hinted data transfer objects (DTO) which can cross the boundaries in the application architecture.

Usage

Dacite is based on a single function - dacite.from_dict. This function takes 3 parameters:

• data_class - data class type

• data - dictionary of input data

• config (optional) - configuration of the creation process, instance of dacite.Config class

Configuration is a (data) class with following fields:

• remap

• flattened

• prefixed

• cast

• transform

The examples below show all features of from_dict function and usage of all Config parameters.

Nested structures

You can pass a data with nested dictionaries and it will create a proper result.

@dataclass
class A:
    x: str
    y: int


@dataclass
class B:
    a: A


data = {
    'a': {
        'x': 'test',
        'y': 1,
    }
}

result = from_dict(data_class=B, data=data)

assert result == B(a=A(x='test', y=1))

Optional fields

Whenever your data class has a Optional field and you will not provide input data for this field, it will take the None value.

from typing import Optional

@dataclass
class A:
    x: str
    y: Optional[int]


data = {
    'x': 'test',
}

result = from_dict(data_class=A, data=data)

assert result == A(x='test', y=None)

Unions

If your field can accept multiple types, you should use Union. Dacite will try to match data with provided types one by one. If none will match, it will raise UnionMatchError exception.

from typing import Union

@dataclass
class A:
    x: str

@dataclass
class B:
    y: int

@dataclass
class C:
    u: Union[A, B]


data = {
    'u': {
        'y': 1,
    },
}

result = from_dict(data_class=C, data=data)

assert result == C(u=B(y=1))

Collections

Dacite supports fields defined as collections. It works for both - basic types and data classes.

@dataclass
class A:
    x: str
    y: int


@dataclass
class B:
    a_list: List[A]


data = {
    'a_list': [
        {
            'x': 'test1',
            'y': 1,
        },
        {
            'x': 'test2',
            'y': 2,
        }
    ],
}

result = from_dict(data_class=B, data=data)

assert result == B(a_list=[A(x='test1', y=1), A(x='test2', y=2)])

Remapping

If your input data key does not match with a data class field name, you can use Config.remap argument to handle this case. You have to pass dictionary with a following mapping: {'data_class_field': 'input_field'}

@dataclass
class A:
    x: str


data = {
    'y': 'test',
}

result = from_dict(data_class=A, data=data, config=Config(remap={'x': 'y'}))

assert result == A(x='test')

Flattened

You often receive a flat structure which you want to convert to something more sophisticated. In this case you can use Config.flattened argument. You have to pass list of flattened fields.

@dataclass
class A:
    x: str
    y: int


@dataclass
class B:
    a: A
    z: float


data = {
    'x': 'test',
    'y': 1,
    'z': 2.0,
}

result = from_dict(data_class=B, data=data, config=Config(flattened=['a']))

assert result == B(a=A(x='test', y=1), z=2.0)

Prefixed

Sometimes your data is prefixed rather than nested. To handle this case, you have to use Config.prefixed argument, just pass a following mapping: {'data_class_field': 'prefix'}

@dataclass
class A:
    x: str
    y: int


@dataclass
class B:
    a: A
    z: float


data = {
    'a_x': 'test',
    'a_y': 1,
    'z': 2.0,
}

result = from_dict(data_class=B, data=data, config=Config(prefixed={'a': 'a_'}))

assert result == B(a=A(x='test', y=1), z=2.0)

Casting

Input values are not casted by default. If you want to use field type information to transform input value from one type to another, you have to pass given field name as an element of the Config.cast argument list.

@dataclass
class A:
    x: str


data = {
    'x': 1,
}

result = from_dict(data_class=A, data=data, config=Config(cast=['x']))

assert result == A(x='1')

Transformation

You can use Config.transform argument if you want to transform the input data into the new value. You have to pass a following mapping: {'data_class_field': callable}, where callable is a Callable[[Any], Any].

@dataclass
class A:
    x: str


data = {
    'x': 'TEST',
}

result = from_dict(data_class=A, data=data, config=Config(transform={'x': str.lower}))

assert result == A(x='test')

Exceptions

Whenever something goes wrong, from_dict will raise adequate exception. There are a few of them:

• WrongTypeError - raised when a type of a input value does not match with a type of a data class field

• MissingValueError - raised when you don’t provide a value for a required field

• InvalidConfigurationError - raised when you provide a invalid value (a field name or a input data key) for a configuration

• UnionMatchError - raised when provided data does not match any type of Union

Authors

Created by Konrad Hałas.