Traditional syntax: SIGNAL and SLOT QtCore.SIGNAL and QtCore.SLOT macros allow Python to interface with Qt signal and slot delivery mechanisms. This is the old way of using signals and slots. The example below uses the well known clicked signal from a QPushButton.The connect method has a non python-friendly syntax. As a result, class attributes cannot be used to set default values for instance variables defined by slots; otherwise, the class attribute would overwrite the descriptor assignment. If a class defines a slot also defined in a base class, the instance variable defined by the base class slot is inaccessible (except by retrieving its.
This document is a quick cheat sheet showing how the PEP 484 typeannotation notation represents various common types in Python 3.
Technically many of the type annotations shown below are redundant,because mypy can derive them from the type of the expression. Somany of the examples have a dual purpose: show how to write theannotation, and show the inferred types.
Python 3.6 introduced a syntax for annotating variables in PEP 526and we use it in most examples.
Python 3 supports an annotation syntax for function declarations.
When you’re puzzled or when things are complicated¶
Standard “duck types”¶
In typical Python code, many functions that can take a list or a dictas an argument only need their argument to be somehow “list-like” or“dict-like”. A specific meaning of “list-like” or “dict-like” (orsomething-else-like) is called a “duck type”, and several duck typesthat are common in idiomatic Python are standardized.
Coroutines and asyncio¶
See Typing async/await for the full detail on typing coroutines and asynchronous code.
Decorator functions can be expressed via generics. SeeDeclaring decorators for the more details.
This module provides a decorator and functions for automaticallyadding generated special methods such as
__repr__() to user-defined classes. It was originally describedin PEP 557.
The member variables to use in these generated methods are definedusing PEP 526 type annotations. For example this code:
Will add, among other things, a
__init__() that looks like:
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Note that this method is automatically added to the class: it is notdirectly specified in the
InventoryItem definition shown above.
New in version 3.7.
Module-level decorators, classes, and functions¶
dataclass(*, init=True, repr=True, eq=True, order=False, unsafe_hash=False, frozen=False)¶
This function is a decorator that is used to add generatedspecial methods to classes, as described below.
dataclass() decorator examines the class to find
field is defined as class variable that has atype annotation. With twoexceptions 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 theorder in which they appear in the class definition.
dataclass() decorator will add various “dunder” methods tothe class, described below. If any of the added methods alreadyexist on the class, the behavior depends on the parameter, as documentedbelow. The decorator returns the same class that is called on; no newclass is created.
dataclass() is used just as a simple decorator with no parameters,it acts as if it has the default values documented in thissignature. That is, these three uses of
The parameters to
init: If true (the default), a
__init__()method will begenerated.
If the class already defines
__init__(), this parameter isignored.
repr: If true (the default), a
__repr__()method will begenerated. The generated repr string will have the class name andthe name and repr of each field, in the order they are defined inthe class. Fields that are marked as being excluded from the reprare not included. For example:
If the class already defines
__repr__(), this parameter isignored.
eq: If true (the default), an
__eq__()method will begenerated. This method compares the class as if it were a tupleof its fields, in order. Both instances in the comparison mustbe of the identical type.
If the class already defines
__eq__(), this parameter isignored.
order: If true (the default is
__ge__()methods will begenerated. These compare the class as if it were a tuple of itsfields, in order. Both instances in the comparison must be of theidentical type. If
orderis true and
eqis false, a
If the class already defines any of
False(the default), a
__hash__()methodis generated according to how
__hash__()is used by built-in
hash(), and when objects areadded 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’sintent, the existence and behavior of
__eq__(), and the values ofthe
frozenflags in the
dataclass()will not implicitly add a
__hash__()method unless it is safe to do so. Neither will it add or change anexisting explicitly defined
__hash__()method. Setting the classattribute
__hash__=Nonehas a specific meaning to Python, asdescribed in the
__hash__()is not explicit defined, or if it is set to
dataclass()may add an implicit
__hash__()method.Although not recommended, you can force
dataclass()to create a
unsafe_hash=True. This might be the caseif 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 resultin a
frozenare both true, by default
__hash__()method for you. If
eqis true and
__hash__()will be set to
None, marking itunhashable (which it is, since it is mutable). If
__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 willgenerate an exception. This emulates read-only frozen instances. If
__delattr__()is defined in the class, then
TypeErroris raised. See the discussion below.
fields may optionally specify a default value, using normalPython syntax:
In this example, both
b will be included in the added
__init__() method, which will be defined as:
TypeError will be raised if a field without a default valuefollows a field with a default value. This is true either when thisoccurs in a single class, or as a result of class inheritance.
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 isrequired. There are, however, some dataclass features thatrequire additional per-field information. To satisfy this need foradditional information, you can replace the default field valuewith a call to the provided
field() function. For example:
As shown above, the
MISSING value is a sentinel object used todetect if the
default_factory parameters areprovided. This sentinel is used because
None is a valid valuefor
default. No code should directly use the
The parameters to
default: If provided, this will be the default value for thisfield. This is needed because the
field()call itselfreplaces the normal position of the default value.
default_factory: If provided, it must be a zero-argumentcallable that will be called when a default value is needed forthis field. Among other purposes, this can be used to specifyfields with mutable default values, as discussed below. It is anerror to specify both
init: If true (the default), this field is included as aparameter to the generated
__init__()method. Hilton hotels near columbia sc.
repr: If true (the default), this field is included in thestring returned by the generated
compare: If true (the default), this field is included in thegenerated equality and comparison methods (
__gt__(), et al.).
hash: This can be a bool or
None. If true, this field isincluded in the generated
None(thedefault), use the value of
compare: this would normally bethe expected behavior. A field should be considered in the hashif it’s used for comparisons. Setting this value to anythingother than
One possible reason to set
compare=Truewould be if a field is expensive to compute a hash value for,that field is needed for equality testing, and there are otherfields that contribute to the type’s hash value. Even if a fieldis excluded from the hash, it will still be used for comparisons.
metadata: This can be a mapping or None. None is treated asan empty dict. This value is wrapped in
MappingProxyType()to make it read-only, and exposedon the
Fieldobject. It is not used at all by DataClasses, and is provided as a third-party extension mechanism.Multiple third-parties can each have their own key, to use as anamespace 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 bereplaced by the specified
default value. If no
default isprovided, then the class attribute will be deleted. The intent isthat after the
dataclass() decorator runs, the classattributes will all contain the default values for the fields, justas if the default value itself were specified. For example,after:
The class attribute
C.z will be
10, the class attribute
C.t will be
20, and the class attributes
C.y will not be set.
Field objects describe each defined field. These objectsare 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.
metadatahave the identical meaning andvalues as they do in the
Other attributes may exist, but they are private and must not beinspected or relied on.
Returns a tuple of
Field objects that define the fields for thisdataclass. 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
asdict(instance, *, dict_factory=dict)¶
Converts the dataclass
instance to a dict (by using thefactory function
dict_factory). Each dataclass is convertedto a dict of its fields, as
name:value pairs. dataclasses, dicts,lists, and tuples are recursed into. For example:
instance is not a dataclass instance.
astuple(instance, *, tuple_factory=tuple)¶
Converts the dataclass
instance to a tuple (by using thefactory function
tuple_factory). Each dataclass is convertedto a tuple of its field values. dataclasses, dicts, lists, andtuples are recursed into.
Continuing from the previous example:
instance is not a dataclass instance.
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 definedin
fields, base classes as given in
bases, and initializedwith a namespace as given in
fields is aniterable whose elements are each either
(name,type,Field). If just
name is supplied,
typing.Any is used for
type. The values of
frozen havethe same meaning as they do in
This function is not strictly required, because any Pythonmechanism for creating a new class with
__annotations__ canthen apply the
dataclass() function to convert that class toa dataclass. This function is provided as a convenience. Forexample:
Is equivalent to:
replace(instance, /, **changes)¶
Creates a new object of the same type of
instance, replacingfields with values from
instance is not a DataClass, raises
TypeError. If values in
changes do notspecify fields, raises
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 bespecified on the call to
replace() so that they can be passed to
It is an error for
changes to contain any fields that aredefined as having
ValueError will be raisedin this case.
Be forewarned about how
init=False fields work during a call to
replace(). They are not copied from the source object, butrather are initialized in
__post_init__(), if they’reinitialized at all. It is expected that
init=False fields willbe rarely and judiciously used. If they are used, it might be wiseto have alternate class constructors, or perhaps a custom
replace() (or similarly named) method which handles instancecopying.
True if its parameter is a dataclass or an instance of one,otherwise return
If you need to know if a class is an instance of a dataclass (andnot a dataclass itself), then add a further check for
__init__() code will call a method named
__post_init__() is defined on theclass. It will normally be called as
self.__post_init__().However, if any
InitVar fields are defined, they will also bepassed to
__post_init__() in the order they were defined in theclass. If no
__init__() method is generated, then
__post_init__() will not automatically be called.
Among other uses, this allows for initializing field values thatdepend on one or more other fields. For example:
See the section below on init-only variables for ways to passparameters to
__post_init__(). Also see the warning about how
One of two places where
dataclass() actually inspects the typeof a field is to determine if a field is a class variable as definedin PEP 526. It does this by checking if the type of the field is
typing.ClassVar. If a field is a
ClassVar, it is excludedfrom consideration as a field and is ignored by the dataclassmechanisms. Such
ClassVar pseudo-fields are not returned by themodule-level
The other place where
dataclass() inspects a type annotation is todetermine if a field is an init-only variable. It does this by seeingif the type of a field is of type
dataclasses.InitVar. If a fieldis an
InitVar, it is considered a pseudo-field called an init-onlyfield. As it is not a true field, it is not returned by themodule-level
fields() function. Init-only fields are added asparameters to the generated
__init__() method, and are passed tothe optional
__post_init__() method. They are not otherwise usedby dataclasses.
For example, suppose a field will be initialized from a database, if avalue is not provided when creating the class:
In this case,
fields() will return
Field objects for
j, but not for
It is not possible to create truly immutable Python objects. However,by passing
frozen=True to the
dataclass() decorator you canemulate immutability. In that case, dataclasses will add
__delattr__() methods to the class. Thesemethods will raise a
FrozenInstanceError when invoked.
There is a tiny performance penalty when using
__init__() cannot use simple assignment to initialize fields, andmust use
When the dataclass is being created by the
dataclass() decorator,it looks through all of the class’s base classes in reverse MRO (thatis, 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 fieldsto the ordered mapping. All of the generated methods will use thiscombined, calculated ordered mapping of fields. Because the fieldsare in insertion order, derived classes override base classes. Anexample:
The final list of fields is, in order,
z. The finaltype of
int, as specified in class
__init__() method for
C will look like:
Default factory functions¶
field() specifies a
default_factory, it is called withzero arguments when a default value for the field is needed. Forexample, to create a new instance of a list, use:
If a field is excluded from
init=False)and the field also specifies
default_factory, then the defaultfactory function will always be called from the generated
__init__() function. This happens because there is no otherway to give the field an initial value.
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Mutable default values¶
Python stores default member variable values in class attributes.Consider this example, not using dataclasses:
Note that the two instances of class
C share the same classvariable
x, as expected.
Using dataclasses, if this code was valid:
it would generate code similar to:
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 creationthey also share this behavior. There is no general way for DataClasses to detect this condition. Instead, dataclasses will raise a
TypeError if it detects a default parameter of type
set. This is a partial solution, but it does protectagainst many common errors.
Using default factory functions is a way to create new instances ofmutable types as default values for fields:
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Raised when an implicitly defined
__delattr__() is called on a dataclass which was defined with