3. 資料模型¶

3.1. 物件、數值和型別¶

物件是 Python 為資料的抽象表示方式。一個 Python 程式當中的所有資料皆由物件或物件之間的關係來呈現。程式碼也都是以物件呈現的。

每個物件都有一個識別性、型別，和數值。物件的識別性在物件建立後永遠不會改變；你也可以把它想成是該物件在記憶體中的位址。is 運算子會比較兩個物件的識別性是否相同；id() 函式則會回傳代表一個該物件的識別性的整數。

在 CPython 當中，id(x) 就是 x 所儲存在的記憶體位址。

一個物件的型別決定了該物件所支援的操作（例如「它有長度嗎？」），也同時定義該型別的物件能夠擁有的數值。type() 函式會回傳一個物件的型別（而該型別本身也是一個物件）。如同它的識別性，一個物件的型別 (type) 也是不可變的。[1]

某些物件的數值可被改變，這種物件稱作「可變的」(mutable)；建立後數值不能變更的物件則稱作「不可變的」(immutable)。（不可變的容器物件中如果包含對於可變物件的參照，則後者的數值改變的時候前者的數值也會跟著一起改變；這種時候該容器仍會被當成是不可變的，因為它包含的物件集合仍然無法變更。因此可變或不可變嚴格說起並不等同於數值是否能被改變，它的定義有其他不明顯的細節。）一個物件是否為可變取決於它的型別；舉例來說，數字、字串和 tuple 是不可變的，而字典與串列則是可變的。

物件永遠不會被明示的摧毀；但當它們變得不再能夠存取的時候可能會被作為垃圾回收。每個實作都能延後垃圾回收或是乾脆忽略它 --- 垃圾回收如何進行完全取決於各個實作，只要沒有被回收的物件仍是可達的。

CPython 目前使用一種參照計數的方案，並提供可選的循環連結垃圾延遲偵測，這個方案會在大部分物件變得不可存取時馬上回收它們，但不保證能夠回收包含循環參照的垃圾。關於控制循環垃圾回收的資訊請見 gc 模組的說明文件。其他實作的行為不會相同，CPython 也有可能改變，因此請不要仰賴物件在變得不可存取時能夠馬上被最終化（亦即你應該總是明確關閉檔案）。

請注意，使用一個實作的追蹤或除錯工具可能會讓原本能夠回收的物件被維持存活。也請注意，使用 try...except 陳述式來抓捕例外也可能會讓物件維持存活。

某些物件包含對於「外部」資源的參照，像是開啟的檔案或是視窗。基本上這些資源會在物件被回收時釋放，但因為垃圾回收不保證會發生，這種物件也會提供明確釋放外部資源的方式 --- 通常是 close() method。強烈建議各個程式明確關閉這種物件。try...finally 陳述式與 with 陳述式提供進行明確關閉的方便手段。

某些物件包含對於其他物件的參照；這種物件被叫做「容器」。容器的範例有 tuple、串列與字典。這些參照是容器的數值的一部分。通常當我們提到容器的數值的時候，我們指的是其中包含的物件的數值，而不是它們的識別性；但當我們提到容器是否可變的時候，我們指的是直接包含在其中的物件的識別性。因此，如果一個不可變的容器（像一個 tuple）包含對於可變物件的參照，該可變物件被變更時該容器的數值也會跟著變更。

型別幾乎影響物件行為的所有面向。就連物件識別性的重要性某種程度上也受型別影響：對於不可變的型別，計算新數值的操作可能其實會回傳一個某個相同型別且相同數值的現存物件的參照；對於可變型別這則不會發生。舉例來說，在進行 a = 1; b = 1 之後，a 和 b 可能會參照同一個物件，也可能不會，取決於所使用的實作。這是因為 int 是不可變的型別，因此 1 的參照可以重複利用。這個行為取決於所使用的實作，因此不應該依賴它，但在進行物件識別性測試的時候還是需要注意有這件事情。而在進行 c = []; d = [] 之後，c 和 d 則保證會參照兩個不同、獨特、且新建立的空白串列。（請注意，e = f = [] 則會將同一個物件同時指派給 e 和 f。）

3.2. 標準型別階層¶

Below is a list of the types that are built into Python. Extension modules (written in C, Java, or other languages, depending on the implementation) can define additional types. Future versions of Python may add types to the type hierarchy (e.g., rational numbers, efficiently stored arrays of integers, etc.), although such additions will often be provided via the standard library instead.

Some of the type descriptions below contain a paragraph listing 'special attributes.' These are attributes that provide access to the implementation and are not intended for general use. Their definition may change in the future.

3.2.1. None¶

這個型別只有一個數值。只有一個物件有這個數值。這個物件由內建名稱 None 存取。它用來在許多情況下代表數值不存在，例如沒有明確回傳任何東西的函式就會回傳這個物件。它的真值是 false。

3.2.2. NotImplemented¶

這個型別只有一個數值。只有一個物件有這個數值。這個物件由內建名稱 NotImplemented 存取。數字方法和 rich comparison 方法應該在沒有為所提供的運算元實作該操作的時候回傳這個數值。（直譯器接下來則會依運算子嘗試反轉的操作或是其他的後備方案。）它不應該在預期布林值的情境中被計算。

更多細節請見實作算術操作。

在 3.9 版的變更: Evaluating NotImplemented in a boolean context was deprecated.

在 3.14 版的變更: 在預期布林值的情境中計算 NotImplemented 現在會引發 TypeError。它先前會計算為 True，並自 Python 3.9 起會發出 DeprecationWarning。

3.2.3. Ellipsis¶

這個型別只有一個數值。只有一個物件有這個數值。這個物件由文本 ... 或內建名稱 Ellipsis 存取。它的真值是 true。

3.2.4. `numbers.Number`¶

These are created by numeric literals and returned as results by arithmetic operators and arithmetic built-in functions. Numeric objects are immutable; once created their value never changes. Python numbers are of course strongly related to mathematical numbers, but subject to the limitations of numerical representation in computers.

The string representations of the numeric classes, computed by __repr__() and __str__(), have the following properties:

They are valid numeric literals which, when passed to their class constructor, produce an object having the value of the original numeric.
The representation is in base 10, when possible.
Leading zeros, possibly excepting a single zero before a decimal point, are not shown.
Trailing zeros, possibly excepting a single zero after a decimal point, are not shown.
A sign is shown only when the number is negative.

Python distinguishes between integers, floating-point numbers, and complex numbers:

3.2.4.1. `numbers.Integral`¶

These represent elements from the mathematical set of integers (positive and negative).

備註

The rules for integer representation are intended to give the most meaningful interpretation of shift and mask operations involving negative integers.

There are two types of integers:

Integers (int): These represent numbers in an unlimited range, subject to available (virtual) memory only. For the purpose of shift and mask operations, a binary representation is assumed, and negative numbers are represented in a variant of 2's complement which gives the illusion of an infinite string of sign bits extending to the left.
Booleans (bool): These represent the truth values False and True. The two objects representing the values False and True are the only Boolean objects. The Boolean type is a subtype of the integer type, and Boolean values behave like the values 0 and 1, respectively, in almost all contexts, the exception being that when converted to a string, the strings "False" or "True" are returned, respectively.

3.2.4.2. `numbers.Real` (`float`)¶

These represent machine-level double precision floating-point numbers. You are at the mercy of the underlying machine architecture (and C or Java implementation) for the accepted range and handling of overflow. Python does not support single-precision floating-point numbers; the savings in processor and memory usage that are usually the reason for using these are dwarfed by the overhead of using objects in Python, so there is no reason to complicate the language with two kinds of floating-point numbers.

3.2.4.3. `numbers.Complex` (`complex`)¶

These represent complex numbers as a pair of machine-level double precision floating-point numbers. The same caveats apply as for floating-point numbers. The real and imaginary parts of a complex number z can be retrieved through the read-only attributes z.real and z.imag.

3.2.5. Sequences¶

These represent finite ordered sets indexed by non-negative numbers. The built-in function len() returns the number of items of a sequence. When the length of a sequence is n, the index set contains the numbers 0, 1, ..., n-1. Item i of sequence a is selected by a[i]. Some sequences, including built-in sequences, interpret negative subscripts by adding the sequence length. For example, a[-2] equals a[n-2], the second to last item of sequence a with length n.

The resulting value must be a nonnegative integer less than the number of items in the sequence. If it is not, an IndexError is raised.

Sequences also support slicing: a[start:stop] selects all items with index k such that start <= k < stop. When used as an expression, a slice is a sequence of the same type. The comment above about negative subscripts also applies to negative slice positions. Note that no error is raised if a slice position is less than zero or larger than the length of the sequence.

If start is missing or None, slicing behaves as if start was zero. If stop is missing or None, slicing behaves as if stop was equal to the length of the sequence.

Some sequences also support "extended slicing" with a third "step" parameter: a[i:j:k] selects all items of a with index x where x = i + n*k, n >= 0 and i <= x < j.

Sequences are distinguished according to their mutability:

3.2.5.1. Immutable sequences¶

An object of an immutable sequence type cannot change once it is created. (If the object contains references to other objects, these other objects may be mutable and may be changed; however, the collection of objects directly referenced by an immutable object cannot change.)

The following types are immutable sequences:

字串 (String)

A string (str) is a sequence of values that represent characters, or more formally, Unicode code points. All the code points in the range 0 to 0x10FFFF can be represented in a string.

Python doesn't have a dedicated character type. Instead, every code point in the string is represented as a string object with length 1.

The built-in function ord() converts a code point from its string form to an integer in the range 0 to 0x10FFFF; chr() converts an integer in the range 0 to 0x10FFFF to the corresponding length 1 string object. str.encode() can be used to convert a str to bytes using the given text encoding, and bytes.decode() can be used to achieve the opposite.

Tuple（元組）

The items of a tuple are arbitrary Python objects. Tuples of two or more items are formed by comma-separated lists of expressions. A tuple of one item (a 'singleton') can be formed by affixing a comma to an expression (an expression by itself does not create a tuple, since parentheses must be usable for grouping of expressions). An empty tuple can be formed by an empty pair of parentheses.

位元組

A bytes object is an immutable array. The items are 8-bit bytes, represented by integers in the range 0 <= x < 256. Bytes literals (like b'abc') and the built-in bytes() constructor can be used to create bytes objects. Also, bytes objects can be decoded to strings via the decode() method.

3.2.5.2. 可變序列¶

Mutable sequences can be changed after they are created. The subscription and slicing notations can be used as the target of assignment and del (delete) statements.

備註

The collections and array module provide additional examples of mutable sequence types.

There are currently two intrinsic mutable sequence types:

List（串列）: The items of a list are arbitrary Python objects. Lists are formed by placing a comma-separated list of expressions in square brackets. (Note that there are no special cases needed to form lists of length 0 or 1.)
位元組陣列: A bytearray object is a mutable array. They are created by the built-in bytearray() constructor. Aside from being mutable (and hence unhashable), byte arrays otherwise provide the same interface and functionality as immutable bytes objects.

3.2.6. Set（集合）型別¶

These represent unordered, finite sets of unique, immutable objects. As such, they cannot be indexed by any subscript. However, they can be iterated over, and the built-in function len() returns the number of items in a set. Common uses for sets are fast membership testing, removing duplicates from a sequence, and computing mathematical operations such as intersection, union, difference, and symmetric difference.

For set elements, the same immutability rules apply as for dictionary keys. Note that numeric types obey the normal rules for numeric comparison: if two numbers compare equal (e.g., 1 and 1.0), only one of them can be contained in a set.

There are currently two intrinsic set types:

Set（集合）: These represent a mutable set. They are created by the built-in set() constructor and can be modified afterwards by several methods, such as add().
Frozen set（凍結集合）: These represent an immutable set. They are created by the built-in frozenset() constructor. As a frozenset is immutable and hashable, it can be used again as an element of another set, or as a dictionary key.

3.2.7. 對映¶

These represent finite sets of objects indexed by arbitrary index sets. The subscript notation a[k] selects the item indexed by k from the mapping a; this can be used in expressions and as the target of assignments or del statements. The built-in function len() returns the number of items in a mapping.

There is currently a single intrinsic mapping type:

3.2.7.1. 字典¶

These represent finite sets of objects indexed by nearly arbitrary values. The only types of values not acceptable as keys are values containing lists or dictionaries or other mutable types that are compared by value rather than by object identity, the reason being that the efficient implementation of dictionaries requires a key's hash value to remain constant. Numeric types used for keys obey the normal rules for numeric comparison: if two numbers compare equal (e.g., 1 and 1.0) then they can be used interchangeably to index the same dictionary entry.

Dictionaries preserve insertion order, meaning that keys will be produced in the same order they were added sequentially over the dictionary. Replacing an existing key does not change the order, however removing a key and re-inserting it will add it to the end instead of keeping its old place.

Dictionaries are mutable; they can be created by the {} notation (see section Dictionary displays).

The extension modules dbm.ndbm and dbm.gnu provide additional examples of mapping types, as does the collections module.

在 3.7 版的變更: Dictionaries did not preserve insertion order in versions of Python before 3.6. In CPython 3.6, insertion order was preserved, but it was considered an implementation detail at that time rather than a language guarantee.

3.2.8. 可呼叫型別¶

These are the types to which the function call operation (see section Calls) can be applied:

3.2.8.1. 自訂函式¶

A user-defined function object is created by a function definition (see section 函式定義). It should be called with an argument list containing the same number of items as the function's formal parameter list.

3.2.8.1.1. 特殊唯讀屬性¶

屬性	含義
function.__builtins__¶	A reference to the `dictionary` that holds the function's builtins namespace. 在 3.10 版被加入.
function.__globals__¶	A reference to the `dictionary` that holds the function's global variables -- the global namespace of the module in which the function was defined.
function.__closure__¶	`None` or a `tuple` of cells that contain bindings for the names specified in the `co_freevars` attribute of the function's `code object`. A cell object has the attribute `cell_contents`. This can be used to get the value of the cell, as well as set the value.

屬性

含義

function.__builtins__¶

A reference to the dictionary that holds the function's builtins namespace.

在 3.10 版被加入.

function.__globals__¶

A reference to the dictionary that holds the function's global variables -- the global namespace of the module in which the function was defined.

function.__closure__¶

None or a tuple of cells that contain bindings for the names specified in the co_freevars attribute of the function's code object.

A cell object has the attribute cell_contents. This can be used to get the value of the cell, as well as set the value.

3.2.8.1.2. 特殊可寫屬性¶

Most of these attributes check the type of the assigned value:

屬性	含義
function.__doc__¶	函式的文件字串，若不可用則為 `None`。
function.__name__¶	The function's name. See also: `__name__ attributes`.
function.__qualname__¶	The function's qualified name. See also: `__qualname__ attributes`. 在 3.3 版被加入.
function.__module__¶	The name of the module the function was defined in, or `None` if unavailable.
function.__defaults__¶	A `tuple` containing default parameter values for those parameters that have defaults, or `None` if no parameters have a default value.
function.__code__¶	代表編譯函式主體的程式碼物件。
function.__dict__¶	The namespace supporting arbitrary function attributes. See also: `__dict__ attributes`.
function.__annotations__¶	A `dictionary` containing annotations of parameters. The keys of the dictionary are the parameter names, and `'return'` for the return annotation, if provided. See also: `object.__annotations__`. 在 3.14 版的變更: Annotations are now lazily evaluated. See PEP 649.
function.__annotate__¶	The annotate function for this function, or `None` if the function has no annotations. See `object.__annotate__`. 在 3.14 版被加入.
function.__kwdefaults__¶	A `dictionary` containing defaults for keyword-only parameters.
function.__type_params__¶	A `tuple` containing the type parameters of a generic function. 在 3.12 版被加入.

Function objects also support getting and setting arbitrary attributes, which can be used, for example, to attach metadata to functions. Regular attribute dot-notation is used to get and set such attributes.

CPython 實作細節： CPython's current implementation only supports function attributes on user-defined functions. Function attributes on built-in functions may be supported in the future.

Additional information about a function's definition can be retrieved from its code object (accessible via the __code__ attribute).

3.2.8.2. 實例方法¶

An instance method object combines a class, a class instance and any callable object (normally a user-defined function).

特殊唯讀屬性：

method.__self__¶	Refers to the class instance object to which the method is bound
method.__func__¶	Refers to the original function object
method.__doc__¶	The method's documentation (same as `method.__func__.__doc__`). A `string` if the original function had a docstring, else `None`.
method.__name__¶	The name of the method (same as `method.__func__.__name__`)
method.__module__¶	The name of the module the method was defined in, or `None` if unavailable.

Methods also support accessing (but not setting) the arbitrary function attributes on the underlying function object.

User-defined method objects may be created when getting an attribute of a class (perhaps via an instance of that class), if that attribute is a user-defined function object or a classmethod object.

When an instance method object is created by retrieving a user-defined function object from a class via one of its instances, its __self__ attribute is the instance, and the method object is said to be bound. The new method's __func__ attribute is the original function object.

When an instance method object is created by retrieving a classmethod object from a class or instance, its __self__ attribute is the class itself, and its __func__ attribute is the function object underlying the class method.

When an instance method object is called, the underlying function (__func__) is called, inserting the class instance (__self__) in front of the argument list. For instance, when C is a class which contains a definition for a function f(), and x is an instance of C, calling x.f(1) is equivalent to calling C.f(x, 1).

When an instance method object is derived from a classmethod object, the "class instance" stored in __self__ will actually be the class itself, so that calling either x.f(1) or C.f(1) is equivalent to calling f(C,1) where f is the underlying function.

It is important to note that user-defined functions which are attributes of a class instance are not converted to bound methods; this only happens when the function is an attribute of the class.

3.2.8.3. 產生器函式¶

A function or method which uses the yield statement (see section yield 陳述式) is called a generator function. Such a function, when called, always returns an iterator object which can be used to execute the body of the function: calling the iterator's iterator.__next__() method will cause the function to execute until it provides a value using the yield statement. When the function executes a return statement or falls off the end, a StopIteration exception is raised and the iterator will have reached the end of the set of values to be returned.

3.2.8.4. Coroutine（協程）函式¶

A function or method which is defined using async def is called a coroutine function. Such a function, when called, returns a coroutine object. It may contain await expressions, as well as async with and async for statements. See also the 協程物件 section.

3.2.8.5. 非同步產生器函式¶

A function or method which is defined using async def and which uses the yield statement is called a asynchronous generator function. Such a function, when called, returns an asynchronous iterator object which can be used in an async for statement to execute the body of the function.

Calling the asynchronous iterator's aiterator.__anext__ method will return an awaitable which when awaited will execute until it provides a value using the yield expression. When the function executes an empty return statement or falls off the end, a StopAsyncIteration exception is raised and the asynchronous iterator will have reached the end of the set of values to be yielded.

3.2.8.6. 內建函式¶

一個內建函式物件是一個 C 函式的 wrapper。內建函式的範例有 len() 和 math.sin()（math 是一個標準的內建模組）。內建函式的引數數量與其型別由其包裝的 C 函式所決定。特殊唯讀屬性：

__doc__ 是函式的文件字串，若不可用則為 None。請見 function.__doc__。
__name__ 是函式的名稱。請見 function.__name__。
__self__ is set to None (but see the next item).
__module__ is the name of the module the function was defined in or None if unavailable. See function.__module__.

3.2.8.7. 內建方法¶

This is really a different disguise of a built-in function, this time containing an object passed to the C function as an implicit extra argument. An example of a built-in method is alist.append(), assuming alist is a list object. In this case, the special read-only attribute __self__ is set to the object denoted by alist. (The attribute has the same semantics as it does with other instance methods.)

3.2.8.8. Classes¶

Classes are callable. These objects normally act as factories for new instances of themselves, but variations are possible for class types that override __new__(). The arguments of the call are passed to __new__() and, in the typical case, to __init__() to initialize the new instance.

3.2.8.9. 類別實例¶

Instances of arbitrary classes can be made callable by defining a __call__() method in their class.

3.2.9. 模組¶

Modules are a basic organizational unit of Python code, and are created by the import system as invoked either by the import statement, or by calling functions such as importlib.import_module() and built-in __import__(). A module object has a namespace implemented by a dictionary object (this is the dictionary referenced by the __globals__ attribute of functions defined in the module). Attribute references are translated to lookups in this dictionary, e.g., m.x is equivalent to m.__dict__["x"]. A module object does not contain the code object used to initialize the module (since it isn't needed once the initialization is done).

Attribute assignment updates the module's namespace dictionary, e.g., m.x = 1 is equivalent to m.__dict__["x"] = 1.

3.2.9.1. Import-related attributes on module objects¶

Module objects have the following attributes that relate to the import system. When a module is created using the machinery associated with the import system, these attributes are filled in based on the module's spec, before the loader executes and loads the module.

To create a module dynamically rather than using the import system, it's recommended to use importlib.util.module_from_spec(), which will set the various import-controlled attributes to appropriate values. It's also possible to use the types.ModuleType constructor to create modules directly, but this technique is more error-prone, as most attributes must be manually set on the module object after it has been created when using this approach.

警示

With the exception of __name__, it is strongly recommended that you rely on __spec__ and its attributes instead of any of the other individual attributes listed in this subsection. Note that updating an attribute on __spec__ will not update the corresponding attribute on the module itself:

>>> import typing
>>> typing.__name__, typing.__spec__.name
('typing', 'typing')
>>> typing.__spec__.name = 'spelling'
>>> typing.__name__, typing.__spec__.name
('typing', 'spelling')
>>> typing.__name__ = 'keyboard_smashing'
>>> typing.__name__, typing.__spec__.name
('keyboard_smashing', 'spelling')

module.__name__¶

The name used to uniquely identify the module in the import system. For a directly executed module, this will be set to "__main__".

This attribute must be set to the fully qualified name of the module. It is expected to match the value of module.__spec__.name.

module.__spec__¶

A record of the module's import-system-related state.

Set to the module spec that was used when importing the module. See 模組規格 for more details.

在 3.4 版被加入.

module.__package__¶

The package a module belongs to.

If the module is top-level (that is, not a part of any specific package) then the attribute should be set to '' (the empty string). Otherwise, it should be set to the name of the module's package (which can be equal to module.__name__ if the module itself is a package). See PEP 366 for further details.

This attribute is used instead of __name__ to calculate explicit relative imports for main modules. It defaults to None for modules created dynamically using the types.ModuleType constructor; use importlib.util.module_from_spec() instead to ensure the attribute is set to a str.

It is strongly recommended that you use module.__spec__.parent instead of module.__package__. __package__ is now only used as a fallback if __spec__.parent is not set, and this fallback path is deprecated.

在 3.4 版的變更: This attribute now defaults to None for modules created dynamically using the types.ModuleType constructor. Previously the attribute was optional.

在 3.6 版的變更: The value of __package__ is expected to be the same as __spec__.parent. __package__ is now only used as a fallback during import resolution if __spec__.parent is not defined.

在 3.10 版的變更: ImportWarning is raised if an import resolution falls back to __package__ instead of __spec__.parent.

在 3.12 版的變更: Raise DeprecationWarning instead of ImportWarning when falling back to __package__ during import resolution.

自從版本 3.13 後不推薦使用，將會自版本 3.15 中移除。: __package__ will cease to be set or taken into consideration by the import system or standard library.

module.__loader__¶

The loader object that the import machinery used to load the module.

This attribute is mostly useful for introspection, but can be used for additional loader-specific functionality, for example getting data associated with a loader.

__loader__ defaults to None for modules created dynamically using the types.ModuleType constructor; use importlib.util.module_from_spec() instead to ensure the attribute is set to a loader object.

It is strongly recommended that you use module.__spec__.loader instead of module.__loader__.

在 3.4 版的變更: This attribute now defaults to None for modules created dynamically using the types.ModuleType constructor. Previously the attribute was optional.

自從版本 3.12 後不推薦使用，將會自版本 3.16 中移除。: 在模組上設定 __loader__ 而沒有設定 __spec__.loader 的做法已被棄用。在 Python 3.16 中，引入系統或標準函式庫將不再設定或考慮 __loader__。

module.__path__¶

A (possibly empty) sequence of strings enumerating the locations where the package's submodules will be found. Non-package modules should not have a __path__ attribute. See 模組上的 __path__ 屬性 for more details.

It is strongly recommended that you use module.__spec__.submodule_search_locations instead of module.__path__.

module.__file__¶

module.__cached__¶

__file__ and __cached__ are both optional attributes that may or may not be set. Both attributes should be a str when they are available.

__file__ indicates the pathname of the file from which the module was loaded (if loaded from a file), or the pathname of the shared library file for extension modules loaded dynamically from a shared library. It might be missing for certain types of modules, such as C modules that are statically linked into the interpreter, and the import system may opt to leave it unset if it has no semantic meaning (for example, a module loaded from a database).

If __file__ is set then the __cached__ attribute might also be set, which is the path to any compiled version of the code (for example, a byte-compiled file). The file does not need to exist to set this attribute; the path can simply point to where the compiled file would exist (see PEP 3147).

Note that __cached__ may be set even if __file__ is not set. However, that scenario is quite atypical. Ultimately, the loader is what makes use of the module spec provided by the finder (from which __file__ and __cached__ are derived). So if a loader can load from a cached module but otherwise does not load from a file, that atypical scenario may be appropriate.

It is strongly recommended that you use module.__spec__.cached instead of module.__cached__.

自從版本 3.13 後不推薦使用，將會自版本 3.15 中移除。: Setting __cached__ on a module while failing to set __spec__.cached is deprecated. In Python 3.15, __cached__ will cease to be set or taken into consideration by the import system or standard library.

3.2.9.2. 模組物件的其他可寫入屬性¶

As well as the import-related attributes listed above, module objects also have the following writable attributes:

module.__doc__¶: 模組的文件字串，若不可用則為 None。請見 __doc__ attributes。

module.__annotations__¶: A dictionary containing variable annotations collected during module body execution. For best practices on working with __annotations__, see annotationlib.

在 3.14 版的變更: Annotations are now lazily evaluated. See PEP 649.

module.__annotate__¶: The annotate function for this module, or None if the module has no annotations. See also: __annotate__ attributes.

在 3.14 版被加入.

3.2.9.3. 模組字典¶

Module objects also have the following special read-only attribute:

module.__dict__¶: The module's namespace as a dictionary object. Uniquely among the attributes listed here, __dict__ cannot be accessed as a global variable from within a module; it can only be accessed as an attribute on module objects.

CPython 實作細節： Because of the way CPython clears module dictionaries, the module dictionary will be cleared when the module falls out of scope even if the dictionary still has live references. To avoid this, copy the dictionary or keep the module around while using its dictionary directly.

3.2.10. Custom classes¶

Custom class types are typically created by class definitions (see section 類別定義). A class has a namespace implemented by a dictionary object. Class attribute references are translated to lookups in this dictionary, e.g., C.x is translated to C.__dict__["x"] (although there are a number of hooks which allow for other means of locating attributes). When the attribute name is not found there, the attribute search continues in the base classes. This search of the base classes uses the C3 method resolution order which behaves correctly even in the presence of 'diamond' inheritance structures where there are multiple inheritance paths leading back to a common ancestor. Additional details on the C3 MRO used by Python can be found at Python 2.3 方法解析順序.

When a class attribute reference (for class C, say) would yield a class method object, it is transformed into an instance method object whose __self__ attribute is C. When it would yield a staticmethod object, it is transformed into the object wrapped by the static method object. See section 實作描述器 for another way in which attributes retrieved from a class may differ from those actually contained in its __dict__.

Class attribute assignments update the class's dictionary, never the dictionary of a base class.

A class object can be called (see above) to yield a class instance (see below).

3.2.10.1. 特殊屬性¶

屬性	含義
type.__name__¶	The class's name. See also: `__name__ attributes`.
type.__qualname__¶	The class's qualified name. See also: `__qualname__ attributes`.
type.__module__¶	The name of the module in which the class was defined.
type.__dict__¶	A `mapping proxy` providing a read-only view of the class's namespace. See also: `__dict__ attributes`.
type.__bases__¶	A `tuple` containing the class's bases. In most cases, for a class defined as `class X(A, B, C)`, `X.__bases__` will be exactly equal to `(A, B, C)`.
type.__base__¶	CPython 實作細節： The single base class in the inheritance chain that is responsible for the memory layout of instances. This attribute corresponds to `tp_base` at the C level.
type.__doc__¶	The class's documentation string, or `None` if undefined. Not inherited by subclasses.
type.__annotations__¶	A dictionary containing variable annotations collected during class body execution. See also: `__annotations__ attributes`. For best practices on working with `__annotations__`, please see `annotationlib`. Use `annotationlib.get_annotations()` instead of accessing this attribute directly. 警告 Accessing the `__annotations__` attribute directly on a class object may return annotations for the wrong class, specifically in certain cases where the class, its base class, or a metaclass is defined under `from __future__ import annotations`. See 749 for details. This attribute does not exist on certain builtin classes. On user-defined classes without `__annotations__`, it is an empty dictionary. 在 3.14 版的變更: Annotations are now lazily evaluated. See PEP 649.
type.__annotate__()¶	The annotate function for this class, or `None` if the class has no annotations. See also: `__annotate__ attributes`. 在 3.14 版被加入.
type.__type_params__¶	A `tuple` containing the type parameters of a generic class. 在 3.12 版被加入.
type.__static_attributes__¶	A `tuple` containing names of attributes of this class which are assigned through `self.X` from any function in its body. 在 3.13 版被加入.
type.__firstlineno__¶	The line number of the first line of the class definition, including decorators. Setting the `__module__` attribute removes the `__firstlineno__` item from the type's dictionary. 在 3.13 版被加入.
type.__mro__¶	The `tuple` of classes that are considered when looking for base classes during method resolution.

3.2.10.2. 特殊方法¶

In addition to the special attributes described above, all Python classes also have the following two methods available:

type.mro()¶: This method can be overridden by a metaclass to customize the method resolution order for its instances. It is called at class instantiation, and its result is stored in __mro__.

type.__subclasses__()¶

Each class keeps a list of weak references to its immediate subclasses. This method returns a list of all those references still alive. The list is in definition order. Example:

>>> class A: pass
>>> class B(A): pass
>>> A.__subclasses__()
[<class 'B'>]

3.2.11. 類別實例¶

A class instance is created by calling a class object (see above). A class instance has a namespace implemented as a dictionary which is the first place in which attribute references are searched. When an attribute is not found there, and the instance's class has an attribute by that name, the search continues with the class attributes. If a class attribute is found that is a user-defined function object, it is transformed into an instance method object whose __self__ attribute is the instance. Static method and class method objects are also transformed; see above under "Classes". See section 實作描述器 for another way in which attributes of a class retrieved via its instances may differ from the objects actually stored in the class's __dict__. If no class attribute is found, and the object's class has a __getattr__() method, that is called to satisfy the lookup.

Attribute assignments and deletions update the instance's dictionary, never a class's dictionary. If the class has a __setattr__() or __delattr__() method, this is called instead of updating the instance dictionary directly.

Class instances can pretend to be numbers, sequences, or mappings if they have methods with certain special names. See section Special method names.

3.2.11.1. 特殊屬性¶

object.__class__¶: The class to which a class instance belongs.

object.__dict__¶: A dictionary or other mapping object used to store an object's (writable) attributes. Not all instances have a __dict__ attribute; see the section on __slots__ for more details.

3.2.12. I/O objects (also known as file objects)¶

A file object represents an open file. Various shortcuts are available to create file objects: the open() built-in function, and also os.popen(), os.fdopen(), and the makefile() method of socket objects (and perhaps by other functions or methods provided by extension modules).

File objects implement common methods, listed below, to simplify usage in generic code. They are expected to be With 陳述式的情境管理器.

The objects sys.stdin, sys.stdout and sys.stderr are initialized to file objects corresponding to the interpreter's standard input, output and error streams; they are all open in text mode and therefore follow the interface defined by the io.TextIOBase abstract class.

file.read(size=-1, /)¶: Retrieve up to size data from the file. As a convenience if size is unspecified or -1 retrieve all data available.

file.write(data, /)¶: Store data to the file.

file.close()¶: Flush any buffers and close the underlying file.

3.2.13. Internal types¶

A few types used internally by the interpreter are exposed to the user. Their definitions may change with future versions of the interpreter, but they are mentioned here for completeness.

3.2.13.1. 程式碼物件¶

Code objects represent byte-compiled executable Python code, or bytecode. The difference between a code object and a function object is that the function object contains an explicit reference to the function's globals (the module in which it was defined), while a code object contains no context; also the default argument values are stored in the function object, not in the code object (because they represent values calculated at run-time). Unlike function objects, code objects are immutable and contain no references (directly or indirectly) to mutable objects.

3.2.13.1.1. 特殊唯讀屬性¶

codeobject.co_name¶	函式名稱
codeobject.co_qualname¶	The fully qualified function name 在 3.11 版被加入.
codeobject.co_argcount¶	The total number of positional parameters (including positional-only parameters and parameters with default values) that the function has
codeobject.co_posonlyargcount¶	The number of positional-only parameters (including arguments with default values) that the function has
codeobject.co_kwonlyargcount¶	The number of keyword-only parameters (including arguments with default values) that the function has
codeobject.co_nlocals¶	The number of local variables used by the function (including parameters)
codeobject.co_varnames¶	A `tuple` containing the names of the local variables in the function (starting with the parameter names)
codeobject.co_cellvars¶	A `tuple` containing the names of local variables that are referenced from at least one nested scope inside the function
codeobject.co_freevars¶	A `tuple` containing the names of free (closure) variables that a nested scope references in an outer scope. See also `function.__closure__`. Note: references to global and builtin names are not included.
codeobject.co_code¶	A string representing the sequence of bytecode instructions in the function
codeobject.co_consts¶	A `tuple` containing the literals used by the bytecode in the function
codeobject.co_names¶	A `tuple` containing the names used by the bytecode in the function
codeobject.co_filename¶	The name of the file from which the code was compiled
codeobject.co_firstlineno¶	The line number of the first line of the function
codeobject.co_lnotab¶	A string encoding the mapping from bytecode offsets to line numbers. For details, see the source code of the interpreter. 在 3.12 版之後被棄用: This attribute of code objects is deprecated, and may be removed in Python 3.15.
codeobject.co_stacksize¶	The required stack size of the code object
codeobject.co_flags¶	An `integer` encoding a number of flags for the interpreter.

The following flag bits are defined for co_flags: bit 0x04 is set if the function uses the *arguments syntax to accept an arbitrary number of positional arguments; bit 0x08 is set if the function uses the **keywords syntax to accept arbitrary keyword arguments; bit 0x20 is set if the function is a generator. See Code Objects Bit Flags for details on the semantics of each flags that might be present.

Future feature declarations (for example, from __future__ import division) also use bits in co_flags to indicate whether a code object was compiled with a particular feature enabled. See compiler_flag.

Other bits in co_flags are reserved for internal use.

If a code object represents a function and has a docstring, the CO_HAS_DOCSTRING bit is set in co_flags and the first item in co_consts is the docstring of the function.

3.2.13.1.2. 用於程式碼物件的方法¶

codeobject.co_positions()¶

Returns an iterable over the source code positions of each bytecode instruction in the code object.

The iterator returns tuples containing the (start_line, end_line, start_column, end_column). The i-th tuple corresponds to the position of the source code that compiled to the i-th code unit. Column information is 0-indexed utf-8 byte offsets on the given source line.

This positional information can be missing. A non-exhaustive lists of cases where this may happen:

Running the interpreter with -X no_debug_ranges.
Loading a pyc file compiled while using -X no_debug_ranges.
Position tuples corresponding to artificial instructions.
Line and column numbers that can't be represented due to implementation specific limitations.

When this occurs, some or all of the tuple elements can be None.

在 3.11 版被加入.

備註

This feature requires storing column positions in code objects which may result in a small increase of disk usage of compiled Python files or interpreter memory usage. To avoid storing the extra information and/or deactivate printing the extra traceback information, the -X no_debug_ranges command line flag or the PYTHONNODEBUGRANGES environment variable can be used.

codeobject.co_lines()¶

Returns an iterator that yields information about successive ranges of bytecodes. Each item yielded is a (start, end, lineno) tuple:

start (an int) represents the offset (inclusive) of the start of the bytecode range
end (an int) represents the offset (exclusive) of the end of the bytecode range
lineno is an int representing the line number of the bytecode range, or None if the bytecodes in the given range have no line number

The items yielded will have the following properties:

The first range yielded will have a start of 0.
The (start, end) ranges will be non-decreasing and consecutive. That is, for any pair of tuples, the start of the second will be equal to the end of the first.
No range will be backwards: end >= start for all triples.
The last tuple yielded will have end equal to the size of the bytecode.

Zero-width ranges, where start == end, are allowed. Zero-width ranges are used for lines that are present in the source code, but have been eliminated by the bytecode compiler.

在 3.10 版被加入.

也參考

PEP 626 - Precise line numbers for debugging and other tools.: The PEP that introduced the co_lines() method.

codeobject.replace(**kwargs)¶

Return a copy of the code object with new values for the specified fields.

Code objects are also supported by the generic function copy.replace().

在 3.8 版被加入.

3.2.13.2. Frame objects¶

Frame objects represent execution frames. They may occur in traceback objects, and are also passed to registered trace functions.

3.2.13.2.1. 特殊唯讀屬性¶

frame.f_back¶	Points to the previous stack frame (towards the caller), or `None` if this is the bottom stack frame
frame.f_code¶	在這個 frame 中執行的程式碼物件 (code object)。存取這個屬性會引發一個附帶引數 `obj` 與 `"f_code"` 的稽核事件 `object.__getattr__`。
frame.f_locals¶	The mapping used by the frame to look up local variables. If the frame refers to an optimized scope, this may return a write-through proxy object. 在 3.13 版的變更: Return a proxy for optimized scopes.
frame.f_globals¶	The dictionary used by the frame to look up global variables
frame.f_builtins¶	The dictionary used by the frame to look up built-in (intrinsic) names
frame.f_lasti¶	The "precise instruction" of the frame object (this is an index into the bytecode string of the code object)
frame.f_generator¶	The generator or coroutine object that owns this frame, or `None` if the frame is a normal function. 在 3.14 版被加入.

3.2.13.2.2. 特殊可寫屬性¶

frame.f_trace¶	If not `None`, this is a function called for various events during code execution (this is used by debuggers). Normally an event is triggered for each new source line (see `f_trace_lines`).
frame.f_trace_lines¶	Set this attribute to `False` to disable triggering a tracing event for each source line.
frame.f_trace_opcodes¶	Set this attribute to `True` to allow per-opcode events to be requested. Note that this may lead to undefined interpreter behaviour if exceptions raised by the trace function escape to the function being traced.
frame.f_lineno¶	The current line number of the frame -- writing to this from within a trace function jumps to the given line (only for the bottom-most frame). A debugger can implement a Jump command (aka Set Next Statement) by writing to this attribute.

3.2.13.2.3. Frame object methods¶

Frame objects support one method:

frame.clear()¶

This method clears all references to local variables held by the frame. Also, if the frame belonged to a generator, the generator is finalized. This helps break reference cycles involving frame objects (for example when catching an exception and storing its traceback for later use).

RuntimeError is raised if the frame is currently executing or suspended.

在 3.4 版被加入.

在 3.13 版的變更: Attempting to clear a suspended frame raises RuntimeError (as has always been the case for executing frames).

3.2.13.3. Traceback objects¶

Traceback objects represent the stack trace of an exception. A traceback object is implicitly created when an exception occurs, and may also be explicitly created by calling types.TracebackType.

在 3.7 版的變更: Traceback objects can now be explicitly instantiated from Python code.

For implicitly created tracebacks, when the search for an exception handler unwinds the execution stack, at each unwound level a traceback object is inserted in front of the current traceback. When an exception handler is entered, the stack trace is made available to the program. (See section try 陳述式.) It is accessible as the third item of the tuple returned by sys.exc_info(), and as the __traceback__ attribute of the caught exception.

When the program contains no suitable handler, the stack trace is written (nicely formatted) to the standard error stream; if the interpreter is interactive, it is also made available to the user as sys.last_traceback.

For explicitly created tracebacks, it is up to the creator of the traceback to determine how the tb_next attributes should be linked to form a full stack trace.

特殊唯讀屬性：

traceback.tb_frame¶

Points to the execution frame of the current level.

存取此屬性會引發一個附帶引數 obj 與 "tb_frame" 的稽核事件 object.__getattr__。

traceback.tb_lineno¶

Gives the line number where the exception occurred

traceback.tb_lasti¶

Indicates the "precise instruction".

The line number and last instruction in the traceback may differ from the line number of its frame object if the exception occurred in a try statement with no matching except clause or with a finally clause.

traceback.tb_next¶: The special writable attribute tb_next is the next level in the stack trace (towards the frame where the exception occurred), or None if there is no next level.

在 3.7 版的變更: 此屬性現在可寫入

3.2.13.4. Slice objects¶

Slice objects are used to represent slices for __getitem__() methods. They are also created by the built-in slice() function.

Special read-only attributes: start is the lower bound; stop is the upper bound; step is the step value; each is None if omitted. These attributes can have any type.

Slice objects support one method:

slice.indices(self, length)¶: This method takes a single integer argument length and computes information about the slice that the slice object would describe if applied to a sequence of length items. It returns a tuple of three integers; respectively these are the start and stop indices and the step or stride length of the slice. Missing or out-of-bounds indices are handled in a manner consistent with regular slices.

3.2.13.5. Static method objects¶

Static method objects provide a way of defeating the transformation of function objects to method objects described above. A static method object is a wrapper around any other object, usually a user-defined method object. When a static method object is retrieved from a class or a class instance, the object actually returned is the wrapped object, which is not subject to any further transformation. Static method objects are also callable. Static method objects are created by the built-in staticmethod() constructor.

3.2.13.6. Class method objects¶

A class method object, like a static method object, is a wrapper around another object that alters the way in which that object is retrieved from classes and class instances. The behaviour of class method objects upon such retrieval is described above, under "instance methods". Class method objects are created by the built-in classmethod() constructor.

3.3. Special method names¶

A class can implement certain operations that are invoked by special syntax (such as arithmetic operations or subscripting and slicing) by defining methods with special names. This is Python's approach to operator overloading, allowing classes to define their own behavior with respect to language operators. For instance, if a class defines a method named __getitem__(), and x is an instance of this class, then x[i] is roughly equivalent to type(x).__getitem__(x, i). Except where mentioned, attempts to execute an operation raise an exception when no appropriate method is defined (typically AttributeError or TypeError).

Setting a special method to None indicates that the corresponding operation is not available. For example, if a class sets __iter__() to None, the class is not iterable, so calling iter() on its instances will raise a TypeError (without falling back to __getitem__()). [2]

When implementing a class that emulates any built-in type, it is important that the emulation only be implemented to the degree that it makes sense for the object being modelled. For example, some sequences may work well with retrieval of individual elements, but extracting a slice may not make sense. (One example of this is the NodeList interface in the W3C's Document Object Model.)

3.3.1. Basic customization¶

object.__new__(cls[, ...])¶

Called to create a new instance of class cls. __new__() is a static method (special-cased so you need not declare it as such) that takes the class of which an instance was requested as its first argument. The remaining arguments are those passed to the object constructor expression (the call to the class). The return value of __new__() should be the new object instance (usually an instance of cls).

Typical implementations create a new instance of the class by invoking the superclass's __new__() method using super().__new__(cls[, ...]) with appropriate arguments and then modifying the newly created instance as necessary before returning it.

If __new__() is invoked during object construction and it returns an instance of cls, then the new instance’s __init__() method will be invoked like __init__(self[, ...]), where self is the new instance and the remaining arguments are the same as were passed to the object constructor.

If __new__() does not return an instance of cls, then the new instance's __init__() method will not be invoked.

__new__() is intended mainly to allow subclasses of immutable types (like int, str, or tuple) to customize instance creation. It is also commonly overridden in custom metaclasses in order to customize class creation.

object.__init__(self[, ...])¶

Called after the instance has been created (by __new__()), but before it is returned to the caller. The arguments are those passed to the class constructor expression. If a base class has an __init__() method, the derived class's __init__() method, if any, must explicitly call it to ensure proper initialization of the base class part of the instance; for example: super().__init__([args...]).

Because __new__() and __init__() work together in constructing objects (__new__() to create it, and __init__() to customize it), no non-None value may be returned by __init__(); doing so will cause a TypeError to be raised at runtime.

object.__del__(self)¶

Called when the instance is about to be destroyed. This is also called a finalizer or (improperly) a destructor. If a base class has a __del__() method, the derived class's __del__() method, if any, must explicitly call it to ensure proper deletion of the base class part of the instance.

It is possible (though not recommended!) for the __del__() method to postpone destruction of the instance by creating a new reference to it. This is called object resurrection. It is implementation-dependent whether __del__() is called a second time when a resurrected object is about to be destroyed; the current CPython implementation only calls it once.

It is not guaranteed that __del__() methods are called for objects that still exist when the interpreter exits. weakref.finalize provides a straightforward way to register a cleanup function to be called when an object is garbage collected.

備註

del x doesn't directly call x.__del__() --- the former decrements the reference count for x by one, and the latter is only called when x's reference count reaches zero.

CPython 實作細節： It is possible for a reference cycle to prevent the reference count of an object from going to zero. In this case, the cycle will be later detected and deleted by the cyclic garbage collector. A common cause of reference cycles is when an exception has been caught in a local variable. The frame's locals then reference the exception, which references its own traceback, which references the locals of all frames caught in the traceback.

也參考

gc 模組的文件。

警告

Due to the precarious circumstances under which __del__() methods are invoked, exceptions that occur during their execution are ignored, and a warning is printed to sys.stderr instead. In particular:

__del__() can be invoked when arbitrary code is being executed, including from any arbitrary thread. If __del__() needs to take a lock or invoke any other blocking resource, it may deadlock as the resource may already be taken by the code that gets interrupted to execute __del__().
__del__() can be executed during interpreter shutdown. As a consequence, the global variables it needs to access (including other modules) may already have been deleted or set to None. Python guarantees that globals whose name begins with a single underscore are deleted from their module before other globals are deleted; if no other references to such globals exist, this may help in assuring that imported modules are still available at the time when the __del__() method is called.

object.__repr__(self)¶

Called by the repr() built-in function to compute the "official" string representation of an object. If at all possible, this should look like a valid Python expression that could be used to recreate an object with the same value (given an appropriate environment). If this is not possible, a string of the form <...some useful description...> should be returned. The return value must be a string object. If a class defines __repr__() but not __str__(), then __repr__() is also used when an "informal" string representation of instances of that class is required.

This is typically used for debugging, so it is important that the representation is information-rich and unambiguous. A default implementation is provided by the object class itself.

object.__str__(self)¶

Called by str(object), the default __format__() implementation, and the built-in function print(), to compute the "informal" or nicely printable string representation of an object. The return value must be a str object.

This method differs from object.__repr__() in that there is no expectation that __str__() return a valid Python expression: a more convenient or concise representation can be used.

The default implementation defined by the built-in type object calls object.__repr__().

object.__bytes__(self)¶: Called by bytes to compute a byte-string representation of an object. This should return a bytes object. The object class itself does not provide this method.

object.__format__(self, format_spec)¶

Called by the format() built-in function, and by extension, evaluation of formatted string literals and the str.format() method, to produce a "formatted" string representation of an object. The format_spec argument is a string that contains a description of the formatting options desired. The interpretation of the format_spec argument is up to the type implementing __format__(), however most classes will either delegate formatting to one of the built-in types, or use a similar formatting option syntax.

See 格式規格 (Format Specification) 迷你語言 for a description of the standard formatting syntax.

回傳值必須是個字串物件。

The default implementation by the object class should be given an empty format_spec string. It delegates to __str__().

在 3.4 版的變更: The __format__ method of object itself raises a TypeError if passed any non-empty string.

在 3.7 版的變更: object.__format__(x, '') is now equivalent to str(x) rather than format(str(x), '').

object.__lt__(self, other)¶

object.__le__(self, other)¶

object.__eq__(self, other)¶

object.__ne__(self, other)¶

object.__gt__(self, other)¶

object.__ge__(self, other)¶

These are the so-called "rich comparison" methods. The correspondence between operator symbols and method names is as follows: x<y calls x.__lt__(y), x<=y calls x.__le__(y), x==y calls x.__eq__(y), x!=y calls x.__ne__(y), x>y calls x.__gt__(y), and x>=y calls x.__ge__(y).

A rich comparison method may return the singleton NotImplemented if it does not implement the operation for a given pair of arguments. By convention, False and True are returned for a successful comparison. However, these methods can return any value, so if the comparison operator is used in a Boolean context (e.g., in the condition of an if statement), Python will call bool() on the value to determine if the result is true or false.

By default, object implements __eq__() by using is, returning NotImplemented in the case of a false comparison: True if x is y else NotImplemented. For __ne__(), by default it delegates to __eq__() and inverts the result unless it is NotImplemented. There are no other implied relationships among the comparison operators or default implementations; for example, the truth of (x<y or x==y) does not imply x<=y. To automatically generate ordering operations from a single root operation, see functools.total_ordering().

By default, the object class provides implementations consistent with Value comparisons: equality compares according to object identity, and order comparisons raise TypeError. Each default method may generate these results directly, but may also return NotImplemented.

See the paragraph on __hash__() for some important notes on creating hashable objects which support custom comparison operations and are usable as dictionary keys.

There are no swapped-argument versions of these methods (to be used when the left argument does not support the operation but the right argument does); rather, __lt__() and __gt__() are each other's reflection, __le__() and __ge__() are each other's reflection, and __eq__() and __ne__() are their own reflection. If the operands are of different types, and the right operand's type is a direct or indirect subclass of the left operand's type, the reflected method of the right operand has priority, otherwise the left operand's method has priority. Virtual subclassing is not considered.

When no appropriate method returns any value other than NotImplemented, the == and != operators will fall back to is and is not, respectively.

object.__hash__(self)¶

Called by built-in function hash() and for operations on members of hashed collections including set, frozenset, and dict. The __hash__() method should return an integer. The only required property is that objects which compare equal have the same hash value; it is advised to mix together the hash values of the components of the object that also play a part in comparison of objects by packing them into a tuple and hashing the tuple. Example:

def __hash__(self):
    return hash((self.name, self.nick, self.color))

備註

hash() truncates the value returned from an object's custom __hash__() method to the size of a Py_ssize_t. This is typically 8 bytes on 64-bit builds and 4 bytes on 32-bit builds. If an object's __hash__() must interoperate on builds of different bit sizes, be sure to check the width on all supported builds. An easy way to do this is with python -c "import sys; print(sys.hash_info.width)".

If a class does not define an __eq__() method it should not define a __hash__() operation either; if it defines __eq__() but not __hash__(), its instances will not be usable as items in hashable collections. If a class defines mutable objects and implements an __eq__() method, it should not implement __hash__(), since the implementation of hashable collections requires that a key's hash value is immutable (if the object's hash value changes, it will be in the wrong hash bucket).

User-defined classes have __eq__() and __hash__() methods by default (inherited from the object class); with them, all objects compare unequal (except with themselves) and x.__hash__() returns an appropriate value such that x == y implies both that x is y and hash(x) == hash(y).

A class that overrides __eq__() and does not define __hash__() will have its __hash__() implicitly set to None. When the __hash__() method of a class is None, instances of the class will raise an appropriate TypeError when a program attempts to retrieve their hash value, and will also be correctly identified as unhashable when checking isinstance(obj, collections.abc.Hashable).

If a class that overrides __eq__() needs to retain the implementation of __hash__() from a parent class, the interpreter must be told this explicitly by setting __hash__ = <ParentClass>.__hash__.

If a class that does not override __eq__() wishes to suppress hash support, it should include __hash__ = None in the class definition. A class which defines its own __hash__() that explicitly raises a TypeError would be incorrectly identified as hashable by an isinstance(obj, collections.abc.Hashable) call.

備註

By default, the __hash__() values of str and bytes objects are "salted" with an unpredictable random value. Although they remain constant within an individual Python process, they are not predictable between repeated invocations of Python.

This is intended to provide protection against a denial-of-service caused by carefully chosen inputs that exploit the worst case performance of a dict insertion, O(n²) complexity. See https://ocert.org/advisories/ocert-2011-003.html for details.

Changing hash values affects the iteration order of sets. Python has never made guarantees about this ordering (and it typically varies between 32-bit and 64-bit builds).

另請參閱

3. 資料模型¶

3.1. 物件、數值和型別¶

3.2. 標準型別階層¶

3.2.1. None¶

3.2.2. NotImplemented¶

3.2.3. Ellipsis¶

3.2.4. numbers.Number¶

3.2.4.1. numbers.Integral¶

3.2.4.2. numbers.Real (float)¶

3.2.4.3. numbers.Complex (complex)¶

3.2.5. Sequences¶

3.2.5.1. Immutable sequences¶

3.2.5.2. 可變序列¶

3.2.6. Set（集合）型別¶

3.2.7. 對映¶

3.2.7.1. 字典¶

3.2.8. 可呼叫型別¶

3.2.8.1. 自訂函式¶

3.2.8.1.1. 特殊唯讀屬性¶

3.2.8.1.2. 特殊可寫屬性¶

3.2.8.2. 實例方法¶

3.2.8.3. 產生器函式¶

3.2.8.4. Coroutine（協程）函式¶

3.2.8.5. 非同步產生器函式¶

3.2.8.6. 內建函式¶

3.2.8.7. 內建方法¶

3.2.8.8. Classes¶

3.2.8.9. 類別實例¶

3.2.9. 模組¶

3.2.9.1. Import-related attributes on module objects¶

3.2.9.2. 模組物件的其他可寫入屬性¶

3.2.9.3. 模組字典¶

3.2.10. Custom classes¶

3.2.10.1. 特殊屬性¶

3.2.10.2. 特殊方法¶

3.2.11. 類別實例¶

3.2.11.1. 特殊屬性¶

3.2.12. I/O objects (also known as file objects)¶

3.2.13. Internal types¶

3.2.13.1. 程式碼物件¶

3.2.13.1.1. 特殊唯讀屬性¶

3.2.13.1.2. 用於程式碼物件的方法¶

3.2.13.2. Frame objects¶

3.2.13.2.1. 特殊唯讀屬性¶

3.2.13.2.2. 特殊可寫屬性¶

3.2.13.2.3. Frame object methods¶

3.2.13.3. Traceback objects¶

3.2.13.4. Slice objects¶

3.2.13.5. Static method objects¶

3.2.13.6. Class method objects¶

3.3. Special method names¶

3.3.1. Basic customization¶

3.2.4. `numbers.Number`¶

3.2.4.1. `numbers.Integral`¶

3.2.4.2. `numbers.Real` (`float`)¶

3.2.4.3. `numbers.Complex` (`complex`)¶