dataclassy 0.3

A reimplementation of data classes in Python

dataclassy

dataclassy is a reimplementation of data classes in Python — an alternative to the built-in dataclasses module that avoids many of its common pitfalls. dataclassy is designed to be more flexible, less verbose, and more powerful than dataclasses, while retaining a familiar interface.

What are data classes?

Simply put, data classes are classes optimised for storing data. In this sense they are similar to record or struct types in other languages. However, Python's data classes can also have methods, making them more reminiscent of Scala's case classes. In Python, data classes take the form of a decorator which, when applied to a class, automatically generates methods to set the class's fields from arguments to its constructor, represent it as a string, and more.

Why use dataclassy?

Data classes from dataclassy offer the following advantages over those from dataclasses:

• Cleaner code: no messy InitVar, ClassVar, field or __post_init__
• Friendly inheritance:
  • No need to apply a decorator to each and every subclass - just once and all following classes will also be data classes
  • Complete freedom in field ordering - no headaches if a field with a default value follows a field without one
• Optional generation of:
  • __slots__, significantly improving memory efficiency and lookup performance
  • **kwargs, simplifying dataclass instantiation from dictionaries
  • An __iter__ method, enabling data class destructuring
• Internal fields (marked with _ or __) are excluded from __repr__ by default

In addition, dataclassy:

• Has no dependencies
• Supports Python 3.6 and up
• Has 100% test coverage

All in a tiny, tidy codebase that's a fraction of the size of Python's dataclasses or other alternatives like attrs.

Usage

Installation

Install the latest stable version from PyPI with pip:

pip install dataclassy

Or install the latest development version straight from this repository:

pip install https://github.com/biqqles/dataclassy/archive/master.zip -U

Migration

For simple use cases it is possible to instantly migrate from dataclasses to dataclassy by simply changing

from dataclasses import dataclass

to

from dataclassy import dataclass

dataclassy implements all of dataclasses' functions, and it also includes aliases for the functions as_dict (asdict) and as_tuple (astuple) to assist in migration from dataclasses. Constructs from dataclasses like field, InitVar and __post_init__ are not supported nor required in dataclassy because it uses Python's built-in syntactic features to accomplish the same things.

Examples

The basics

To define a data class, simply apply the @dataclass decorator to a class definition:

@dataclass  # with default parameters
class Pet:
    name: str
    age: int
    species: str
    foods: List[str] = []
    fluffy: bool

Without arguments to the decorator, the resulting class will behave very similarly to its equivalent from the built-in module. However, dataclassy's decorator has some additional options over dataclasses', and it is also inherited so that subclasses of data classes are automatically data classes too.

The decorator generates various methods for the class. Which ones exactly depend on the options to the decorator. For example, @dataclass(repr=False) will prevent a __repr__ method from being generated. @dataclass is equivalent to using the decorator with default parameters (i.e. @dataclass and @dataclass() are equivalent). Options to the decorator are detailed fully in the next section.

You can exclude a class attribute from dataclassy's mechanisms entirely by simply defining it without a type annotation. This can be used for class variables and constants.

Default values

Default values for fields work exactly as default arguments to functions (and in fact this is how they are implemented), with one difference: for mutable defaults, a copy is automatically created for each class instance. This means that a new copy of the list field foods in Pet above will be created each time it is instantiated, so that appending to that attribute in one instance will not affect other instances.

Inheritance

Unlike dataclasses, dataclassy's decorator only needs to be applied once, and all subclasses will become data classes with the same options as the parent class. The decorator can still be reapplied to subclasses in order to apply new parameters.

To change the type, or to add or change the default value of a field in a subclass, simply redeclare it in the subclass.

Post-initialisation logic

You can define custom post-initialisation logic (such as calculating new fields based on the value of others) by defining an __init__ method. This works because dataclassy uses __new__ to set the attributes of the class before __init__ is called. This is demonstrated in the following example:

@dataclass
class CustomInit:
    a: int
    b: int

    def __init__(self, a, b, c):
        self.d = (self.a + self.b) / c

When this class is instantiated with CustomInit(1, 2, 3), it gets a new instance attribute d which is calculated using the value of the __init__-only parameter c.

When defining an __init__, you must ensure that the signature includes, in order, all fields of the class in addition to your init-only parameters, either by explicitly listing them as shown, or by using *args and/or **kwargs (e.g. ``def init(self, *args, c). Note however that this syntax makes c` a keyword-only argument). This prevents ambiguity when initialising the class. Python will enforce this by raising a `RuntimeError` if you do not.

This mechanism performs the roles of dataclasses' InitVar and __post_init__.

API

Decorator

@dataclass(init=True, repr=True, eq=True, iter=False, frozen=False, kwargs=False, slots=False, hide_internals=True)

The decorator used to signify that a class definition should become a data class. The decorator returns a new data class with generated methods as detailed below. If the class already defines a particular method, it will not be replaced with a generated one.

Without arguments, its behaviour is, superficially, almost identical to its equivalent in the built-in module. However, dataclassy's decorator only needs to be applied once, and all subclasses will become data classes with the same parameters. The decorator can still be reapplied to subclasses in order to change parameters.

A data class' fields are defined using Python's type annotations syntax. To change the type or default value of a field in a subclass, simply redeclare it.

This decorator takes advantage of two equally important features available in Python 3.6 and up: variable type annotations and dictionaries being ordered (the latter is formally only standardised in Python 3.7 but is the case for all implementations of Python 3.6, i.e. CPython and PyPy).

Decorator options

The term "field", as used in this section, refers to a class-level variable with a type annotation. For more information, see the documentation for fields() below.

init

If true (the default), generate a __new__ method that has as parameters all fields up its inheritance chain. These are ordered in definition order, with all fields with default values placed towards the end, following all fields without them. The method initialises the class by applying these parameters to the class as attributes.

This ordering is an important distinction from dataclasses, where all fields are simply ordered in definition order, and is what allows dataclassy's data classes to be far more flexible in terms of inheritance.

You can verify the signature of the generated initialiser for any class using signature from the inspect module. For example, print(inspect.signature(Pet)) will output (name: str, age: int, species: str, foods: List[str] = []).

A shallow copy will be created for mutable arguments (defined as those defining a copy method). This means that default field values that are mutable (e.g. a list) will not be mutated between instances.

repr

If true (the default), generate a __repr__ method that displays all fields (or if hide_internals is true, all fields excluding internal ones) of the data class instance and their values.

eq

If true (the default), generate an __eq__ method that compares this data class to another as if they were tuples created by as_tuple.

iter

If true, generate an __iter__ method that returns the values of the class's fields, in order of definition. This can be used to destructure a data class instance, as with a Scala case class or a Python namedtuple.

kwargs

If true, add **kwargs to the end of the parameter list for __init__. This simplifies data class instantiation from dictionaries that may have keys in addition to the fields of the dataclass (i.e. SomeDataClass(**some_dict)).

slots

If true, generate a __slots__ attribute for the class. This reduces the memory footprint of instances and attribute lookup overhead. However, __slots__ come with a few restrictions (for example, multiple inheritance becomes tricky) that you should be aware of.

frozen

If true, data class instances are nominally immutable: fields cannot be overwritten or deleted after initialisation in __init__. Attempting to do so will raise an AttributeError.

hide_internals

If true (the default), internal fields are not included in the generated __repr__.

Functions

is_dataclass(obj)

Returns True if obj is a data class as implemented in this module.

is_dataclass_instance(obj)

Returns True if obj is an instance of a data class as implemented in this module.

fields(dataclass, internals=False)

Return a dict of dataclass's fields and their values. internals selects whether to include internal fields.

A field is defined as a class-level variable with a type annotation. This means that class variables and constants are not fields, assuming they do not have annotations as indicated above.

as_dict(dataclass dict_factory=dict)

Recursively create a dict of a dataclass instance's fields and their values.

This function is recursively called on data classes, named tuples and iterables.

as_tuple(dataclass)

Recursively create a tuple of the values of a dataclass instance's fields, in definition order.

This function is recursively called on data classes, named tuples and iterables.

make_dataclass(name, fields, defaults, bases=(), **options)

Dynamically create a data class with name name, fields fields, default field values defaults and inheriting from bases.

replace(dataclass, **changes)

Return a new copy of dataclass with field values replaced as specified in changes.

Type hints

Internal

The Internal type wrapper marks a field as being "internal" to the data class. Fields which begin with the "internal use" idiomatic indicator _ or the private field interpreter indicator __ are automatically treated as internal fields. The Internal type wrapper therefore serves as an alternative method of indicating that a field is internal for situations where you are unable to name your fields in this way.

DataClass

Use this type hint to indicate that a variable, parameter or field should be a generic data class instance. For example, dataclassy uses these in the signatures of as_dict, as_tuple and fields to show that these functions should be called on data class instances.

To be added

• The missing decorator options from dataclasses: order=False and unsafe_hash=False.