594 lines
23 KiB
ReStructuredText
594 lines
23 KiB
ReStructuredText
:mod:`dataclasses` --- Data Classes
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===================================
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.. module:: dataclasses
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:synopsis: Generate special methods on user-defined classes.
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.. moduleauthor:: Eric V. Smith <eric@trueblade.com>
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.. sectionauthor:: Eric V. Smith <eric@trueblade.com>
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**Source code:** :source:`Lib/dataclasses.py`
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--------------
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This module provides a decorator and functions for automatically
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adding generated :term:`special method`\s such as :meth:`__init__` and
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:meth:`__repr__` to user-defined classes. It was originally described
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in :pep:`557`.
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The member variables to use in these generated methods are defined
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using :pep:`526` type annotations. For example this code::
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@dataclass
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class InventoryItem:
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'''Class for keeping track of an item in inventory.'''
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name: str
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unit_price: float
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quantity_on_hand: int = 0
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def total_cost(self) -> float:
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return self.unit_price * self.quantity_on_hand
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Will add, among other things, a :meth:`__init__` that looks like::
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def __init__(self, name: str, unit_price: float, quantity_on_hand: int=0):
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self.name = name
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self.unit_price = unit_price
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self.quantity_on_hand = quantity_on_hand
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Note that this method is automatically added to the class: it is not
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directly specified in the ``InventoryItem`` definition shown above.
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.. versionadded:: 3.7
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Module-level decorators, classes, and functions
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-----------------------------------------------
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.. decorator:: dataclass(*, init=True, repr=True, eq=True, order=False, unsafe_hash=False, frozen=False)
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This function is a :term:`decorator` that is used to add generated
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:term:`special method`\s to classes, as described below.
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The :func:`dataclass` decorator examines the class to find
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``field``\s. A ``field`` is defined as class variable that has a
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:term:`type annotation <variable annotation>`. With two
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exceptions described below, nothing in :func:`dataclass`
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examines the type specified in the variable annotation.
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The order of the fields in all of the generated methods is the
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order in which they appear in the class definition.
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The :func:`dataclass` decorator will add various "dunder" methods to
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the class, described below. If any of the added methods already
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exist on the class, the behavior depends on the parameter, as documented
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below. The decorator returns the same class that is called on; no new
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class is created.
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If :func:`dataclass` is used just as a simple decorator with no parameters,
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it acts as if it has the default values documented in this
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signature. That is, these three uses of :func:`dataclass` are
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equivalent::
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@dataclass
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class C:
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...
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@dataclass()
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class C:
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...
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@dataclass(init=True, repr=True, eq=True, order=False, unsafe_hash=False, frozen=False)
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class C:
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...
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The parameters to :func:`dataclass` are:
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- ``init``: If true (the default), a :meth:`__init__` method will be
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generated.
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If the class already defines :meth:`__init__`, this parameter is
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ignored.
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- ``repr``: If true (the default), a :meth:`__repr__` method will be
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generated. The generated repr string will have the class name and
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the name and repr of each field, in the order they are defined in
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the class. Fields that are marked as being excluded from the repr
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are not included. For example:
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``InventoryItem(name='widget', unit_price=3.0, quantity_on_hand=10)``.
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If the class already defines :meth:`__repr__`, this parameter is
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ignored.
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- ``eq``: If true (the default), an :meth:`__eq__` method will be
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generated. This method compares the class as if it were a tuple
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of its fields, in order. Both instances in the comparison must
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be of the identical type.
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If the class already defines :meth:`__eq__`, this parameter is
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ignored.
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- ``order``: If true (the default is ``False``), :meth:`__lt__`,
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:meth:`__le__`, :meth:`__gt__`, and :meth:`__ge__` methods will be
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generated. These compare the class as if it were a tuple of its
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fields, in order. Both instances in the comparison must be of the
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identical type. If ``order`` is true and ``eq`` is false, a
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:exc:`ValueError` is raised.
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If the class already defines any of :meth:`__lt__`,
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:meth:`__le__`, :meth:`__gt__`, or :meth:`__ge__`, then
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:exc:`TypeError` is raised.
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- ``unsafe_hash``: If ``False`` (the default), a :meth:`__hash__` method
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is generated according to how ``eq`` and ``frozen`` are set.
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:meth:`__hash__` is used by built-in :meth:`hash()`, and when objects are
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added to hashed collections such as dictionaries and sets. Having a
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:meth:`__hash__` implies that instances of the class are immutable.
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Mutability is a complicated property that depends on the programmer's
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intent, the existence and behavior of :meth:`__eq__`, and the values of
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the ``eq`` and ``frozen`` flags in the :func:`dataclass` decorator.
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By default, :func:`dataclass` will not implicitly add a :meth:`__hash__`
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method unless it is safe to do so. Neither will it add or change an
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existing explicitly defined :meth:`__hash__` method. Setting the class
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attribute ``__hash__ = None`` has a specific meaning to Python, as
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described in the :meth:`__hash__` documentation.
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If :meth:`__hash__` is not explicit defined, or if it is set to ``None``,
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then :func:`dataclass` *may* add an implicit :meth:`__hash__` method.
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Although not recommended, you can force :func:`dataclass` to create a
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:meth:`__hash__` method with ``unsafe_hash=True``. This might be the case
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if your class is logically immutable but can nonetheless be mutated.
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This is a specialized use case and should be considered carefully.
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Here are the rules governing implicit creation of a :meth:`__hash__`
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method. Note that you cannot both have an explicit :meth:`__hash__`
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method in your dataclass and set ``unsafe_hash=True``; this will result
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in a :exc:`TypeError`.
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If ``eq`` and ``frozen`` are both true, by default :func:`dataclass` will
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generate a :meth:`__hash__` method for you. If ``eq`` is true and
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``frozen`` is false, :meth:`__hash__` will be set to ``None``, marking it
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unhashable (which it is, since it is mutable). If ``eq`` is false,
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:meth:`__hash__` will be left untouched meaning the :meth:`__hash__`
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method of the superclass will be used (if the superclass is
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:class:`object`, this means it will fall back to id-based hashing).
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- ``frozen``: If true (the default is ``False``), assigning to fields will
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generate an exception. This emulates read-only frozen instances. If
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:meth:`__setattr__` or :meth:`__delattr__` is defined in the class, then
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:exc:`TypeError` is raised. See the discussion below.
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``field``\s may optionally specify a default value, using normal
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Python syntax::
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@dataclass
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class C:
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a: int # 'a' has no default value
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b: int = 0 # assign a default value for 'b'
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In this example, both ``a`` and ``b`` will be included in the added
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:meth:`__init__` method, which will be defined as::
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def __init__(self, a: int, b: int = 0):
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:exc:`TypeError` will be raised if a field without a default value
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follows a field with a default value. This is true either when this
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occurs in a single class, or as a result of class inheritance.
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.. function:: field(*, default=MISSING, default_factory=MISSING, repr=True, hash=None, init=True, compare=True, metadata=None)
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For common and simple use cases, no other functionality is
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required. There are, however, some dataclass features that
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require additional per-field information. To satisfy this need for
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additional information, you can replace the default field value
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with a call to the provided :func:`field` function. For example::
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@dataclass
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class C:
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mylist: List[int] = field(default_factory=list)
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c = C()
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c.mylist += [1, 2, 3]
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As shown above, the ``MISSING`` value is a sentinel object used to
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detect if the ``default`` and ``default_factory`` parameters are
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provided. This sentinel is used because ``None`` is a valid value
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for ``default``. No code should directly use the ``MISSING``
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value.
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The parameters to :func:`field` are:
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- ``default``: If provided, this will be the default value for this
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field. This is needed because the :meth:`field` call itself
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replaces the normal position of the default value.
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- ``default_factory``: If provided, it must be a zero-argument
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callable that will be called when a default value is needed for
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this field. Among other purposes, this can be used to specify
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fields with mutable default values, as discussed below. It is an
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error to specify both ``default`` and ``default_factory``.
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- ``init``: If true (the default), this field is included as a
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parameter to the generated :meth:`__init__` method.
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- ``repr``: If true (the default), this field is included in the
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string returned by the generated :meth:`__repr__` method.
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- ``compare``: If true (the default), this field is included in the
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generated equality and comparison methods (:meth:`__eq__`,
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:meth:`__gt__`, et al.).
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- ``hash``: This can be a bool or ``None``. If true, this field is
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included in the generated :meth:`__hash__` method. If ``None`` (the
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default), use the value of ``compare``: this would normally be
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the expected behavior. A field should be considered in the hash
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if it's used for comparisons. Setting this value to anything
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other than ``None`` is discouraged.
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One possible reason to set ``hash=False`` but ``compare=True``
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would be if a field is expensive to compute a hash value for,
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that field is needed for equality testing, and there are other
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fields that contribute to the type's hash value. Even if a field
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is excluded from the hash, it will still be used for comparisons.
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- ``metadata``: This can be a mapping or None. None is treated as
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an empty dict. This value is wrapped in
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:func:`~types.MappingProxyType` to make it read-only, and exposed
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on the :class:`Field` object. It is not used at all by Data
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Classes, and is provided as a third-party extension mechanism.
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Multiple third-parties can each have their own key, to use as a
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namespace in the metadata.
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If the default value of a field is specified by a call to
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:func:`field()`, then the class attribute for this field will be
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replaced by the specified ``default`` value. If no ``default`` is
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provided, then the class attribute will be deleted. The intent is
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that after the :func:`dataclass` decorator runs, the class
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attributes will all contain the default values for the fields, just
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as if the default value itself were specified. For example,
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after::
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@dataclass
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class C:
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x: int
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y: int = field(repr=False)
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z: int = field(repr=False, default=10)
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t: int = 20
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The class attribute ``C.z`` will be ``10``, the class attribute
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``C.t`` will be ``20``, and the class attributes ``C.x`` and
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``C.y`` will not be set.
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.. class:: Field
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:class:`Field` objects describe each defined field. These objects
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are created internally, and are returned by the :func:`fields`
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module-level method (see below). Users should never instantiate a
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:class:`Field` object directly. Its documented attributes are:
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- ``name``: The name of the field.
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- ``type``: The type of the field.
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- ``default``, ``default_factory``, ``init``, ``repr``, ``hash``,
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``compare``, and ``metadata`` have the identical meaning and
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values as they do in the :func:`field` declaration.
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Other attributes may exist, but they are private and must not be
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inspected or relied on.
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.. function:: fields(class_or_instance)
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Returns a tuple of :class:`Field` objects that define the fields for this
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dataclass. Accepts either a dataclass, or an instance of a dataclass.
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Raises :exc:`TypeError` if not passed a dataclass or instance of one.
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Does not return pseudo-fields which are ``ClassVar`` or ``InitVar``.
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.. function:: asdict(instance, *, dict_factory=dict)
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Converts the dataclass ``instance`` to a dict (by using the
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factory function ``dict_factory``). Each dataclass is converted
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to a dict of its fields, as ``name: value`` pairs. dataclasses, dicts,
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lists, and tuples are recursed into. For example::
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@dataclass
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class Point:
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x: int
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y: int
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@dataclass
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class C:
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mylist: List[Point]
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p = Point(10, 20)
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assert asdict(p) == {'x': 10, 'y': 20}
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c = C([Point(0, 0), Point(10, 4)])
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assert asdict(c) == {'mylist': [{'x': 0, 'y': 0}, {'x': 10, 'y': 4}]}
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Raises :exc:`TypeError` if ``instance`` is not a dataclass instance.
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.. function:: astuple(instance, *, tuple_factory=tuple)
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Converts the dataclass ``instance`` to a tuple (by using the
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factory function ``tuple_factory``). Each dataclass is converted
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to a tuple of its field values. dataclasses, dicts, lists, and
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tuples are recursed into.
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Continuing from the previous example::
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assert astuple(p) == (10, 20)
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assert astuple(c) == ([(0, 0), (10, 4)],)
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Raises :exc:`TypeError` if ``instance`` is not a dataclass instance.
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.. function:: make_dataclass(cls_name, fields, *, bases=(), namespace=None, init=True, repr=True, eq=True, order=False, unsafe_hash=False, frozen=False)
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Creates a new dataclass with name ``cls_name``, fields as defined
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in ``fields``, base classes as given in ``bases``, and initialized
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with a namespace as given in ``namespace``. ``fields`` is an
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iterable whose elements are each either ``name``, ``(name, type)``,
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or ``(name, type, Field)``. If just ``name`` is supplied,
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``typing.Any`` is used for ``type``. The values of ``init``,
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``repr``, ``eq``, ``order``, ``unsafe_hash``, and ``frozen`` have
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the same meaning as they do in :func:`dataclass`.
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This function is not strictly required, because any Python
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mechanism for creating a new class with ``__annotations__`` can
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then apply the :func:`dataclass` function to convert that class to
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a dataclass. This function is provided as a convenience. For
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example::
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C = make_dataclass('C',
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[('x', int),
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'y',
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('z', int, field(default=5))],
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namespace={'add_one': lambda self: self.x + 1})
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Is equivalent to::
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@dataclass
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class C:
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x: int
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y: 'typing.Any'
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z: int = 5
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def add_one(self):
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return self.x + 1
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.. function:: replace(instance, **changes)
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Creates a new object of the same type of ``instance``, replacing
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fields with values from ``changes``. If ``instance`` is not a Data
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Class, raises :exc:`TypeError`. If values in ``changes`` do not
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specify fields, raises :exc:`TypeError`.
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The newly returned object is created by calling the :meth:`__init__`
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method of the dataclass. This ensures that
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:meth:`__post_init__`, if present, is also called.
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Init-only variables without default values, if any exist, must be
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specified on the call to :func:`replace` so that they can be passed to
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:meth:`__init__` and :meth:`__post_init__`.
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It is an error for ``changes`` to contain any fields that are
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defined as having ``init=False``. A :exc:`ValueError` will be raised
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in this case.
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Be forewarned about how ``init=False`` fields work during a call to
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:func:`replace`. They are not copied from the source object, but
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rather are initialized in :meth:`__post_init__`, if they're
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initialized at all. It is expected that ``init=False`` fields will
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be rarely and judiciously used. If they are used, it might be wise
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to have alternate class constructors, or perhaps a custom
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``replace()`` (or similarly named) method which handles instance
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copying.
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.. function:: is_dataclass(class_or_instance)
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Return ``True`` if its parameter is a dataclass or an instance of one,
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otherwise return ``False``.
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If you need to know if a class is an instance of a dataclass (and
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not a dataclass itself), then add a further check for ``not
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isinstance(obj, type)``::
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def is_dataclass_instance(obj):
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return is_dataclass(obj) and not isinstance(obj, type)
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Post-init processing
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--------------------
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The generated :meth:`__init__` code will call a method named
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:meth:`__post_init__`, if :meth:`__post_init__` is defined on the
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class. It will normally be called as ``self.__post_init__()``.
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However, if any ``InitVar`` fields are defined, they will also be
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passed to :meth:`__post_init__` in the order they were defined in the
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class. If no :meth:`__init__` method is generated, then
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:meth:`__post_init__` will not automatically be called.
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Among other uses, this allows for initializing field values that
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depend on one or more other fields. For example::
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@dataclass
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class C:
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a: float
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b: float
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c: float = field(init=False)
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def __post_init__(self):
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self.c = self.a + self.b
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See the section below on init-only variables for ways to pass
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parameters to :meth:`__post_init__`. Also see the warning about how
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:func:`replace` handles ``init=False`` fields.
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Class variables
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---------------
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One of two places where :func:`dataclass` actually inspects the type
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of a field is to determine if a field is a class variable as defined
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in :pep:`526`. It does this by checking if the type of the field is
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``typing.ClassVar``. If a field is a ``ClassVar``, it is excluded
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from consideration as a field and is ignored by the dataclass
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mechanisms. Such ``ClassVar`` pseudo-fields are not returned by the
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module-level :func:`fields` function.
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Init-only variables
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-------------------
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The other place where :func:`dataclass` inspects a type annotation is to
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determine if a field is an init-only variable. It does this by seeing
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if the type of a field is of type ``dataclasses.InitVar``. If a field
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is an ``InitVar``, it is considered a pseudo-field called an init-only
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field. As it is not a true field, it is not returned by the
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module-level :func:`fields` function. Init-only fields are added as
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parameters to the generated :meth:`__init__` method, and are passed to
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the optional :meth:`__post_init__` method. They are not otherwise used
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by dataclasses.
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For example, suppose a field will be initialized from a database, if a
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value is not provided when creating the class::
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@dataclass
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class C:
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i: int
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j: int = None
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database: InitVar[DatabaseType] = None
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def __post_init__(self, database):
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if self.j is None and database is not None:
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self.j = database.lookup('j')
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c = C(10, database=my_database)
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In this case, :func:`fields` will return :class:`Field` objects for ``i`` and
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``j``, but not for ``database``.
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Frozen instances
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----------------
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It is not possible to create truly immutable Python objects. However,
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by passing ``frozen=True`` to the :meth:`dataclass` decorator you can
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emulate immutability. In that case, dataclasses will add
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:meth:`__setattr__` and :meth:`__delattr__` methods to the class. These
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methods will raise a :exc:`FrozenInstanceError` when invoked.
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There is a tiny performance penalty when using ``frozen=True``:
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:meth:`__init__` cannot use simple assignment to initialize fields, and
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must use :meth:`object.__setattr__`.
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Inheritance
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-----------
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When the dataclass is being created by the :meth:`dataclass` decorator,
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it looks through all of the class's base classes in reverse MRO (that
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is, starting at :class:`object`) and, for each dataclass that it finds,
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adds the fields from that base class to an ordered mapping of fields.
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After all of the base class fields are added, it adds its own fields
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to the ordered mapping. All of the generated methods will use this
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combined, calculated ordered mapping of fields. Because the fields
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are in insertion order, derived classes override base classes. An
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example::
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@dataclass
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class Base:
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x: Any = 15.0
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y: int = 0
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@dataclass
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class C(Base):
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z: int = 10
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x: int = 15
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The final list of fields is, in order, ``x``, ``y``, ``z``. The final
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type of ``x`` is ``int``, as specified in class ``C``.
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The generated :meth:`__init__` method for ``C`` will look like::
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def __init__(self, x: int = 15, y: int = 0, z: int = 10):
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Default factory functions
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-------------------------
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If a :func:`field` specifies a ``default_factory``, it is called with
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zero arguments when a default value for the field is needed. For
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example, to create a new instance of a list, use::
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mylist: list = field(default_factory=list)
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If a field is excluded from :meth:`__init__` (using ``init=False``)
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and the field also specifies ``default_factory``, then the default
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factory function will always be called from the generated
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:meth:`__init__` function. This happens because there is no other
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way to give the field an initial value.
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Mutable default values
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----------------------
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Python stores default member variable values in class attributes.
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Consider this example, not using dataclasses::
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class C:
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x = []
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def add(self, element):
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self.x.append(element)
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o1 = C()
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o2 = C()
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o1.add(1)
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o2.add(2)
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assert o1.x == [1, 2]
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assert o1.x is o2.x
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Note that the two instances of class ``C`` share the same class
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variable ``x``, as expected.
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Using dataclasses, *if* this code was valid::
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@dataclass
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class D:
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x: List = []
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def add(self, element):
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self.x += element
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it would generate code similar to::
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class D:
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x = []
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def __init__(self, x=x):
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self.x = x
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def add(self, element):
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self.x += element
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assert D().x is D().x
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This has the same issue as the original example using class ``C``.
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That is, two instances of class ``D`` that do not specify a value for
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``x`` when creating a class instance will share the same copy of
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``x``. Because dataclasses just use normal Python class creation
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they also share this behavior. There is no general way for Data
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Classes to detect this condition. Instead, dataclasses will raise a
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:exc:`TypeError` if it detects a default parameter of type ``list``,
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``dict``, or ``set``. This is a partial solution, but it does protect
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against many common errors.
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Using default factory functions is a way to create new instances of
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mutable types as default values for fields::
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@dataclass
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class D:
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x: list = field(default_factory=list)
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assert D().x is not D().x
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Exceptions
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----------
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.. exception:: FrozenInstanceError
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Raised when an implicitly defined :meth:`__setattr__` or
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:meth:`__delattr__` is called on a dataclass which was defined with
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``frozen=True``.
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