"dataclasses" --- Data Classes
******************************

**Source code:** Lib/dataclasses.py

======================================================================

This module provides a decorator and functions for automatically
adding generated *special method*s such as "__init__()" and
"__repr__()" to user-defined classes.  It was originally described in
**PEP 557**.

The member variables to use in these generated methods are defined
using **PEP 526** type annotations.  For example this code:

   from dataclasses import dataclass

   @dataclass
   class InventoryItem:
       """Class for keeping track of an item in inventory."""
       name: str
       unit_price: float
       quantity_on_hand: int = 0

       def total_cost(self) -> float:
           return self.unit_price * self.quantity_on_hand

Will add, among other things, a "__init__()" that looks like:

   def __init__(self, name: str, unit_price: float, quantity_on_hand: int=0):
       self.name = name
       self.unit_price = unit_price
       self.quantity_on_hand = quantity_on_hand

Note that this method is automatically added to the class: it is not
directly specified in the "InventoryItem" definition shown above.

New in version 3.7.


Module-level decorators, classes, and functions
===============================================

@dataclasses.dataclass(*, init=True, repr=True, eq=True, order=False, unsafe_hash=False, frozen=False)

   This function is a *decorator* that is used to add generated
   *special method*s to classes, as described below.

   The "dataclass()" decorator examines the class to find "field"s.  A
   "field" is defined as class variable that has a *type annotation*.
   With two exceptions described below, nothing in "dataclass()"
   examines the type specified in the variable annotation.

   The order of the fields in all of the generated methods is the
   order in which they appear in the class definition.

   The "dataclass()" decorator will add various "dunder" methods to
   the class, described below.  If any of the added methods already
   exist on the class, the behavior depends on the parameter, as
   documented below. The decorator returns the same class that is
   called on; no new class is created.

   If "dataclass()" is used just as a simple decorator with no
   parameters, it acts as if it has the default values documented in
   this signature.  That is, these three uses of "dataclass()" are
   equivalent:

      @dataclass
      class C:
          ...

      @dataclass()
      class C:
          ...

      @dataclass(init=True, repr=True, eq=True, order=False, unsafe_hash=False, frozen=False)
      class C:
         ...

   The parameters to "dataclass()" are:

   * "init": If true (the default), a "__init__()" method will be
     generated.

     If the class already defines "__init__()", this parameter is
     ignored.

   * "repr": If true (the default), a "__repr__()" method will be
     generated.  The generated repr string will have the class name
     and the name and repr of each field, in the order they are
     defined in the class.  Fields that are marked as being excluded
     from the repr are not included.  For example:
     "InventoryItem(name='widget', unit_price=3.0,
     quantity_on_hand=10)".

     If the class already defines "__repr__()", this parameter is
     ignored.

   * "eq": If true (the default), an "__eq__()" method will be
     generated.  This method compares the class as if it were a tuple
     of its fields, in order.  Both instances in the comparison must
     be of the identical type.

     If the class already defines "__eq__()", this parameter is
     ignored.

   * "order": If true (the default is "False"), "__lt__()",
     "__le__()", "__gt__()", and "__ge__()" methods will be generated.
     These compare the class as if it were a tuple of its fields, in
     order.  Both instances in the comparison must be of the identical
     type.  If "order" is true and "eq" is false, a "ValueError" is
     raised.

     If the class already defines any of "__lt__()", "__le__()",
     "__gt__()", or "__ge__()", then "TypeError" is raised.

   * "unsafe_hash": If "False" (the default), a "__hash__()" method is
     generated according to how "eq" and "frozen" are set.

     "__hash__()" is used by built-in "hash()", and when objects are
     added to hashed collections such as dictionaries and sets.
     Having a "__hash__()" implies that instances of the class are
     immutable. Mutability is a complicated property that depends on
     the programmer's intent, the existence and behavior of
     "__eq__()", and the values of the "eq" and "frozen" flags in the
     "dataclass()" decorator.

     By default, "dataclass()" will not implicitly add a "__hash__()"
     method unless it is safe to do so.  Neither will it add or change
     an existing explicitly defined "__hash__()" method.  Setting the
     class attribute "__hash__ = None" has a specific meaning to
     Python, as described in the "__hash__()" documentation.

     If "__hash__()" is not explicit defined, or if it is set to
     "None", then "dataclass()" *may* add an implicit "__hash__()"
     method. Although not recommended, you can force "dataclass()" to
     create a "__hash__()" method with "unsafe_hash=True". This might
     be the case if your class is logically immutable but can
     nonetheless be mutated. This is a specialized use case and should
     be considered carefully.

     Here are the rules governing implicit creation of a "__hash__()"
     method.  Note that you cannot both have an explicit "__hash__()"
     method in your dataclass and set "unsafe_hash=True"; this will
     result in a "TypeError".

     If "eq" and "frozen" are both true, by default "dataclass()" will
     generate a "__hash__()" method for you.  If "eq" is true and
     "frozen" is false, "__hash__()" will be set to "None", marking it
     unhashable (which it is, since it is mutable).  If "eq" is false,
     "__hash__()" will be left untouched meaning the "__hash__()"
     method of the superclass will be used (if the superclass is
     "object", this means it will fall back to id-based hashing).

   * "frozen": If true (the default is "False"), assigning to fields
     will generate an exception.  This emulates read-only frozen
     instances.  If "__setattr__()" or "__delattr__()" is defined in
     the class, then "TypeError" is raised.  See the discussion below.

   "field"s may optionally specify a default value, using normal
   Python syntax:

      @dataclass
      class C:
          a: int       # 'a' has no default value
          b: int = 0   # assign a default value for 'b'

   In this example, both "a" and "b" will be included in the added
   "__init__()" method, which will be defined as:

      def __init__(self, a: int, b: int = 0):

   "TypeError" will be raised if a field without a default value
   follows a field with a default value.  This is true either when
   this occurs in a single class, or as a result of class inheritance.

dataclasses.field(*, default=MISSING, default_factory=MISSING, repr=True, hash=None, init=True, compare=True, metadata=None)

   For common and simple use cases, no other functionality is
   required.  There are, however, some dataclass features that require
   additional per-field information.  To satisfy this need for
   additional information, you can replace the default field value
   with a call to the provided "field()" function.  For example:

      @dataclass
      class C:
          mylist: list[int] = field(default_factory=list)

      c = C()
      c.mylist += [1, 2, 3]

   As shown above, the "MISSING" value is a sentinel object used to
   detect if the "default" and "default_factory" parameters are
   provided.  This sentinel is used because "None" is a valid value
   for "default".  No code should directly use the "MISSING" value.

   The parameters to "field()" are:

   * "default": If provided, this will be the default value for this
     field.  This is needed because the "field()" call itself replaces
     the normal position of the default value.

   * "default_factory": If provided, it must be a zero-argument
     callable that will be called when a default value is needed for
     this field.  Among other purposes, this can be used to specify
     fields with mutable default values, as discussed below.  It is an
     error to specify both "default" and "default_factory".

   * "init": If true (the default), this field is included as a
     parameter to the generated "__init__()" method.

   * "repr": If true (the default), this field is included in the
     string returned by the generated "__repr__()" method.

   * "compare": If true (the default), this field is included in the
     generated equality and comparison methods ("__eq__()",
     "__gt__()", et al.).

   * "hash": This can be a bool or "None".  If true, this field is
     included in the generated "__hash__()" method.  If "None" (the
     default), use the value of "compare": this would normally be the
     expected behavior.  A field should be considered in the hash if
     it's used for comparisons.  Setting this value to anything other
     than "None" is discouraged.

     One possible reason to set "hash=False" but "compare=True" would
     be if a field is expensive to compute a hash value for, that
     field is needed for equality testing, and there are other fields
     that contribute to the type's hash value.  Even if a field is
     excluded from the hash, it will still be used for comparisons.

   * "metadata": This can be a mapping or None. None is treated as an
     empty dict.  This value is wrapped in "MappingProxyType()" to
     make it read-only, and exposed on the "Field" object. It is not
     used at all by Data Classes, and is provided as a third-party
     extension mechanism. Multiple third-parties can each have their
     own key, to use as a namespace in the metadata.

   If the default value of a field is specified by a call to
   "field()", then the class attribute for this field will be replaced
   by the specified "default" value.  If no "default" is provided,
   then the class attribute will be deleted.  The intent is that after
   the "dataclass()" decorator runs, the class attributes will all
   contain the default values for the fields, just as if the default
   value itself were specified.  For example, after:

      @dataclass
      class C:
          x: int
          y: int = field(repr=False)
          z: int = field(repr=False, default=10)
          t: int = 20

   The class attribute "C.z" will be "10", the class attribute "C.t"
   will be "20", and the class attributes "C.x" and "C.y" will not be
   set.

class dataclasses.Field

   "Field" objects describe each defined field. These objects are
   created internally, and are returned by the "fields()" module-level
   method (see below).  Users should never instantiate a "Field"
   object directly.  Its documented attributes are:

      * "name": The name of the field.

      * "type": The type of the field.

      * "default", "default_factory", "init", "repr", "hash",
        "compare", and "metadata" have the identical meaning and
        values as they do in the "field()" declaration.

   Other attributes may exist, but they are private and must not be
   inspected or relied on.

dataclasses.fields(class_or_instance)

   Returns a tuple of "Field" objects that define the fields for this
   dataclass.  Accepts either a dataclass, or an instance of a
   dataclass. Raises "TypeError" if not passed a dataclass or instance
   of one. Does not return pseudo-fields which are "ClassVar" or
   "InitVar".

dataclasses.asdict(instance, *, dict_factory=dict)

   Converts the dataclass "instance" to a dict (by using the factory
   function "dict_factory").  Each dataclass is converted to a dict of
   its fields, as "name: value" pairs.  dataclasses, dicts, lists, and
   tuples are recursed into.  For example:

      @dataclass
      class Point:
           x: int
           y: int

      @dataclass
      class C:
           mylist: list[Point]

      p = Point(10, 20)
      assert asdict(p) == {'x': 10, 'y': 20}

      c = C([Point(0, 0), Point(10, 4)])
      assert asdict(c) == {'mylist': [{'x': 0, 'y': 0}, {'x': 10, 'y': 4}]}

   Raises "TypeError" if "instance" is not a dataclass instance.

dataclasses.astuple(instance, *, tuple_factory=tuple)

   Converts the dataclass "instance" to a tuple (by using the factory
   function "tuple_factory").  Each dataclass is converted to a tuple
   of its field values.  dataclasses, dicts, lists, and tuples are
   recursed into.

   Continuing from the previous example:

      assert astuple(p) == (10, 20)
      assert astuple(c) == ([(0, 0), (10, 4)],)

   Raises "TypeError" if "instance" is not a dataclass instance.

dataclasses.make_dataclass(cls_name, fields, *, bases=(), namespace=None, init=True, repr=True, eq=True, order=False, unsafe_hash=False, frozen=False)

   Creates a new dataclass with name "cls_name", fields as defined in
   "fields", base classes as given in "bases", and initialized with a
   namespace as given in "namespace".  "fields" is an iterable whose
   elements are each either "name", "(name, type)", or "(name, type,
   Field)".  If just "name" is supplied, "typing.Any" is used for
   "type".  The values of "init", "repr", "eq", "order",
   "unsafe_hash", and "frozen" have the same meaning as they do in
   "dataclass()".

   This function is not strictly required, because any Python
   mechanism for creating a new class with "__annotations__" can then
   apply the "dataclass()" function to convert that class to a
   dataclass.  This function is provided as a convenience.  For
   example:

      C = make_dataclass('C',
                         [('x', int),
                           'y',
                          ('z', int, field(default=5))],
                         namespace={'add_one': lambda self: self.x + 1})

   Is equivalent to:

      @dataclass
      class C:
          x: int
          y: 'typing.Any'
          z: int = 5

          def add_one(self):
              return self.x + 1

dataclasses.replace(instance, /, **changes)

   Creates a new object of the same type of "instance", replacing
   fields with values from "changes".  If "instance" is not a Data
   Class, raises "TypeError".  If values in "changes" do not specify
   fields, raises "TypeError".

   The newly returned object is created by calling the "__init__()"
   method of the dataclass.  This ensures that "__post_init__()", if
   present, is also called.

   Init-only variables without default values, if any exist, must be
   specified on the call to "replace()" so that they can be passed to
   "__init__()" and "__post_init__()".

   It is an error for "changes" to contain any fields that are defined
   as having "init=False".  A "ValueError" will be raised in this
   case.

   Be forewarned about how "init=False" fields work during a call to
   "replace()".  They are not copied from the source object, but
   rather are initialized in "__post_init__()", if they're initialized
   at all.  It is expected that "init=False" fields will be rarely and
   judiciously used.  If they are used, it might be wise to have
   alternate class constructors, or perhaps a custom "replace()" (or
   similarly named) method which handles instance copying.

dataclasses.is_dataclass(class_or_instance)

   Return "True" if its parameter is a dataclass or an instance of
   one, otherwise return "False".

   If you need to know if a class is an instance of a dataclass (and
   not a dataclass itself), then add a further check for "not
   isinstance(obj, type)":

      def is_dataclass_instance(obj):
          return is_dataclass(obj) and not isinstance(obj, type)


Post-init processing
====================

The generated "__init__()" code will call a method named
"__post_init__()", if "__post_init__()" is defined on the class.  It
will normally be called as "self.__post_init__()". However, if any
"InitVar" fields are defined, they will also be passed to
"__post_init__()" in the order they were defined in the class.  If no
"__init__()" method is generated, then "__post_init__()" will not
automatically be called.

Among other uses, this allows for initializing field values that
depend on one or more other fields.  For example:

   @dataclass
   class C:
       a: float
       b: float
       c: float = field(init=False)

       def __post_init__(self):
           self.c = self.a + self.b

See the section below on init-only variables for ways to pass
parameters to "__post_init__()".  Also see the warning about how
"replace()" handles "init=False" fields.


Class variables
===============

One of two places where "dataclass()" actually inspects the type of a
field is to determine if a field is a class variable as defined in
**PEP 526**.  It does this by checking if the type of the field is
"typing.ClassVar".  If a field is a "ClassVar", it is excluded from
consideration as a field and is ignored by the dataclass mechanisms.
Such "ClassVar" pseudo-fields are not returned by the module-level
"fields()" function.


Init-only variables
===================

The other place where "dataclass()" inspects a type annotation is to
determine if a field is an init-only variable.  It does this by seeing
if the type of a field is of type "dataclasses.InitVar".  If a field
is an "InitVar", it is considered a pseudo-field called an init-only
field.  As it is not a true field, it is not returned by the module-
level "fields()" function.  Init-only fields are added as parameters
to the generated "__init__()" method, and are passed to the optional
"__post_init__()" method.  They are not otherwise used by dataclasses.

For example, suppose a field will be initialized from a database, if a
value is not provided when creating the class:

   @dataclass
   class C:
       i: int
       j: int = None
       database: InitVar[DatabaseType] = None

       def __post_init__(self, database):
           if self.j is None and database is not None:
               self.j = database.lookup('j')

   c = C(10, database=my_database)

In this case, "fields()" will return "Field" objects for "i" and "j",
but not for "database".


Frozen instances
================

It is not possible to create truly immutable Python objects.  However,
by passing "frozen=True" to the "dataclass()" decorator you can
emulate immutability.  In that case, dataclasses will add
"__setattr__()" and "__delattr__()" methods to the class.  These
methods will raise a "FrozenInstanceError" when invoked.

There is a tiny performance penalty when using "frozen=True":
"__init__()" cannot use simple assignment to initialize fields, and
must use "object.__setattr__()".


Inheritance
===========

When the dataclass is being created by the "dataclass()" decorator, it
looks through all of the class's base classes in reverse MRO (that is,
starting at "object") and, for each dataclass that it finds, adds the
fields from that base class to an ordered mapping of fields. After all
of the base class fields are added, it adds its own fields to the
ordered mapping.  All of the generated methods will use this combined,
calculated ordered mapping of fields.  Because the fields are in
insertion order, derived classes override base classes.  An example:

   @dataclass
   class Base:
       x: Any = 15.0
       y: int = 0

   @dataclass
   class C(Base):
       z: int = 10
       x: int = 15

The final list of fields is, in order, "x", "y", "z".  The final type
of "x" is "int", as specified in class "C".

The generated "__init__()" method for "C" will look like:

   def __init__(self, x: int = 15, y: int = 0, z: int = 10):


Default factory functions
=========================

   If a "field()" specifies a "default_factory", it is called with
   zero arguments when a default value for the field is needed.  For
   example, to create a new instance of a list, use:

      mylist: list = field(default_factory=list)

   If a field is excluded from "__init__()" (using "init=False") and
   the field also specifies "default_factory", then the default
   factory function will always be called from the generated
   "__init__()" function.  This happens because there is no other way
   to give the field an initial value.


Mutable default values
======================

   Python stores default member variable values in class attributes.
   Consider this example, not using dataclasses:

      class C:
          x = []
          def add(self, element):
              self.x.append(element)

      o1 = C()
      o2 = C()
      o1.add(1)
      o2.add(2)
      assert o1.x == [1, 2]
      assert o1.x is o2.x

   Note that the two instances of class "C" share the same class
   variable "x", as expected.

   Using dataclasses, *if* this code was valid:

      @dataclass
      class D:
          x: List = []
          def add(self, element):
              self.x += element

   it would generate code similar to:

      class D:
          x = []
          def __init__(self, x=x):
              self.x = x
          def add(self, element):
              self.x += element

      assert D().x is D().x

   This has the same issue as the original example using class "C".
   That is, two instances of class "D" that do not specify a value for
   "x" when creating a class instance will share the same copy of "x".
   Because dataclasses just use normal Python class creation they also
   share this behavior.  There is no general way for Data Classes to
   detect this condition.  Instead, dataclasses will raise a
   "TypeError" if it detects a default parameter of type "list",
   "dict", or "set".  This is a partial solution, but it does protect
   against many common errors.

   Using default factory functions is a way to create new instances of
   mutable types as default values for fields:

      @dataclass
      class D:
          x: list = field(default_factory=list)

      assert D().x is not D().x


Exceptions
==========

exception dataclasses.FrozenInstanceError

   Raised when an implicitly defined "__setattr__()" or
   "__delattr__()" is called on a dataclass which was defined with
   "frozen=True".
