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 typedata- dictionary of input dataconfig(optional) - configuration of the creation process, instance ofdacite.Configclass
Configuration is a (data) class with following fields:
type_hookscastforward_referencescheck_typesstrictstrict_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 fieldMissingValueError- raised when you don't provide a value for a required fieldUnionMatchError- raised when provided data does not match any type ofUnionForwardReferenceError- raised when undefined forward reference encountered in dataclassUnexpectedDataError- raised whenstrictmode is enabled and the input data has not matching keysStrictUnionMatchError- raised whenstrict_unions_matchmode is enabled and the input data has ambiguousUnionmatch
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 [email protected]: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 cacheThe caching is completely transparent from the interface perspective.
Changelog
Follow dacite updates in CHANGELOG.
Authors
Created by Konrad Hałas.