dataclassy 0.1.0

An alternative to Python's dataclasses

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.

Why use dataclassy?

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

• Friendly inheritance:
  • No need to apply a decorator to each 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:

• Is pure Python, with zero dependencies
• Supports Python 3.6 and up

All in a tidy codebase that's a fraction of the size of Python's dataclasses!

Usage

Installation

Install the latest version from this repository with pip:

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

Migration

For basic applications, it is possible to instantly migrate from dataclasses to dataclassy by simply changing

from dataclasses import dataclass

to

from dataclassy import dataclass

dataclassy also includes aliases for its functions - asdict for as_dict, astuple for as_tuple and make_dataclass for create_dataclass - to assist in migration from dataclasses.

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. Usage is simple:

@dataclass  # with default parameters
class Pet:
    name: str
    age: int
    species: str
    foods: List[str] = []
    SOME_CONSTANT = 232

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 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. Data classes can of course also contain methods.

As shown, class variables and constants are represented by the absence of type annotations.

Decorator parameters

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 an __init__ 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.

This 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] = []).

This generated __init__ will assign its parameters to the fields of the new dataclass instance.

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. For example, a copy of the list field foods of Pet will be created for each instance, meaning that appending to that attribute in one instance will not affect other 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 intantiation 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.

create_dataclass(name, fields, defaults, bases=())

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

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

• An equivalent for __post_init__