To help you get started, we've collected the most common ways that dacite is being used within popular public projects.

dacite

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

Installation

To install dacite, simply use pip:

$ pip install dacite

Requirements

Minimum Python version supported by dacite is 3.6.

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
• forward references
• collections
• custom type hooks

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.

It's important to mention that dacite is not a data validation library. There are dozens of awesome data validation projects and it doesn't make sense to duplicate this functionality within dacite. If you want to validate your data first, you should combine dacite with one of data validation library.

Please check Use Case section for a real-life example.

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:

• type_hooks
• cast
• forward_references
• check_types
• strict
• strict_unions_match

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)])

Type hooks

You can use Config.type_hooks argument if you want to transform the input data of a data class field with given type into the new value. You have to pass a following mapping: {Type: 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(type_hooks={str: str.lower}))

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

If a data class field type is a Optional[T] you can pass both - Optional[T] or just T - as a key in type_hooks. The same with generic collections, e.g. when a field has type List[T] you can use List[T] to transform whole collection or T to transform each item.

Casting

It's a very common case that you want to create an instance of a field type from the input data with just calling your type with the input value. Of course you can use type_hooks={T: T} to achieve this goal but cast=[T] is an easier and more expressive way. It also works with base classes - if T is a base class of type S, all fields of type S will be also "casted".

from enum import Enum

class E(Enum):
    X = 'x'
    Y = 'y'
    Z = 'z'

@dataclass
class A:
    e: E


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

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

# or

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

assert result == A(e=E.X)

Forward References

Definition of forward references can be passed as a {'name': Type} mapping to Config.forward_references. This dict is passed to typing.get_type_hints() as the globalns param when evaluating each field's type.

@dataclass
class X:
    y: "Y"

@dataclass
class Y:
    s: str

data = from_dict(X, {"y": {"s": "text"}}, Config(forward_references={"Y": Y}))
assert data == X(Y("text"))

Types checking

There are rare cases when dacite built-in type checker can not validate your types (e.g. custom generic class) or you have such functionality covered by other library and you don't want to validate your types twice. In such case you can disable type checking with Config(check_types=False). By default types checking is enabled.

T = TypeVar('T')


class X(Generic[T]):
    pass


@dataclass
class A:
    x: X[str]


x = X[str]()

assert from_dict(A, {'x': x}, config=Config(check_types=False)) == A(x=x)

Strict mode

By default from_dict ignores additional keys (not matching data class field) in the input data. If you want change this behaviour set Config.strict to True. In case of unexpected key from_dict will raise UnexpectedDataError exception.

Strict unions match

Union allows to define multiple possible types for a given field. By default dacite is trying to find the first matching type for a provided data and it returns instance of this type. It means that it's possible that there are other matching types further on the Union types list. With strict_unions_match only a single match is allowed, otherwise dacite raises StrictUnionMatchError.

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
• UnionMatchError - raised when provided data does not match any type of Union
• ForwardReferenceError - raised when undefined forward reference encountered in dataclass
• UnexpectedDataError - raised when strict mode is enabled and the input data has not matching keys
• StrictUnionMatchError - raised when strict_unions_match mode is enabled and the input data has ambiguous Union match

Development

First of all - if you want to submit your pull request, thank you very much! I really appreciate your support.

Please remember that every new feature, bug fix or improvement should be tested. 100% code coverage is a must-have.

We are using a few static code analysis tools to increase the code quality (black, mypy, pylint). Please make sure that you are not generating any errors/warnings before you submit your PR. You can find current configuration in .github/* directory.

Last but not least, if you want to introduce new feature, please discuss it first within an issue.

How to start

Clone dacite repository:

$ git clone git@github.com:konradhalas/dacite.git

Create and activate virtualenv in the way you like:

$ python3 -m venv dacite-env
$ source dacite-env/bin/activate

Install all dacite dependencies:

$ pip install -e .[dev]

And, optionally but recommended, install pre-commit hook for black:

$ pre-commit install

To run tests you just have to fire:

$ pytest

Performance testing

dacite is a small library, but its use is potentially very extensive. Thus, it is crucial to ensure good performance of the library.

We achieve that with the help of pytest-benchmark library, and a suite of dedicated performance tests which can be found in the tests/performance directory. The CI process runs these tests automatically, but they can also be helpful locally, while developing the library.

Whenever you run pytest command, a new benchmark report is saved to /.benchmarks directory. You can easily compare these reports by running: pytest-benchmark compare, which will load all the runs and display them in a table, where you can compare the performance of each run.

You can even specify which particular runs you want to compare, e.g. pytest-benchmark compare 0001 0003 0005.

Use case

There are many cases when we receive "raw" data (Python dicts) as a input to our system. HTTP request payload is a very common use case. In most web frameworks we receive request data as a simple dictionary. Instead of passing this dict down to your "business" code, it's a good idea to create something more "robust".

Following example is a simple flask app - it has single /products endpoint. You can use this endpoint to "create" product in your system. Our core create_product function expects data class as a parameter. Thanks to dacite we can easily build such data class from POST request payload.

from dataclasses import dataclass
from typing import List

from flask import Flask, request, Response

import dacite

app = Flask(__name__)


@dataclass
class ProductVariantData:
    code: str
    description: str = ''
    stock: int = 0


@dataclass
class ProductData:
    name: str
    price: float
    variants: List[ProductVariantData]


def create_product(product_data: ProductData) -> None:
    pass  # your business logic here


@app.route("/products", methods=['POST'])
def products():
    product_data = dacite.from_dict(
        data_class=ProductData,
        data=request.get_json(),
    )
    create_product(product_data=product_data)
    return Response(status=201)

What if we want to validate our data (e.g. check if code has 6 characters)? Such features are out of scope of dacite but we can easily combine it with one of data validation library. Let's try with marshmallow.

First of all we have to define our data validation schemas:

from marshmallow import Schema, fields, ValidationError


def validate_code(code):
    if len(code) != 6:
        raise ValidationError('Code must have 6 characters.')


class ProductVariantDataSchema(Schema):
    code = fields.Str(required=True, validate=validate_code)
    description = fields.Str(required=False)
    stock = fields.Int(required=False)


class ProductDataSchema(Schema):
    name = fields.Str(required=True)
    price = fields.Decimal(required=True)
    variants = fields.Nested(ProductVariantDataSchema(many=True))

And use them within our endpoint:

@app.route("/products", methods=['POST'])
def products():
    schema = ProductDataSchema()
    result, errors = schema.load(request.get_json())
    if errors:
        return Response(
            response=json.dumps(errors), 
            status=400, 
            mimetype='application/json',
        )
    product_data = dacite.from_dict(
        data_class=ProductData,
        data=result,
    )
    create_product(product_data=product_data)
    return Response(status=201)

Still dacite helps us to create data class from "raw" dict with validated data.

Cache

dacite uses some LRU caching to improve its performance where possible. To use the caching utility:

from dacite import set_cache_size, get_cache_size, clear_cache

get_cache_size()  # outputs the current LRU max_size, default is 2048
set_cache_size(4096)  # set LRU max_size to 4096
set_cache_size(None)  # set LRU max_size to None
clear_cache()  # Clear the cache

The caching is completely transparent from the interface perspective.

Changelog

Follow dacite updates in CHANGELOG.

Authors

Created by Konrad Hałas.