⚝
One Hat Cyber Team
⚝
Your IP:
172.22.0.1
Server IP:
151.80.20.34
Server:
Linux 794f04d97d5e 5.15.0-143-generic #153-Ubuntu SMP Fri Jun 13 19:10:45 UTC 2025 x86_64
Server Software:
Apache/2.4.62 (Debian)
PHP Version:
8.2.28
Buat File
|
Buat Folder
Eksekusi
Dir :
~
/
lib
/
python3.11
/
pydoc_data
/
__pycache__
/
View File Name :
topics.cpython-311.pyc
� ��h� � �� � i d d�dd�dd�dd�dd �d d �d d �dd�dd�dd�dd�dd�dd�dd�d d �d d �d d!�i d"d#�d$d%�d&d'�d(d)�d*d+�d,d-�d.d/�d0d1�d2d3�d4d5�d6d7�d8d9�d:d;�d<d=�d>d?�d@dA�dBdC��i dDdE�dFdG�dHdI�dJdK�dLdM�dNdO�dPdQ�dRd=�dSdT�dUdV�dWdX�dYdZ�d[d\�d]d^�d_d`�dadb�dcdd��i dedf�dgdh�didj�dkdl�dmdn�dodp�dqdr�dsdt�dudv�dwdx�dydz�d{d|�d}d~�dd��d�d��d�d��d�d���d�d�d�d�d�d�d�d�d�d�d�d�� �Z d�S )��assertau The "assert" statement ********************** Assert statements are a convenient way to insert debugging assertions into a program: assert_stmt ::= "assert" expression ["," expression] The simple form, "assert expression", is equivalent to if __debug__: if not expression: raise AssertionError The extended form, "assert expression1, expression2", is equivalent to if __debug__: if not expression1: raise AssertionError(expression2) These equivalences assume that "__debug__" and "AssertionError" refer to the built-in variables with those names. In the current implementation, the built-in variable "__debug__" is "True" under normal circumstances, "False" when optimization is requested (command line option "-O"). The current code generator emits no code for an assert statement when optimization is requested at compile time. Note that it is unnecessary to include the source code for the expression that failed in the error message; it will be displayed as part of the stack trace. Assignments to "__debug__" are illegal. The value for the built-in variable is determined when the interpreter starts. � assignmentu�+ Assignment statements ********************* Assignment statements are used to (re)bind names to values and to modify attributes or items of mutable objects: assignment_stmt ::= (target_list "=")+ (starred_expression | yield_expression) target_list ::= target ("," target)* [","] target ::= identifier | "(" [target_list] ")" | "[" [target_list] "]" | attributeref | subscription | slicing | "*" target (See section Primaries for the syntax definitions for *attributeref*, *subscription*, and *slicing*.) An assignment statement evaluates the expression list (remember that this can be a single expression or a comma-separated list, the latter yielding a tuple) and assigns the single resulting object to each of the target lists, from left to right. Assignment is defined recursively depending on the form of the target (list). When a target is part of a mutable object (an attribute reference, subscription or slicing), the mutable object must ultimately perform the assignment and decide about its validity, and may raise an exception if the assignment is unacceptable. The rules observed by various types and the exceptions raised are given with the definition of the object types (see section The standard type hierarchy). Assignment of an object to a target list, optionally enclosed in parentheses or square brackets, is recursively defined as follows. * If the target list is a single target with no trailing comma, optionally in parentheses, the object is assigned to that target. * Else: * If the target list contains one target prefixed with an asterisk, called a “starred” target: The object must be an iterable with at least as many items as there are targets in the target list, minus one. The first items of the iterable are assigned, from left to right, to the targets before the starred target. The final items of the iterable are assigned to the targets after the starred target. A list of the remaining items in the iterable is then assigned to the starred target (the list can be empty). * Else: The object must be an iterable with the same number of items as there are targets in the target list, and the items are assigned, from left to right, to the corresponding targets. Assignment of an object to a single target is recursively defined as follows. * If the target is an identifier (name): * If the name does not occur in a "global" or "nonlocal" statement in the current code block: the name is bound to the object in the current local namespace. * Otherwise: the name is bound to the object in the global namespace or the outer namespace determined by "nonlocal", respectively. The name is rebound if it was already bound. This may cause the reference count for the object previously bound to the name to reach zero, causing the object to be deallocated and its destructor (if it has one) to be called. * If the target is an attribute reference: The primary expression in the reference is evaluated. It should yield an object with assignable attributes; if this is not the case, "TypeError" is raised. That object is then asked to assign the assigned object to the given attribute; if it cannot perform the assignment, it raises an exception (usually but not necessarily "AttributeError"). Note: If the object is a class instance and the attribute reference occurs on both sides of the assignment operator, the right-hand side expression, "a.x" can access either an instance attribute or (if no instance attribute exists) a class attribute. The left-hand side target "a.x" is always set as an instance attribute, creating it if necessary. Thus, the two occurrences of "a.x" do not necessarily refer to the same attribute: if the right-hand side expression refers to a class attribute, the left-hand side creates a new instance attribute as the target of the assignment: class Cls: x = 3 # class variable inst = Cls() inst.x = inst.x + 1 # writes inst.x as 4 leaving Cls.x as 3 This description does not necessarily apply to descriptor attributes, such as properties created with "property()". * If the target is a subscription: The primary expression in the reference is evaluated. It should yield either a mutable sequence object (such as a list) or a mapping object (such as a dictionary). Next, the subscript expression is evaluated. If the primary is a mutable sequence object (such as a list), the subscript must yield an integer. If it is negative, the sequence’s length is added to it. The resulting value must be a nonnegative integer less than the sequence’s length, and the sequence is asked to assign the assigned object to its item with that index. If the index is out of range, "IndexError" is raised (assignment to a subscripted sequence cannot add new items to a list). If the primary is a mapping object (such as a dictionary), the subscript must have a type compatible with the mapping’s key type, and the mapping is then asked to create a key/datum pair which maps the subscript to the assigned object. This can either replace an existing key/value pair with the same key value, or insert a new key/value pair (if no key with the same value existed). For user-defined objects, the "__setitem__()" method is called with appropriate arguments. * If the target is a slicing: The primary expression in the reference is evaluated. It should yield a mutable sequence object (such as a list). The assigned object should be a sequence object of the same type. Next, the lower and upper bound expressions are evaluated, insofar they are present; defaults are zero and the sequence’s length. The bounds should evaluate to integers. If either bound is negative, the sequence’s length is added to it. The resulting bounds are clipped to lie between zero and the sequence’s length, inclusive. Finally, the sequence object is asked to replace the slice with the items of the assigned sequence. The length of the slice may be different from the length of the assigned sequence, thus changing the length of the target sequence, if the target sequence allows it. **CPython implementation detail:** In the current implementation, the syntax for targets is taken to be the same as for expressions, and invalid syntax is rejected during the code generation phase, causing less detailed error messages. Although the definition of assignment implies that overlaps between the left-hand side and the right-hand side are ‘simultaneous’ (for example "a, b = b, a" swaps two variables), overlaps *within* the collection of assigned-to variables occur left-to-right, sometimes resulting in confusion. For instance, the following program prints "[0, 2]": x = [0, 1] i = 0 i, x[i] = 1, 2 # i is updated, then x[i] is updated print(x) See also: **PEP 3132** - Extended Iterable Unpacking The specification for the "*target" feature. Augmented assignment statements =============================== Augmented assignment is the combination, in a single statement, of a binary operation and an assignment statement: augmented_assignment_stmt ::= augtarget augop (expression_list | yield_expression) augtarget ::= identifier | attributeref | subscription | slicing augop ::= "+=" | "-=" | "*=" | "@=" | "/=" | "//=" | "%=" | "**=" | ">>=" | "<<=" | "&=" | "^=" | "|=" (See section Primaries for the syntax definitions of the last three symbols.) An augmented assignment evaluates the target (which, unlike normal assignment statements, cannot be an unpacking) and the expression list, performs the binary operation specific to the type of assignment on the two operands, and assigns the result to the original target. The target is only evaluated once. An augmented assignment expression like "x += 1" can be rewritten as "x = x + 1" to achieve a similar, but not exactly equal effect. In the augmented version, "x" is only evaluated once. Also, when possible, the actual operation is performed *in-place*, meaning that rather than creating a new object and assigning that to the target, the old object is modified instead. Unlike normal assignments, augmented assignments evaluate the left- hand side *before* evaluating the right-hand side. For example, "a[i] += f(x)" first looks-up "a[i]", then it evaluates "f(x)" and performs the addition, and lastly, it writes the result back to "a[i]". With the exception of assigning to tuples and multiple targets in a single statement, the assignment done by augmented assignment statements is handled the same way as normal assignments. Similarly, with the exception of the possible *in-place* behavior, the binary operation performed by augmented assignment is the same as the normal binary operations. For targets which are attribute references, the same caveat about class and instance attributes applies as for regular assignments. Annotated assignment statements =============================== *Annotation* assignment is the combination, in a single statement, of a variable or attribute annotation and an optional assignment statement: annotated_assignment_stmt ::= augtarget ":" expression ["=" (starred_expression | yield_expression)] The difference from normal Assignment statements is that only a single target is allowed. For simple names as assignment targets, if in class or module scope, the annotations are evaluated and stored in a special class or module attribute "__annotations__" that is a dictionary mapping from variable names (mangled if private) to evaluated annotations. This attribute is writable and is automatically created at the start of class or module body execution, if annotations are found statically. For expressions as assignment targets, the annotations are evaluated if in class or module scope, but not stored. If a name is annotated in a function scope, then this name is local for that scope. Annotations are never evaluated and stored in function scopes. If the right hand side is present, an annotated assignment performs the actual assignment before evaluating annotations (where applicable). If the right hand side is not present for an expression target, then the interpreter evaluates the target except for the last "__setitem__()" or "__setattr__()" call. See also: **PEP 526** - Syntax for Variable Annotations The proposal that added syntax for annotating the types of variables (including class variables and instance variables), instead of expressing them through comments. **PEP 484** - Type hints The proposal that added the "typing" module to provide a standard syntax for type annotations that can be used in static analysis tools and IDEs. Changed in version 3.8: Now annotated assignments allow the same expressions in the right hand side as regular assignments. Previously, some expressions (like un-parenthesized tuple expressions) caused a syntax error. �asyncu� Coroutines ********** New in version 3.5. Coroutine function definition ============================= async_funcdef ::= [decorators] "async" "def" funcname "(" [parameter_list] ")" ["->" expression] ":" suite Execution of Python coroutines can be suspended and resumed at many points (see *coroutine*). "await" expressions, "async for" and "async with" can only be used in the body of a coroutine function. Functions defined with "async def" syntax are always coroutine functions, even if they do not contain "await" or "async" keywords. It is a "SyntaxError" to use a "yield from" expression inside the body of a coroutine function. An example of a coroutine function: async def func(param1, param2): do_stuff() await some_coroutine() Changed in version 3.7: "await" and "async" are now keywords; previously they were only treated as such inside the body of a coroutine function. The "async for" statement ========================= async_for_stmt ::= "async" for_stmt An *asynchronous iterable* provides an "__aiter__" method that directly returns an *asynchronous iterator*, which can call asynchronous code in its "__anext__" method. The "async for" statement allows convenient iteration over asynchronous iterables. The following code: async for TARGET in ITER: SUITE else: SUITE2 Is semantically equivalent to: iter = (ITER) iter = type(iter).__aiter__(iter) running = True while running: try: TARGET = await type(iter).__anext__(iter) except StopAsyncIteration: running = False else: SUITE else: SUITE2 See also "__aiter__()" and "__anext__()" for details. It is a "SyntaxError" to use an "async for" statement outside the body of a coroutine function. The "async with" statement ========================== async_with_stmt ::= "async" with_stmt An *asynchronous context manager* is a *context manager* that is able to suspend execution in its *enter* and *exit* methods. The following code: async with EXPRESSION as TARGET: SUITE is semantically equivalent to: manager = (EXPRESSION) aenter = type(manager).__aenter__ aexit = type(manager).__aexit__ value = await aenter(manager) hit_except = False try: TARGET = value SUITE except: hit_except = True if not await aexit(manager, *sys.exc_info()): raise finally: if not hit_except: await aexit(manager, None, None, None) See also "__aenter__()" and "__aexit__()" for details. It is a "SyntaxError" to use an "async with" statement outside the body of a coroutine function. See also: **PEP 492** - Coroutines with async and await syntax The proposal that made coroutines a proper standalone concept in Python, and added supporting syntax. -[ Footnotes ]- [1] The exception is propagated to the invocation stack unless there is a "finally" clause which happens to raise another exception. That new exception causes the old one to be lost. [2] In pattern matching, a sequence is defined as one of the following: * a class that inherits from "collections.abc.Sequence" * a Python class that has been registered as "collections.abc.Sequence" * a builtin class that has its (CPython) "Py_TPFLAGS_SEQUENCE" bit set * a class that inherits from any of the above The following standard library classes are sequences: * "array.array" * "collections.deque" * "list" * "memoryview" * "range" * "tuple" Note: Subject values of type "str", "bytes", and "bytearray" do not match sequence patterns. [3] In pattern matching, a mapping is defined as one of the following: * a class that inherits from "collections.abc.Mapping" * a Python class that has been registered as "collections.abc.Mapping" * a builtin class that has its (CPython) "Py_TPFLAGS_MAPPING" bit set * a class that inherits from any of the above The standard library classes "dict" and "types.MappingProxyType" are mappings. [4] A string literal appearing as the first statement in the function body is transformed into the function’s "__doc__" attribute and therefore the function’s *docstring*. [5] A string literal appearing as the first statement in the class body is transformed into the namespace’s "__doc__" item and therefore the class’s *docstring*. zatom-identifiersa� Identifiers (Names) ******************* An identifier occurring as an atom is a name. See section Identifiers and keywords for lexical definition and section Naming and binding for documentation of naming and binding. When the name is bound to an object, evaluation of the atom yields that object. When a name is not bound, an attempt to evaluate it raises a "NameError" exception. **Private name mangling:** When an identifier that textually occurs in a class definition begins with two or more underscore characters and does not end in two or more underscores, it is considered a *private name* of that class. Private names are transformed to a longer form before code is generated for them. The transformation inserts the class name, with leading underscores removed and a single underscore inserted, in front of the name. For example, the identifier "__spam" occurring in a class named "Ham" will be transformed to "_Ham__spam". This transformation is independent of the syntactical context in which the identifier is used. If the transformed name is extremely long (longer than 255 characters), implementation defined truncation may happen. If the class name consists only of underscores, no transformation is done. z atom-literalsu Literals ******** Python supports string and bytes literals and various numeric literals: literal ::= stringliteral | bytesliteral | integer | floatnumber | imagnumber Evaluation of a literal yields an object of the given type (string, bytes, integer, floating point number, complex number) with the given value. The value may be approximated in the case of floating point and imaginary (complex) literals. See section Literals for details. All literals correspond to immutable data types, and hence the object’s identity is less important than its value. Multiple evaluations of literals with the same value (either the same occurrence in the program text or a different occurrence) may obtain the same object or a different object with the same value. zattribute-accessuj5 Customizing attribute access **************************** The following methods can be defined to customize the meaning of attribute access (use of, assignment to, or deletion of "x.name") for class instances. object.__getattr__(self, name) Called when the default attribute access fails with an "AttributeError" (either "__getattribute__()" raises an "AttributeError" because *name* is not an instance attribute or an attribute in the class tree for "self"; or "__get__()" of a *name* property raises "AttributeError"). This method should either return the (computed) attribute value or raise an "AttributeError" exception. Note that if the attribute is found through the normal mechanism, "__getattr__()" is not called. (This is an intentional asymmetry between "__getattr__()" and "__setattr__()".) This is done both for efficiency reasons and because otherwise "__getattr__()" would have no way to access other attributes of the instance. Note that at least for instance variables, you can fake total control by not inserting any values in the instance attribute dictionary (but instead inserting them in another object). See the "__getattribute__()" method below for a way to actually get total control over attribute access. object.__getattribute__(self, name) Called unconditionally to implement attribute accesses for instances of the class. If the class also defines "__getattr__()", the latter will not be called unless "__getattribute__()" either calls it explicitly or raises an "AttributeError". This method should return the (computed) attribute value or raise an "AttributeError" exception. In order to avoid infinite recursion in this method, its implementation should always call the base class method with the same name to access any attributes it needs, for example, "object.__getattribute__(self, name)". Note: This method may still be bypassed when looking up special methods as the result of implicit invocation via language syntax or built-in functions. See Special method lookup. For certain sensitive attribute accesses, raises an auditing event "object.__getattr__" with arguments "obj" and "name". object.__setattr__(self, name, value) Called when an attribute assignment is attempted. This is called instead of the normal mechanism (i.e. store the value in the instance dictionary). *name* is the attribute name, *value* is the value to be assigned to it. If "__setattr__()" wants to assign to an instance attribute, it should call the base class method with the same name, for example, "object.__setattr__(self, name, value)". For certain sensitive attribute assignments, raises an auditing event "object.__setattr__" with arguments "obj", "name", "value". object.__delattr__(self, name) Like "__setattr__()" but for attribute deletion instead of assignment. This should only be implemented if "del obj.name" is meaningful for the object. For certain sensitive attribute deletions, raises an auditing event "object.__delattr__" with arguments "obj" and "name". object.__dir__(self) Called when "dir()" is called on the object. A sequence must be returned. "dir()" converts the returned sequence to a list and sorts it. Customizing module attribute access =================================== Special names "__getattr__" and "__dir__" can be also used to customize access to module attributes. The "__getattr__" function at the module level should accept one argument which is the name of an attribute and return the computed value or raise an "AttributeError". If an attribute is not found on a module object through the normal lookup, i.e. "object.__getattribute__()", then "__getattr__" is searched in the module "__dict__" before raising an "AttributeError". If found, it is called with the attribute name and the result is returned. The "__dir__" function should accept no arguments, and return a sequence of strings that represents the names accessible on module. If present, this function overrides the standard "dir()" search on a module. For a more fine grained customization of the module behavior (setting attributes, properties, etc.), one can set the "__class__" attribute of a module object to a subclass of "types.ModuleType". For example: import sys from types import ModuleType class VerboseModule(ModuleType): def __repr__(self): return f'Verbose {self.__name__}' def __setattr__(self, attr, value): print(f'Setting {attr}...') super().__setattr__(attr, value) sys.modules[__name__].__class__ = VerboseModule Note: Defining module "__getattr__" and setting module "__class__" only affect lookups made using the attribute access syntax – directly accessing the module globals (whether by code within the module, or via a reference to the module’s globals dictionary) is unaffected. Changed in version 3.5: "__class__" module attribute is now writable. New in version 3.7: "__getattr__" and "__dir__" module attributes. See also: **PEP 562** - Module __getattr__ and __dir__ Describes the "__getattr__" and "__dir__" functions on modules. Implementing Descriptors ======================== The following methods only apply when an instance of the class containing the method (a so-called *descriptor* class) appears in an *owner* class (the descriptor must be in either the owner’s class dictionary or in the class dictionary for one of its parents). In the examples below, “the attribute” refers to the attribute whose name is the key of the property in the owner class’ "__dict__". object.__get__(self, instance, owner=None) Called to get the attribute of the owner class (class attribute access) or of an instance of that class (instance attribute access). The optional *owner* argument is the owner class, while *instance* is the instance that the attribute was accessed through, or "None" when the attribute is accessed through the *owner*. This method should return the computed attribute value or raise an "AttributeError" exception. **PEP 252** specifies that "__get__()" is callable with one or two arguments. Python’s own built-in descriptors support this specification; however, it is likely that some third-party tools have descriptors that require both arguments. Python’s own "__getattribute__()" implementation always passes in both arguments whether they are required or not. object.__set__(self, instance, value) Called to set the attribute on an instance *instance* of the owner class to a new value, *value*. Note, adding "__set__()" or "__delete__()" changes the kind of descriptor to a “data descriptor”. See Invoking Descriptors for more details. object.__delete__(self, instance) Called to delete the attribute on an instance *instance* of the owner class. The attribute "__objclass__" is interpreted by the "inspect" module as specifying the class where this object was defined (setting this appropriately can assist in runtime introspection of dynamic class attributes). For callables, it may indicate that an instance of the given type (or a subclass) is expected or required as the first positional argument (for example, CPython sets this attribute for unbound methods that are implemented in C). Invoking Descriptors ==================== In general, a descriptor is an object attribute with “binding behavior”, one whose attribute access has been overridden by methods in the descriptor protocol: "__get__()", "__set__()", and "__delete__()". If any of those methods are defined for an object, it is said to be a descriptor. The default behavior for attribute access is to get, set, or delete the attribute from an object’s dictionary. For instance, "a.x" has a lookup chain starting with "a.__dict__['x']", then "type(a).__dict__['x']", and continuing through the base classes of "type(a)" excluding metaclasses. However, if the looked-up value is an object defining one of the descriptor methods, then Python may override the default behavior and invoke the descriptor method instead. Where this occurs in the precedence chain depends on which descriptor methods were defined and how they were called. The starting point for descriptor invocation is a binding, "a.x". How the arguments are assembled depends on "a": Direct Call The simplest and least common call is when user code directly invokes a descriptor method: "x.__get__(a)". Instance Binding If binding to an object instance, "a.x" is transformed into the call: "type(a).__dict__['x'].__get__(a, type(a))". Class Binding If binding to a class, "A.x" is transformed into the call: "A.__dict__['x'].__get__(None, A)". Super Binding A dotted lookup such as "super(A, a).x" searches "a.__class__.__mro__" for a base class "B" following "A" and then returns "B.__dict__['x'].__get__(a, A)". If not a descriptor, "x" is returned unchanged. For instance bindings, the precedence of descriptor invocation depends on which descriptor methods are defined. A descriptor can define any combination of "__get__()", "__set__()" and "__delete__()". If it does not define "__get__()", then accessing the attribute will return the descriptor object itself unless there is a value in the object’s instance dictionary. If the descriptor defines "__set__()" and/or "__delete__()", it is a data descriptor; if it defines neither, it is a non-data descriptor. Normally, data descriptors define both "__get__()" and "__set__()", while non-data descriptors have just the "__get__()" method. Data descriptors with "__get__()" and "__set__()" (and/or "__delete__()") defined always override a redefinition in an instance dictionary. In contrast, non-data descriptors can be overridden by instances. Python methods (including those decorated with "@staticmethod" and "@classmethod") are implemented as non-data descriptors. Accordingly, instances can redefine and override methods. This allows individual instances to acquire behaviors that differ from other instances of the same class. The "property()" function is implemented as a data descriptor. Accordingly, instances cannot override the behavior of a property. __slots__ ========= *__slots__* allow us to explicitly declare data members (like properties) and deny the creation of "__dict__" and *__weakref__* (unless explicitly declared in *__slots__* or available in a parent.) The space saved over using "__dict__" can be significant. Attribute lookup speed can be significantly improved as well. object.__slots__ This class variable can be assigned a string, iterable, or sequence of strings with variable names used by instances. *__slots__* reserves space for the declared variables and prevents the automatic creation of "__dict__" and *__weakref__* for each instance. Notes on using *__slots__* -------------------------- * When inheriting from a class without *__slots__*, the "__dict__" and *__weakref__* attribute of the instances will always be accessible. * Without a "__dict__" variable, instances cannot be assigned new variables not listed in the *__slots__* definition. Attempts to assign to an unlisted variable name raises "AttributeError". If dynamic assignment of new variables is desired, then add "'__dict__'" to the sequence of strings in the *__slots__* declaration. * Without a *__weakref__* variable for each instance, classes defining *__slots__* do not support "weak references" to its instances. If weak reference support is needed, then add "'__weakref__'" to the sequence of strings in the *__slots__* declaration. * *__slots__* are implemented at the class level by creating descriptors for each variable name. 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. * The action of a *__slots__* declaration is not limited to the class where it is defined. *__slots__* declared in parents are available in child classes. However, child subclasses will get a "__dict__" and *__weakref__* unless they also define *__slots__* (which should only contain names of any *additional* slots). * 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 descriptor directly from the base class). This renders the meaning of the program undefined. In the future, a check may be added to prevent this. * Nonempty *__slots__* does not work for classes derived from “variable-length” built-in types such as "int", "bytes" and "tuple". * Any non-string *iterable* may be assigned to *__slots__*. * If a "dictionary" is used to assign *__slots__*, the dictionary keys will be used as the slot names. The values of the dictionary can be used to provide per-attribute docstrings that will be recognised by "inspect.getdoc()" and displayed in the output of "help()". * "__class__" assignment works only if both classes have the same *__slots__*. * Multiple inheritance with multiple slotted parent classes can be used, but only one parent is allowed to have attributes created by slots (the other bases must have empty slot layouts) - violations raise "TypeError". * If an *iterator* is used for *__slots__* then a *descriptor* is created for each of the iterator’s values. However, the *__slots__* attribute will be an empty iterator. zattribute-referencesa� Attribute references ******************** An attribute reference is a primary followed by a period and a name: attributeref ::= primary "." identifier The primary must evaluate to an object of a type that supports attribute references, which most objects do. This object is then asked to produce the attribute whose name is the identifier. This production can be customized by overriding the "__getattr__()" method. If this attribute is not available, the exception "AttributeError" is raised. Otherwise, the type and value of the object produced is determined by the object. Multiple evaluations of the same attribute reference may yield different objects. � augassigna� Augmented assignment statements ******************************* Augmented assignment is the combination, in a single statement, of a binary operation and an assignment statement: augmented_assignment_stmt ::= augtarget augop (expression_list | yield_expression) augtarget ::= identifier | attributeref | subscription | slicing augop ::= "+=" | "-=" | "*=" | "@=" | "/=" | "//=" | "%=" | "**=" | ">>=" | "<<=" | "&=" | "^=" | "|=" (See section Primaries for the syntax definitions of the last three symbols.) An augmented assignment evaluates the target (which, unlike normal assignment statements, cannot be an unpacking) and the expression list, performs the binary operation specific to the type of assignment on the two operands, and assigns the result to the original target. The target is only evaluated once. An augmented assignment expression like "x += 1" can be rewritten as "x = x + 1" to achieve a similar, but not exactly equal effect. In the augmented version, "x" is only evaluated once. Also, when possible, the actual operation is performed *in-place*, meaning that rather than creating a new object and assigning that to the target, the old object is modified instead. Unlike normal assignments, augmented assignments evaluate the left- hand side *before* evaluating the right-hand side. For example, "a[i] += f(x)" first looks-up "a[i]", then it evaluates "f(x)" and performs the addition, and lastly, it writes the result back to "a[i]". With the exception of assigning to tuples and multiple targets in a single statement, the assignment done by augmented assignment statements is handled the same way as normal assignments. Similarly, with the exception of the possible *in-place* behavior, the binary operation performed by augmented assignment is the same as the normal binary operations. For targets which are attribute references, the same caveat about class and instance attributes applies as for regular assignments. �awaitz�Await expression **************** Suspend the execution of *coroutine* on an *awaitable* object. Can only be used inside a *coroutine function*. await_expr ::= "await" primary New in version 3.5. �binaryu Binary arithmetic operations **************************** The binary arithmetic operations have the conventional priority levels. Note that some of these operations also apply to certain non- numeric types. Apart from the power operator, there are only two levels, one for multiplicative operators and one for additive operators: m_expr ::= u_expr | m_expr "*" u_expr | m_expr "@" m_expr | m_expr "//" u_expr | m_expr "/" u_expr | m_expr "%" u_expr a_expr ::= m_expr | a_expr "+" m_expr | a_expr "-" m_expr The "*" (multiplication) operator yields the product of its arguments. The arguments must either both be numbers, or one argument must be an integer and the other must be a sequence. In the former case, the numbers are converted to a common type and then multiplied together. In the latter case, sequence repetition is performed; a negative repetition factor yields an empty sequence. This operation can be customized using the special "__mul__()" and "__rmul__()" methods. The "@" (at) operator is intended to be used for matrix multiplication. No builtin Python types implement this operator. New in version 3.5. The "/" (division) and "//" (floor division) operators yield the quotient of their arguments. The numeric arguments are first converted to a common type. Division of integers yields a float, while floor division of integers results in an integer; the result is that of mathematical division with the ‘floor’ function applied to the result. Division by zero raises the "ZeroDivisionError" exception. This operation can be customized using the special "__truediv__()" and "__floordiv__()" methods. The "%" (modulo) operator yields the remainder from the division of the first argument by the second. The numeric arguments are first converted to a common type. A zero right argument raises the "ZeroDivisionError" exception. The arguments may be floating point numbers, e.g., "3.14%0.7" equals "0.34" (since "3.14" equals "4*0.7 + 0.34".) The modulo operator always yields a result with the same sign as its second operand (or zero); the absolute value of the result is strictly smaller than the absolute value of the second operand [1]. The floor division and modulo operators are connected by the following identity: "x == (x//y)*y + (x%y)". Floor division and modulo are also connected with the built-in function "divmod()": "divmod(x, y) == (x//y, x%y)". [2]. In addition to performing the modulo operation on numbers, the "%" operator is also overloaded by string objects to perform old-style string formatting (also known as interpolation). The syntax for string formatting is described in the Python Library Reference, section printf-style String Formatting. The *modulo* operation can be customized using the special "__mod__()" method. The floor division operator, the modulo operator, and the "divmod()" function are not defined for complex numbers. Instead, convert to a floating point number using the "abs()" function if appropriate. The "+" (addition) operator yields the sum of its arguments. The arguments must either both be numbers or both be sequences of the same type. In the former case, the numbers are converted to a common type and then added together. In the latter case, the sequences are concatenated. This operation can be customized using the special "__add__()" and "__radd__()" methods. The "-" (subtraction) operator yields the difference of its arguments. The numeric arguments are first converted to a common type. This operation can be customized using the special "__sub__()" method. �bitwisea< Binary bitwise operations ************************* Each of the three bitwise operations has a different priority level: and_expr ::= shift_expr | and_expr "&" shift_expr xor_expr ::= and_expr | xor_expr "^" and_expr or_expr ::= xor_expr | or_expr "|" xor_expr The "&" operator yields the bitwise AND of its arguments, which must be integers or one of them must be a custom object overriding "__and__()" or "__rand__()" special methods. The "^" operator yields the bitwise XOR (exclusive OR) of its arguments, which must be integers or one of them must be a custom object overriding "__xor__()" or "__rxor__()" special methods. The "|" operator yields the bitwise (inclusive) OR of its arguments, which must be integers or one of them must be a custom object overriding "__or__()" or "__ror__()" special methods. zbltin-code-objectsu� Code Objects ************ Code objects are used by the implementation to represent “pseudo- compiled” executable Python code such as a function body. They differ from function objects because they don’t contain a reference to their global execution environment. Code objects are returned by the built- in "compile()" function and can be extracted from function objects through their "__code__" attribute. See also the "code" module. Accessing "__code__" raises an auditing event "object.__getattr__" with arguments "obj" and ""__code__"". A code object can be executed or evaluated by passing it (instead of a source string) to the "exec()" or "eval()" built-in functions. See The standard type hierarchy for more information. zbltin-ellipsis-objecta. The Ellipsis Object ******************* This object is commonly used by slicing (see Slicings). It supports no special operations. There is exactly one ellipsis object, named "Ellipsis" (a built-in name). "type(Ellipsis)()" produces the "Ellipsis" singleton. It is written as "Ellipsis" or "...". zbltin-null-objectu The Null Object *************** This object is returned by functions that don’t explicitly return a value. It supports no special operations. There is exactly one null object, named "None" (a built-in name). "type(None)()" produces the same singleton. It is written as "None". zbltin-type-objectsu5 Type Objects ************ Type objects represent the various object types. An object’s type is accessed by the built-in function "type()". There are no special operations on types. The standard module "types" defines names for all standard built-in types. Types are written like this: "<class 'int'>". �booleansa� Boolean operations ****************** or_test ::= and_test | or_test "or" and_test and_test ::= not_test | and_test "and" not_test not_test ::= comparison | "not" not_test In the context of Boolean operations, and also when expressions are used by control flow statements, the following values are interpreted as false: "False", "None", numeric zero of all types, and empty strings and containers (including strings, tuples, lists, dictionaries, sets and frozensets). All other values are interpreted as true. User-defined objects can customize their truth value by providing a "__bool__()" method. The operator "not" yields "True" if its argument is false, "False" otherwise. The expression "x and y" first evaluates *x*; if *x* is false, its value is returned; otherwise, *y* is evaluated and the resulting value is returned. The expression "x or y" first evaluates *x*; if *x* is true, its value is returned; otherwise, *y* is evaluated and the resulting value is returned. Note that neither "and" nor "or" restrict the value and type they return to "False" and "True", but rather return the last evaluated argument. This is sometimes useful, e.g., if "s" is a string that should be replaced by a default value if it is empty, the expression "s or 'foo'" yields the desired value. Because "not" has to create a new value, it returns a boolean value regardless of the type of its argument (for example, "not 'foo'" produces "False" rather than "''".) �breaka$ The "break" statement ********************* break_stmt ::= "break" "break" may only occur syntactically nested in a "for" or "while" loop, but not nested in a function or class definition within that loop. It terminates the nearest enclosing loop, skipping the optional "else" clause if the loop has one. If a "for" loop is terminated by "break", the loop control target keeps its current value. When "break" passes control out of a "try" statement with a "finally" clause, that "finally" clause is executed before really leaving the loop. zcallable-typesu Emulating callable objects ************************** object.__call__(self[, args...]) Called when the instance is “called” as a function; if this method is defined, "x(arg1, arg2, ...)" roughly translates to "type(x).__call__(x, arg1, ...)". �callsu� Calls ***** A call calls a callable object (e.g., a *function*) with a possibly empty series of *arguments*: call ::= primary "(" [argument_list [","] | comprehension] ")" argument_list ::= positional_arguments ["," starred_and_keywords] ["," keywords_arguments] | starred_and_keywords ["," keywords_arguments] | keywords_arguments positional_arguments ::= positional_item ("," positional_item)* positional_item ::= assignment_expression | "*" expression starred_and_keywords ::= ("*" expression | keyword_item) ("," "*" expression | "," keyword_item)* keywords_arguments ::= (keyword_item | "**" expression) ("," keyword_item | "," "**" expression)* keyword_item ::= identifier "=" expression An optional trailing comma may be present after the positional and keyword arguments but does not affect the semantics. The primary must evaluate to a callable object (user-defined functions, built-in functions, methods of built-in objects, class objects, methods of class instances, and all objects having a "__call__()" method are callable). All argument expressions are evaluated before the call is attempted. Please refer to section Function definitions for the syntax of formal *parameter* lists. If keyword arguments are present, they are first converted to positional arguments, as follows. First, a list of unfilled slots is created for the formal parameters. If there are N positional arguments, they are placed in the first N slots. Next, for each keyword argument, the identifier is used to determine the corresponding slot (if the identifier is the same as the first formal parameter name, the first slot is used, and so on). If the slot is already filled, a "TypeError" exception is raised. Otherwise, the argument is placed in the slot, filling it (even if the expression is "None", it fills the slot). When all arguments have been processed, the slots that are still unfilled are filled with the corresponding default value from the function definition. (Default values are calculated, once, when the function is defined; thus, a mutable object such as a list or dictionary used as default value will be shared by all calls that don’t specify an argument value for the corresponding slot; this should usually be avoided.) If there are any unfilled slots for which no default value is specified, a "TypeError" exception is raised. Otherwise, the list of filled slots is used as the argument list for the call. **CPython implementation detail:** An implementation may provide built-in functions whose positional parameters do not have names, even if they are ‘named’ for the purpose of documentation, and which therefore cannot be supplied by keyword. In CPython, this is the case for functions implemented in C that use "PyArg_ParseTuple()" to parse their arguments. If there are more positional arguments than there are formal parameter slots, a "TypeError" exception is raised, unless a formal parameter using the syntax "*identifier" is present; in this case, that formal parameter receives a tuple containing the excess positional arguments (or an empty tuple if there were no excess positional arguments). If any keyword argument does not correspond to a formal parameter name, a "TypeError" exception is raised, unless a formal parameter using the syntax "**identifier" is present; in this case, that formal parameter receives a dictionary containing the excess keyword arguments (using the keywords as keys and the argument values as corresponding values), or a (new) empty dictionary if there were no excess keyword arguments. If the syntax "*expression" appears in the function call, "expression" must evaluate to an *iterable*. Elements from these iterables are treated as if they were additional positional arguments. For the call "f(x1, x2, *y, x3, x4)", if *y* evaluates to a sequence *y1*, …, *yM*, this is equivalent to a call with M+4 positional arguments *x1*, *x2*, *y1*, …, *yM*, *x3*, *x4*. A consequence of this is that although the "*expression" syntax may appear *after* explicit keyword arguments, it is processed *before* the keyword arguments (and any "**expression" arguments – see below). So: >>> def f(a, b): ... print(a, b) ... >>> f(b=1, *(2,)) 2 1 >>> f(a=1, *(2,)) Traceback (most recent call last): File "<stdin>", line 1, in <module> TypeError: f() got multiple values for keyword argument 'a' >>> f(1, *(2,)) 1 2 It is unusual for both keyword arguments and the "*expression" syntax to be used in the same call, so in practice this confusion does not often arise. If the syntax "**expression" appears in the function call, "expression" must evaluate to a *mapping*, the contents of which are treated as additional keyword arguments. If a parameter matching a key has already been given a value (by an explicit keyword argument, or from another unpacking), a "TypeError" exception is raised. When "**expression" is used, each key in this mapping must be a string. Each value from the mapping is assigned to the first formal parameter eligible for keyword assignment whose name is equal to the key. A key need not be a Python identifier (e.g. ""max-temp °F"" is acceptable, although it will not match any formal parameter that could be declared). If there is no match to a formal parameter the key-value pair is collected by the "**" parameter, if there is one, or if there is not, a "TypeError" exception is raised. Formal parameters using the syntax "*identifier" or "**identifier" cannot be used as positional argument slots or as keyword argument names. Changed in version 3.5: Function calls accept any number of "*" and "**" unpackings, positional arguments may follow iterable unpackings ("*"), and keyword arguments may follow dictionary unpackings ("**"). Originally proposed by **PEP 448**. A call always returns some value, possibly "None", unless it raises an exception. How this value is computed depends on the type of the callable object. If it is— a user-defined function: The code block for the function is executed, passing it the argument list. The first thing the code block will do is bind the formal parameters to the arguments; this is described in section Function definitions. When the code block executes a "return" statement, this specifies the return value of the function call. a built-in function or method: The result is up to the interpreter; see Built-in Functions for the descriptions of built-in functions and methods. a class object: A new instance of that class is returned. a class instance method: The corresponding user-defined function is called, with an argument list that is one longer than the argument list of the call: the instance becomes the first argument. a class instance: The class must define a "__call__()" method; the effect is then the same as if that method was called. �classu� Class definitions ***************** A class definition defines a class object (see section The standard type hierarchy): classdef ::= [decorators] "class" classname [inheritance] ":" suite inheritance ::= "(" [argument_list] ")" classname ::= identifier A class definition is an executable statement. The inheritance list usually gives a list of base classes (see Metaclasses for more advanced uses), so each item in the list should evaluate to a class object which allows subclassing. Classes without an inheritance list inherit, by default, from the base class "object"; hence, class Foo: pass is equivalent to class Foo(object): pass The class’s suite is then executed in a new execution frame (see Naming and binding), using a newly created local namespace and the original global namespace. (Usually, the suite contains mostly function definitions.) When the class’s suite finishes execution, its execution frame is discarded but its local namespace is saved. [5] A class object is then created using the inheritance list for the base classes and the saved local namespace for the attribute dictionary. The class name is bound to this class object in the original local namespace. The order in which attributes are defined in the class body is preserved in the new class’s "__dict__". Note that this is reliable only right after the class is created and only for classes that were defined using the definition syntax. Class creation can be customized heavily using metaclasses. Classes can also be decorated: just like when decorating functions, @f1(arg) @f2 class Foo: pass is roughly equivalent to class Foo: pass Foo = f1(arg)(f2(Foo)) The evaluation rules for the decorator expressions are the same as for function decorators. The result is then bound to the class name. Changed in version 3.9: Classes may be decorated with any valid "assignment_expression". Previously, the grammar was much more restrictive; see **PEP 614** for details. **Programmer’s note:** Variables defined in the class definition are class attributes; they are shared by instances. Instance attributes can be set in a method with "self.name = value". Both class and instance attributes are accessible through the notation “"self.name"”, and an instance attribute hides a class attribute with the same name when accessed in this way. Class attributes can be used as defaults for instance attributes, but using mutable values there can lead to unexpected results. Descriptors can be used to create instance variables with different implementation details. See also: **PEP 3115** - Metaclasses in Python 3000 The proposal that changed the declaration of metaclasses to the current syntax, and the semantics for how classes with metaclasses are constructed. **PEP 3129** - Class Decorators The proposal that added class decorators. Function and method decorators were introduced in **PEP 318**. � comparisonsu( Comparisons *********** Unlike C, all comparison operations in Python have the same priority, which is lower than that of any arithmetic, shifting or bitwise operation. Also unlike C, expressions like "a < b < c" have the interpretation that is conventional in mathematics: comparison ::= or_expr (comp_operator or_expr)* comp_operator ::= "<" | ">" | "==" | ">=" | "<=" | "!=" | "is" ["not"] | ["not"] "in" Comparisons yield boolean values: "True" or "False". Custom *rich comparison methods* may return non-boolean values. In this case Python will call "bool()" on such value in boolean contexts. Comparisons can be chained arbitrarily, e.g., "x < y <= z" is equivalent to "x < y and y <= z", except that "y" is evaluated only once (but in both cases "z" is not evaluated at all when "x < y" is found to be false). Formally, if *a*, *b*, *c*, …, *y*, *z* are expressions and *op1*, *op2*, …, *opN* are comparison operators, then "a op1 b op2 c ... y opN z" is equivalent to "a op1 b and b op2 c and ... y opN z", except that each expression is evaluated at most once. Note that "a op1 b op2 c" doesn’t imply any kind of comparison between *a* and *c*, so that, e.g., "x < y > z" is perfectly legal (though perhaps not pretty). Value comparisons ================= The operators "<", ">", "==", ">=", "<=", and "!=" compare the values of two objects. The objects do not need to have the same type. Chapter Objects, values and types states that objects have a value (in addition to type and identity). The value of an object is a rather abstract notion in Python: For example, there is no canonical access method for an object’s value. Also, there is no requirement that the value of an object should be constructed in a particular way, e.g. comprised of all its data attributes. Comparison operators implement a particular notion of what the value of an object is. One can think of them as defining the value of an object indirectly, by means of their comparison implementation. Because all types are (direct or indirect) subtypes of "object", they inherit the default comparison behavior from "object". Types can customize their comparison behavior by implementing *rich comparison methods* like "__lt__()", described in Basic customization. The default behavior for equality comparison ("==" and "!=") is based on the identity of the objects. Hence, equality comparison of instances with the same identity results in equality, and equality comparison of instances with different identities results in inequality. A motivation for this default behavior is the desire that all objects should be reflexive (i.e. "x is y" implies "x == y"). A default order comparison ("<", ">", "<=", and ">=") is not provided; an attempt raises "TypeError". A motivation for this default behavior is the lack of a similar invariant as for equality. The behavior of the default equality comparison, that instances with different identities are always unequal, may be in contrast to what types will need that have a sensible definition of object value and value-based equality. Such types will need to customize their comparison behavior, and in fact, a number of built-in types have done that. The following list describes the comparison behavior of the most important built-in types. * Numbers of built-in numeric types (Numeric Types — int, float, complex) and of the standard library types "fractions.Fraction" and "decimal.Decimal" can be compared within and across their types, with the restriction that complex numbers do not support order comparison. Within the limits of the types involved, they compare mathematically (algorithmically) correct without loss of precision. The not-a-number values "float('NaN')" and "decimal.Decimal('NaN')" are special. Any ordered comparison of a number to a not-a-number value is false. A counter-intuitive implication is that not-a-number values are not equal to themselves. For example, if "x = float('NaN')", "3 < x", "x < 3" and "x == x" are all false, while "x != x" is true. This behavior is compliant with IEEE 754. * "None" and "NotImplemented" are singletons. **PEP 8** advises that comparisons for singletons should always be done with "is" or "is not", never the equality operators. * Binary sequences (instances of "bytes" or "bytearray") can be compared within and across their types. They compare lexicographically using the numeric values of their elements. * Strings (instances of "str") compare lexicographically using the numerical Unicode code points (the result of the built-in function "ord()") of their characters. [3] Strings and binary sequences cannot be directly compared. * Sequences (instances of "tuple", "list", or "range") can be compared only within each of their types, with the restriction that ranges do not support order comparison. Equality comparison across these types results in inequality, and ordering comparison across these types raises "TypeError". Sequences compare lexicographically using comparison of corresponding elements. The built-in containers typically assume identical objects are equal to themselves. That lets them bypass equality tests for identical objects to improve performance and to maintain their internal invariants. Lexicographical comparison between built-in collections works as follows: * For two collections to compare equal, they must be of the same type, have the same length, and each pair of corresponding elements must compare equal (for example, "[1,2] == (1,2)" is false because the type is not the same). * Collections that support order comparison are ordered the same as their first unequal elements (for example, "[1,2,x] <= [1,2,y]" has the same value as "x <= y"). If a corresponding element does not exist, the shorter collection is ordered first (for example, "[1,2] < [1,2,3]" is true). * Mappings (instances of "dict") compare equal if and only if they have equal "(key, value)" pairs. Equality comparison of the keys and values enforces reflexivity. Order comparisons ("<", ">", "<=", and ">=") raise "TypeError". * Sets (instances of "set" or "frozenset") can be compared within and across their types. They define order comparison operators to mean subset and superset tests. Those relations do not define total orderings (for example, the two sets "{1,2}" and "{2,3}" are not equal, nor subsets of one another, nor supersets of one another). Accordingly, sets are not appropriate arguments for functions which depend on total ordering (for example, "min()", "max()", and "sorted()" produce undefined results given a list of sets as inputs). Comparison of sets enforces reflexivity of its elements. * Most other built-in types have no comparison methods implemented, so they inherit the default comparison behavior. User-defined classes that customize their comparison behavior should follow some consistency rules, if possible: * Equality comparison should be reflexive. In other words, identical objects should compare equal: "x is y" implies "x == y" * Comparison should be symmetric. In other words, the following expressions should have the same result: "x == y" and "y == x" "x != y" and "y != x" "x < y" and "y > x" "x <= y" and "y >= x" * Comparison should be transitive. The following (non-exhaustive) examples illustrate that: "x > y and y > z" implies "x > z" "x < y and y <= z" implies "x < z" * Inverse comparison should result in the boolean negation. In other words, the following expressions should have the same result: "x == y" and "not x != y" "x < y" and "not x >= y" (for total ordering) "x > y" and "not x <= y" (for total ordering) The last two expressions apply to totally ordered collections (e.g. to sequences, but not to sets or mappings). See also the "total_ordering()" decorator. * The "hash()" result should be consistent with equality. Objects that are equal should either have the same hash value, or be marked as unhashable. Python does not enforce these consistency rules. In fact, the not-a-number values are an example for not following these rules. Membership test operations ========================== The operators "in" and "not in" test for membership. "x in s" evaluates to "True" if *x* is a member of *s*, and "False" otherwise. "x not in s" returns the negation of "x in s". All built-in sequences and set types support this as well as dictionary, for which "in" tests whether the dictionary has a given key. For container types such as list, tuple, set, frozenset, dict, or collections.deque, the expression "x in y" is equivalent to "any(x is e or x == e for e in y)". For the string and bytes types, "x in y" is "True" if and only if *x* is a substring of *y*. An equivalent test is "y.find(x) != -1". Empty strings are always considered to be a substring of any other string, so """ in "abc"" will return "True". For user-defined classes which define the "__contains__()" method, "x in y" returns "True" if "y.__contains__(x)" returns a true value, and "False" otherwise. For user-defined classes which do not define "__contains__()" but do define "__iter__()", "x in y" is "True" if some value "z", for which the expression "x is z or x == z" is true, is produced while iterating over "y". If an exception is raised during the iteration, it is as if "in" raised that exception. Lastly, the old-style iteration protocol is tried: if a class defines "__getitem__()", "x in y" is "True" if and only if there is a non- negative integer index *i* such that "x is y[i] or x == y[i]", and no lower integer index raises the "IndexError" exception. (If any other exception is raised, it is as if "in" raised that exception). The operator "not in" is defined to have the inverse truth value of "in". Identity comparisons ==================== The operators "is" and "is not" test for an object’s identity: "x is y" is true if and only if *x* and *y* are the same object. An Object’s identity is determined using the "id()" function. "x is not y" yields the inverse truth value. [4] �compoundu�� Compound statements ******************* Compound statements contain (groups of) other statements; they affect or control the execution of those other statements in some way. In general, compound statements span multiple lines, although in simple incarnations a whole compound statement may be contained in one line. The "if", "while" and "for" statements implement traditional control flow constructs. "try" specifies exception handlers and/or cleanup code for a group of statements, while the "with" statement allows the execution of initialization and finalization code around a block of code. Function and class definitions are also syntactically compound statements. A compound statement consists of one or more ‘clauses.’ A clause consists of a header and a ‘suite.’ The clause headers of a particular compound statement are all at the same indentation level. Each clause header begins with a uniquely identifying keyword and ends with a colon. A suite is a group of statements controlled by a clause. A suite can be one or more semicolon-separated simple statements on the same line as the header, following the header’s colon, or it can be one or more indented statements on subsequent lines. Only the latter form of a suite can contain nested compound statements; the following is illegal, mostly because it wouldn’t be clear to which "if" clause a following "else" clause would belong: if test1: if test2: print(x) Also note that the semicolon binds tighter than the colon in this context, so that in the following example, either all or none of the "print()" calls are executed: if x < y < z: print(x); print(y); print(z) Summarizing: compound_stmt ::= if_stmt | while_stmt | for_stmt | try_stmt | with_stmt | match_stmt | funcdef | classdef | async_with_stmt | async_for_stmt | async_funcdef suite ::= stmt_list NEWLINE | NEWLINE INDENT statement+ DEDENT statement ::= stmt_list NEWLINE | compound_stmt stmt_list ::= simple_stmt (";" simple_stmt)* [";"] Note that statements always end in a "NEWLINE" possibly followed by a "DEDENT". Also note that optional continuation clauses always begin with a keyword that cannot start a statement, thus there are no ambiguities (the ‘dangling "else"’ problem is solved in Python by requiring nested "if" statements to be indented). The formatting of the grammar rules in the following sections places each clause on a separate line for clarity. The "if" statement ================== The "if" statement is used for conditional execution: if_stmt ::= "if" assignment_expression ":" suite ("elif" assignment_expression ":" suite)* ["else" ":" suite] It selects exactly one of the suites by evaluating the expressions one by one until one is found to be true (see section Boolean operations for the definition of true and false); then that suite is executed (and no other part of the "if" statement is executed or evaluated). If all expressions are false, the suite of the "else" clause, if present, is executed. The "while" statement ===================== The "while" statement is used for repeated execution as long as an expression is true: while_stmt ::= "while" assignment_expression ":" suite ["else" ":" suite] This repeatedly tests the expression and, if it is true, executes the first suite; if the expression is false (which may be the first time it is tested) the suite of the "else" clause, if present, is executed and the loop terminates. A "break" statement executed in the first suite terminates the loop without executing the "else" clause’s suite. A "continue" statement executed in the first suite skips the rest of the suite and goes back to testing the expression. The "for" statement =================== The "for" statement is used to iterate over the elements of a sequence (such as a string, tuple or list) or other iterable object: for_stmt ::= "for" target_list "in" starred_list ":" suite ["else" ":" suite] The "starred_list" expression is evaluated once; it should yield an *iterable* object. An *iterator* is created for that iterable. The first item provided by the iterator is then assigned to the target list using the standard rules for assignments (see Assignment statements), and the suite is executed. This repeats for each item provided by the iterator. When the iterator is exhausted, the suite in the "else" clause, if present, is executed, and the loop terminates. A "break" statement executed in the first suite terminates the loop without executing the "else" clause’s suite. A "continue" statement executed in the first suite skips the rest of the suite and continues with the next item, or with the "else" clause if there is no next item. The for-loop makes assignments to the variables in the target list. This overwrites all previous assignments to those variables including those made in the suite of the for-loop: for i in range(10): print(i) i = 5 # this will not affect the for-loop # because i will be overwritten with the next # index in the range Names in the target list are not deleted when the loop is finished, but if the sequence is empty, they will not have been assigned to at all by the loop. Hint: the built-in type "range()" represents immutable arithmetic sequences of integers. For instance, iterating "range(3)" successively yields 0, 1, and then 2. Changed in version 3.11: Starred elements are now allowed in the expression list. The "try" statement =================== The "try" statement specifies exception handlers and/or cleanup code for a group of statements: try_stmt ::= try1_stmt | try2_stmt | try3_stmt try1_stmt ::= "try" ":" suite ("except" [expression ["as" identifier]] ":" suite)+ ["else" ":" suite] ["finally" ":" suite] try2_stmt ::= "try" ":" suite ("except" "*" expression ["as" identifier] ":" suite)+ ["else" ":" suite] ["finally" ":" suite] try3_stmt ::= "try" ":" suite "finally" ":" suite Additional information on exceptions can be found in section Exceptions, and information on using the "raise" statement to generate exceptions may be found in section The raise statement. "except" clause --------------- The "except" clause(s) specify one or more exception handlers. When no exception occurs in the "try" clause, no exception handler is executed. When an exception occurs in the "try" suite, a search for an exception handler is started. This search inspects the "except" clauses in turn until one is found that matches the exception. An expression-less "except" clause, if present, must be last; it matches any exception. For an "except" clause with an expression, that expression is evaluated, and the clause matches the exception if the resulting object is “compatible” with the exception. An object is compatible with an exception if the object is the class or a *non- virtual base class* of the exception object, or a tuple containing an item that is the class or a non-virtual base class of the exception object. If no "except" clause matches the exception, the search for an exception handler continues in the surrounding code and on the invocation stack. [1] If the evaluation of an expression in the header of an "except" clause raises an exception, the original search for a handler is canceled and a search starts for the new exception in the surrounding code and on the call stack (it is treated as if the entire "try" statement raised the exception). When a matching "except" clause is found, the exception is assigned to the target specified after the "as" keyword in that "except" clause, if present, and the "except" clause’s suite is executed. All "except" clauses must have an executable block. When the end of this block is reached, execution continues normally after the entire "try" statement. (This means that if two nested handlers exist for the same exception, and the exception occurs in the "try" clause of the inner handler, the outer handler will not handle the exception.) When an exception has been assigned using "as target", it is cleared at the end of the "except" clause. This is as if except E as N: foo was translated to except E as N: try: foo finally: del N This means the exception must be assigned to a different name to be able to refer to it after the "except" clause. Exceptions are cleared because with the traceback attached to them, they form a reference cycle with the stack frame, keeping all locals in that frame alive until the next garbage collection occurs. Before an "except" clause’s suite is executed, details about the exception are stored in the "sys" module and can be accessed via "sys.exc_info()". "sys.exc_info()" returns a 3-tuple consisting of the exception class, the exception instance and a traceback object (see section The standard type hierarchy) identifying the point in the program where the exception occurred. The details about the exception accessed via "sys.exc_info()" are restored to their previous values when leaving an exception handler: >>> print(sys.exc_info()) (None, None, None) >>> try: ... raise TypeError ... except: ... print(sys.exc_info()) ... try: ... raise ValueError ... except: ... print(sys.exc_info()) ... print(sys.exc_info()) ... (<class 'TypeError'>, TypeError(), <traceback object at 0x10efad080>) (<class 'ValueError'>, ValueError(), <traceback object at 0x10efad040>) (<class 'TypeError'>, TypeError(), <traceback object at 0x10efad080>) >>> print(sys.exc_info()) (None, None, None) "except*" clause ---------------- The "except*" clause(s) are used for handling "ExceptionGroup"s. The exception type for matching is interpreted as in the case of "except", but in the case of exception groups we can have partial matches when the type matches some of the exceptions in the group. This means that multiple "except*" clauses can execute, each handling part of the exception group. Each clause executes at most once and handles an exception group of all matching exceptions. Each exception in the group is handled by at most one "except*" clause, the first that matches it. >>> try: ... raise ExceptionGroup("eg", ... [ValueError(1), TypeError(2), OSError(3), OSError(4)]) ... except* TypeError as e: ... print(f'caught {type(e)} with nested {e.exceptions}') ... except* OSError as e: ... print(f'caught {type(e)} with nested {e.exceptions}') ... caught <class 'ExceptionGroup'> with nested (TypeError(2),) caught <class 'ExceptionGroup'> with nested (OSError(3), OSError(4)) + Exception Group Traceback (most recent call last): | File "<stdin>", line 2, in <module> | ExceptionGroup: eg +-+---------------- 1 ---------------- | ValueError: 1 +------------------------------------ Any remaining exceptions that were not handled by any "except*" clause are re-raised at the end, combined into an exception group along with all exceptions that were raised from within "except*" clauses. If the raised exception is not an exception group and its type matches one of the "except*" clauses, it is caught and wrapped by an exception group with an empty message string. >>> try: ... raise BlockingIOError ... except* BlockingIOError as e: ... print(repr(e)) ... ExceptionGroup('', (BlockingIOError())) An "except*" clause must have a matching type, and this type cannot be a subclass of "BaseExceptionGroup". It is not possible to mix "except" and "except*" in the same "try". "break", "continue" and "return" cannot appear in an "except*" clause. "else" clause ------------- The optional "else" clause is executed if the control flow leaves the "try" suite, no exception was raised, and no "return", "continue", or "break" statement was executed. Exceptions in the "else" clause are not handled by the preceding "except" clauses. "finally" clause ---------------- If "finally" is present, it specifies a ‘cleanup’ handler. The "try" clause is executed, including any "except" and "else" clauses. If an exception occurs in any of the clauses and is not handled, the exception is temporarily saved. The "finally" clause is executed. If there is a saved exception it is re-raised at the end of the "finally" clause. If the "finally" clause raises another exception, the saved exception is set as the context of the new exception. If the "finally" clause executes a "return", "break" or "continue" statement, the saved exception is discarded: >>> def f(): ... try: ... 1/0 ... finally: ... return 42 ... >>> f() 42 The exception information is not available to the program during execution of the "finally" clause. When a "return", "break" or "continue" statement is executed in the "try" suite of a "try"…"finally" statement, the "finally" clause is also executed ‘on the way out.’ The return value of a function is determined by the last "return" statement executed. Since the "finally" clause always executes, a "return" statement executed in the "finally" clause will always be the last one executed: >>> def foo(): ... try: ... return 'try' ... finally: ... return 'finally' ... >>> foo() 'finally' Changed in version 3.8: Prior to Python 3.8, a "continue" statement was illegal in the "finally" clause due to a problem with the implementation. The "with" statement ==================== The "with" statement is used to wrap the execution of a block with methods defined by a context manager (see section With Statement Context Managers). This allows common "try"…"except"…"finally" usage patterns to be encapsulated for convenient reuse. with_stmt ::= "with" ( "(" with_stmt_contents ","? ")" | with_stmt_contents ) ":" suite with_stmt_contents ::= with_item ("," with_item)* with_item ::= expression ["as" target] The execution of the "with" statement with one “item” proceeds as follows: 1. The context expression (the expression given in the "with_item") is evaluated to obtain a context manager. 2. The context manager’s "__enter__()" is loaded for later use. 3. The context manager’s "__exit__()" is loaded for later use. 4. The context manager’s "__enter__()" method is invoked. 5. If a target was included in the "with" statement, the return value from "__enter__()" is assigned to it. Note: The "with" statement guarantees that if the "__enter__()" method returns without an error, then "__exit__()" will always be called. Thus, if an error occurs during the assignment to the target list, it will be treated the same as an error occurring within the suite would be. See step 7 below. 6. The suite is executed. 7. The context manager’s "__exit__()" method is invoked. If an exception caused the suite to be exited, its type, value, and traceback are passed as arguments to "__exit__()". Otherwise, three "None" arguments are supplied. If the suite was exited due to an exception, and the return value from the "__exit__()" method was false, the exception is reraised. If the return value was true, the exception is suppressed, and execution continues with the statement following the "with" statement. If the suite was exited for any reason other than an exception, the return value from "__exit__()" is ignored, and execution proceeds at the normal location for the kind of exit that was taken. The following code: with EXPRESSION as TARGET: SUITE is semantically equivalent to: manager = (EXPRESSION) enter = type(manager).__enter__ exit = type(manager).__exit__ value = enter(manager) hit_except = False try: TARGET = value SUITE except: hit_except = True if not exit(manager, *sys.exc_info()): raise finally: if not hit_except: exit(manager, None, None, None) With more than one item, the context managers are processed as if multiple "with" statements were nested: with A() as a, B() as b: SUITE is semantically equivalent to: with A() as a: with B() as b: SUITE You can also write multi-item context managers in multiple lines if the items are surrounded by parentheses. For example: with ( A() as a, B() as b, ): SUITE Changed in version 3.1: Support for multiple context expressions. Changed in version 3.10: Support for using grouping parentheses to break the statement in multiple lines. See also: **PEP 343** - The “with” statement The specification, background, and examples for the Python "with" statement. The "match" statement ===================== New in version 3.10. The match statement is used for pattern matching. Syntax: match_stmt ::= 'match' subject_expr ":" NEWLINE INDENT case_block+ DEDENT subject_expr ::= star_named_expression "," star_named_expressions? | named_expression case_block ::= 'case' patterns [guard] ":" block Note: This section uses single quotes to denote soft keywords. Pattern matching takes a pattern as input (following "case") and a subject value (following "match"). The pattern (which may contain subpatterns) is matched against the subject value. The outcomes are: * A match success or failure (also termed a pattern success or failure). * Possible binding of matched values to a name. The prerequisites for this are further discussed below. The "match" and "case" keywords are soft keywords. See also: * **PEP 634** – Structural Pattern Matching: Specification * **PEP 636** – Structural Pattern Matching: Tutorial Overview -------- Here’s an overview of the logical flow of a match statement: 1. The subject expression "subject_expr" is evaluated and a resulting subject value obtained. If the subject expression contains a comma, a tuple is constructed using the standard rules. 2. Each pattern in a "case_block" is attempted to match with the subject value. The specific rules for success or failure are described below. The match attempt can also bind some or all of the standalone names within the pattern. The precise pattern binding rules vary per pattern type and are specified below. **Name bindings made during a successful pattern match outlive the executed block and can be used after the match statement**. Note: During failed pattern matches, some subpatterns may succeed. Do not rely on bindings being made for a failed match. Conversely, do not rely on variables remaining unchanged after a failed match. The exact behavior is dependent on implementation and may vary. This is an intentional decision made to allow different implementations to add optimizations. 3. If the pattern succeeds, the corresponding guard (if present) is evaluated. In this case all name bindings are guaranteed to have happened. * If the guard evaluates as true or is missing, the "block" inside "case_block" is executed. * Otherwise, the next "case_block" is attempted as described above. * If there are no further case blocks, the match statement is completed. Note: Users should generally never rely on a pattern being evaluated. Depending on implementation, the interpreter may cache values or use other optimizations which skip repeated evaluations. A sample match statement: >>> flag = False >>> match (100, 200): ... case (100, 300): # Mismatch: 200 != 300 ... print('Case 1') ... case (100, 200) if flag: # Successful match, but guard fails ... print('Case 2') ... case (100, y): # Matches and binds y to 200 ... print(f'Case 3, y: {y}') ... case _: # Pattern not attempted ... print('Case 4, I match anything!') ... Case 3, y: 200 In this case, "if flag" is a guard. Read more about that in the next section. Guards ------ guard ::= "if" named_expression A "guard" (which is part of the "case") must succeed for code inside the "case" block to execute. It takes the form: "if" followed by an expression. The logical flow of a "case" block with a "guard" follows: 1. Check that the pattern in the "case" block succeeded. If the pattern failed, the "guard" is not evaluated and the next "case" block is checked. 2. If the pattern succeeded, evaluate the "guard". * If the "guard" condition evaluates as true, the case block is selected. * If the "guard" condition evaluates as false, the case block is not selected. * If the "guard" raises an exception during evaluation, the exception bubbles up. Guards are allowed to have side effects as they are expressions. Guard evaluation must proceed from the first to the last case block, one at a time, skipping case blocks whose pattern(s) don’t all succeed. (I.e., guard evaluation must happen in order.) Guard evaluation must stop once a case block is selected. Irrefutable Case Blocks ----------------------- An irrefutable case block is a match-all case block. A match statement may have at most one irrefutable case block, and it must be last. A case block is considered irrefutable if it has no guard and its pattern is irrefutable. A pattern is considered irrefutable if we can prove from its syntax alone that it will always succeed. Only the following patterns are irrefutable: * AS Patterns whose left-hand side is irrefutable * OR Patterns containing at least one irrefutable pattern * Capture Patterns * Wildcard Patterns * parenthesized irrefutable patterns Patterns -------- Note: This section uses grammar notations beyond standard EBNF: * the notation "SEP.RULE+" is shorthand for "RULE (SEP RULE)*" * the notation "!RULE" is shorthand for a negative lookahead assertion The top-level syntax for "patterns" is: patterns ::= open_sequence_pattern | pattern pattern ::= as_pattern | or_pattern closed_pattern ::= | literal_pattern | capture_pattern | wildcard_pattern | value_pattern | group_pattern | sequence_pattern | mapping_pattern | class_pattern The descriptions below will include a description “in simple terms” of what a pattern does for illustration purposes (credits to Raymond Hettinger for a document that inspired most of the descriptions). Note that these descriptions are purely for illustration purposes and **may not** reflect the underlying implementation. Furthermore, they do not cover all valid forms. OR Patterns ~~~~~~~~~~~ An OR pattern is two or more patterns separated by vertical bars "|". Syntax: or_pattern ::= "|".closed_pattern+ Only the final subpattern may be irrefutable, and each subpattern must bind the same set of names to avoid ambiguity. An OR pattern matches each of its subpatterns in turn to the subject value, until one succeeds. The OR pattern is then considered successful. Otherwise, if none of the subpatterns succeed, the OR pattern fails. In simple terms, "P1 | P2 | ..." will try to match "P1", if it fails it will try to match "P2", succeeding immediately if any succeeds, failing otherwise. AS Patterns ~~~~~~~~~~~ An AS pattern matches an OR pattern on the left of the "as" keyword against a subject. Syntax: as_pattern ::= or_pattern "as" capture_pattern If the OR pattern fails, the AS pattern fails. Otherwise, the AS pattern binds the subject to the name on the right of the as keyword and succeeds. "capture_pattern" cannot be a a "_". In simple terms "P as NAME" will match with "P", and on success it will set "NAME = <subject>". Literal Patterns ~~~~~~~~~~~~~~~~ A literal pattern corresponds to most literals in Python. Syntax: literal_pattern ::= signed_number | signed_number "+" NUMBER | signed_number "-" NUMBER | strings | "None" | "True" | "False" | signed_number: NUMBER | "-" NUMBER The rule "strings" and the token "NUMBER" are defined in the standard Python grammar. Triple-quoted strings are supported. Raw strings and byte strings are supported. Formatted string literals are not supported. The forms "signed_number '+' NUMBER" and "signed_number '-' NUMBER" are for expressing complex numbers; they require a real number on the left and an imaginary number on the right. E.g. "3 + 4j". In simple terms, "LITERAL" will succeed only if "<subject> == LITERAL". For the singletons "None", "True" and "False", the "is" operator is used. Capture Patterns ~~~~~~~~~~~~~~~~ A capture pattern binds the subject value to a name. Syntax: capture_pattern ::= !'_' NAME A single underscore "_" is not a capture pattern (this is what "!'_'" expresses). It is instead treated as a "wildcard_pattern". In a given pattern, a given name can only be bound once. E.g. "case x, x: ..." is invalid while "case [x] | x: ..." is allowed. Capture patterns always succeed. The binding follows scoping rules established by the assignment expression operator in **PEP 572**; the name becomes a local variable in the closest containing function scope unless there’s an applicable "global" or "nonlocal" statement. In simple terms "NAME" will always succeed and it will set "NAME = <subject>". Wildcard Patterns ~~~~~~~~~~~~~~~~~ A wildcard pattern always succeeds (matches anything) and binds no name. Syntax: wildcard_pattern ::= '_' "_" is a soft keyword within any pattern, but only within patterns. It is an identifier, as usual, even within "match" subject expressions, "guard"s, and "case" blocks. In simple terms, "_" will always succeed. Value Patterns ~~~~~~~~~~~~~~ A value pattern represents a named value in Python. Syntax: value_pattern ::= attr attr ::= name_or_attr "." NAME name_or_attr ::= attr | NAME The dotted name in the pattern is looked up using standard Python name resolution rules. The pattern succeeds if the value found compares equal to the subject value (using the "==" equality operator). In simple terms "NAME1.NAME2" will succeed only if "<subject> == NAME1.NAME2" Note: If the same value occurs multiple times in the same match statement, the interpreter may cache the first value found and reuse it rather than repeat the same lookup. This cache is strictly tied to a given execution of a given match statement. Group Patterns ~~~~~~~~~~~~~~ A group pattern allows users to add parentheses around patterns to emphasize the intended grouping. Otherwise, it has no additional syntax. Syntax: group_pattern ::= "(" pattern ")" In simple terms "(P)" has the same effect as "P". Sequence Patterns ~~~~~~~~~~~~~~~~~ A sequence pattern contains several subpatterns to be matched against sequence elements. The syntax is similar to the unpacking of a list or tuple. sequence_pattern ::= "[" [maybe_sequence_pattern] "]" | "(" [open_sequence_pattern] ")" open_sequence_pattern ::= maybe_star_pattern "," [maybe_sequence_pattern] maybe_sequence_pattern ::= ",".maybe_star_pattern+ ","? maybe_star_pattern ::= star_pattern | pattern star_pattern ::= "*" (capture_pattern | wildcard_pattern) There is no difference if parentheses or square brackets are used for sequence patterns (i.e. "(...)" vs "[...]" ). Note: A single pattern enclosed in parentheses without a trailing comma (e.g. "(3 | 4)") is a group pattern. While a single pattern enclosed in square brackets (e.g. "[3 | 4]") is still a sequence pattern. At most one star subpattern may be in a sequence pattern. The star subpattern may occur in any position. If no star subpattern is present, the sequence pattern is a fixed-length sequence pattern; otherwise it is a variable-length sequence pattern. The following is the logical flow for matching a sequence pattern against a subject value: 1. If the subject value is not a sequence [2], the sequence pattern fails. 2. If the subject value is an instance of "str", "bytes" or "bytearray" the sequence pattern fails. 3. The subsequent steps depend on whether the sequence pattern is fixed or variable-length. If the sequence pattern is fixed-length: 1. If the length of the subject sequence is not equal to the number of subpatterns, the sequence pattern fails 2. Subpatterns in the sequence pattern are matched to their corresponding items in the subject sequence from left to right. Matching stops as soon as a subpattern fails. If all subpatterns succeed in matching their corresponding item, the sequence pattern succeeds. Otherwise, if the sequence pattern is variable-length: 1. If the length of the subject sequence is less than the number of non-star subpatterns, the sequence pattern fails. 2. The leading non-star subpatterns are matched to their corresponding items as for fixed-length sequences. 3. If the previous step succeeds, the star subpattern matches a list formed of the remaining subject items, excluding the remaining items corresponding to non-star subpatterns following the star subpattern. 4. Remaining non-star subpatterns are matched to their corresponding subject items, as for a fixed-length sequence. Note: The length of the subject sequence is obtained via "len()" (i.e. via the "__len__()" protocol). This length may be cached by the interpreter in a similar manner as value patterns. In simple terms "[P1, P2, P3," … ", P<N>]" matches only if all the following happens: * check "<subject>" is a sequence * "len(subject) == <N>" * "P1" matches "<subject>[0]" (note that this match can also bind names) * "P2" matches "<subject>[1]" (note that this match can also bind names) * … and so on for the corresponding pattern/element. Mapping Patterns ~~~~~~~~~~~~~~~~ A mapping pattern contains one or more key-value patterns. The syntax is similar to the construction of a dictionary. Syntax: mapping_pattern ::= "{" [items_pattern] "}" items_pattern ::= ",".key_value_pattern+ ","? key_value_pattern ::= (literal_pattern | value_pattern) ":" pattern | double_star_pattern double_star_pattern ::= "**" capture_pattern At most one double star pattern may be in a mapping pattern. The double star pattern must be the last subpattern in the mapping pattern. Duplicate keys in mapping patterns are disallowed. Duplicate literal keys will raise a "SyntaxError". Two keys that otherwise have the same value will raise a "ValueError" at runtime. The following is the logical flow for matching a mapping pattern against a subject value: 1. If the subject value is not a mapping [3],the mapping pattern fails. 2. If every key given in the mapping pattern is present in the subject mapping, and the pattern for each key matches the corresponding item of the subject mapping, the mapping pattern succeeds. 3. If duplicate keys are detected in the mapping pattern, the pattern is considered invalid. A "SyntaxError" is raised for duplicate literal values; or a "ValueError" for named keys of the same value. Note: Key-value pairs are matched using the two-argument form of the mapping subject’s "get()" method. Matched key-value pairs must already be present in the mapping, and not created on-the-fly via "__missing__()" or "__getitem__()". In simple terms "{KEY1: P1, KEY2: P2, ... }" matches only if all the following happens: * check "<subject>" is a mapping * "KEY1 in <subject>" * "P1" matches "<subject>[KEY1]" * … and so on for the corresponding KEY/pattern pair. Class Patterns ~~~~~~~~~~~~~~ A class pattern represents a class and its positional and keyword arguments (if any). Syntax: class_pattern ::= name_or_attr "(" [pattern_arguments ","?] ")" pattern_arguments ::= positional_patterns ["," keyword_patterns] | keyword_patterns positional_patterns ::= ",".pattern+ keyword_patterns ::= ",".keyword_pattern+ keyword_pattern ::= NAME "=" pattern The same keyword should not be repeated in class patterns. The following is the logical flow for matching a class pattern against a subject value: 1. If "name_or_attr" is not an instance of the builtin "type" , raise "TypeError". 2. If the subject value is not an instance of "name_or_attr" (tested via "isinstance()"), the class pattern fails. 3. If no pattern arguments are present, the pattern succeeds. Otherwise, the subsequent steps depend on whether keyword or positional argument patterns are present. For a number of built-in types (specified below), a single positional subpattern is accepted which will match the entire subject; for these types keyword patterns also work as for other types. If only keyword patterns are present, they are processed as follows, one by one: I. The keyword is looked up as an attribute on the subject. * If this raises an exception other than "AttributeError", the exception bubbles up. * If this raises "AttributeError", the class pattern has failed. * Else, the subpattern associated with the keyword pattern is matched against the subject’s attribute value. If this fails, the class pattern fails; if this succeeds, the match proceeds to the next keyword. II. If all keyword patterns succeed, the class pattern succeeds. If any positional patterns are present, they are converted to keyword patterns using the "__match_args__" attribute on the class "name_or_attr" before matching: I. The equivalent of "getattr(cls, "__match_args__", ())" is called. * If this raises an exception, the exception bubbles up. * If the returned value is not a tuple, the conversion fails and "TypeError" is raised. * If there are more positional patterns than "len(cls.__match_args__)", "TypeError" is raised. * Otherwise, positional pattern "i" is converted to a keyword pattern using "__match_args__[i]" as the keyword. "__match_args__[i]" must be a string; if not "TypeError" is raised. * If there are duplicate keywords, "TypeError" is raised. See also: Customizing positional arguments in class pattern matching II. Once all positional patterns have been converted to keyword patterns, the match proceeds as if there were only keyword patterns. For the following built-in types the handling of positional subpatterns is different: * "bool" * "bytearray" * "bytes" * "dict" * "float" * "frozenset" * "int" * "list" * "set" * "str" * "tuple" These classes accept a single positional argument, and the pattern there is matched against the whole object rather than an attribute. For example "int(0|1)" matches the value "0", but not the value "0.0". In simple terms "CLS(P1, attr=P2)" matches only if the following happens: * "isinstance(<subject>, CLS)" * convert "P1" to a keyword pattern using "CLS.__match_args__" * For each keyword argument "attr=P2": * "hasattr(<subject>, "attr")" * "P2" matches "<subject>.attr" * … and so on for the corresponding keyword argument/pattern pair. See also: * **PEP 634** – Structural Pattern Matching: Specification * **PEP 636** – Structural Pattern Matching: Tutorial Function definitions ==================== A function definition defines a user-defined function object (see section The standard type hierarchy): funcdef ::= [decorators] "def" funcname "(" [parameter_list] ")" ["->" expression] ":" suite decorators ::= decorator+ decorator ::= "@" assignment_expression NEWLINE parameter_list ::= defparameter ("," defparameter)* "," "/" ["," [parameter_list_no_posonly]] | parameter_list_no_posonly parameter_list_no_posonly ::= defparameter ("," defparameter)* ["," [parameter_list_starargs]] | parameter_list_starargs parameter_list_starargs ::= "*" [parameter] ("," defparameter)* ["," ["**" parameter [","]]] | "**" parameter [","] parameter ::= identifier [":" expression] defparameter ::= parameter ["=" expression] funcname ::= identifier A function definition is an executable statement. Its execution binds the function name in the current local namespace to a function object (a wrapper around the executable code for the function). This function object contains a reference to the current global namespace as the global namespace to be used when the function is called. The function definition does not execute the function body; this gets executed only when the function is called. [4] A function definition may be wrapped by one or more *decorator* expressions. Decorator expressions are evaluated when the function is defined, in the scope that contains the function definition. The result must be a callable, which is invoked with the function object as the only argument. The returned value is bound to the function name instead of the function object. Multiple decorators are applied in nested fashion. For example, the following code @f1(arg) @f2 def func(): pass is roughly equivalent to def func(): pass func = f1(arg)(f2(func)) except that the original function is not temporarily bound to the name "func". Changed in version 3.9: Functions may be decorated with any valid "assignment_expression". Previously, the grammar was much more restrictive; see **PEP 614** for details. When one or more *parameters* have the form *parameter* "=" *expression*, the function is said to have “default parameter values.” For a parameter with a default value, the corresponding *argument* may be omitted from a call, in which case the parameter’s default value is substituted. If a parameter has a default value, all following parameters up until the “"*"” must also have a default value — this is a syntactic restriction that is not expressed by the grammar. **Default parameter values are evaluated from left to right when the function definition is executed.** This means that the expression is evaluated once, when the function is defined, and that the same “pre- computed” value is used for each call. This is especially important to understand when a default parameter value is a mutable object, such as a list or a dictionary: if the function modifies the object (e.g. by appending an item to a list), the default parameter value is in effect modified. This is generally not what was intended. A way around this is to use "None" as the default, and explicitly test for it in the body of the function, e.g.: def whats_on_the_telly(penguin=None): if penguin is None: penguin = [] penguin.append("property of the zoo") return penguin Function call semantics are described in more detail in section Calls. A function call always assigns values to all parameters mentioned in the parameter list, either from positional arguments, from keyword arguments, or from default values. If the form “"*identifier"” is present, it is initialized to a tuple receiving any excess positional parameters, defaulting to the empty tuple. If the form “"**identifier"” is present, it is initialized to a new ordered mapping receiving any excess keyword arguments, defaulting to a new empty mapping of the same type. Parameters after “"*"” or “"*identifier"” are keyword-only parameters and may only be passed by keyword arguments. Parameters before “"/"” are positional-only parameters and may only be passed by positional arguments. Changed in version 3.8: The "/" function parameter syntax may be used to indicate positional-only parameters. See **PEP 570** for details. Parameters may have an *annotation* of the form “": expression"” following the parameter name. Any parameter may have an annotation, even those of the form "*identifier" or "**identifier". Functions may have “return” annotation of the form “"-> expression"” after the parameter list. These annotations can be any valid Python expression. The presence of annotations does not change the semantics of a function. The annotation values are available as values of a dictionary keyed by the parameters’ names in the "__annotations__" attribute of the function object. If the "annotations" import from "__future__" is used, annotations are preserved as strings at runtime which enables postponed evaluation. Otherwise, they are evaluated when the function definition is executed. In this case annotations may be evaluated in a different order than they appear in the source code. It is also possible to create anonymous functions (functions not bound to a name), for immediate use in expressions. This uses lambda expressions, described in section Lambdas. Note that the lambda expression is merely a shorthand for a simplified function definition; a function defined in a “"def"” statement can be passed around or assigned to another name just like a function defined by a lambda expression. The “"def"” form is actually more powerful since it allows the execution of multiple statements and annotations. **Programmer’s note:** Functions are first-class objects. A “"def"” statement executed inside a function definition defines a local function that can be returned or passed around. Free variables used in the nested function can access the local variables of the function containing the def. See section Naming and binding for details. See also: **PEP 3107** - Function Annotations The original specification for function annotations. **PEP 484** - Type Hints Definition of a standard meaning for annotations: type hints. **PEP 526** - Syntax for Variable Annotations Ability to type hint variable declarations, including class variables and instance variables **PEP 563** - Postponed Evaluation of Annotations Support for forward references within annotations by preserving annotations in a string form at runtime instead of eager evaluation. Class definitions ================= A class definition defines a class object (see section The standard type hierarchy): classdef ::= [decorators] "class" classname [inheritance] ":" suite inheritance ::= "(" [argument_list] ")" classname ::= identifier A class definition is an executable statement. The inheritance list usually gives a list of base classes (see Metaclasses for more advanced uses), so each item in the list should evaluate to a class object which allows subclassing. Classes without an inheritance list inherit, by default, from the base class "object"; hence, class Foo: pass is equivalent to class Foo(object): pass The class’s suite is then executed in a new execution frame (see Naming and binding), using a newly created local namespace and the original global namespace. (Usually, the suite contains mostly function definitions.) When the class’s suite finishes execution, its execution frame is discarded but its local namespace is saved. [5] A class object is then created using the inheritance list for the base classes and the saved local namespace for the attribute dictionary. The class name is bound to this class object in the original local namespace. The order in which attributes are defined in the class body is preserved in the new class’s "__dict__". Note that this is reliable only right after the class is created and only for classes that were defined using the definition syntax. Class creation can be customized heavily using metaclasses. Classes can also be decorated: just like when decorating functions, @f1(arg) @f2 class Foo: pass is roughly equivalent to class Foo: pass Foo = f1(arg)(f2(Foo)) The evaluation rules for the decorator expressions are the same as for function decorators. The result is then bound to the class name. Changed in version 3.9: Classes may be decorated with any valid "assignment_expression". Previously, the grammar was much more restrictive; see **PEP 614** for details. **Programmer’s note:** Variables defined in the class definition are class attributes; they are shared by instances. Instance attributes can be set in a method with "self.name = value". Both class and instance attributes are accessible through the notation “"self.name"”, and an instance attribute hides a class attribute with the same name when accessed in this way. Class attributes can be used as defaults for instance attributes, but using mutable values there can lead to unexpected results. Descriptors can be used to create instance variables with different implementation details. See also: **PEP 3115** - Metaclasses in Python 3000 The proposal that changed the declaration of metaclasses to the current syntax, and the semantics for how classes with metaclasses are constructed. **PEP 3129** - Class Decorators The proposal that added class decorators. Function and method decorators were introduced in **PEP 318**. Coroutines ========== New in version 3.5. Coroutine function definition ----------------------------- async_funcdef ::= [decorators] "async" "def" funcname "(" [parameter_list] ")" ["->" expression] ":" suite Execution of Python coroutines can be suspended and resumed at many points (see *coroutine*). "await" expressions, "async for" and "async with" can only be used in the body of a coroutine function. Functions defined with "async def" syntax are always coroutine functions, even if they do not contain "await" or "async" keywords. It is a "SyntaxError" to use a "yield from" expression inside the body of a coroutine function. An example of a coroutine function: async def func(param1, param2): do_stuff() await some_coroutine() Changed in version 3.7: "await" and "async" are now keywords; previously they were only treated as such inside the body of a coroutine function. The "async for" statement ------------------------- async_for_stmt ::= "async" for_stmt An *asynchronous iterable* provides an "__aiter__" method that directly returns an *asynchronous iterator*, which can call asynchronous code in its "__anext__" method. The "async for" statement allows convenient iteration over asynchronous iterables. The following code: async for TARGET in ITER: SUITE else: SUITE2 Is semantically equivalent to: iter = (ITER) iter = type(iter).__aiter__(iter) running = True while running: try: TARGET = await type(iter).__anext__(iter) except StopAsyncIteration: running = False else: SUITE else: SUITE2 See also "__aiter__()" and "__anext__()" for details. It is a "SyntaxError" to use an "async for" statement outside the body of a coroutine function. The "async with" statement -------------------------- async_with_stmt ::= "async" with_stmt An *asynchronous context manager* is a *context manager* that is able to suspend execution in its *enter* and *exit* methods. The following code: async with EXPRESSION as TARGET: SUITE is semantically equivalent to: manager = (EXPRESSION) aenter = type(manager).__aenter__ aexit = type(manager).__aexit__ value = await aenter(manager) hit_except = False try: TARGET = value SUITE except: hit_except = True if not await aexit(manager, *sys.exc_info()): raise finally: if not hit_except: await aexit(manager, None, None, None) See also "__aenter__()" and "__aexit__()" for details. It is a "SyntaxError" to use an "async with" statement outside the body of a coroutine function. See also: **PEP 492** - Coroutines with async and await syntax The proposal that made coroutines a proper standalone concept in Python, and added supporting syntax. -[ Footnotes ]- [1] The exception is propagated to the invocation stack unless there is a "finally" clause which happens to raise another exception. That new exception causes the old one to be lost. [2] In pattern matching, a sequence is defined as one of the following: * a class that inherits from "collections.abc.Sequence" * a Python class that has been registered as "collections.abc.Sequence" * a builtin class that has its (CPython) "Py_TPFLAGS_SEQUENCE" bit set * a class that inherits from any of the above The following standard library classes are sequences: * "array.array" * "collections.deque" * "list" * "memoryview" * "range" * "tuple" Note: Subject values of type "str", "bytes", and "bytearray" do not match sequence patterns. [3] In pattern matching, a mapping is defined as one of the following: * a class that inherits from "collections.abc.Mapping" * a Python class that has been registered as "collections.abc.Mapping" * a builtin class that has its (CPython) "Py_TPFLAGS_MAPPING" bit set * a class that inherits from any of the above The standard library classes "dict" and "types.MappingProxyType" are mappings. [4] A string literal appearing as the first statement in the function body is transformed into the function’s "__doc__" attribute and therefore the function’s *docstring*. [5] A string literal appearing as the first statement in the class body is transformed into the namespace’s "__doc__" item and therefore the class’s *docstring*. zcontext-managersu� With Statement Context Managers ******************************* A *context manager* is an object that defines the runtime context to be established when executing a "with" statement. The context manager handles the entry into, and the exit from, the desired runtime context for the execution of the block of code. Context managers are normally invoked using the "with" statement (described in section The with statement), but can also be used by directly invoking their methods. Typical uses of context managers include saving and restoring various kinds of global state, locking and unlocking resources, closing opened files, etc. For more information on context managers, see Context Manager Types. object.__enter__(self) Enter the runtime context related to this object. The "with" statement will bind this method’s return value to the target(s) specified in the "as" clause of the statement, if any. object.__exit__(self, exc_type, exc_value, traceback) Exit the runtime context related to this object. The parameters describe the exception that caused the context to be exited. If the context was exited without an exception, all three arguments will be "None". If an exception is supplied, and the method wishes to suppress the exception (i.e., prevent it from being propagated), it should return a true value. Otherwise, the exception will be processed normally upon exit from this method. Note that "__exit__()" methods should not reraise the passed-in exception; this is the caller’s responsibility. See also: **PEP 343** - The “with” statement The specification, background, and examples for the Python "with" statement. �continuea� The "continue" statement ************************ continue_stmt ::= "continue" "continue" may only occur syntactically nested in a "for" or "while" loop, but not nested in a function or class definition within that loop. It continues with the next cycle of the nearest enclosing loop. When "continue" passes control out of a "try" statement with a "finally" clause, that "finally" clause is executed before really starting the next loop cycle. � conversionsu� Arithmetic conversions ********************** When a description of an arithmetic operator below uses the phrase “the numeric arguments are converted to a common type”, this means that the operator implementation for built-in types works as follows: * If either argument is a complex number, the other is converted to complex; * otherwise, if either argument is a floating point number, the other is converted to floating point; * otherwise, both must be integers and no conversion is necessary. Some additional rules apply for certain operators (e.g., a string as a left argument to the ‘%’ operator). Extensions must define their own conversion behavior. � customizationu^5 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. Note: "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 implementation detail:** 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. See also: Documentation for the "gc" module. Warning: 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. object.__str__(self) Called by "str(object)" and the built-in functions "format()" and "print()" to compute the “informal” or nicely printable string representation of an object. The return value must be a string 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. 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 Format Specification Mini-Language for a description of the standard formatting syntax. The return value must be a string object. Changed in version 3.4: The __format__ method of "object" itself raises a "TypeError" if passed any non-empty string. Changed in version 3.7: "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()". 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 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. 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)) Note: "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; 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. Note: 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(n^2) complexity. See http://www.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).See also "PYTHONHASHSEED". Changed in version 3.3: Hash randomization is enabled by default. object.__bool__(self) Called to implement truth value testing and the built-in operation "bool()"; should return "False" or "True". When this method is not defined, "__len__()" is called, if it is defined, and the object is considered true if its result is nonzero. If a class defines neither "__len__()" nor "__bool__()", all its instances are considered true. �debuggeru�K "pdb" — The Python Debugger *************************** **Source code:** Lib/pdb.py ====================================================================== The module "pdb" defines an interactive source code debugger for Python programs. It supports setting (conditional) breakpoints and single stepping at the source line level, inspection of stack frames, source code listing, and evaluation of arbitrary Python code in the context of any stack frame. It also supports post-mortem debugging and can be called under program control. The debugger is extensible – it is actually defined as the class "Pdb". This is currently undocumented but easily understood by reading the source. The extension interface uses the modules "bdb" and "cmd". See also: Module "faulthandler" Used to dump Python tracebacks explicitly, on a fault, after a timeout, or on a user signal. Module "traceback" Standard interface to extract, format and print stack traces of Python programs. The debugger’s prompt is "(Pdb)". Typical usage to run a program under control of the debugger is: >>> import pdb >>> import mymodule >>> pdb.run('mymodule.test()') > <string>(0)?() (Pdb) continue > <string>(1)?() (Pdb) continue NameError: 'spam' > <string>(1)?() (Pdb) Changed in version 3.3: Tab-completion via the "readline" module is available for commands and command arguments, e.g. the current global and local names are offered as arguments of the "p" command. "pdb.py" can also be invoked as a script to debug other scripts. For example: python3 -m pdb myscript.py When invoked as a script, pdb will automatically enter post-mortem debugging if the program being debugged exits abnormally. After post- mortem debugging (or after normal exit of the program), pdb will restart the program. Automatic restarting preserves pdb’s state (such as breakpoints) and in most cases is more useful than quitting the debugger upon program’s exit. New in version 3.2: "pdb.py" now accepts a "-c" option that executes commands as if given in a ".pdbrc" file, see Debugger Commands. New in version 3.7: "pdb.py" now accepts a "-m" option that execute modules similar to the way "python3 -m" does. As with a script, the debugger will pause execution just before the first line of the module. The typical usage to break into the debugger is to insert: import pdb; pdb.set_trace() at the location you want to break into the debugger, and then run the program. You can then step through the code following this statement, and continue running without the debugger using the "continue" command. New in version 3.7: The built-in "breakpoint()", when called with defaults, can be used instead of "import pdb; pdb.set_trace()". The typical usage to inspect a crashed program is: >>> import pdb >>> import mymodule >>> mymodule.test() Traceback (most recent call last): File "<stdin>", line 1, in <module> File "./mymodule.py", line 4, in test test2() File "./mymodule.py", line 3, in test2 print(spam) NameError: spam >>> pdb.pm() > ./mymodule.py(3)test2() -> print(spam) (Pdb) The module defines the following functions; each enters the debugger in a slightly different way: pdb.run(statement, globals=None, locals=None) Execute the *statement* (given as a string or a code object) under debugger control. The debugger prompt appears before any code is executed; you can set breakpoints and type "continue", or you can step through the statement using "step" or "next" (all these commands are explained below). The optional *globals* and *locals* arguments specify the environment in which the code is executed; by default the dictionary of the module "__main__" is used. (See the explanation of the built-in "exec()" or "eval()" functions.) pdb.runeval(expression, globals=None, locals=None) Evaluate the *expression* (given as a string or a code object) under debugger control. When "runeval()" returns, it returns the value of the expression. Otherwise this function is similar to "run()". pdb.runcall(function, *args, **kwds) Call the *function* (a function or method object, not a string) with the given arguments. When "runcall()" returns, it returns whatever the function call returned. The debugger prompt appears as soon as the function is entered. pdb.set_trace(*, header=None) Enter the debugger at the calling stack frame. This is useful to hard-code a breakpoint at a given point in a program, even if the code is not otherwise being debugged (e.g. when an assertion fails). If given, *header* is printed to the console just before debugging begins. Changed in version 3.7: The keyword-only argument *header*. pdb.post_mortem(traceback=None) Enter post-mortem debugging of the given *traceback* object. If no *traceback* is given, it uses the one of the exception that is currently being handled (an exception must be being handled if the default is to be used). pdb.pm() Enter post-mortem debugging of the traceback found in "sys.last_traceback". The "run*" functions and "set_trace()" are aliases for instantiating the "Pdb" class and calling the method of the same name. If you want to access further features, you have to do this yourself: class pdb.Pdb(completekey='tab', stdin=None, stdout=None, skip=None, nosigint=False, readrc=True) "Pdb" is the debugger class. The *completekey*, *stdin* and *stdout* arguments are passed to the underlying "cmd.Cmd" class; see the description there. The *skip* argument, if given, must be an iterable of glob-style module name patterns. The debugger will not step into frames that originate in a module that matches one of these patterns. [1] By default, Pdb sets a handler for the SIGINT signal (which is sent when the user presses "Ctrl-C" on the console) when you give a "continue" command. This allows you to break into the debugger again by pressing "Ctrl-C". If you want Pdb not to touch the SIGINT handler, set *nosigint* to true. The *readrc* argument defaults to true and controls whether Pdb will load .pdbrc files from the filesystem. Example call to enable tracing with *skip*: import pdb; pdb.Pdb(skip=['django.*']).set_trace() Raises an auditing event "pdb.Pdb" with no arguments. New in version 3.1: The *skip* argument. New in version 3.2: The *nosigint* argument. Previously, a SIGINT handler was never set by Pdb. Changed in version 3.6: The *readrc* argument. run(statement, globals=None, locals=None) runeval(expression, globals=None, locals=None) runcall(function, *args, **kwds) set_trace() See the documentation for the functions explained above. Debugger Commands ================= The commands recognized by the debugger are listed below. Most commands can be abbreviated to one or two letters as indicated; e.g. "h(elp)" means that either "h" or "help" can be used to enter the help command (but not "he" or "hel", nor "H" or "Help" or "HELP"). Arguments to commands must be separated by whitespace (spaces or tabs). Optional arguments are enclosed in square brackets ("[]") in the command syntax; the square brackets must not be typed. Alternatives in the command syntax are separated by a vertical bar ("|"). Entering a blank line repeats the last command entered. Exception: if the last command was a "list" command, the next 11 lines are listed. Commands that the debugger doesn’t recognize are assumed to be Python statements and are executed in the context of the program being debugged. Python statements can also be prefixed with an exclamation point ("!"). This is a powerful way to inspect the program being debugged; it is even possible to change a variable or call a function. When an exception occurs in such a statement, the exception name is printed but the debugger’s state is not changed. The debugger supports aliases. Aliases can have parameters which allows one a certain level of adaptability to the context under examination. Multiple commands may be entered on a single line, separated by ";;". (A single ";" is not used as it is the separator for multiple commands in a line that is passed to the Python parser.) No intelligence is applied to separating the commands; the input is split at the first ";;" pair, even if it is in the middle of a quoted string. A workaround for strings with double semicolons is to use implicit string concatenation "';'';'" or "";"";"". If a file ".pdbrc" exists in the user’s home directory or in the current directory, it is read with "'utf-8'" encoding and executed as if it had been typed at the debugger prompt. This is particularly useful for aliases. If both files exist, the one in the home directory is read first and aliases defined there can be overridden by the local file. Changed in version 3.11: ".pdbrc" is now read with "'utf-8'" encoding. Previously, it was read with the system locale encoding. Changed in version 3.2: ".pdbrc" can now contain commands that continue debugging, such as "continue" or "next". Previously, these commands had no effect. h(elp) [command] Without argument, print the list of available commands. With a *command* as argument, print help about that command. "help pdb" displays the full documentation (the docstring of the "pdb" module). Since the *command* argument must be an identifier, "help exec" must be entered to get help on the "!" command. w(here) Print a stack trace, with the most recent frame at the bottom. An arrow indicates the current frame, which determines the context of most commands. d(own) [count] Move the current frame *count* (default one) levels down in the stack trace (to a newer frame). u(p) [count] Move the current frame *count* (default one) levels up in the stack trace (to an older frame). b(reak) [([filename:]lineno | function) [, condition]] With a *lineno* argument, set a break there in the current file. With a *function* argument, set a break at the first executable statement within that function. The line number may be prefixed with a filename and a colon, to specify a breakpoint in another file (probably one that hasn’t been loaded yet). The file is searched on "sys.path". Note that each breakpoint is assigned a number to which all the other breakpoint commands refer. If a second argument is present, it is an expression which must evaluate to true before the breakpoint is honored. Without argument, list all breaks, including for each breakpoint, the number of times that breakpoint has been hit, the current ignore count, and the associated condition if any. tbreak [([filename:]lineno | function) [, condition]] Temporary breakpoint, which is removed automatically when it is first hit. The arguments are the same as for "break". cl(ear) [filename:lineno | bpnumber ...] With a *filename:lineno* argument, clear all the breakpoints at this line. With a space separated list of breakpoint numbers, clear those breakpoints. Without argument, clear all breaks (but first ask confirmation). disable [bpnumber ...] Disable the breakpoints given as a space separated list of breakpoint numbers. Disabling a breakpoint means it cannot cause the program to stop execution, but unlike clearing a breakpoint, it remains in the list of breakpoints and can be (re-)enabled. enable [bpnumber ...] Enable the breakpoints specified. ignore bpnumber [count] Set the ignore count for the given breakpoint number. If count is omitted, the ignore count is set to 0. A breakpoint becomes active when the ignore count is zero. When non-zero, the count is decremented each time the breakpoint is reached and the breakpoint is not disabled and any associated condition evaluates to true. condition bpnumber [condition] Set a new *condition* for the breakpoint, an expression which must evaluate to true before the breakpoint is honored. If *condition* is absent, any existing condition is removed; i.e., the breakpoint is made unconditional. commands [bpnumber] Specify a list of commands for breakpoint number *bpnumber*. The commands themselves appear on the following lines. Type a line containing just "end" to terminate the commands. An example: (Pdb) commands 1 (com) p some_variable (com) end (Pdb) To remove all commands from a breakpoint, type "commands" and follow it immediately with "end"; that is, give no commands. With no *bpnumber* argument, "commands" refers to the last breakpoint set. You can use breakpoint commands to start your program up again. Simply use the "continue" command, or "step", or any other command that resumes execution. Specifying any command resuming execution (currently "continue", "step", "next", "return", "jump", "quit" and their abbreviations) terminates the command list (as if that command was immediately followed by end). This is because any time you resume execution (even with a simple next or step), you may encounter another breakpoint—which could have its own command list, leading to ambiguities about which list to execute. If you use the ‘silent’ command in the command list, the usual message about stopping at a breakpoint is not printed. This may be desirable for breakpoints that are to print a specific message and then continue. If none of the other commands print anything, you see no sign that the breakpoint was reached. s(tep) Execute the current line, stop at the first possible occasion (either in a function that is called or on the next line in the current function). n(ext) Continue execution until the next line in the current function is reached or it returns. (The difference between "next" and "step" is that "step" stops inside a called function, while "next" executes called functions at (nearly) full speed, only stopping at the next line in the current function.) unt(il) [lineno] Without argument, continue execution until the line with a number greater than the current one is reached. With a line number, continue execution until a line with a number greater or equal to that is reached. In both cases, also stop when the current frame returns. Changed in version 3.2: Allow giving an explicit line number. r(eturn) Continue execution until the current function returns. c(ont(inue)) Continue execution, only stop when a breakpoint is encountered. j(ump) lineno Set the next line that will be executed. Only available in the bottom-most frame. This lets you jump back and execute code again, or jump forward to skip code that you don’t want to run. It should be noted that not all jumps are allowed – for instance it is not possible to jump into the middle of a "for" loop or out of a "finally" clause. l(ist) [first[, last]] List source code for the current file. Without arguments, list 11 lines around the current line or continue the previous listing. With "." as argument, list 11 lines around the current line. With one argument, list 11 lines around at that line. With two arguments, list the given range; if the second argument is less than the first, it is interpreted as a count. The current line in the current frame is indicated by "->". If an exception is being debugged, the line where the exception was originally raised or propagated is indicated by ">>", if it differs from the current line. New in version 3.2: The ">>" marker. ll | longlist List all source code for the current function or frame. Interesting lines are marked as for "list". New in version 3.2. a(rgs) Print the argument list of the current function. p expression Evaluate the *expression* in the current context and print its value. Note: "print()" can also be used, but is not a debugger command — this executes the Python "print()" function. pp expression Like the "p" command, except the value of the expression is pretty- printed using the "pprint" module. whatis expression Print the type of the *expression*. source expression Try to get source code for the given object and display it. New in version 3.2. display [expression] Display the value of the expression if it changed, each time execution stops in the current frame. Without expression, list all display expressions for the current frame. New in version 3.2. undisplay [expression] Do not display the expression any more in the current frame. Without expression, clear all display expressions for the current frame. New in version 3.2. interact Start an interactive interpreter (using the "code" module) whose global namespace contains all the (global and local) names found in the current scope. New in version 3.2. alias [name [command]] Create an alias called *name* that executes *command*. The command must *not* be enclosed in quotes. Replaceable parameters can be indicated by "%1", "%2", and so on, while "%*" is replaced by all the parameters. If no command is given, the current alias for *name* is shown. If no arguments are given, all aliases are listed. Aliases may be nested and can contain anything that can be legally typed at the pdb prompt. Note that internal pdb commands *can* be overridden by aliases. Such a command is then hidden until the alias is removed. Aliasing is recursively applied to the first word of the command line; all other words in the line are left alone. As an example, here are two useful aliases (especially when placed in the ".pdbrc" file): # Print instance variables (usage "pi classInst") alias pi for k in %1.__dict__.keys(): print("%1.",k,"=",%1.__dict__[k]) # Print instance variables in self alias ps pi self unalias name Delete the specified alias. ! statement Execute the (one-line) *statement* in the context of the current stack frame. The exclamation point can be omitted unless the first word of the statement resembles a debugger command. To set a global variable, you can prefix the assignment command with a "global" statement on the same line, e.g.: (Pdb) global list_options; list_options = ['-l'] (Pdb) run [args ...] restart [args ...] Restart the debugged Python program. If an argument is supplied, it is split with "shlex" and the result is used as the new "sys.argv". History, breakpoints, actions and debugger options are preserved. "restart" is an alias for "run". q(uit) Quit from the debugger. The program being executed is aborted. debug code Enter a recursive debugger that steps through the code argument (which is an arbitrary expression or statement to be executed in the current environment). retval Print the return value for the last return of a function. -[ Footnotes ]- [1] Whether a frame is considered to originate in a certain module is determined by the "__name__" in the frame globals. �dela� The "del" statement ******************* del_stmt ::= "del" target_list Deletion is recursively defined very similar to the way assignment is defined. Rather than spelling it out in full details, here are some hints. Deletion of a target list recursively deletes each target, from left to right. Deletion of a name removes the binding of that name from the local or global namespace, depending on whether the name occurs in a "global" statement in the same code block. If the name is unbound, a "NameError" exception will be raised. Deletion of attribute references, subscriptions and slicings is passed to the primary object involved; deletion of a slicing is in general equivalent to assignment of an empty slice of the right type (but even this is determined by the sliced object). Changed in version 3.2: Previously it was illegal to delete a name from the local namespace if it occurs as a free variable in a nested block. �dictu Dictionary displays ******************* A dictionary display is a possibly empty series of key/datum pairs enclosed in curly braces: dict_display ::= "{" [key_datum_list | dict_comprehension] "}" key_datum_list ::= key_datum ("," key_datum)* [","] key_datum ::= expression ":" expression | "**" or_expr dict_comprehension ::= expression ":" expression comp_for A dictionary display yields a new dictionary object. If a comma-separated sequence of key/datum pairs is given, they are evaluated from left to right to define the entries of the dictionary: each key object is used as a key into the dictionary to store the corresponding datum. This means that you can specify the same key multiple times in the key/datum list, and the final dictionary’s value for that key will be the last one given. A double asterisk "**" denotes *dictionary unpacking*. Its operand must be a *mapping*. Each mapping item is added to the new dictionary. Later values replace values already set by earlier key/datum pairs and earlier dictionary unpackings. New in version 3.5: Unpacking into dictionary displays, originally proposed by **PEP 448**. A dict comprehension, in contrast to list and set comprehensions, needs two expressions separated with a colon followed by the usual “for” and “if” clauses. When the comprehension is run, the resulting key and value elements are inserted in the new dictionary in the order they are produced. Restrictions on the types of the key values are listed earlier in section The standard type hierarchy. (To summarize, the key type should be *hashable*, which excludes all mutable objects.) Clashes between duplicate keys are not detected; the last datum (textually rightmost in the display) stored for a given key value prevails. Changed in version 3.8: Prior to Python 3.8, in dict comprehensions, the evaluation order of key and value was not well-defined. In CPython, the value was evaluated before the key. Starting with 3.8, the key is evaluated before the value, as proposed by **PEP 572**. zdynamic-featuresa� Interaction with dynamic features ********************************* Name resolution of free variables occurs at runtime, not at compile time. This means that the following code will print 42: i = 10 def f(): print(i) i = 42 f() The "eval()" and "exec()" functions do not have access to the full environment for resolving names. Names may be resolved in the local and global namespaces of the caller. Free variables are not resolved in the nearest enclosing namespace, but in the global namespace. [1] The "exec()" and "eval()" functions have optional arguments to override the global and local namespace. If only one namespace is specified, it is used for both. �elseaX The "if" statement ****************** The "if" statement is used for conditional execution: if_stmt ::= "if" assignment_expression ":" suite ("elif" assignment_expression ":" suite)* ["else" ":" suite] It selects exactly one of the suites by evaluating the expressions one by one until one is found to be true (see section Boolean operations for the definition of true and false); then that suite is executed (and no other part of the "if" statement is executed or evaluated). If all expressions are false, the suite of the "else" clause, if present, is executed. � exceptionsu� Exceptions ********** Exceptions are a means of breaking out of the normal flow of control of a code block in order to handle errors or other exceptional conditions. An exception is *raised* at the point where the error is detected; it may be *handled* by the surrounding code block or by any code block that directly or indirectly invoked the code block where the error occurred. The Python interpreter raises an exception when it detects a run-time error (such as division by zero). A Python program can also explicitly raise an exception with the "raise" statement. Exception handlers are specified with the "try" … "except" statement. The "finally" clause of such a statement can be used to specify cleanup code which does not handle the exception, but is executed whether an exception occurred or not in the preceding code. Python uses the “termination” model of error handling: an exception handler can find out what happened and continue execution at an outer level, but it cannot repair the cause of the error and retry the failing operation (except by re-entering the offending piece of code from the top). When an exception is not handled at all, the interpreter terminates execution of the program, or returns to its interactive main loop. In either case, it prints a stack traceback, except when the exception is "SystemExit". Exceptions are identified by class instances. The "except" clause is selected depending on the class of the instance: it must reference the class of the instance or a *non-virtual base class* thereof. The instance can be received by the handler and can carry additional information about the exceptional condition. Note: Exception messages are not part of the Python API. Their contents may change from one version of Python to the next without warning and should not be relied on by code which will run under multiple versions of the interpreter. See also the description of the "try" statement in section The try statement and "raise" statement in section The raise statement. -[ Footnotes ]- [1] This limitation occurs because the code that is executed by these operations is not available at the time the module is compiled. � execmodeluE% Execution model *************** Structure of a program ====================== A Python program is constructed from code blocks. A *block* is a piece of Python program text that is executed as a unit. The following are blocks: a module, a function body, and a class definition. Each command typed interactively is a block. A script file (a file given as standard input to the interpreter or specified as a command line argument to the interpreter) is a code block. A script command (a command specified on the interpreter command line with the "-c" option) is a code block. A module run as a top level script (as module "__main__") from the command line using a "-m" argument is also a code block. The string argument passed to the built-in functions "eval()" and "exec()" is a code block. A code block is executed in an *execution frame*. A frame contains some administrative information (used for debugging) and determines where and how execution continues after the code block’s execution has completed. Naming and binding ================== Binding of names ---------------- *Names* refer to objects. Names are introduced by name binding operations. The following constructs bind names: * formal parameters to functions, * class definitions, * function definitions, * assignment expressions, * targets that are identifiers if occurring in an assignment: * "for" loop header, * after "as" in a "with" statement, "except" clause, "except*" clause, or in the as-pattern in structural pattern matching, * in a capture pattern in structural pattern matching * "import" statements. The "import" statement of the form "from ... import *" binds all names defined in the imported module, except those beginning with an underscore. This form may only be used at the module level. A target occurring in a "del" statement is also considered bound for this purpose (though the actual semantics are to unbind the name). Each assignment or import statement occurs within a block defined by a class or function definition or at the module level (the top-level code block). If a name is bound in a block, it is a local variable of that block, unless declared as "nonlocal" or "global". If a name is bound at the module level, it is a global variable. (The variables of the module code block are local and global.) If a variable is used in a code block but not defined there, it is a *free variable*. Each occurrence of a name in the program text refers to the *binding* of that name established by the following name resolution rules. Resolution of names ------------------- A *scope* defines the visibility of a name within a block. If a local variable is defined in a block, its scope includes that block. If the definition occurs in a function block, the scope extends to any blocks contained within the defining one, unless a contained block introduces a different binding for the name. When a name is used in a code block, it is resolved using the nearest enclosing scope. The set of all such scopes visible to a code block is called the block’s *environment*. When a name is not found at all, a "NameError" exception is raised. If the current scope is a function scope, and the name refers to a local variable that has not yet been bound to a value at the point where the name is used, an "UnboundLocalError" exception is raised. "UnboundLocalError" is a subclass of "NameError". If a name binding operation occurs anywhere within a code block, all uses of the name within the block are treated as references to the current block. This can lead to errors when a name is used within a block before it is bound. This rule is subtle. Python lacks declarations and allows name binding operations to occur anywhere within a code block. The local variables of a code block can be determined by scanning the entire text of the block for name binding operations. See the FAQ entry on UnboundLocalError for examples. If the "global" statement occurs within a block, all uses of the names specified in the statement refer to the bindings of those names in the top-level namespace. Names are resolved in the top-level namespace by searching the global namespace, i.e. the namespace of the module containing the code block, and the builtins namespace, the namespace of the module "builtins". The global namespace is searched first. If the names are not found there, the builtins namespace is searched. The "global" statement must precede all uses of the listed names. The "global" statement has the same scope as a name binding operation in the same block. If the nearest enclosing scope for a free variable contains a global statement, the free variable is treated as a global. The "nonlocal" statement causes corresponding names to refer to previously bound variables in the nearest enclosing function scope. "SyntaxError" is raised at compile time if the given name does not exist in any enclosing function scope. The namespace for a module is automatically created the first time a module is imported. The main module for a script is always called "__main__". Class definition blocks and arguments to "exec()" and "eval()" are special in the context of name resolution. A class definition is an executable statement that may use and define names. These references follow the normal rules for name resolution with an exception that unbound local variables are looked up in the global namespace. The namespace of the class definition becomes the attribute dictionary of the class. The scope of names defined in a class block is limited to the class block; it does not extend to the code blocks of methods – this includes comprehensions and generator expressions since they are implemented using a function scope. This means that the following will fail: class A: a = 42 b = list(a + i for i in range(10)) Builtins and restricted execution --------------------------------- **CPython implementation detail:** Users should not touch "__builtins__"; it is strictly an implementation detail. Users wanting to override values in the builtins namespace should "import" the "builtins" module and modify its attributes appropriately. The builtins namespace associated with the execution of a code block is actually found by looking up the name "__builtins__" in its global namespace; this should be a dictionary or a module (in the latter case the module’s dictionary is used). By default, when in the "__main__" module, "__builtins__" is the built-in module "builtins"; when in any other module, "__builtins__" is an alias for the dictionary of the "builtins" module itself. Interaction with dynamic features --------------------------------- Name resolution of free variables occurs at runtime, not at compile time. This means that the following code will print 42: i = 10 def f(): print(i) i = 42 f() The "eval()" and "exec()" functions do not have access to the full environment for resolving names. Names may be resolved in the local and global namespaces of the caller. Free variables are not resolved in the nearest enclosing namespace, but in the global namespace. [1] The "exec()" and "eval()" functions have optional arguments to override the global and local namespace. If only one namespace is specified, it is used for both. Exceptions ========== Exceptions are a means of breaking out of the normal flow of control of a code block in order to handle errors or other exceptional conditions. An exception is *raised* at the point where the error is detected; it may be *handled* by the surrounding code block or by any code block that directly or indirectly invoked the code block where the error occurred. The Python interpreter raises an exception when it detects a run-time error (such as division by zero). A Python program can also explicitly raise an exception with the "raise" statement. Exception handlers are specified with the "try" … "except" statement. The "finally" clause of such a statement can be used to specify cleanup code which does not handle the exception, but is executed whether an exception occurred or not in the preceding code. Python uses the “termination” model of error handling: an exception handler can find out what happened and continue execution at an outer level, but it cannot repair the cause of the error and retry the failing operation (except by re-entering the offending piece of code from the top). When an exception is not handled at all, the interpreter terminates execution of the program, or returns to its interactive main loop. In either case, it prints a stack traceback, except when the exception is "SystemExit". Exceptions are identified by class instances. The "except" clause is selected depending on the class of the instance: it must reference the class of the instance or a *non-virtual base class* thereof. The instance can be received by the handler and can carry additional information about the exceptional condition. Note: Exception messages are not part of the Python API. Their contents may change from one version of Python to the next without warning and should not be relied on by code which will run under multiple versions of the interpreter. See also the description of the "try" statement in section The try statement and "raise" statement in section The raise statement. -[ Footnotes ]- [1] This limitation occurs because the code that is executed by these operations is not available at the time the module is compiled. � exprlistsuz Expression lists **************** expression_list ::= expression ("," expression)* [","] starred_list ::= starred_item ("," starred_item)* [","] starred_expression ::= expression | (starred_item ",")* [starred_item] starred_item ::= assignment_expression | "*" or_expr Except when part of a list or set display, an expression list containing at least one comma yields a tuple. The length of the tuple is the number of expressions in the list. The expressions are evaluated from left to right. An asterisk "*" denotes *iterable unpacking*. Its operand must be an *iterable*. The iterable is expanded into a sequence of items, which are included in the new tuple, list, or set, at the site of the unpacking. New in version 3.5: Iterable unpacking in expression lists, originally proposed by **PEP 448**. The trailing comma is required only to create a single tuple (a.k.a. a *singleton*); it is optional in all other cases. A single expression without a trailing comma doesn’t create a tuple, but rather yields the value of that expression. (To create an empty tuple, use an empty pair of parentheses: "()".) �floatinga� Floating point literals *********************** Floating point literals are described by the following lexical definitions: floatnumber ::= pointfloat | exponentfloat pointfloat ::= [digitpart] fraction | digitpart "." exponentfloat ::= (digitpart | pointfloat) exponent digitpart ::= digit (["_"] digit)* fraction ::= "." digitpart exponent ::= ("e" | "E") ["+" | "-"] digitpart Note that the integer and exponent parts are always interpreted using radix 10. For example, "077e010" is legal, and denotes the same number as "77e10". The allowed range of floating point literals is implementation-dependent. As in integer literals, underscores are supported for digit grouping. Some examples of floating point literals: 3.14 10. .001 1e100 3.14e-10 0e0 3.14_15_93 Changed in version 3.6: Underscores are now allowed for grouping purposes in literals. �foru" The "for" statement ******************* The "for" statement is used to iterate over the elements of a sequence (such as a string, tuple or list) or other iterable object: for_stmt ::= "for" target_list "in" starred_list ":" suite ["else" ":" suite] The "starred_list" expression is evaluated once; it should yield an *iterable* object. An *iterator* is created for that iterable. The first item provided by the iterator is then assigned to the target list using the standard rules for assignments (see Assignment statements), and the suite is executed. This repeats for each item provided by the iterator. When the iterator is exhausted, the suite in the "else" clause, if present, is executed, and the loop terminates. A "break" statement executed in the first suite terminates the loop without executing the "else" clause’s suite. A "continue" statement executed in the first suite skips the rest of the suite and continues with the next item, or with the "else" clause if there is no next item. The for-loop makes assignments to the variables in the target list. This overwrites all previous assignments to those variables including those made in the suite of the for-loop: for i in range(10): print(i) i = 5 # this will not affect the for-loop # because i will be overwritten with the next # index in the range Names in the target list are not deleted when the loop is finished, but if the sequence is empty, they will not have been assigned to at all by the loop. Hint: the built-in type "range()" represents immutable arithmetic sequences of integers. For instance, iterating "range(3)" successively yields 0, 1, and then 2. Changed in version 3.11: Starred elements are now allowed in the expression list. � formatstringsu�b Format String Syntax ******************** The "str.format()" method and the "Formatter" class share the same syntax for format strings (although in the case of "Formatter", subclasses can define their own format string syntax). The syntax is related to that of formatted string literals, but it is less sophisticated and, in particular, does not support arbitrary expressions. Format strings contain “replacement fields” surrounded by curly braces "{}". Anything that is not contained in braces is considered literal text, which is copied unchanged to the output. If you need to include a brace character in the literal text, it can be escaped by doubling: "{{" and "}}". The grammar for a replacement field is as follows: replacement_field ::= "{" [field_name] ["!" conversion] [":" format_spec] "}" field_name ::= arg_name ("." attribute_name | "[" element_index "]")* arg_name ::= [identifier | digit+] attribute_name ::= identifier element_index ::= digit+ | index_string index_string ::= <any source character except "]"> + conversion ::= "r" | "s" | "a" format_spec ::= <described in the next section> In less formal terms, the replacement field can start with a *field_name* that specifies the object whose value is to be formatted and inserted into the output instead of the replacement field. The *field_name* is optionally followed by a *conversion* field, which is preceded by an exclamation point "'!'", and a *format_spec*, which is preceded by a colon "':'". These specify a non-default format for the replacement value. See also the Format Specification Mini-Language section. The *field_name* itself begins with an *arg_name* that is either a number or a keyword. If it’s a number, it refers to a positional argument, and if it’s a keyword, it refers to a named keyword argument. If the numerical arg_names in a format string are 0, 1, 2, … in sequence, they can all be omitted (not just some) and the numbers 0, 1, 2, … will be automatically inserted in that order. Because *arg_name* is not quote-delimited, it is not possible to specify arbitrary dictionary keys (e.g., the strings "'10'" or "':-]'") within a format string. The *arg_name* can be followed by any number of index or attribute expressions. An expression of the form "'.name'" selects the named attribute using "getattr()", while an expression of the form "'[index]'" does an index lookup using "__getitem__()". Changed in version 3.1: The positional argument specifiers can be omitted for "str.format()", so "'{} {}'.format(a, b)" is equivalent to "'{0} {1}'.format(a, b)". Changed in version 3.4: The positional argument specifiers can be omitted for "Formatter". Some simple format string examples: "First, thou shalt count to {0}" # References first positional argument "Bring me a {}" # Implicitly references the first positional argument "From {} to {}" # Same as "From {0} to {1}" "My quest is {name}" # References keyword argument 'name' "Weight in tons {0.weight}" # 'weight' attribute of first positional arg "Units destroyed: {players[0]}" # First element of keyword argument 'players'. The *conversion* field causes a type coercion before formatting. Normally, the job of formatting a value is done by the "__format__()" method of the value itself. However, in some cases it is desirable to force a type to be formatted as a string, overriding its own definition of formatting. By converting the value to a string before calling "__format__()", the normal formatting logic is bypassed. Three conversion flags are currently supported: "'!s'" which calls "str()" on the value, "'!r'" which calls "repr()" and "'!a'" which calls "ascii()". Some examples: "Harold's a clever {0!s}" # Calls str() on the argument first "Bring out the holy {name!r}" # Calls repr() on the argument first "More {!a}" # Calls ascii() on the argument first The *format_spec* field contains a specification of how the value should be presented, including such details as field width, alignment, padding, decimal precision and so on. Each value type can define its own “formatting mini-language” or interpretation of the *format_spec*. Most built-in types support a common formatting mini-language, which is described in the next section. A *format_spec* field can also include nested replacement fields within it. These nested replacement fields may contain a field name, conversion flag and format specification, but deeper nesting is not allowed. The replacement fields within the format_spec are substituted before the *format_spec* string is interpreted. This allows the formatting of a value to be dynamically specified. See the Format examples section for some examples. Format Specification Mini-Language ================================== “Format specifications” are used within replacement fields contained within a format string to define how individual values are presented (see Format String Syntax and Formatted string literals). They can also be passed directly to the built-in "format()" function. Each formattable type may define how the format specification is to be interpreted. Most built-in types implement the following options for format specifications, although some of the formatting options are only supported by the numeric types. A general convention is that an empty format specification produces the same result as if you had called "str()" on the value. A non-empty format specification typically modifies the result. The general form of a *standard format specifier* is: format_spec ::= [[fill]align][sign][z][#][0][width][grouping_option][.precision][type] fill ::= <any character> align ::= "<" | ">" | "=" | "^" sign ::= "+" | "-" | " " width ::= digit+ grouping_option ::= "_" | "," precision ::= digit+ type ::= "b" | "c" | "d" | "e" | "E" | "f" | "F" | "g" | "G" | "n" | "o" | "s" | "x" | "X" | "%" If a valid *align* value is specified, it can be preceded by a *fill* character that can be any character and defaults to a space if omitted. It is not possible to use a literal curly brace (”"{"” or “"}"”) as the *fill* character in a formatted string literal or when using the "str.format()" method. However, it is possible to insert a curly brace with a nested replacement field. This limitation doesn’t affect the "format()" function. The meaning of the various alignment options is as follows: +-----------+------------------------------------------------------------+ | Option | Meaning | |===========|============================================================| | "'<'" | Forces the field to be left-aligned within the available | | | space (this is the default for most objects). | +-----------+------------------------------------------------------------+ | "'>'" | Forces the field to be right-aligned within the available | | | space (this is the default for numbers). | +-----------+------------------------------------------------------------+ | "'='" | Forces the padding to be placed after the sign (if any) | | | but before the digits. This is used for printing fields | | | in the form ‘+000000120’. This alignment option is only | | | valid for numeric types. It becomes the default for | | | numbers when ‘0’ immediately precedes the field width. | +-----------+------------------------------------------------------------+ | "'^'" | Forces the field to be centered within the available | | | space. | +-----------+------------------------------------------------------------+ Note that unless a minimum field width is defined, the field width will always be the same size as the data to fill it, so that the alignment option has no meaning in this case. The *sign* option is only valid for number types, and can be one of the following: +-----------+------------------------------------------------------------+ | Option | Meaning | |===========|============================================================| | "'+'" | indicates that a sign should be used for both positive as | | | well as negative numbers. | +-----------+------------------------------------------------------------+ | "'-'" | indicates that a sign should be used only for negative | | | numbers (this is the default behavior). | +-----------+------------------------------------------------------------+ | space | indicates that a leading space should be used on positive | | | numbers, and a minus sign on negative numbers. | +-----------+------------------------------------------------------------+ The "'z'" option coerces negative zero floating-point values to positive zero after rounding to the format precision. This option is only valid for floating-point presentation types. Changed in version 3.11: Added the "'z'" option (see also **PEP 682**). The "'#'" option causes the “alternate form” to be used for the conversion. The alternate form is defined differently for different types. This option is only valid for integer, float and complex types. For integers, when binary, octal, or hexadecimal output is used, this option adds the respective prefix "'0b'", "'0o'", "'0x'", or "'0X'" to the output value. For float and complex the alternate form causes the result of the conversion to always contain a decimal- point character, even if no digits follow it. Normally, a decimal- point character appears in the result of these conversions only if a digit follows it. In addition, for "'g'" and "'G'" conversions, trailing zeros are not removed from the result. The "','" option signals the use of a comma for a thousands separator. For a locale aware separator, use the "'n'" integer presentation type instead. Changed in version 3.1: Added the "','" option (see also **PEP 378**). The "'_'" option signals the use of an underscore for a thousands separator for floating point presentation types and for integer presentation type "'d'". For integer presentation types "'b'", "'o'", "'x'", and "'X'", underscores will be inserted every 4 digits. For other presentation types, specifying this option is an error. Changed in version 3.6: Added the "'_'" option (see also **PEP 515**). *width* is a decimal integer defining the minimum total field width, including any prefixes, separators, and other formatting characters. If not specified, then the field width will be determined by the content. When no explicit alignment is given, preceding the *width* field by a zero ("'0'") character enables sign-aware zero-padding for numeric types. This is equivalent to a *fill* character of "'0'" with an *alignment* type of "'='". Changed in version 3.10: Preceding the *width* field by "'0'" no longer affects the default alignment for strings. The *precision* is a decimal integer indicating how many digits should be displayed after the decimal point for presentation types "'f'" and "'F'", or before and after the decimal point for presentation types "'g'" or "'G'". For string presentation types the field indicates the maximum field size - in other words, how many characters will be used from the field content. The *precision* is not allowed for integer presentation types. Finally, the *type* determines how the data should be presented. The available string presentation types are: +-----------+------------------------------------------------------------+ | Type | Meaning | |===========|============================================================| | "'s'" | String format. This is the default type for strings and | | | may be omitted. | +-----------+------------------------------------------------------------+ | None | The same as "'s'". | +-----------+------------------------------------------------------------+ The available integer presentation types are: +-----------+------------------------------------------------------------+ | Type | Meaning | |===========|============================================================| | "'b'" | Binary format. Outputs the number in base 2. | +-----------+------------------------------------------------------------+ | "'c'" | Character. Converts the integer to the corresponding | | | unicode character before printing. | +-----------+------------------------------------------------------------+ | "'d'" | Decimal Integer. Outputs the number in base 10. | +-----------+------------------------------------------------------------+ | "'o'" | Octal format. Outputs the number in base 8. | +-----------+------------------------------------------------------------+ | "'x'" | Hex format. Outputs the number in base 16, using lower- | | | case letters for the digits above 9. | +-----------+------------------------------------------------------------+ | "'X'" | Hex format. Outputs the number in base 16, using upper- | | | case letters for the digits above 9. In case "'#'" is | | | specified, the prefix "'0x'" will be upper-cased to "'0X'" | | | as well. | +-----------+------------------------------------------------------------+ | "'n'" | Number. This is the same as "'d'", except that it uses the | | | current locale setting to insert the appropriate number | | | separator characters. | +-----------+------------------------------------------------------------+ | None | The same as "'d'". | +-----------+------------------------------------------------------------+ In addition to the above presentation types, integers can be formatted with the floating point presentation types listed below (except "'n'" and "None"). When doing so, "float()" is used to convert the integer to a floating point number before formatting. The available presentation types for "float" and "Decimal" values are: +-----------+------------------------------------------------------------+ | Type | Meaning | |===========|============================================================| | "'e'" | Scientific notation. For a given precision "p", formats | | | the number in scientific notation with the letter ‘e’ | | | separating the coefficient from the exponent. The | | | coefficient has one digit before and "p" digits after the | | | decimal point, for a total of "p + 1" significant digits. | | | With no precision given, uses a precision of "6" digits | | | after the decimal point for "float", and shows all | | | coefficient digits for "Decimal". If no digits follow the | | | decimal point, the decimal point is also removed unless | | | the "#" option is used. | +-----------+------------------------------------------------------------+ | "'E'" | Scientific notation. Same as "'e'" except it uses an upper | | | case ‘E’ as the separator character. | +-----------+------------------------------------------------------------+ | "'f'" | Fixed-point notation. For a given precision "p", formats | | | the number as a decimal number with exactly "p" digits | | | following the decimal point. With no precision given, uses | | | a precision of "6" digits after the decimal point for | | | "float", and uses a precision large enough to show all | | | coefficient digits for "Decimal". If no digits follow the | | | decimal point, the decimal point is also removed unless | | | the "#" option is used. | +-----------+------------------------------------------------------------+ | "'F'" | Fixed-point notation. Same as "'f'", but converts "nan" to | | | "NAN" and "inf" to "INF". | +-----------+------------------------------------------------------------+ | "'g'" | General format. For a given precision "p >= 1", this | | | rounds the number to "p" significant digits and then | | | formats the result in either fixed-point format or in | | | scientific notation, depending on its magnitude. A | | | precision of "0" is treated as equivalent to a precision | | | of "1". The precise rules are as follows: suppose that | | | the result formatted with presentation type "'e'" and | | | precision "p-1" would have exponent "exp". Then, if "m <= | | | exp < p", where "m" is -4 for floats and -6 for | | | "Decimals", the number is formatted with presentation type | | | "'f'" and precision "p-1-exp". Otherwise, the number is | | | formatted with presentation type "'e'" and precision | | | "p-1". In both cases insignificant trailing zeros are | | | removed from the significand, and the decimal point is | | | also removed if there are no remaining digits following | | | it, unless the "'#'" option is used. With no precision | | | given, uses a precision of "6" significant digits for | | | "float". For "Decimal", the coefficient of the result is | | | formed from the coefficient digits of the value; | | | scientific notation is used for values smaller than "1e-6" | | | in absolute value and values where the place value of the | | | least significant digit is larger than 1, and fixed-point | | | notation is used otherwise. Positive and negative | | | infinity, positive and negative zero, and nans, are | | | formatted as "inf", "-inf", "0", "-0" and "nan" | | | respectively, regardless of the precision. | +-----------+------------------------------------------------------------+ | "'G'" | General format. Same as "'g'" except switches to "'E'" if | | | the number gets too large. The representations of infinity | | | and NaN are uppercased, too. | +-----------+------------------------------------------------------------+ | "'n'" | Number. This is the same as "'g'", except that it uses the | | | current locale setting to insert the appropriate number | | | separator characters. | +-----------+------------------------------------------------------------+ | "'%'" | Percentage. Multiplies the number by 100 and displays in | | | fixed ("'f'") format, followed by a percent sign. | +-----------+------------------------------------------------------------+ | None | For "float" this is the same as "'g'", except that when | | | fixed-point notation is used to format the result, it | | | always includes at least one digit past the decimal point. | | | The precision used is as large as needed to represent the | | | given value faithfully. For "Decimal", this is the same | | | as either "'g'" or "'G'" depending on the value of | | | "context.capitals" for the current decimal context. The | | | overall effect is to match the output of "str()" as | | | altered by the other format modifiers. | +-----------+------------------------------------------------------------+ Format examples =============== This section contains examples of the "str.format()" syntax and comparison with the old "%"-formatting. In most of the cases the syntax is similar to the old "%"-formatting, with the addition of the "{}" and with ":" used instead of "%". For example, "'%03.2f'" can be translated to "'{:03.2f}'". The new format syntax also supports new and different options, shown in the following examples. Accessing arguments by position: >>> '{0}, {1}, {2}'.format('a', 'b', 'c') 'a, b, c' >>> '{}, {}, {}'.format('a', 'b', 'c') # 3.1+ only 'a, b, c' >>> '{2}, {1}, {0}'.format('a', 'b', 'c') 'c, b, a' >>> '{2}, {1}, {0}'.format(*'abc') # unpacking argument sequence 'c, b, a' >>> '{0}{1}{0}'.format('abra', 'cad') # arguments' indices can be repeated 'abracadabra' Accessing arguments by name: >>> 'Coordinates: {latitude}, {longitude}'.format(latitude='37.24N', longitude='-115.81W') 'Coordinates: 37.24N, -115.81W' >>> coord = {'latitude': '37.24N', 'longitude': '-115.81W'} >>> 'Coordinates: {latitude}, {longitude}'.format(**coord) 'Coordinates: 37.24N, -115.81W' Accessing arguments’ attributes: >>> c = 3-5j >>> ('The complex number {0} is formed from the real part {0.real} ' ... 'and the imaginary part {0.imag}.').format(c) 'The complex number (3-5j) is formed from the real part 3.0 and the imaginary part -5.0.' >>> class Point: ... def __init__(self, x, y): ... self.x, self.y = x, y ... def __str__(self): ... return 'Point({self.x}, {self.y})'.format(self=self) ... >>> str(Point(4, 2)) 'Point(4, 2)' Accessing arguments’ items: >>> coord = (3, 5) >>> 'X: {0[0]}; Y: {0[1]}'.format(coord) 'X: 3; Y: 5' Replacing "%s" and "%r": >>> "repr() shows quotes: {!r}; str() doesn't: {!s}".format('test1', 'test2') "repr() shows quotes: 'test1'; str() doesn't: test2" Aligning the text and specifying a width: >>> '{:<30}'.format('left aligned') 'left aligned ' >>> '{:>30}'.format('right aligned') ' right aligned' >>> '{:^30}'.format('centered') ' centered ' >>> '{:*^30}'.format('centered') # use '*' as a fill char '***********centered***********' Replacing "%+f", "%-f", and "% f" and specifying a sign: >>> '{:+f}; {:+f}'.format(3.14, -3.14) # show it always '+3.140000; -3.140000' >>> '{: f}; {: f}'.format(3.14, -3.14) # show a space for positive numbers ' 3.140000; -3.140000' >>> '{:-f}; {:-f}'.format(3.14, -3.14) # show only the minus -- same as '{:f}; {:f}' '3.140000; -3.140000' Replacing "%x" and "%o" and converting the value to different bases: >>> # format also supports binary numbers >>> "int: {0:d}; hex: {0:x}; oct: {0:o}; bin: {0:b}".format(42) 'int: 42; hex: 2a; oct: 52; bin: 101010' >>> # with 0x, 0o, or 0b as prefix: >>> "int: {0:d}; hex: {0:#x}; oct: {0:#o}; bin: {0:#b}".format(42) 'int: 42; hex: 0x2a; oct: 0o52; bin: 0b101010' Using the comma as a thousands separator: >>> '{:,}'.format(1234567890) '1,234,567,890' Expressing a percentage: >>> points = 19 >>> total = 22 >>> 'Correct answers: {:.2%}'.format(points/total) 'Correct answers: 86.36%' Using type-specific formatting: >>> import datetime >>> d = datetime.datetime(2010, 7, 4, 12, 15, 58) >>> '{:%Y-%m-%d %H:%M:%S}'.format(d) '2010-07-04 12:15:58' Nesting arguments and more complex examples: >>> for align, text in zip('<^>', ['left', 'center', 'right']): ... '{0:{fill}{align}16}'.format(text, fill=align, align=align) ... 'left<<<<<<<<<<<<' '^^^^^center^^^^^' '>>>>>>>>>>>right' >>> >>> octets = [192, 168, 0, 1] >>> '{:02X}{:02X}{:02X}{:02X}'.format(*octets) 'C0A80001' >>> int(_, 16) 3232235521 >>> >>> width = 5 >>> for num in range(5,12): ... for base in 'dXob': ... print('{0:{width}{base}}'.format(num, base=base, width=width), end=' ') ... print() ... 5 5 5 101 6 6 6 110 7 7 7 111 8 8 10 1000 9 9 11 1001 10 A 12 1010 11 B 13 1011 �functionu� Function definitions ******************** A function definition defines a user-defined function object (see section The standard type hierarchy): funcdef ::= [decorators] "def" funcname "(" [parameter_list] ")" ["->" expression] ":" suite decorators ::= decorator+ decorator ::= "@" assignment_expression NEWLINE parameter_list ::= defparameter ("," defparameter)* "," "/" ["," [parameter_list_no_posonly]] | parameter_list_no_posonly parameter_list_no_posonly ::= defparameter ("," defparameter)* ["," [parameter_list_starargs]] | parameter_list_starargs parameter_list_starargs ::= "*" [parameter] ("," defparameter)* ["," ["**" parameter [","]]] | "**" parameter [","] parameter ::= identifier [":" expression] defparameter ::= parameter ["=" expression] funcname ::= identifier A function definition is an executable statement. Its execution binds the function name in the current local namespace to a function object (a wrapper around the executable code for the function). This function object contains a reference to the current global namespace as the global namespace to be used when the function is called. The function definition does not execute the function body; this gets executed only when the function is called. [4] A function definition may be wrapped by one or more *decorator* expressions. Decorator expressions are evaluated when the function is defined, in the scope that contains the function definition. The result must be a callable, which is invoked with the function object as the only argument. The returned value is bound to the function name instead of the function object. Multiple decorators are applied in nested fashion. For example, the following code @f1(arg) @f2 def func(): pass is roughly equivalent to def func(): pass func = f1(arg)(f2(func)) except that the original function is not temporarily bound to the name "func". Changed in version 3.9: Functions may be decorated with any valid "assignment_expression". Previously, the grammar was much more restrictive; see **PEP 614** for details. When one or more *parameters* have the form *parameter* "=" *expression*, the function is said to have “default parameter values.” For a parameter with a default value, the corresponding *argument* may be omitted from a call, in which case the parameter’s default value is substituted. If a parameter has a default value, all following parameters up until the “"*"” must also have a default value — this is a syntactic restriction that is not expressed by the grammar. **Default parameter values are evaluated from left to right when the function definition is executed.** This means that the expression is evaluated once, when the function is defined, and that the same “pre- computed” value is used for each call. This is especially important to understand when a default parameter value is a mutable object, such as a list or a dictionary: if the function modifies the object (e.g. by appending an item to a list), the default parameter value is in effect modified. This is generally not what was intended. A way around this is to use "None" as the default, and explicitly test for it in the body of the function, e.g.: def whats_on_the_telly(penguin=None): if penguin is None: penguin = [] penguin.append("property of the zoo") return penguin Function call semantics are described in more detail in section Calls. A function call always assigns values to all parameters mentioned in the parameter list, either from positional arguments, from keyword arguments, or from default values. If the form “"*identifier"” is present, it is initialized to a tuple receiving any excess positional parameters, defaulting to the empty tuple. If the form “"**identifier"” is present, it is initialized to a new ordered mapping receiving any excess keyword arguments, defaulting to a new empty mapping of the same type. Parameters after “"*"” or “"*identifier"” are keyword-only parameters and may only be passed by keyword arguments. Parameters before “"/"” are positional-only parameters and may only be passed by positional arguments. Changed in version 3.8: The "/" function parameter syntax may be used to indicate positional-only parameters. See **PEP 570** for details. Parameters may have an *annotation* of the form “": expression"” following the parameter name. Any parameter may have an annotation, even those of the form "*identifier" or "**identifier". Functions may have “return” annotation of the form “"-> expression"” after the parameter list. These annotations can be any valid Python expression. The presence of annotations does not change the semantics of a function. The annotation values are available as values of a dictionary keyed by the parameters’ names in the "__annotations__" attribute of the function object. If the "annotations" import from "__future__" is used, annotations are preserved as strings at runtime which enables postponed evaluation. Otherwise, they are evaluated when the function definition is executed. In this case annotations may be evaluated in a different order than they appear in the source code. It is also possible to create anonymous functions (functions not bound to a name), for immediate use in expressions. This uses lambda expressions, described in section Lambdas. Note that the lambda expression is merely a shorthand for a simplified function definition; a function defined in a “"def"” statement can be passed around or assigned to another name just like a function defined by a lambda expression. The “"def"” form is actually more powerful since it allows the execution of multiple statements and annotations. **Programmer’s note:** Functions are first-class objects. A “"def"” statement executed inside a function definition defines a local function that can be returned or passed around. Free variables used in the nested function can access the local variables of the function containing the def. See section Naming and binding for details. See also: **PEP 3107** - Function Annotations The original specification for function annotations. **PEP 484** - Type Hints Definition of a standard meaning for annotations: type hints. **PEP 526** - Syntax for Variable Annotations Ability to type hint variable declarations, including class variables and instance variables **PEP 563** - Postponed Evaluation of Annotations Support for forward references within annotations by preserving annotations in a string form at runtime instead of eager evaluation. �globalu� The "global" statement ********************** global_stmt ::= "global" identifier ("," identifier)* The "global" statement is a declaration which holds for the entire current code block. It means that the listed identifiers are to be interpreted as globals. It would be impossible to assign to a global variable without "global", although free variables may refer to globals without being declared global. Names listed in a "global" statement must not be used in the same code block textually preceding that "global" statement. Names listed in a "global" statement must not be defined as formal parameters, or as targets in "with" statements or "except" clauses, or in a "for" target list, "class" definition, function definition, "import" statement, or variable annotation. **CPython implementation detail:** The current implementation does not enforce some of these restrictions, but programs should not abuse this freedom, as future implementations may enforce them or silently change the meaning of the program. **Programmer’s note:** "global" is a directive to the parser. It applies only to code parsed at the same time as the "global" statement. In particular, a "global" statement contained in a string or code object supplied to the built-in "exec()" function does not affect the code block *containing* the function call, and code contained in such a string is unaffected by "global" statements in the code containing the function call. The same applies to the "eval()" and "compile()" functions. z id-classesu� Reserved classes of identifiers ******************************* Certain classes of identifiers (besides keywords) have special meanings. These classes are identified by the patterns of leading and trailing underscore characters: "_*" Not imported by "from module import *". "_" In a "case" pattern within a "match" statement, "_" is a soft keyword that denotes a wildcard. Separately, the interactive interpreter makes the result of the last evaluation available in the variable "_". (It is stored in the "builtins" module, alongside built-in functions like "print".) Elsewhere, "_" is a regular identifier. It is often used to name “special” items, but it is not special to Python itself. Note: The name "_" is often used in conjunction with internationalization; refer to the documentation for the "gettext" module for more information on this convention.It is also commonly used for unused variables. "__*__" System-defined names, informally known as “dunder” names. These names are defined by the interpreter and its implementation (including the standard library). Current system names are discussed in the Special method names section and elsewhere. More will likely be defined in future versions of Python. *Any* use of "__*__" names, in any context, that does not follow explicitly documented use, is subject to breakage without warning. "__*" Class-private names. Names in this category, when used within the context of a class definition, are re-written to use a mangled form to help avoid name clashes between “private” attributes of base and derived classes. See section Identifiers (Names). � identifiersuY Identifiers and keywords ************************ Identifiers (also referred to as *names*) are described by the following lexical definitions. The syntax of identifiers in Python is based on the Unicode standard annex UAX-31, with elaboration and changes as defined below; see also **PEP 3131** for further details. Within the ASCII range (U+0001..U+007F), the valid characters for identifiers are the same as in Python 2.x: the uppercase and lowercase letters "A" through "Z", the underscore "_" and, except for the first character, the digits "0" through "9". Python 3.0 introduces additional characters from outside the ASCII range (see **PEP 3131**). For these characters, the classification uses the version of the Unicode Character Database as included in the "unicodedata" module. Identifiers are unlimited in length. Case is significant. identifier ::= xid_start xid_continue* id_start ::= <all characters in general categories Lu, Ll, Lt, Lm, Lo, Nl, the underscore, and characters with the Other_ID_Start property> id_continue ::= <all characters in id_start, plus characters in the categories Mn, Mc, Nd, Pc and others with the Other_ID_Continue property> xid_start ::= <all characters in id_start whose NFKC normalization is in "id_start xid_continue*"> xid_continue ::= <all characters in id_continue whose NFKC normalization is in "id_continue*"> The Unicode category codes mentioned above stand for: * *Lu* - uppercase letters * *Ll* - lowercase letters * *Lt* - titlecase letters * *Lm* - modifier letters * *Lo* - other letters * *Nl* - letter numbers * *Mn* - nonspacing marks * *Mc* - spacing combining marks * *Nd* - decimal numbers * *Pc* - connector punctuations * *Other_ID_Start* - explicit list of characters in PropList.txt to support backwards compatibility * *Other_ID_Continue* - likewise All identifiers are converted into the normal form NFKC while parsing; comparison of identifiers is based on NFKC. A non-normative HTML file listing all valid identifier characters for Unicode 14.0.0 can be found at https://www.unicode.org/Public/14.0.0/ucd/DerivedCoreProperties.txt Keywords ======== The following identifiers are used as reserved words, or *keywords* of the language, and cannot be used as ordinary identifiers. They must be spelled exactly as written here: False await else import pass None break except in raise True class finally is return and continue for lambda try as def from nonlocal while assert del global not with async elif if or yield Soft Keywords ============= New in version 3.10. Some identifiers are only reserved under specific contexts. These are known as *soft keywords*. The identifiers "match", "case" and "_" can syntactically act as keywords in contexts related to the pattern matching statement, but this distinction is done at the parser level, not when tokenizing. As soft keywords, their use with pattern matching is possible while still preserving compatibility with existing code that uses "match", "case" and "_" as identifier names. Reserved classes of identifiers =============================== Certain classes of identifiers (besides keywords) have special meanings. These classes are identified by the patterns of leading and trailing underscore characters: "_*" Not imported by "from module import *". "_" In a "case" pattern within a "match" statement, "_" is a soft keyword that denotes a wildcard. Separately, the interactive interpreter makes the result of the last evaluation available in the variable "_". (It is stored in the "builtins" module, alongside built-in functions like "print".) Elsewhere, "_" is a regular identifier. It is often used to name “special” items, but it is not special to Python itself. Note: The name "_" is often used in conjunction with internationalization; refer to the documentation for the "gettext" module for more information on this convention.It is also commonly used for unused variables. "__*__" System-defined names, informally known as “dunder” names. These names are defined by the interpreter and its implementation (including the standard library). Current system names are discussed in the Special method names section and elsewhere. More will likely be defined in future versions of Python. *Any* use of "__*__" names, in any context, that does not follow explicitly documented use, is subject to breakage without warning. "__*" Class-private names. Names in this category, when used within the context of a class definition, are re-written to use a mangled form to help avoid name clashes between “private” attributes of base and derived classes. See section Identifiers (Names). �if� imaginarya5 Imaginary literals ****************** Imaginary literals are described by the following lexical definitions: imagnumber ::= (floatnumber | digitpart) ("j" | "J") An imaginary literal yields a complex number with a real part of 0.0. Complex numbers are represented as a pair of floating point numbers and have the same restrictions on their range. To create a complex number with a nonzero real part, add a floating point number to it, e.g., "(3+4j)". Some examples of imaginary literals: 3.14j 10.j 10j .001j 1e100j 3.14e-10j 3.14_15_93j �importu�" The "import" statement ********************** import_stmt ::= "import" module ["as" identifier] ("," module ["as" identifier])* | "from" relative_module "import" identifier ["as" identifier] ("," identifier ["as" identifier])* | "from" relative_module "import" "(" identifier ["as" identifier] ("," identifier ["as" identifier])* [","] ")" | "from" relative_module "import" "*" module ::= (identifier ".")* identifier relative_module ::= "."* module | "."+ The basic import statement (no "from" clause) is executed in two steps: 1. find a module, loading and initializing it if necessary 2. define a name or names in the local namespace for the scope where the "import" statement occurs. When the statement contains multiple clauses (separated by commas) the two steps are carried out separately for each clause, just as though the clauses had been separated out into individual import statements. The details of the first step, finding and loading modules, are described in greater detail in the section on the import system, which also describes the various types of packages and modules that can be imported, as well as all the hooks that can be used to customize the import system. Note that failures in this step may indicate either that the module could not be located, *or* that an error occurred while initializing the module, which includes execution of the module’s code. If the requested module is retrieved successfully, it will be made available in the local namespace in one of three ways: * If the module name is followed by "as", then the name following "as" is bound directly to the imported module. * If no other name is specified, and the module being imported is a top level module, the module’s name is bound in the local namespace as a reference to the imported module * If the module being imported is *not* a top level module, then the name of the top level package that contains the module is bound in the local namespace as a reference to the top level package. The imported module must be accessed using its full qualified name rather than directly The "from" form uses a slightly more complex process: 1. find the module specified in the "from" clause, loading and initializing it if necessary; 2. for each of the identifiers specified in the "import" clauses: 1. check if the imported module has an attribute by that name 2. if not, attempt to import a submodule with that name and then check the imported module again for that attribute 3. if the attribute is not found, "ImportError" is raised. 4. otherwise, a reference to that value is stored in the local namespace, using the name in the "as" clause if it is present, otherwise using the attribute name Examples: import foo # foo imported and bound locally import foo.bar.baz # foo, foo.bar, and foo.bar.baz imported, foo bound locally import foo.bar.baz as fbb # foo, foo.bar, and foo.bar.baz imported, foo.bar.baz bound as fbb from foo.bar import baz # foo, foo.bar, and foo.bar.baz imported, foo.bar.baz bound as baz from foo import attr # foo imported and foo.attr bound as attr If the list of identifiers is replaced by a star ("'*'"), all public names defined in the module are bound in the local namespace for the scope where the "import" statement occurs. The *public names* defined by a module are determined by checking the module’s namespace for a variable named "__all__"; if defined, it must be a sequence of strings which are names defined or imported by that module. The names given in "__all__" are all considered public and are required to exist. If "__all__" is not defined, the set of public names includes all names found in the module’s namespace which do not begin with an underscore character ("'_'"). "__all__" should contain the entire public API. It is intended to avoid accidentally exporting items that are not part of the API (such as library modules which were imported and used within the module). The wild card form of import — "from module import *" — is only allowed at the module level. Attempting to use it in class or function definitions will raise a "SyntaxError". When specifying what module to import you do not have to specify the absolute name of the module. When a module or package is contained within another package it is possible to make a relative import within the same top package without having to mention the package name. By using leading dots in the specified module or package after "from" you can specify how high to traverse up the current package hierarchy without specifying exact names. One leading dot means the current package where the module making the import exists. Two dots means up one package level. Three dots is up two levels, etc. So if you execute "from . import mod" from a module in the "pkg" package then you will end up importing "pkg.mod". If you execute "from ..subpkg2 import mod" from within "pkg.subpkg1" you will import "pkg.subpkg2.mod". The specification for relative imports is contained in the Package Relative Imports section. "importlib.import_module()" is provided to support applications that determine dynamically the modules to be loaded. Raises an auditing event "import" with arguments "module", "filename", "sys.path", "sys.meta_path", "sys.path_hooks". Future statements ================= A *future statement* is a directive to the compiler that a particular module should be compiled using syntax or semantics that will be available in a specified future release of Python where the feature becomes standard. The future statement is intended to ease migration to future versions of Python that introduce incompatible changes to the language. It allows use of the new features on a per-module basis before the release in which the feature becomes standard. future_stmt ::= "from" "__future__" "import" feature ["as" identifier] ("," feature ["as" identifier])* | "from" "__future__" "import" "(" feature ["as" identifier] ("," feature ["as" identifier])* [","] ")" feature ::= identifier A future statement must appear near the top of the module. The only lines that can appear before a future statement are: * the module docstring (if any), * comments, * blank lines, and * other future statements. The only feature that requires using the future statement is "annotations" (see **PEP 563**). All historical features enabled by the future statement are still recognized by Python 3. The list includes "absolute_import", "division", "generators", "generator_stop", "unicode_literals", "print_function", "nested_scopes" and "with_statement". They are all redundant because they are always enabled, and only kept for backwards compatibility. A future statement is recognized and treated specially at compile time: Changes to the semantics of core constructs are often implemented by generating different code. It may even be the case that a new feature introduces new incompatible syntax (such as a new reserved word), in which case the compiler may need to parse the module differently. Such decisions cannot be pushed off until runtime. For any given release, the compiler knows which feature names have been defined, and raises a compile-time error if a future statement contains a feature not known to it. The direct runtime semantics are the same as for any import statement: there is a standard module "__future__", described later, and it will be imported in the usual way at the time the future statement is executed. The interesting runtime semantics depend on the specific feature enabled by the future statement. Note that there is nothing special about the statement: import __future__ [as name] That is not a future statement; it’s an ordinary import statement with no special semantics or syntax restrictions. Code compiled by calls to the built-in functions "exec()" and "compile()" that occur in a module "M" containing a future statement will, by default, use the new syntax or semantics associated with the future statement. This can be controlled by optional arguments to "compile()" — see the documentation of that function for details. A future statement typed at an interactive interpreter prompt will take effect for the rest of the interpreter session. If an interpreter is started with the "-i" option, is passed a script name to execute, and the script includes a future statement, it will be in effect in the interactive session started after the script is executed. See also: **PEP 236** - Back to the __future__ The original proposal for the __future__ mechanism. �ina Membership test operations ************************** The operators "in" and "not in" test for membership. "x in s" evaluates to "True" if *x* is a member of *s*, and "False" otherwise. "x not in s" returns the negation of "x in s". All built-in sequences and set types support this as well as dictionary, for which "in" tests whether the dictionary has a given key. For container types such as list, tuple, set, frozenset, dict, or collections.deque, the expression "x in y" is equivalent to "any(x is e or x == e for e in y)". For the string and bytes types, "x in y" is "True" if and only if *x* is a substring of *y*. An equivalent test is "y.find(x) != -1". Empty strings are always considered to be a substring of any other string, so """ in "abc"" will return "True". For user-defined classes which define the "__contains__()" method, "x in y" returns "True" if "y.__contains__(x)" returns a true value, and "False" otherwise. For user-defined classes which do not define "__contains__()" but do define "__iter__()", "x in y" is "True" if some value "z", for which the expression "x is z or x == z" is true, is produced while iterating over "y". If an exception is raised during the iteration, it is as if "in" raised that exception. Lastly, the old-style iteration protocol is tried: if a class defines "__getitem__()", "x in y" is "True" if and only if there is a non- negative integer index *i* such that "x is y[i] or x == y[i]", and no lower integer index raises the "IndexError" exception. (If any other exception is raised, it is as if "in" raised that exception). The operator "not in" is defined to have the inverse truth value of "in". �integersaV Integer literals **************** Integer literals are described by the following lexical definitions: integer ::= decinteger | bininteger | octinteger | hexinteger decinteger ::= nonzerodigit (["_"] digit)* | "0"+ (["_"] "0")* bininteger ::= "0" ("b" | "B") (["_"] bindigit)+ octinteger ::= "0" ("o" | "O") (["_"] octdigit)+ hexinteger ::= "0" ("x" | "X") (["_"] hexdigit)+ nonzerodigit ::= "1"..."9" digit ::= "0"..."9" bindigit ::= "0" | "1" octdigit ::= "0"..."7" hexdigit ::= digit | "a"..."f" | "A"..."F" There is no limit for the length of integer literals apart from what can be stored in available memory. Underscores are ignored for determining the numeric value of the literal. They can be used to group digits for enhanced readability. One underscore can occur between digits, and after base specifiers like "0x". Note that leading zeros in a non-zero decimal number are not allowed. This is for disambiguation with C-style octal literals, which Python used before version 3.0. Some examples of integer literals: 7 2147483647 0o177 0b100110111 3 79228162514264337593543950336 0o377 0xdeadbeef 100_000_000_000 0b_1110_0101 Changed in version 3.6: Underscores are now allowed for grouping purposes in literals. �lambdaa Lambdas ******* lambda_expr ::= "lambda" [parameter_list] ":" expression Lambda expressions (sometimes called lambda forms) are used to create anonymous functions. The expression "lambda parameters: expression" yields a function object. The unnamed object behaves like a function object defined with: def <lambda>(parameters): return expression See section Function definitions for the syntax of parameter lists. Note that functions created with lambda expressions cannot contain statements or annotations. �listsa/ List displays ************* A list display is a possibly empty series of expressions enclosed in square brackets: list_display ::= "[" [starred_list | comprehension] "]" A list display yields a new list object, the contents being specified by either a list of expressions or a comprehension. When a comma- separated list of expressions is supplied, its elements are evaluated from left to right and placed into the list object in that order. When a comprehension is supplied, the list is constructed from the elements resulting from the comprehension. �namingu� Naming and binding ****************** Binding of names ================ *Names* refer to objects. Names are introduced by name binding operations. The following constructs bind names: * formal parameters to functions, * class definitions, * function definitions, * assignment expressions, * targets that are identifiers if occurring in an assignment: * "for" loop header, * after "as" in a "with" statement, "except" clause, "except*" clause, or in the as-pattern in structural pattern matching, * in a capture pattern in structural pattern matching * "import" statements. The "import" statement of the form "from ... import *" binds all names defined in the imported module, except those beginning with an underscore. This form may only be used at the module level. A target occurring in a "del" statement is also considered bound for this purpose (though the actual semantics are to unbind the name). Each assignment or import statement occurs within a block defined by a class or function definition or at the module level (the top-level code block). If a name is bound in a block, it is a local variable of that block, unless declared as "nonlocal" or "global". If a name is bound at the module level, it is a global variable. (The variables of the module code block are local and global.) If a variable is used in a code block but not defined there, it is a *free variable*. Each occurrence of a name in the program text refers to the *binding* of that name established by the following name resolution rules. Resolution of names =================== A *scope* defines the visibility of a name within a block. If a local variable is defined in a block, its scope includes that block. If the definition occurs in a function block, the scope extends to any blocks contained within the defining one, unless a contained block introduces a different binding for the name. When a name is used in a code block, it is resolved using the nearest enclosing scope. The set of all such scopes visible to a code block is called the block’s *environment*. When a name is not found at all, a "NameError" exception is raised. If the current scope is a function scope, and the name refers to a local variable that has not yet been bound to a value at the point where the name is used, an "UnboundLocalError" exception is raised. "UnboundLocalError" is a subclass of "NameError". If a name binding operation occurs anywhere within a code block, all uses of the name within the block are treated as references to the current block. This can lead to errors when a name is used within a block before it is bound. This rule is subtle. Python lacks declarations and allows name binding operations to occur anywhere within a code block. The local variables of a code block can be determined by scanning the entire text of the block for name binding operations. See the FAQ entry on UnboundLocalError for examples. If the "global" statement occurs within a block, all uses of the names specified in the statement refer to the bindings of those names in the top-level namespace. Names are resolved in the top-level namespace by searching the global namespace, i.e. the namespace of the module containing the code block, and the builtins namespace, the namespace of the module "builtins". The global namespace is searched first. If the names are not found there, the builtins namespace is searched. The "global" statement must precede all uses of the listed names. The "global" statement has the same scope as a name binding operation in the same block. If the nearest enclosing scope for a free variable contains a global statement, the free variable is treated as a global. The "nonlocal" statement causes corresponding names to refer to previously bound variables in the nearest enclosing function scope. "SyntaxError" is raised at compile time if the given name does not exist in any enclosing function scope. The namespace for a module is automatically created the first time a module is imported. The main module for a script is always called "__main__". Class definition blocks and arguments to "exec()" and "eval()" are special in the context of name resolution. A class definition is an executable statement that may use and define names. These references follow the normal rules for name resolution with an exception that unbound local variables are looked up in the global namespace. The namespace of the class definition becomes the attribute dictionary of the class. The scope of names defined in a class block is limited to the class block; it does not extend to the code blocks of methods – this includes comprehensions and generator expressions since they are implemented using a function scope. This means that the following will fail: class A: a = 42 b = list(a + i for i in range(10)) Builtins and restricted execution ================================= **CPython implementation detail:** Users should not touch "__builtins__"; it is strictly an implementation detail. Users wanting to override values in the builtins namespace should "import" the "builtins" module and modify its attributes appropriately. The builtins namespace associated with the execution of a code block is actually found by looking up the name "__builtins__" in its global namespace; this should be a dictionary or a module (in the latter case the module’s dictionary is used). By default, when in the "__main__" module, "__builtins__" is the built-in module "builtins"; when in any other module, "__builtins__" is an alias for the dictionary of the "builtins" module itself. Interaction with dynamic features ================================= Name resolution of free variables occurs at runtime, not at compile time. This means that the following code will print 42: i = 10 def f(): print(i) i = 42 f() The "eval()" and "exec()" functions do not have access to the full environment for resolving names. Names may be resolved in the local and global namespaces of the caller. Free variables are not resolved in the nearest enclosing namespace, but in the global namespace. [1] The "exec()" and "eval()" functions have optional arguments to override the global and local namespace. If only one namespace is specified, it is used for both. �nonlocala� The "nonlocal" statement ************************ nonlocal_stmt ::= "nonlocal" identifier ("," identifier)* The "nonlocal" statement causes the listed identifiers to refer to previously bound variables in the nearest enclosing scope excluding globals. This is important because the default behavior for binding is to search the local namespace first. The statement allows encapsulated code to rebind variables outside of the local scope besides the global (module) scope. Names listed in a "nonlocal" statement, unlike those listed in a "global" statement, must refer to pre-existing bindings in an enclosing scope (the scope in which a new binding should be created cannot be determined unambiguously). Names listed in a "nonlocal" statement must not collide with pre- existing bindings in the local scope. See also: **PEP 3104** - Access to Names in Outer Scopes The specification for the "nonlocal" statement. �numbersu� Numeric literals **************** There are three types of numeric literals: integers, floating point numbers, and imaginary numbers. There are no complex literals (complex numbers can be formed by adding a real number and an imaginary number). Note that numeric literals do not include a sign; a phrase like "-1" is actually an expression composed of the unary operator ‘"-"’ and the literal "1". z numeric-typesu� Emulating numeric types *********************** The following methods can be defined to emulate numeric objects. Methods corresponding to operations that are not supported by the particular kind of number implemented (e.g., bitwise operations for non-integral numbers) should be left undefined. object.__add__(self, other) object.__sub__(self, other) object.__mul__(self, other) object.__matmul__(self, other) object.__truediv__(self, other) object.__floordiv__(self, other) object.__mod__(self, other) object.__divmod__(self, other) object.__pow__(self, other[, modulo]) object.__lshift__(self, other) object.__rshift__(self, other) object.__and__(self, other) object.__xor__(self, other) object.__or__(self, other) These methods are called to implement the binary arithmetic operations ("+", "-", "*", "@", "/", "//", "%", "divmod()", "pow()", "**", "<<", ">>", "&", "^", "|"). For instance, to evaluate the expression "x + y", where *x* is an instance of a class that has an "__add__()" method, "type(x).__add__(x, y)" is called. The "__divmod__()" method should be the equivalent to using "__floordiv__()" and "__mod__()"; it should not be related to "__truediv__()". Note that "__pow__()" should be defined to accept an optional third argument if the ternary version of the built-in "pow()" function is to be supported. If one of those methods does not support the operation with the supplied arguments, it should return "NotImplemented". object.__radd__(self, other) object.__rsub__(self, other) object.__rmul__(self, other) object.__rmatmul__(self, other) object.__rtruediv__(self, other) object.__rfloordiv__(self, other) object.__rmod__(self, other) object.__rdivmod__(self, other) object.__rpow__(self, other[, modulo]) object.__rlshift__(self, other) object.__rrshift__(self, other) object.__rand__(self, other) object.__rxor__(self, other) object.__ror__(self, other) These methods are called to implement the binary arithmetic operations ("+", "-", "*", "@", "/", "//", "%", "divmod()", "pow()", "**", "<<", ">>", "&", "^", "|") with reflected (swapped) operands. These functions are only called if the left operand does not support the corresponding operation [3] and the operands are of different types. [4] For instance, to evaluate the expression "x - y", where *y* is an instance of a class that has an "__rsub__()" method, "type(y).__rsub__(y, x)" is called if "type(x).__sub__(x, y)" returns *NotImplemented*. Note that ternary "pow()" will not try calling "__rpow__()" (the coercion rules would become too complicated). Note: If the right operand’s type is a subclass of the left operand’s type and that subclass provides a different implementation of the reflected method for the operation, this method will be called before the left operand’s non-reflected method. This behavior allows subclasses to override their ancestors’ operations. object.__iadd__(self, other) object.__isub__(self, other) object.__imul__(self, other) object.__imatmul__(self, other) object.__itruediv__(self, other) object.__ifloordiv__(self, other) object.__imod__(self, other) object.__ipow__(self, other[, modulo]) object.__ilshift__(self, other) object.__irshift__(self, other) object.__iand__(self, other) object.__ixor__(self, other) object.__ior__(self, other) These methods are called to implement the augmented arithmetic assignments ("+=", "-=", "*=", "@=", "/=", "//=", "%=", "**=", "<<=", ">>=", "&=", "^=", "|="). These methods should attempt to do the operation in-place (modifying *self*) and return the result (which could be, but does not have to be, *self*). If a specific method is not defined, the augmented assignment falls back to the normal methods. For instance, if *x* is an instance of a class with an "__iadd__()" method, "x += y" is equivalent to "x = x.__iadd__(y)" . Otherwise, "x.__add__(y)" and "y.__radd__(x)" are considered, as with the evaluation of "x + y". In certain situations, augmented assignment can result in unexpected errors (see Why does a_tuple[i] += [‘item’] raise an exception when the addition works?), but this behavior is in fact part of the data model. object.__neg__(self) object.__pos__(self) object.__abs__(self) object.__invert__(self) Called to implement the unary arithmetic operations ("-", "+", "abs()" and "~"). object.__complex__(self) object.__int__(self) object.__float__(self) Called to implement the built-in functions "complex()", "int()" and "float()". Should return a value of the appropriate type. object.__index__(self) Called to implement "operator.index()", and whenever Python needs to losslessly convert the numeric object to an integer object (such as in slicing, or in the built-in "bin()", "hex()" and "oct()" functions). Presence of this method indicates that the numeric object is an integer type. Must return an integer. If "__int__()", "__float__()" and "__complex__()" are not defined then corresponding built-in functions "int()", "float()" and "complex()" fall back to "__index__()". object.__round__(self[, ndigits]) object.__trunc__(self) object.__floor__(self) object.__ceil__(self) Called to implement the built-in function "round()" and "math" functions "trunc()", "floor()" and "ceil()". Unless *ndigits* is passed to "__round__()" all these methods should return the value of the object truncated to an "Integral" (typically an "int"). The built-in function "int()" falls back to "__trunc__()" if neither "__int__()" nor "__index__()" is defined. Changed in version 3.11: The delegation of "int()" to "__trunc__()" is deprecated. �objectsu Objects, values and types ************************* *Objects* are Python’s abstraction for data. All data in a Python program is represented by objects or by relations between objects. (In a sense, and in conformance to Von Neumann’s model of a “stored program computer”, code is also represented by objects.) Every object has an identity, a type and a value. An object’s *identity* never changes once it has been created; you may think of it as the object’s address in memory. The ‘"is"’ operator compares the identity of two objects; the "id()" function returns an integer representing its identity. **CPython implementation detail:** For CPython, "id(x)" is the memory address where "x" is stored. An object’s type determines the operations that the object supports (e.g., “does it have a length?”) and also defines the possible values for objects of that type. The "type()" function returns an object’s type (which is an object itself). Like its identity, an object’s *type* is also unchangeable. [1] The *value* of some objects can change. Objects whose value can change are said to be *mutable*; objects whose value is unchangeable once they are created are called *immutable*. (The value of an immutable container object that contains a reference to a mutable object can change when the latter’s value is changed; however the container is still considered immutable, because the collection of objects it contains cannot be changed. So, immutability is not strictly the same as having an unchangeable value, it is more subtle.) An object’s mutability is determined by its type; for instance, numbers, strings and tuples are immutable, while dictionaries and lists are mutable. Objects are never explicitly destroyed; however, when they become unreachable they may be garbage-collected. An implementation is allowed to postpone garbage collection or omit it altogether — it is a matter of implementation quality how garbage collection is implemented, as long as no objects are collected that are still reachable. **CPython implementation detail:** CPython currently uses a reference- counting scheme with (optional) delayed detection of cyclically linked garbage, which collects most objects as soon as they become unreachable, but is not guaranteed to collect garbage containing circular references. See the documentation of the "gc" module for information on controlling the collection of cyclic garbage. Other implementations act differently and CPython may change. Do not depend on immediate finalization of objects when they become unreachable (so you should always close files explicitly). Note that the use of the implementation’s tracing or debugging facilities may keep objects alive that would normally be collectable. Also note that catching an exception with a ‘"try"…"except"’ statement may keep objects alive. Some objects contain references to “external” resources such as open files or windows. It is understood that these resources are freed when the object is garbage-collected, but since garbage collection is not guaranteed to happen, such objects also provide an explicit way to release the external resource, usually a "close()" method. Programs are strongly recommended to explicitly close such objects. The ‘"try"…"finally"’ statement and the ‘"with"’ statement provide convenient ways to do this. Some objects contain references to other objects; these are called *containers*. Examples of containers are tuples, lists and dictionaries. The references are part of a container’s value. In most cases, when we talk about the value of a container, we imply the values, not the identities of the contained objects; however, when we talk about the mutability of a container, only the identities of the immediately contained objects are implied. So, if an immutable container (like a tuple) contains a reference to a mutable object, its value changes if that mutable object is changed. Types affect almost all aspects of object behavior. Even the importance of object identity is affected in some sense: for immutable types, operations that compute new values may actually return a reference to any existing object with the same type and value, while for mutable objects this is not allowed. E.g., after "a = 1; b = 1", "a" and "b" may or may not refer to the same object with the value one, depending on the implementation, but after "c = []; d = []", "c" and "d" are guaranteed to refer to two different, unique, newly created empty lists. (Note that "c = d = []" assigns the same object to both "c" and "d".) zoperator-summaryu� Operator precedence ******************* The following table summarizes the operator precedence in Python, from highest precedence (most binding) to lowest precedence (least binding). Operators in the same box have the same precedence. Unless the syntax is explicitly given, operators are binary. Operators in the same box group left to right (except for exponentiation and conditional expressions, which group from right to left). Note that comparisons, membership tests, and identity tests, all have the same precedence and have a left-to-right chaining feature as described in the Comparisons section. +-------------------------------------------------+---------------------------------------+ | Operator | Description | |=================================================|=======================================| | "(expressions...)", "[expressions...]", "{key: | Binding or parenthesized expression, | | value...}", "{expressions...}" | list display, dictionary display, set | | | display | +-------------------------------------------------+---------------------------------------+ | "x[index]", "x[index:index]", | Subscription, slicing, call, | | "x(arguments...)", "x.attribute" | attribute reference | +-------------------------------------------------+---------------------------------------+ | "await x" | Await expression | +-------------------------------------------------+---------------------------------------+ | "**" | Exponentiation [5] | +-------------------------------------------------+---------------------------------------+ | "+x", "-x", "~x" | Positive, negative, bitwise NOT | +-------------------------------------------------+---------------------------------------+ | "*", "@", "/", "//", "%" | Multiplication, matrix | | | multiplication, division, floor | | | division, remainder [6] | +-------------------------------------------------+---------------------------------------+ | "+", "-" | Addition and subtraction | +-------------------------------------------------+---------------------------------------+ | "<<", ">>" | Shifts | +-------------------------------------------------+---------------------------------------+ | "&" | Bitwise AND | +-------------------------------------------------+---------------------------------------+ | "^" | Bitwise XOR | +-------------------------------------------------+---------------------------------------+ | "|" | Bitwise OR | +-------------------------------------------------+---------------------------------------+ | "in", "not in", "is", "is not", "<", "<=", ">", | Comparisons, including membership | | ">=", "!=", "==" | tests and identity tests | +-------------------------------------------------+---------------------------------------+ | "not x" | Boolean NOT | +-------------------------------------------------+---------------------------------------+ | "and" | Boolean AND | +-------------------------------------------------+---------------------------------------+ | "or" | Boolean OR | +-------------------------------------------------+---------------------------------------+ | "if" – "else" | Conditional expression | +-------------------------------------------------+---------------------------------------+ | "lambda" | Lambda expression | +-------------------------------------------------+---------------------------------------+ | ":=" | Assignment expression | +-------------------------------------------------+---------------------------------------+ -[ Footnotes ]- [1] While "abs(x%y) < abs(y)" is true mathematically, for floats it may not be true numerically due to roundoff. For example, and assuming a platform on which a Python float is an IEEE 754 double- precision number, in order that "-1e-100 % 1e100" have the same sign as "1e100", the computed result is "-1e-100 + 1e100", which is numerically exactly equal to "1e100". The function "math.fmod()" returns a result whose sign matches the sign of the first argument instead, and so returns "-1e-100" in this case. Which approach is more appropriate depends on the application. [2] If x is very close to an exact integer multiple of y, it’s possible for "x//y" to be one larger than "(x-x%y)//y" due to rounding. In such cases, Python returns the latter result, in order to preserve that "divmod(x,y)[0] * y + x % y" be very close to "x". [3] The Unicode standard distinguishes between *code points* (e.g. U+0041) and *abstract characters* (e.g. “LATIN CAPITAL LETTER A”). While most abstract characters in Unicode are only represented using one code point, there is a number of abstract characters that can in addition be represented using a sequence of more than one code point. For example, the abstract character “LATIN CAPITAL LETTER C WITH CEDILLA” can be represented as a single *precomposed character* at code position U+00C7, or as a sequence of a *base character* at code position U+0043 (LATIN CAPITAL LETTER C), followed by a *combining character* at code position U+0327 (COMBINING CEDILLA). The comparison operators on strings compare at the level of Unicode code points. This may be counter-intuitive to humans. For example, ""\u00C7" == "\u0043\u0327"" is "False", even though both strings represent the same abstract character “LATIN CAPITAL LETTER C WITH CEDILLA”. To compare strings at the level of abstract characters (that is, in a way intuitive to humans), use "unicodedata.normalize()". [4] Due to automatic garbage-collection, free lists, and the dynamic nature of descriptors, you may notice seemingly unusual behaviour in certain uses of the "is" operator, like those involving comparisons between instance methods, or constants. Check their documentation for more info. [5] The power operator "**" binds less tightly than an arithmetic or bitwise unary operator on its right, that is, "2**-1" is "0.5". [6] The "%" operator is also used for string formatting; the same precedence applies. �passuw The "pass" statement ******************** pass_stmt ::= "pass" "pass" is a null operation — when it is executed, nothing happens. It is useful as a placeholder when a statement is required syntactically, but no code needs to be executed, for example: def f(arg): pass # a function that does nothing (yet) class C: pass # a class with no methods (yet) �powera� The power operator ****************** The power operator binds more tightly than unary operators on its left; it binds less tightly than unary operators on its right. The syntax is: power ::= (await_expr | primary) ["**" u_expr] Thus, in an unparenthesized sequence of power and unary operators, the operators are evaluated from right to left (this does not constrain the evaluation order for the operands): "-1**2" results in "-1". The power operator has the same semantics as the built-in "pow()" function, when called with two arguments: it yields its left argument raised to the power of its right argument. The numeric arguments are first converted to a common type, and the result is of that type. For int operands, the result has the same type as the operands unless the second argument is negative; in that case, all arguments are converted to float and a float result is delivered. For example, "10**2" returns "100", but "10**-2" returns "0.01". Raising "0.0" to a negative power results in a "ZeroDivisionError". Raising a negative number to a fractional power results in a "complex" number. (In earlier versions it raised a "ValueError".) This operation can be customized using the special "__pow__()" method. �raiseu� The "raise" statement ********************* raise_stmt ::= "raise" [expression ["from" expression]] If no expressions are present, "raise" re-raises the exception that is currently being handled, which is also known as the *active exception*. If there isn’t currently an active exception, a "RuntimeError" exception is raised indicating that this is an error. Otherwise, "raise" evaluates the first expression as the exception object. It must be either a subclass or an instance of "BaseException". If it is a class, the exception instance will be obtained when needed by instantiating the class with no arguments. The *type* of the exception is the exception instance’s class, the *value* is the instance itself. A traceback object is normally created automatically when an exception is raised and attached to it as the "__traceback__" attribute, which is writable. You can create an exception and set your own traceback in one step using the "with_traceback()" exception method (which returns the same exception instance, with its traceback set to its argument), like so: raise Exception("foo occurred").with_traceback(tracebackobj) The "from" clause is used for exception chaining: if given, the second *expression* must be another exception class or instance. If the second expression is an exception instance, it will be attached to the raised exception as the "__cause__" attribute (which is writable). If the expression is an exception class, the class will be instantiated and the resulting exception instance will be attached to the raised exception as the "__cause__" attribute. If the raised exception is not handled, both exceptions will be printed: >>> try: ... print(1 / 0) ... except Exception as exc: ... raise RuntimeError("Something bad happened") from exc ... Traceback (most recent call last): File "<stdin>", line 2, in <module> ZeroDivisionError: division by zero The above exception was the direct cause of the following exception: Traceback (most recent call last): File "<stdin>", line 4, in <module> RuntimeError: Something bad happened A similar mechanism works implicitly if a new exception is raised when an exception is already being handled. An exception may be handled when an "except" or "finally" clause, or a "with" statement, is used. The previous exception is then attached as the new exception’s "__context__" attribute: >>> try: ... print(1 / 0) ... except: ... raise RuntimeError("Something bad happened") ... Traceback (most recent call last): File "<stdin>", line 2, in <module> ZeroDivisionError: division by zero During handling of the above exception, another exception occurred: Traceback (most recent call last): File "<stdin>", line 4, in <module> RuntimeError: Something bad happened Exception chaining can be explicitly suppressed by specifying "None" in the "from" clause: >>> try: ... print(1 / 0) ... except: ... raise RuntimeError("Something bad happened") from None ... Traceback (most recent call last): File "<stdin>", line 4, in <module> RuntimeError: Something bad happened Additional information on exceptions can be found in section Exceptions, and information about handling exceptions is in section The try statement. Changed in version 3.3: "None" is now permitted as "Y" in "raise X from Y". New in version 3.3: The "__suppress_context__" attribute to suppress automatic display of the exception context. Changed in version 3.11: If the traceback of the active exception is modified in an "except" clause, a subsequent "raise" statement re- raises the exception with the modified traceback. Previously, the exception was re-raised with the traceback it had when it was caught. �returna The "return" statement ********************** return_stmt ::= "return" [expression_list] "return" may only occur syntactically nested in a function definition, not within a nested class definition. If an expression list is present, it is evaluated, else "None" is substituted. "return" leaves the current function call with the expression list (or "None") as return value. When "return" passes control out of a "try" statement with a "finally" clause, that "finally" clause is executed before really leaving the function. In a generator function, the "return" statement indicates that the generator is done and will cause "StopIteration" to be raised. The returned value (if any) is used as an argument to construct "StopIteration" and becomes the "StopIteration.value" attribute. In an asynchronous generator function, an empty "return" statement indicates that the asynchronous generator is done and will cause "StopAsyncIteration" to be raised. A non-empty "return" statement is a syntax error in an asynchronous generator function. zsequence-typesu� Emulating container types ************************* The following methods can be defined to implement container objects. Containers usually are *sequences* (such as "lists" or "tuples") or *mappings* (like "dictionaries"), but can represent other containers as well. The first set of methods is used either to emulate a sequence or to emulate a mapping; the difference is that for a sequence, the allowable keys should be the integers *k* for which "0 <= k < N" where *N* is the length of the sequence, or "slice" objects, which define a range of items. It is also recommended that mappings provide the methods "keys()", "values()", "items()", "get()", "clear()", "setdefault()", "pop()", "popitem()", "copy()", and "update()" behaving similar to those for Python’s standard "dictionary" objects. The "collections.abc" module provides a "MutableMapping" *abstract base class* to help create those methods from a base set of "__getitem__()", "__setitem__()", "__delitem__()", and "keys()". Mutable sequences should provide methods "append()", "count()", "index()", "extend()", "insert()", "pop()", "remove()", "reverse()" and "sort()", like Python standard "list" objects. Finally, sequence types should implement addition (meaning concatenation) and multiplication (meaning repetition) by defining the methods "__add__()", "__radd__()", "__iadd__()", "__mul__()", "__rmul__()" and "__imul__()" described below; they should not define other numerical operators. It is recommended that both mappings and sequences implement the "__contains__()" method to allow efficient use of the "in" operator; for mappings, "in" should search the mapping’s keys; for sequences, it should search through the values. It is further recommended that both mappings and sequences implement the "__iter__()" method to allow efficient iteration through the container; for mappings, "__iter__()" should iterate through the object’s keys; for sequences, it should iterate through the values. object.__len__(self) Called to implement the built-in function "len()". Should return the length of the object, an integer ">=" 0. Also, an object that doesn’t define a "__bool__()" method and whose "__len__()" method returns zero is considered to be false in a Boolean context. **CPython implementation detail:** In CPython, the length is required to be at most "sys.maxsize". If the length is larger than "sys.maxsize" some features (such as "len()") may raise "OverflowError". To prevent raising "OverflowError" by truth value testing, an object must define a "__bool__()" method. object.__length_hint__(self) Called to implement "operator.length_hint()". Should return an estimated length for the object (which may be greater or less than the actual length). The length must be an integer ">=" 0. The return value may also be "NotImplemented", which is treated the same as if the "__length_hint__" method didn’t exist at all. This method is purely an optimization and is never required for correctness. New in version 3.4. Note: Slicing is done exclusively with the following three methods. A call like a[1:2] = b is translated to a[slice(1, 2, None)] = b and so forth. Missing slice items are always filled in with "None". object.__getitem__(self, key) Called to implement evaluation of "self[key]". For *sequence* types, the accepted keys should be integers and slice objects. Note that the special interpretation of negative indexes (if the class wishes to emulate a *sequence* type) is up to the "__getitem__()" method. If *key* is of an inappropriate type, "TypeError" may be raised; if of a value outside the set of indexes for the sequence (after any special interpretation of negative values), "IndexError" should be raised. For *mapping* types, if *key* is missing (not in the container), "KeyError" should be raised. Note: "for" loops expect that an "IndexError" will be raised for illegal indexes to allow proper detection of the end of the sequence. Note: When subscripting a *class*, the special class method "__class_getitem__()" may be called instead of "__getitem__()". See __class_getitem__ versus __getitem__ for more details. object.__setitem__(self, key, value) Called to implement assignment to "self[key]". Same note as for "__getitem__()". This should only be implemented for mappings if the objects support changes to the values for keys, or if new keys can be added, or for sequences if elements can be replaced. The same exceptions should be raised for improper *key* values as for the "__getitem__()" method. object.__delitem__(self, key) Called to implement deletion of "self[key]". Same note as for "__getitem__()". This should only be implemented for mappings if the objects support removal of keys, or for sequences if elements can be removed from the sequence. The same exceptions should be raised for improper *key* values as for the "__getitem__()" method. object.__missing__(self, key) Called by "dict"."__getitem__()" to implement "self[key]" for dict subclasses when key is not in the dictionary. object.__iter__(self) This method is called when an *iterator* is required for a container. This method should return a new iterator object that can iterate over all the objects in the container. For mappings, it should iterate over the keys of the container. object.__reversed__(self) Called (if present) by the "reversed()" built-in to implement reverse iteration. It should return a new iterator object that iterates over all the objects in the container in reverse order. If the "__reversed__()" method is not provided, the "reversed()" built-in will fall back to using the sequence protocol ("__len__()" and "__getitem__()"). Objects that support the sequence protocol should only provide "__reversed__()" if they can provide an implementation that is more efficient than the one provided by "reversed()". The membership test operators ("in" and "not in") are normally implemented as an iteration through a container. However, container objects can supply the following special method with a more efficient implementation, which also does not require the object be iterable. object.__contains__(self, item) Called to implement membership test operators. Should return true if *item* is in *self*, false otherwise. For mapping objects, this should consider the keys of the mapping rather than the values or the key-item pairs. For objects that don’t define "__contains__()", the membership test first tries iteration via "__iter__()", then the old sequence iteration protocol via "__getitem__()", see this section in the language reference. �shiftinga5 Shifting operations ******************* The shifting operations have lower priority than the arithmetic operations: shift_expr ::= a_expr | shift_expr ("<<" | ">>") a_expr These operators accept integers as arguments. They shift the first argument to the left or right by the number of bits given by the second argument. This operation can be customized using the special "__lshift__()" and "__rshift__()" methods. A right shift by *n* bits is defined as floor division by "pow(2,n)". A left shift by *n* bits is defined as multiplication with "pow(2,n)". �slicingsa� Slicings ******** A slicing selects a range of items in a sequence object (e.g., a string, tuple or list). Slicings may be used as expressions or as targets in assignment or "del" statements. The syntax for a slicing: slicing ::= primary "[" slice_list "]" slice_list ::= slice_item ("," slice_item)* [","] slice_item ::= expression | proper_slice proper_slice ::= [lower_bound] ":" [upper_bound] [ ":" [stride] ] lower_bound ::= expression upper_bound ::= expression stride ::= expression There is ambiguity in the formal syntax here: anything that looks like an expression list also looks like a slice list, so any subscription can be interpreted as a slicing. Rather than further complicating the syntax, this is disambiguated by defining that in this case the interpretation as a subscription takes priority over the interpretation as a slicing (this is the case if the slice list contains no proper slice). The semantics for a slicing are as follows. The primary is indexed (using the same "__getitem__()" method as normal subscription) with a key that is constructed from the slice list, as follows. If the slice list contains at least one comma, the key is a tuple containing the conversion of the slice items; otherwise, the conversion of the lone slice item is the key. The conversion of a slice item that is an expression is that expression. The conversion of a proper slice is a slice object (see section The standard type hierarchy) whose "start", "stop" and "step" attributes are the values of the expressions given as lower bound, upper bound and stride, respectively, substituting "None" for missing expressions. � specialattrsu' Special Attributes ****************** The implementation adds a few special read-only attributes to several object types, where they are relevant. Some of these are not reported by the "dir()" built-in function. object.__dict__ A dictionary or other mapping object used to store an object’s (writable) attributes. instance.__class__ The class to which a class instance belongs. class.__bases__ The tuple of base classes of a class object. definition.__name__ The name of the class, function, method, descriptor, or generator instance. definition.__qualname__ The *qualified name* of the class, function, method, descriptor, or generator instance. New in version 3.3. class.__mro__ This attribute is a tuple of classes that are considered when looking for base classes during method resolution. class.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__". class.__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: >>> int.__subclasses__() [<class 'bool'>] � specialnamesu�� 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.) 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. Note: "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 implementation detail:** 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. See also: Documentation for the "gc" module. Warning: 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. object.__str__(self) Called by "str(object)" and the built-in functions "format()" and "print()" to compute the “informal” or nicely printable string representation of an object. The return value must be a string 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. 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 Format Specification Mini-Language for a description of the standard formatting syntax. The return value must be a string object. Changed in version 3.4: The __format__ method of "object" itself raises a "TypeError" if passed any non-empty string. Changed in version 3.7: "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()". 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 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. 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)) Note: "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; 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. Note: 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(n^2) complexity. See http://www.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).See also "PYTHONHASHSEED". Changed in version 3.3: Hash randomization is enabled by default. object.__bool__(self) Called to implement truth value testing and the built-in operation "bool()"; should return "False" or "True". When this method is not defined, "__len__()" is called, if it is defined, and the object is considered true if its result is nonzero. If a class defines neither "__len__()" nor "__bool__()", all its instances are considered true. Customizing attribute access ============================ The following methods can be defined to customize the meaning of attribute access (use of, assignment to, or deletion of "x.name") for class instances. object.__getattr__(self, name) Called when the default attribute access fails with an "AttributeError" (either "__getattribute__()" raises an "AttributeError" because *name* is not an instance attribute or an attribute in the class tree for "self"; or "__get__()" of a *name* property raises "AttributeError"). This method should either return the (computed) attribute value or raise an "AttributeError" exception. Note that if the attribute is found through the normal mechanism, "__getattr__()" is not called. (This is an intentional asymmetry between "__getattr__()" and "__setattr__()".) This is done both for efficiency reasons and because otherwise "__getattr__()" would have no way to access other attributes of the instance. Note that at least for instance variables, you can fake total control by not inserting any values in the instance attribute dictionary (but instead inserting them in another object). See the "__getattribute__()" method below for a way to actually get total control over attribute access. object.__getattribute__(self, name) Called unconditionally to implement attribute accesses for instances of the class. If the class also defines "__getattr__()", the latter will not be called unless "__getattribute__()" either calls it explicitly or raises an "AttributeError". This method should return the (computed) attribute value or raise an "AttributeError" exception. In order to avoid infinite recursion in this method, its implementation should always call the base class method with the same name to access any attributes it needs, for example, "object.__getattribute__(self, name)". Note: This method may still be bypassed when looking up special methods as the result of implicit invocation via language syntax or built-in functions. See Special method lookup. For certain sensitive attribute accesses, raises an auditing event "object.__getattr__" with arguments "obj" and "name". object.__setattr__(self, name, value) Called when an attribute assignment is attempted. This is called instead of the normal mechanism (i.e. store the value in the instance dictionary). *name* is the attribute name, *value* is the value to be assigned to it. If "__setattr__()" wants to assign to an instance attribute, it should call the base class method with the same name, for example, "object.__setattr__(self, name, value)". For certain sensitive attribute assignments, raises an auditing event "object.__setattr__" with arguments "obj", "name", "value". object.__delattr__(self, name) Like "__setattr__()" but for attribute deletion instead of assignment. This should only be implemented if "del obj.name" is meaningful for the object. For certain sensitive attribute deletions, raises an auditing event "object.__delattr__" with arguments "obj" and "name". object.__dir__(self) Called when "dir()" is called on the object. A sequence must be returned. "dir()" converts the returned sequence to a list and sorts it. Customizing module attribute access ----------------------------------- Special names "__getattr__" and "__dir__" can be also used to customize access to module attributes. The "__getattr__" function at the module level should accept one argument which is the name of an attribute and return the computed value or raise an "AttributeError". If an attribute is not found on a module object through the normal lookup, i.e. "object.__getattribute__()", then "__getattr__" is searched in the module "__dict__" before raising an "AttributeError". If found, it is called with the attribute name and the result is returned. The "__dir__" function should accept no arguments, and return a sequence of strings that represents the names accessible on module. If present, this function overrides the standard "dir()" search on a module. For a more fine grained customization of the module behavior (setting attributes, properties, etc.), one can set the "__class__" attribute of a module object to a subclass of "types.ModuleType". For example: import sys from types import ModuleType class VerboseModule(ModuleType): def __repr__(self): return f'Verbose {self.__name__}' def __setattr__(self, attr, value): print(f'Setting {attr}...') super().__setattr__(attr, value) sys.modules[__name__].__class__ = VerboseModule Note: Defining module "__getattr__" and setting module "__class__" only affect lookups made using the attribute access syntax – directly accessing the module globals (whether by code within the module, or via a reference to the module’s globals dictionary) is unaffected. Changed in version 3.5: "__class__" module attribute is now writable. New in version 3.7: "__getattr__" and "__dir__" module attributes. See also: **PEP 562** - Module __getattr__ and __dir__ Describes the "__getattr__" and "__dir__" functions on modules. Implementing Descriptors ------------------------ The following methods only apply when an instance of the class containing the method (a so-called *descriptor* class) appears in an *owner* class (the descriptor must be in either the owner’s class dictionary or in the class dictionary for one of its parents). In the examples below, “the attribute” refers to the attribute whose name is the key of the property in the owner class’ "__dict__". object.__get__(self, instance, owner=None) Called to get the attribute of the owner class (class attribute access) or of an instance of that class (instance attribute access). The optional *owner* argument is the owner class, while *instance* is the instance that the attribute was accessed through, or "None" when the attribute is accessed through the *owner*. This method should return the computed attribute value or raise an "AttributeError" exception. **PEP 252** specifies that "__get__()" is callable with one or two arguments. Python’s own built-in descriptors support this specification; however, it is likely that some third-party tools have descriptors that require both arguments. Python’s own "__getattribute__()" implementation always passes in both arguments whether they are required or not. object.__set__(self, instance, value) Called to set the attribute on an instance *instance* of the owner class to a new value, *value*. Note, adding "__set__()" or "__delete__()" changes the kind of descriptor to a “data descriptor”. See Invoking Descriptors for more details. object.__delete__(self, instance) Called to delete the attribute on an instance *instance* of the owner class. The attribute "__objclass__" is interpreted by the "inspect" module as specifying the class where this object was defined (setting this appropriately can assist in runtime introspection of dynamic class attributes). For callables, it may indicate that an instance of the given type (or a subclass) is expected or required as the first positional argument (for example, CPython sets this attribute for unbound methods that are implemented in C). Invoking Descriptors -------------------- In general, a descriptor is an object attribute with “binding behavior”, one whose attribute access has been overridden by methods in the descriptor protocol: "__get__()", "__set__()", and "__delete__()". If any of those methods are defined for an object, it is said to be a descriptor. The default behavior for attribute access is to get, set, or delete the attribute from an object’s dictionary. For instance, "a.x" has a lookup chain starting with "a.__dict__['x']", then "type(a).__dict__['x']", and continuing through the base classes of "type(a)" excluding metaclasses. However, if the looked-up value is an object defining one of the descriptor methods, then Python may override the default behavior and invoke the descriptor method instead. Where this occurs in the precedence chain depends on which descriptor methods were defined and how they were called. The starting point for descriptor invocation is a binding, "a.x". How the arguments are assembled depends on "a": Direct Call The simplest and least common call is when user code directly invokes a descriptor method: "x.__get__(a)". Instance Binding If binding to an object instance, "a.x" is transformed into the call: "type(a).__dict__['x'].__get__(a, type(a))". Class Binding If binding to a class, "A.x" is transformed into the call: "A.__dict__['x'].__get__(None, A)". Super Binding A dotted lookup such as "super(A, a).x" searches "a.__class__.__mro__" for a base class "B" following "A" and then returns "B.__dict__['x'].__get__(a, A)". If not a descriptor, "x" is returned unchanged. For instance bindings, the precedence of descriptor invocation depends on which descriptor methods are defined. A descriptor can define any combination of "__get__()", "__set__()" and "__delete__()". If it does not define "__get__()", then accessing the attribute will return the descriptor object itself unless there is a value in the object’s instance dictionary. If the descriptor defines "__set__()" and/or "__delete__()", it is a data descriptor; if it defines neither, it is a non-data descriptor. Normally, data descriptors define both "__get__()" and "__set__()", while non-data descriptors have just the "__get__()" method. Data descriptors with "__get__()" and "__set__()" (and/or "__delete__()") defined always override a redefinition in an instance dictionary. In contrast, non-data descriptors can be overridden by instances. Python methods (including those decorated with "@staticmethod" and "@classmethod") are implemented as non-data descriptors. Accordingly, instances can redefine and override methods. This allows individual instances to acquire behaviors that differ from other instances of the same class. The "property()" function is implemented as a data descriptor. Accordingly, instances cannot override the behavior of a property. __slots__ --------- *__slots__* allow us to explicitly declare data members (like properties) and deny the creation of "__dict__" and *__weakref__* (unless explicitly declared in *__slots__* or available in a parent.) The space saved over using "__dict__" can be significant. Attribute lookup speed can be significantly improved as well. object.__slots__ This class variable can be assigned a string, iterable, or sequence of strings with variable names used by instances. *__slots__* reserves space for the declared variables and prevents the automatic creation of "__dict__" and *__weakref__* for each instance. Notes on using *__slots__* ~~~~~~~~~~~~~~~~~~~~~~~~~~ * When inheriting from a class without *__slots__*, the "__dict__" and *__weakref__* attribute of the instances will always be accessible. * Without a "__dict__" variable, instances cannot be assigned new variables not listed in the *__slots__* definition. Attempts to assign to an unlisted variable name raises "AttributeError". If dynamic assignment of new variables is desired, then add "'__dict__'" to the sequence of strings in the *__slots__* declaration. * Without a *__weakref__* variable for each instance, classes defining *__slots__* do not support "weak references" to its instances. If weak reference support is needed, then add "'__weakref__'" to the sequence of strings in the *__slots__* declaration. * *__slots__* are implemented at the class level by creating descriptors for each variable name. 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. * The action of a *__slots__* declaration is not limited to the class where it is defined. *__slots__* declared in parents are available in child classes. However, child subclasses will get a "__dict__" and *__weakref__* unless they also define *__slots__* (which should only contain names of any *additional* slots). * 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 descriptor directly from the base class). This renders the meaning of the program undefined. In the future, a check may be added to prevent this. * Nonempty *__slots__* does not work for classes derived from “variable-length” built-in types such as "int", "bytes" and "tuple". * Any non-string *iterable* may be assigned to *__slots__*. * If a "dictionary" is used to assign *__slots__*, the dictionary keys will be used as the slot names. The values of the dictionary can be used to provide per-attribute docstrings that will be recognised by "inspect.getdoc()" and displayed in the output of "help()". * "__class__" assignment works only if both classes have the same *__slots__*. * Multiple inheritance with multiple slotted parent classes can be used, but only one parent is allowed to have attributes created by slots (the other bases must have empty slot layouts) - violations raise "TypeError". * If an *iterator* is used for *__slots__* then a *descriptor* is created for each of the iterator’s values. However, the *__slots__* attribute will be an empty iterator. Customizing class creation ========================== Whenever a class inherits from another class, "__init_subclass__()" is called on the parent class. This way, it is possible to write classes which change the behavior of subclasses. This is closely related to class decorators, but where class decorators only affect the specific class they’re applied to, "__init_subclass__" solely applies to future subclasses of the class defining the method. classmethod object.__init_subclass__(cls) This method is called whenever the containing class is subclassed. *cls* is then the new subclass. If defined as a normal instance method, this method is implicitly converted to a class method. Keyword arguments which are given to a new class are passed to the parent’s class "__init_subclass__". For compatibility with other classes using "__init_subclass__", one should take out the needed keyword arguments and pass the others over to the base class, as in: class Philosopher: def __init_subclass__(cls, /, default_name, **kwargs): super().__init_subclass__(**kwargs) cls.default_name = default_name class AustralianPhilosopher(Philosopher, default_name="Bruce"): pass The default implementation "object.__init_subclass__" does nothing, but raises an error if it is called with any arguments. Note: The metaclass hint "metaclass" is consumed by the rest of the type machinery, and is never passed to "__init_subclass__" implementations. The actual metaclass (rather than the explicit hint) can be accessed as "type(cls)". New in version 3.6. When a class is created, "type.__new__()" scans the class variables and makes callbacks to those with a "__set_name__()" hook. object.__set_name__(self, owner, name) Automatically called at the time the owning class *owner* is created. The object has been assigned to *name* in that class: class A: x = C() # Automatically calls: x.__set_name__(A, 'x') If the class variable is assigned after the class is created, "__set_name__()" will not be called automatically. If needed, "__set_name__()" can be called directly: class A: pass c = C() A.x = c # The hook is not called c.__set_name__(A, 'x') # Manually invoke the hook See Creating the class object for more details. New in version 3.6. Metaclasses ----------- By default, classes are constructed using "type()". The class body is executed in a new namespace and the class name is bound locally to the result of "type(name, bases, namespace)". The class creation process can be customized by passing the "metaclass" keyword argument in the class definition line, or by inheriting from an existing class that included such an argument. In the following example, both "MyClass" and "MySubclass" are instances of "Meta": class Meta(type): pass class MyClass(metaclass=Meta): pass class MySubclass(MyClass): pass Any other keyword arguments that are specified in the class definition are passed through to all metaclass operations described below. When a class definition is executed, the following steps occur: * MRO entries are resolved; * the appropriate metaclass is determined; * the class namespace is prepared; * the class body is executed; * the class object is created. Resolving MRO entries --------------------- If a base that appears in class definition is not an instance of "type", then an "__mro_entries__" method is searched on it. If found, it is called with the original bases tuple. This method must return a tuple of classes that will be used instead of this base. The tuple may be empty, in such case the original base is ignored. See also: **PEP 560** - Core support for typing module and generic types Determining the appropriate metaclass ------------------------------------- The appropriate metaclass for a class definition is determined as follows: * if no bases and no explicit metaclass are given, then "type()" is used; * if an explicit metaclass is given and it is *not* an instance of "type()", then it is used directly as the metaclass; * if an instance of "type()" is given as the explicit metaclass, or bases are defined, then the most derived metaclass is used. The most derived metaclass is selected from the explicitly specified metaclass (if any) and the metaclasses (i.e. "type(cls)") of all specified base classes. The most derived metaclass is one which is a subtype of *all* of these candidate metaclasses. If none of the candidate metaclasses meets that criterion, then the class definition will fail with "TypeError". Preparing the class namespace ----------------------------- Once the appropriate metaclass has been identified, then the class namespace is prepared. If the metaclass has a "__prepare__" attribute, it is called as "namespace = metaclass.__prepare__(name, bases, **kwds)" (where the additional keyword arguments, if any, come from the class definition). The "__prepare__" method should be implemented as a "classmethod". The namespace returned by "__prepare__" is passed in to "__new__", but when the final class object is created the namespace is copied into a new "dict". If the metaclass has no "__prepare__" attribute, then the class namespace is initialised as an empty ordered mapping. See also: **PEP 3115** - Metaclasses in Python 3000 Introduced the "__prepare__" namespace hook Executing the class body ------------------------ The class body is executed (approximately) as "exec(body, globals(), namespace)". The key difference from a normal call to "exec()" is that lexical scoping allows the class body (including any methods) to reference names from the current and outer scopes when the class definition occurs inside a function. However, even when the class definition occurs inside the function, methods defined inside the class still cannot see names defined at the class scope. Class variables must be accessed through the first parameter of instance or class methods, or through the implicit lexically scoped "__class__" reference described in the next section. Creating the class object ------------------------- Once the class namespace has been populated by executing the class body, the class object is created by calling "metaclass(name, bases, namespace, **kwds)" (the additional keywords passed here are the same as those passed to "__prepare__"). This class object is the one that will be referenced by the zero- argument form of "super()". "__class__" is an implicit closure reference created by the compiler if any methods in a class body refer to either "__class__" or "super". This allows the zero argument form of "super()" to correctly identify the class being defined based on lexical scoping, while the class or instance that was used to make the current call is identified based on the first argument passed to the method. **CPython implementation detail:** In CPython 3.6 and later, the "__class__" cell is passed to the metaclass as a "__classcell__" entry in the class namespace. If present, this must be propagated up to the "type.__new__" call in order for the class to be initialised correctly. Failing to do so will result in a "RuntimeError" in Python 3.8. When using the default metaclass "type", or any metaclass that ultimately calls "type.__new__", the following additional customization steps are invoked after creating the class object: 1. The "type.__new__" method collects all of the attributes in the class namespace that define a "__set_name__()" method; 2. Those "__set_name__" methods are called with the class being defined and the assigned name of that particular attribute; 3. The "__init_subclass__()" hook is called on the immediate parent of the new class in its method resolution order. After the class object is created, it is passed to the class decorators included in the class definition (if any) and the resulting object is bound in the local namespace as the defined class. When a new class is created by "type.__new__", the object provided as the namespace parameter is copied to a new ordered mapping and the original object is discarded. The new copy is wrapped in a read-only proxy, which becomes the "__dict__" attribute of the class object. See also: **PEP 3135** - New super Describes the implicit "__class__" closure reference Uses for metaclasses -------------------- The potential uses for metaclasses are boundless. Some ideas that have been explored include enum, logging, interface checking, automatic delegation, automatic property creation, proxies, frameworks, and automatic resource locking/synchronization. Customizing instance and subclass checks ======================================== The following methods are used to override the default behavior of the "isinstance()" and "issubclass()" built-in functions. In particular, the metaclass "abc.ABCMeta" implements these methods in order to allow the addition of Abstract Base Classes (ABCs) as “virtual base classes” to any class or type (including built-in types), including other ABCs. class.__instancecheck__(self, instance) Return true if *instance* should be considered a (direct or indirect) instance of *class*. If defined, called to implement "isinstance(instance, class)". class.__subclasscheck__(self, subclass) Return true if *subclass* should be considered a (direct or indirect) subclass of *class*. If defined, called to implement "issubclass(subclass, class)". Note that these methods are looked up on the type (metaclass) of a class. They cannot be defined as class methods in the actual class. This is consistent with the lookup of special methods that are called on instances, only in this case the instance is itself a class. See also: **PEP 3119** - Introducing Abstract Base Classes Includes the specification for customizing "isinstance()" and "issubclass()" behavior through "__instancecheck__()" and "__subclasscheck__()", with motivation for this functionality in the context of adding Abstract Base Classes (see the "abc" module) to the language. Emulating generic types ======================= When using *type annotations*, it is often useful to *parameterize* a *generic type* using Python’s square-brackets notation. For example, the annotation "list[int]" might be used to signify a "list" in which all the elements are of type "int". See also: **PEP 484** - Type Hints Introducing Python’s framework for type annotations Generic Alias Types Documentation for objects representing parameterized generic classes Generics, user-defined generics and "typing.Generic" Documentation on how to implement generic classes that can be parameterized at runtime and understood by static type-checkers. A class can *generally* only be parameterized if it defines the special class method "__class_getitem__()". classmethod object.__class_getitem__(cls, key) Return an object representing the specialization of a generic class by type arguments found in *key*. When defined on a class, "__class_getitem__()" is automatically a class method. As such, there is no need for it to be decorated with "@classmethod" when it is defined. The purpose of *__class_getitem__* ---------------------------------- The purpose of "__class_getitem__()" is to allow runtime parameterization of standard-library generic classes in order to more easily apply *type hints* to these classes. To implement custom generic classes that can be parameterized at runtime and understood by static type-checkers, users should either inherit from a standard library class that already implements "__class_getitem__()", or inherit from "typing.Generic", which has its own implementation of "__class_getitem__()". Custom implementations of "__class_getitem__()" on classes defined outside of the standard library may not be understood by third-party type-checkers such as mypy. Using "__class_getitem__()" on any class for purposes other than type hinting is discouraged. *__class_getitem__* versus *__getitem__* ---------------------------------------- Usually, the subscription of an object using square brackets will call the "__getitem__()" instance method defined on the object’s class. However, if the object being subscribed is itself a class, the class method "__class_getitem__()" may be called instead. "__class_getitem__()" should return a GenericAlias object if it is properly defined. Presented with the *expression* "obj[x]", the Python interpreter follows something like the following process to decide whether "__getitem__()" or "__class_getitem__()" should be called: from inspect import isclass def subscribe(obj, x): """Return the result of the expression 'obj[x]'""" class_of_obj = type(obj) # If the class of obj defines __getitem__, # call class_of_obj.__getitem__(obj, x) if hasattr(class_of_obj, '__getitem__'): return class_of_obj.__getitem__(obj, x) # Else, if obj is a class and defines __class_getitem__, # call obj.__class_getitem__(x) elif isclass(obj) and hasattr(obj, '__class_getitem__'): return obj.__class_getitem__(x) # Else, raise an exception else: raise TypeError( f"'{class_of_obj.__name__}' object is not subscriptable" ) In Python, all classes are themselves instances of other classes. The class of a class is known as that class’s *metaclass*, and most classes have the "type" class as their metaclass. "type" does not define "__getitem__()", meaning that expressions such as "list[int]", "dict[str, float]" and "tuple[str, bytes]" all result in "__class_getitem__()" being called: >>> # list has class "type" as its metaclass, like most classes: >>> type(list) <class 'type'> >>> type(dict) == type(list) == type(tuple) == type(str) == type(bytes) True >>> # "list[int]" calls "list.__class_getitem__(int)" >>> list[int] list[int] >>> # list.__class_getitem__ returns a GenericAlias object: >>> type(list[int]) <class 'types.GenericAlias'> However, if a class has a custom metaclass that defines "__getitem__()", subscribing the class may result in different behaviour. An example of this can be found in the "enum" module: >>> from enum import Enum >>> class Menu(Enum): ... """A breakfast menu""" ... SPAM = 'spam' ... BACON = 'bacon' ... >>> # Enum classes have a custom metaclass: >>> type(Menu) <class 'enum.EnumMeta'> >>> # EnumMeta defines __getitem__, >>> # so __class_getitem__ is not called, >>> # and the result is not a GenericAlias object: >>> Menu['SPAM'] <Menu.SPAM: 'spam'> >>> type(Menu['SPAM']) <enum 'Menu'> See also: **PEP 560** - Core Support for typing module and generic types Introducing "__class_getitem__()", and outlining when a subscription results in "__class_getitem__()" being called instead of "__getitem__()" Emulating callable objects ========================== object.__call__(self[, args...]) Called when the instance is “called” as a function; if this method is defined, "x(arg1, arg2, ...)" roughly translates to "type(x).__call__(x, arg1, ...)". Emulating container types ========================= The following methods can be defined to implement container objects. Containers usually are *sequences* (such as "lists" or "tuples") or *mappings* (like "dictionaries"), but can represent other containers as well. The first set of methods is used either to emulate a sequence or to emulate a mapping; the difference is that for a sequence, the allowable keys should be the integers *k* for which "0 <= k < N" where *N* is the length of the sequence, or "slice" objects, which define a range of items. It is also recommended that mappings provide the methods "keys()", "values()", "items()", "get()", "clear()", "setdefault()", "pop()", "popitem()", "copy()", and "update()" behaving similar to those for Python’s standard "dictionary" objects. The "collections.abc" module provides a "MutableMapping" *abstract base class* to help create those methods from a base set of "__getitem__()", "__setitem__()", "__delitem__()", and "keys()". Mutable sequences should provide methods "append()", "count()", "index()", "extend()", "insert()", "pop()", "remove()", "reverse()" and "sort()", like Python standard "list" objects. Finally, sequence types should implement addition (meaning concatenation) and multiplication (meaning repetition) by defining the methods "__add__()", "__radd__()", "__iadd__()", "__mul__()", "__rmul__()" and "__imul__()" described below; they should not define other numerical operators. It is recommended that both mappings and sequences implement the "__contains__()" method to allow efficient use of the "in" operator; for mappings, "in" should search the mapping’s keys; for sequences, it should search through the values. It is further recommended that both mappings and sequences implement the "__iter__()" method to allow efficient iteration through the container; for mappings, "__iter__()" should iterate through the object’s keys; for sequences, it should iterate through the values. object.__len__(self) Called to implement the built-in function "len()". Should return the length of the object, an integer ">=" 0. Also, an object that doesn’t define a "__bool__()" method and whose "__len__()" method returns zero is considered to be false in a Boolean context. **CPython implementation detail:** In CPython, the length is required to be at most "sys.maxsize". If the length is larger than "sys.maxsize" some features (such as "len()") may raise "OverflowError". To prevent raising "OverflowError" by truth value testing, an object must define a "__bool__()" method. object.__length_hint__(self) Called to implement "operator.length_hint()". Should return an estimated length for the object (which may be greater or less than the actual length). The length must be an integer ">=" 0. The return value may also be "NotImplemented", which is treated the same as if the "__length_hint__" method didn’t exist at all. This method is purely an optimization and is never required for correctness. New in version 3.4. Note: Slicing is done exclusively with the following three methods. A call like a[1:2] = b is translated to a[slice(1, 2, None)] = b and so forth. Missing slice items are always filled in with "None". object.__getitem__(self, key) Called to implement evaluation of "self[key]". For *sequence* types, the accepted keys should be integers and slice objects. Note that the special interpretation of negative indexes (if the class wishes to emulate a *sequence* type) is up to the "__getitem__()" method. If *key* is of an inappropriate type, "TypeError" may be raised; if of a value outside the set of indexes for the sequence (after any special interpretation of negative values), "IndexError" should be raised. For *mapping* types, if *key* is missing (not in the container), "KeyError" should be raised. Note: "for" loops expect that an "IndexError" will be raised for illegal indexes to allow proper detection of the end of the sequence. Note: When subscripting a *class*, the special class method "__class_getitem__()" may be called instead of "__getitem__()". See __class_getitem__ versus __getitem__ for more details. object.__setitem__(self, key, value) Called to implement assignment to "self[key]". Same note as for "__getitem__()". This should only be implemented for mappings if the objects support changes to the values for keys, or if new keys can be added, or for sequences if elements can be replaced. The same exceptions should be raised for improper *key* values as for the "__getitem__()" method. object.__delitem__(self, key) Called to implement deletion of "self[key]". Same note as for "__getitem__()". This should only be implemented for mappings if the objects support removal of keys, or for sequences if elements can be removed from the sequence. The same exceptions should be raised for improper *key* values as for the "__getitem__()" method. object.__missing__(self, key) Called by "dict"."__getitem__()" to implement "self[key]" for dict subclasses when key is not in the dictionary. object.__iter__(self) This method is called when an *iterator* is required for a container. This method should return a new iterator object that can iterate over all the objects in the container. For mappings, it should iterate over the keys of the container. object.__reversed__(self) Called (if present) by the "reversed()" built-in to implement reverse iteration. It should return a new iterator object that iterates over all the objects in the container in reverse order. If the "__reversed__()" method is not provided, the "reversed()" built-in will fall back to using the sequence protocol ("__len__()" and "__getitem__()"). Objects that support the sequence protocol should only provide "__reversed__()" if they can provide an implementation that is more efficient than the one provided by "reversed()". The membership test operators ("in" and "not in") are normally implemented as an iteration through a container. However, container objects can supply the following special method with a more efficient implementation, which also does not require the object be iterable. object.__contains__(self, item) Called to implement membership test operators. Should return true if *item* is in *self*, false otherwise. For mapping objects, this should consider the keys of the mapping rather than the values or the key-item pairs. For objects that don’t define "__contains__()", the membership test first tries iteration via "__iter__()", then the old sequence iteration protocol via "__getitem__()", see this section in the language reference. Emulating numeric types ======================= The following methods can be defined to emulate numeric objects. Methods corresponding to operations that are not supported by the particular kind of number implemented (e.g., bitwise operations for non-integral numbers) should be left undefined. object.__add__(self, other) object.__sub__(self, other) object.__mul__(self, other) object.__matmul__(self, other) object.__truediv__(self, other) object.__floordiv__(self, other) object.__mod__(self, other) object.__divmod__(self, other) object.__pow__(self, other[, modulo]) object.__lshift__(self, other) object.__rshift__(self, other) object.__and__(self, other) object.__xor__(self, other) object.__or__(self, other) These methods are called to implement the binary arithmetic operations ("+", "-", "*", "@", "/", "//", "%", "divmod()", "pow()", "**", "<<", ">>", "&", "^", "|"). For instance, to evaluate the expression "x + y", where *x* is an instance of a class that has an "__add__()" method, "type(x).__add__(x, y)" is called. The "__divmod__()" method should be the equivalent to using "__floordiv__()" and "__mod__()"; it should not be related to "__truediv__()". Note that "__pow__()" should be defined to accept an optional third argument if the ternary version of the built-in "pow()" function is to be supported. If one of those methods does not support the operation with the supplied arguments, it should return "NotImplemented". object.__radd__(self, other) object.__rsub__(self, other) object.__rmul__(self, other) object.__rmatmul__(self, other) object.__rtruediv__(self, other) object.__rfloordiv__(self, other) object.__rmod__(self, other) object.__rdivmod__(self, other) object.__rpow__(self, other[, modulo]) object.__rlshift__(self, other) object.__rrshift__(self, other) object.__rand__(self, other) object.__rxor__(self, other) object.__ror__(self, other) These methods are called to implement the binary arithmetic operations ("+", "-", "*", "@", "/", "//", "%", "divmod()", "pow()", "**", "<<", ">>", "&", "^", "|") with reflected (swapped) operands. These functions are only called if the left operand does not support the corresponding operation [3] and the operands are of different types. [4] For instance, to evaluate the expression "x - y", where *y* is an instance of a class that has an "__rsub__()" method, "type(y).__rsub__(y, x)" is called if "type(x).__sub__(x, y)" returns *NotImplemented*. Note that ternary "pow()" will not try calling "__rpow__()" (the coercion rules would become too complicated). Note: If the right operand’s type is a subclass of the left operand’s type and that subclass provides a different implementation of the reflected method for the operation, this method will be called before the left operand’s non-reflected method. This behavior allows subclasses to override their ancestors’ operations. object.__iadd__(self, other) object.__isub__(self, other) object.__imul__(self, other) object.__imatmul__(self, other) object.__itruediv__(self, other) object.__ifloordiv__(self, other) object.__imod__(self, other) object.__ipow__(self, other[, modulo]) object.__ilshift__(self, other) object.__irshift__(self, other) object.__iand__(self, other) object.__ixor__(self, other) object.__ior__(self, other) These methods are called to implement the augmented arithmetic assignments ("+=", "-=", "*=", "@=", "/=", "//=", "%=", "**=", "<<=", ">>=", "&=", "^=", "|="). These methods should attempt to do the operation in-place (modifying *self*) and return the result (which could be, but does not have to be, *self*). If a specific method is not defined, the augmented assignment falls back to the normal methods. For instance, if *x* is an instance of a class with an "__iadd__()" method, "x += y" is equivalent to "x = x.__iadd__(y)" . Otherwise, "x.__add__(y)" and "y.__radd__(x)" are considered, as with the evaluation of "x + y". In certain situations, augmented assignment can result in unexpected errors (see Why does a_tuple[i] += [‘item’] raise an exception when the addition works?), but this behavior is in fact part of the data model. object.__neg__(self) object.__pos__(self) object.__abs__(self) object.__invert__(self) Called to implement the unary arithmetic operations ("-", "+", "abs()" and "~"). object.__complex__(self) object.__int__(self) object.__float__(self) Called to implement the built-in functions "complex()", "int()" and "float()". Should return a value of the appropriate type. object.__index__(self) Called to implement "operator.index()", and whenever Python needs to losslessly convert the numeric object to an integer object (such as in slicing, or in the built-in "bin()", "hex()" and "oct()" functions). Presence of this method indicates that the numeric object is an integer type. Must return an integer. If "__int__()", "__float__()" and "__complex__()" are not defined then corresponding built-in functions "int()", "float()" and "complex()" fall back to "__index__()". object.__round__(self[, ndigits]) object.__trunc__(self) object.__floor__(self) object.__ceil__(self) Called to implement the built-in function "round()" and "math" functions "trunc()", "floor()" and "ceil()". Unless *ndigits* is passed to "__round__()" all these methods should return the value of the object truncated to an "Integral" (typically an "int"). The built-in function "int()" falls back to "__trunc__()" if neither "__int__()" nor "__index__()" is defined. Changed in version 3.11: The delegation of "int()" to "__trunc__()" is deprecated. With Statement Context Managers =============================== A *context manager* is an object that defines the runtime context to be established when executing a "with" statement. The context manager handles the entry into, and the exit from, the desired runtime context for the execution of the block of code. Context managers are normally invoked using the "with" statement (described in section The with statement), but can also be used by directly invoking their methods. Typical uses of context managers include saving and restoring various kinds of global state, locking and unlocking resources, closing opened files, etc. For more information on context managers, see Context Manager Types. object.__enter__(self) Enter the runtime context related to this object. The "with" statement will bind this method’s return value to the target(s) specified in the "as" clause of the statement, if any. object.__exit__(self, exc_type, exc_value, traceback) Exit the runtime context related to this object. The parameters describe the exception that caused the context to be exited. If the context was exited without an exception, all three arguments will be "None". If an exception is supplied, and the method wishes to suppress the exception (i.e., prevent it from being propagated), it should return a true value. Otherwise, the exception will be processed normally upon exit from this method. Note that "__exit__()" methods should not reraise the passed-in exception; this is the caller’s responsibility. See also: **PEP 343** - The “with” statement The specification, background, and examples for the Python "with" statement. Customizing positional arguments in class pattern matching ========================================================== When using a class name in a pattern, positional arguments in the pattern are not allowed by default, i.e. "case MyClass(x, y)" is typically invalid without special support in "MyClass". To be able to use that kind of pattern, the class needs to define a *__match_args__* attribute. object.__match_args__ This class variable can be assigned a tuple of strings. When this class is used in a class pattern with positional arguments, each positional argument will be converted into a keyword argument, using the corresponding value in *__match_args__* as the keyword. The absence of this attribute is equivalent to setting it to "()". For example, if "MyClass.__match_args__" is "("left", "center", "right")" that means that "case MyClass(x, y)" is equivalent to "case MyClass(left=x, center=y)". Note that the number of arguments in the pattern must be smaller than or equal to the number of elements in *__match_args__*; if it is larger, the pattern match attempt will raise a "TypeError". New in version 3.10. See also: **PEP 634** - Structural Pattern Matching The specification for the Python "match" statement. Special method lookup ===================== For custom classes, implicit invocations of special methods are only guaranteed to work correctly if defined on an object’s type, not in the object’s instance dictionary. That behaviour is the reason why the following code raises an exception: >>> class C: ... pass ... >>> c = C() >>> c.__len__ = lambda: 5 >>> len(c) Traceback (most recent call last): File "<stdin>", line 1, in <module> TypeError: object of type 'C' has no len() The rationale behind this behaviour lies with a number of special methods such as "__hash__()" and "__repr__()" that are implemented by all objects, including type objects. If the implicit lookup of these methods used the conventional lookup process, they would fail when invoked on the type object itself: >>> 1 .__hash__() == hash(1) True >>> int.__hash__() == hash(int) Traceback (most recent call last): File "<stdin>", line 1, in <module> TypeError: descriptor '__hash__' of 'int' object needs an argument Incorrectly attempting to invoke an unbound method of a class in this way is sometimes referred to as ‘metaclass confusion’, and is avoided by bypassing the instance when looking up special methods: >>> type(1).__hash__(1) == hash(1) True >>> type(int).__hash__(int) == hash(int) True In addition to bypassing any instance attributes in the interest of correctness, implicit special method lookup generally also bypasses the "__getattribute__()" method even of the object’s metaclass: >>> class Meta(type): ... def __getattribute__(*args): ... print("Metaclass getattribute invoked") ... return type.__getattribute__(*args) ... >>> class C(object, metaclass=Meta): ... def __len__(self): ... return 10 ... def __getattribute__(*args): ... print("Class getattribute invoked") ... return object.__getattribute__(*args) ... >>> c = C() >>> c.__len__() # Explicit lookup via instance Class getattribute invoked 10 >>> type(c).__len__(c) # Explicit lookup via type Metaclass getattribute invoked 10 >>> len(c) # Implicit lookup 10 Bypassing the "__getattribute__()" machinery in this fashion provides significant scope for speed optimisations within the interpreter, at the cost of some flexibility in the handling of special methods (the special method *must* be set on the class object itself in order to be consistently invoked by the interpreter). zstring-methodsu�` String Methods ************** Strings implement all of the common sequence operations, along with the additional methods described below. Strings also support two styles of string formatting, one providing a large degree of flexibility and customization (see "str.format()", Format String Syntax and Custom String Formatting) and the other based on C "printf" style formatting that handles a narrower range of types and is slightly harder to use correctly, but is often faster for the cases it can handle (printf-style String Formatting). The Text Processing Services section of the standard library covers a number of other modules that provide various text related utilities (including regular expression support in the "re" module). str.capitalize() Return a copy of the string with its first character capitalized and the rest lowercased. Changed in version 3.8: The first character is now put into titlecase rather than uppercase. This means that characters like digraphs will only have their first letter capitalized, instead of the full character. str.casefold() Return a casefolded copy of the string. Casefolded strings may be used for caseless matching. Casefolding is similar to lowercasing but more aggressive because it is intended to remove all case distinctions in a string. For example, the German lowercase letter "'ß'" is equivalent to ""ss"". Since it is already lowercase, "lower()" would do nothing to "'ß'"; "casefold()" converts it to ""ss"". The casefolding algorithm is described in section 3.13 of the Unicode Standard. New in version 3.3. str.center(width[, fillchar]) Return centered in a string of length *width*. Padding is done using the specified *fillchar* (default is an ASCII space). The original string is returned if *width* is less than or equal to "len(s)". str.count(sub[, start[, end]]) Return the number of non-overlapping occurrences of substring *sub* in the range [*start*, *end*]. Optional arguments *start* and *end* are interpreted as in slice notation. If *sub* is empty, returns the number of empty strings between characters which is the length of the string plus one. str.encode(encoding='utf-8', errors='strict') Return the string encoded to "bytes". *encoding* defaults to "'utf-8'"; see Standard Encodings for possible values. *errors* controls how encoding errors are handled. If "'strict'" (the default), a "UnicodeError" exception is raised. Other possible values are "'ignore'", "'replace'", "'xmlcharrefreplace'", "'backslashreplace'" and any other name registered via "codecs.register_error()". See Error Handlers for details. For performance reasons, the value of *errors* is not checked for validity unless an encoding error actually occurs, Python Development Mode is enabled or a debug build is used. Changed in version 3.1: Added support for keyword arguments. Changed in version 3.9: The value of the *errors* argument is now checked in Python Development Mode and in debug mode. str.endswith(suffix[, start[, end]]) Return "True" if the string ends with the specified *suffix*, otherwise return "False". *suffix* can also be a tuple of suffixes to look for. With optional *start*, test beginning at that position. With optional *end*, stop comparing at that position. str.expandtabs(tabsize=8) Return a copy of the string where all tab characters are replaced by one or more spaces, depending on the current column and the given tab size. Tab positions occur every *tabsize* characters (default is 8, giving tab positions at columns 0, 8, 16 and so on). To expand the string, the current column is set to zero and the string is examined character by character. If the character is a tab ("\t"), one or more space characters are inserted in the result until the current column is equal to the next tab position. (The tab character itself is not copied.) If the character is a newline ("\n") or return ("\r"), it is copied and the current column is reset to zero. Any other character is copied unchanged and the current column is incremented by one regardless of how the character is represented when printed. >>> '01\t012\t0123\t01234'.expandtabs() '01 012 0123 01234' >>> '01\t012\t0123\t01234'.expandtabs(4) '01 012 0123 01234' str.find(sub[, start[, end]]) Return the lowest index in the string where substring *sub* is found within the slice "s[start:end]". Optional arguments *start* and *end* are interpreted as in slice notation. Return "-1" if *sub* is not found. Note: The "find()" method should be used only if you need to know the position of *sub*. To check if *sub* is a substring or not, use the "in" operator: >>> 'Py' in 'Python' True str.format(*args, **kwargs) Perform a string formatting operation. The string on which this method is called can contain literal text or replacement fields delimited by braces "{}". Each replacement field contains either the numeric index of a positional argument, or the name of a keyword argument. Returns a copy of the string where each replacement field is replaced with the string value of the corresponding argument. >>> "The sum of 1 + 2 is {0}".format(1+2) 'The sum of 1 + 2 is 3' See Format String Syntax for a description of the various formatting options that can be specified in format strings. Note: When formatting a number ("int", "float", "complex", "decimal.Decimal" and subclasses) with the "n" type (ex: "'{:n}'.format(1234)"), the function temporarily sets the "LC_CTYPE" locale to the "LC_NUMERIC" locale to decode "decimal_point" and "thousands_sep" fields of "localeconv()" if they are non-ASCII or longer than 1 byte, and the "LC_NUMERIC" locale is different than the "LC_CTYPE" locale. This temporary change affects other threads. Changed in version 3.7: When formatting a number with the "n" type, the function sets temporarily the "LC_CTYPE" locale to the "LC_NUMERIC" locale in some cases. str.format_map(mapping) Similar to "str.format(**mapping)", except that "mapping" is used directly and not copied to a "dict". This is useful if for example "mapping" is a dict subclass: >>> class Default(dict): ... def __missing__(self, key): ... return key ... >>> '{name} was born in {country}'.format_map(Default(name='Guido')) 'Guido was born in country' New in version 3.2. str.index(sub[, start[, end]]) Like "find()", but raise "ValueError" when the substring is not found. str.isalnum() Return "True" if all characters in the string are alphanumeric and there is at least one character, "False" otherwise. A character "c" is alphanumeric if one of the following returns "True": "c.isalpha()", "c.isdecimal()", "c.isdigit()", or "c.isnumeric()". str.isalpha() Return "True" if all characters in the string are alphabetic and there is at least one character, "False" otherwise. Alphabetic characters are those characters defined in the Unicode character database as “Letter”, i.e., those with general category property being one of “Lm”, “Lt”, “Lu”, “Ll”, or “Lo”. Note that this is different from the “Alphabetic” property defined in the Unicode Standard. str.isascii() Return "True" if the string is empty or all characters in the string are ASCII, "False" otherwise. ASCII characters have code points in the range U+0000-U+007F. New in version 3.7. str.isdecimal() Return "True" if all characters in the string are decimal characters and there is at least one character, "False" otherwise. Decimal characters are those that can be used to form numbers in base 10, e.g. U+0660, ARABIC-INDIC DIGIT ZERO. Formally a decimal character is a character in the Unicode General Category “Nd”. str.isdigit() Return "True" if all characters in the string are digits and there is at least one character, "False" otherwise. Digits include decimal characters and digits that need special handling, such as the compatibility superscript digits. This covers digits which cannot be used to form numbers in base 10, like the Kharosthi numbers. Formally, a digit is a character that has the property value Numeric_Type=Digit or Numeric_Type=Decimal. str.isidentifier() Return "True" if the string is a valid identifier according to the language definition, section Identifiers and keywords. Call "keyword.iskeyword()" to test whether string "s" is a reserved identifier, such as "def" and "class". Example: >>> from keyword import iskeyword >>> 'hello'.isidentifier(), iskeyword('hello') (True, False) >>> 'def'.isidentifier(), iskeyword('def') (True, True) str.islower() Return "True" if all cased characters [4] in the string are lowercase and there is at least one cased character, "False" otherwise. str.isnumeric() Return "True" if all characters in the string are numeric characters, and there is at least one character, "False" otherwise. Numeric characters include digit characters, and all characters that have the Unicode numeric value property, e.g. U+2155, VULGAR FRACTION ONE FIFTH. Formally, numeric characters are those with the property value Numeric_Type=Digit, Numeric_Type=Decimal or Numeric_Type=Numeric. str.isprintable() Return "True" if all characters in the string are printable or the string is empty, "False" otherwise. Nonprintable characters are those characters defined in the Unicode character database as “Other” or “Separator”, excepting the ASCII space (0x20) which is considered printable. (Note that printable characters in this context are those which should not be escaped when "repr()" is invoked on a string. It has no bearing on the handling of strings written to "sys.stdout" or "sys.stderr".) str.isspace() Return "True" if there are only whitespace characters in the string and there is at least one character, "False" otherwise. A character is *whitespace* if in the Unicode character database (see "unicodedata"), either its general category is "Zs" (“Separator, space”), or its bidirectional class is one of "WS", "B", or "S". str.istitle() Return "True" if the string is a titlecased string and there is at least one character, for example uppercase characters may only follow uncased characters and lowercase characters only cased ones. Return "False" otherwise. str.isupper() Return "True" if all cased characters [4] in the string are uppercase and there is at least one cased character, "False" otherwise. >>> 'BANANA'.isupper() True >>> 'banana'.isupper() False >>> 'baNana'.isupper() False >>> ' '.isupper() False str.join(iterable) Return a string which is the concatenation of the strings in *iterable*. A "TypeError" will be raised if there are any non- string values in *iterable*, including "bytes" objects. The separator between elements is the string providing this method. str.ljust(width[, fillchar]) Return the string left justified in a string of length *width*. Padding is done using the specified *fillchar* (default is an ASCII space). The original string is returned if *width* is less than or equal to "len(s)". str.lower() Return a copy of the string with all the cased characters [4] converted to lowercase. The lowercasing algorithm used is described in section 3.13 of the Unicode Standard. str.lstrip([chars]) Return a copy of the string with leading characters removed. The *chars* argument is a string specifying the set of characters to be removed. If omitted or "None", the *chars* argument defaults to removing whitespace. The *chars* argument is not a prefix; rather, all combinations of its values are stripped: >>> ' spacious '.lstrip() 'spacious ' >>> 'www.example.com'.lstrip('cmowz.') 'example.com' See "str.removeprefix()" for a method that will remove a single prefix string rather than all of a set of characters. For example: >>> 'Arthur: three!'.lstrip('Arthur: ') 'ee!' >>> 'Arthur: three!'.removeprefix('Arthur: ') 'three!' static str.maketrans(x[, y[, z]]) This static method returns a translation table usable for "str.translate()". If there is only one argument, it must be a dictionary mapping Unicode ordinals (integers) or characters (strings of length 1) to Unicode ordinals, strings (of arbitrary lengths) or "None". Character keys will then be converted to ordinals. If there are two arguments, they must be strings of equal length, and in the resulting dictionary, each character in x will be mapped to the character at the same position in y. If there is a third argument, it must be a string, whose characters will be mapped to "None" in the result. str.partition(sep) Split the string at the first occurrence of *sep*, and return a 3-tuple containing the part before the separator, the separator itself, and the part after the separator. If the separator is not found, return a 3-tuple containing the string itself, followed by two empty strings. str.removeprefix(prefix, /) If the string starts with the *prefix* string, return "string[len(prefix):]". Otherwise, return a copy of the original string: >>> 'TestHook'.removeprefix('Test') 'Hook' >>> 'BaseTestCase'.removeprefix('Test') 'BaseTestCase' New in version 3.9. str.removesuffix(suffix, /) If the string ends with the *suffix* string and that *suffix* is not empty, return "string[:-len(suffix)]". Otherwise, return a copy of the original string: >>> 'MiscTests'.removesuffix('Tests') 'Misc' >>> 'TmpDirMixin'.removesuffix('Tests') 'TmpDirMixin' New in version 3.9. str.replace(old, new[, count]) Return a copy of the string with all occurrences of substring *old* replaced by *new*. If the optional argument *count* is given, only the first *count* occurrences are replaced. str.rfind(sub[, start[, end]]) Return the highest index in the string where substring *sub* is found, such that *sub* is contained within "s[start:end]". Optional arguments *start* and *end* are interpreted as in slice notation. Return "-1" on failure. str.rindex(sub[, start[, end]]) Like "rfind()" but raises "ValueError" when the substring *sub* is not found. str.rjust(width[, fillchar]) Return the string right justified in a string of length *width*. Padding is done using the specified *fillchar* (default is an ASCII space). The original string is returned if *width* is less than or equal to "len(s)". str.rpartition(sep) Split the string at the last occurrence of *sep*, and return a 3-tuple containing the part before the separator, the separator itself, and the part after the separator. If the separator is not found, return a 3-tuple containing two empty strings, followed by the string itself. str.rsplit(sep=None, maxsplit=-1) Return a list of the words in the string, using *sep* as the delimiter string. If *maxsplit* is given, at most *maxsplit* splits are done, the *rightmost* ones. If *sep* is not specified or "None", any whitespace string is a separator. Except for splitting from the right, "rsplit()" behaves like "split()" which is described in detail below. str.rstrip([chars]) Return a copy of the string with trailing characters removed. The *chars* argument is a string specifying the set of characters to be removed. If omitted or "None", the *chars* argument defaults to removing whitespace. The *chars* argument is not a suffix; rather, all combinations of its values are stripped: >>> ' spacious '.rstrip() ' spacious' >>> 'mississippi'.rstrip('ipz') 'mississ' See "str.removesuffix()" for a method that will remove a single suffix string rather than all of a set of characters. For example: >>> 'Monty Python'.rstrip(' Python') 'M' >>> 'Monty Python'.removesuffix(' Python') 'Monty' str.split(sep=None, maxsplit=-1) Return a list of the words in the string, using *sep* as the delimiter string. If *maxsplit* is given, at most *maxsplit* splits are done (thus, the list will have at most "maxsplit+1" elements). If *maxsplit* is not specified or "-1", then there is no limit on the number of splits (all possible splits are made). If *sep* is given, consecutive delimiters are not grouped together and are deemed to delimit empty strings (for example, "'1,,2'.split(',')" returns "['1', '', '2']"). The *sep* argument may consist of multiple characters (for example, "'1<>2<>3'.split('<>')" returns "['1', '2', '3']"). Splitting an empty string with a specified separator returns "['']". For example: >>> '1,2,3'.split(',') ['1', '2', '3'] >>> '1,2,3'.split(',', maxsplit=1) ['1', '2,3'] >>> '1,2,,3,'.split(',') ['1', '2', '', '3', ''] If *sep* is not specified or is "None", a different splitting algorithm is applied: runs of consecutive whitespace are regarded as a single separator, and the result will contain no empty strings at the start or end if the string has leading or trailing whitespace. Consequently, splitting an empty string or a string consisting of just whitespace with a "None" separator returns "[]". For example: >>> '1 2 3'.split() ['1', '2', '3'] >>> '1 2 3'.split(maxsplit=1) ['1', '2 3'] >>> ' 1 2 3 '.split() ['1', '2', '3'] str.splitlines(keepends=False) Return a list of the lines in the string, breaking at line boundaries. Line breaks are not included in the resulting list unless *keepends* is given and true. This method splits on the following line boundaries. In particular, the boundaries are a superset of *universal newlines*. +-------------------------+-------------------------------+ | Representation | Description | |=========================|===============================| | "\n" | Line Feed | +-------------------------+-------------------------------+ | "\r" | Carriage Return | +-------------------------+-------------------------------+ | "\r\n" | Carriage Return + Line Feed | +-------------------------+-------------------------------+ | "\v" or "\x0b" | Line Tabulation | +-------------------------+-------------------------------+ | "\f" or "\x0c" | Form Feed | +-------------------------+-------------------------------+ | "\x1c" | File Separator | +-------------------------+-------------------------------+ | "\x1d" | Group Separator | +-------------------------+-------------------------------+ | "\x1e" | Record Separator | +-------------------------+-------------------------------+ | "\x85" | Next Line (C1 Control Code) | +-------------------------+-------------------------------+ | "\u2028" | Line Separator | +-------------------------+-------------------------------+ | "\u2029" | Paragraph Separator | +-------------------------+-------------------------------+ Changed in version 3.2: "\v" and "\f" added to list of line boundaries. For example: >>> 'ab c\n\nde fg\rkl\r\n'.splitlines() ['ab c', '', 'de fg', 'kl'] >>> 'ab c\n\nde fg\rkl\r\n'.splitlines(keepends=True) ['ab c\n', '\n', 'de fg\r', 'kl\r\n'] Unlike "split()" when a delimiter string *sep* is given, this method returns an empty list for the empty string, and a terminal line break does not result in an extra line: >>> "".splitlines() [] >>> "One line\n".splitlines() ['One line'] For comparison, "split('\n')" gives: >>> ''.split('\n') [''] >>> 'Two lines\n'.split('\n') ['Two lines', ''] str.startswith(prefix[, start[, end]]) Return "True" if string starts with the *prefix*, otherwise return "False". *prefix* can also be a tuple of prefixes to look for. With optional *start*, test string beginning at that position. With optional *end*, stop comparing string at that position. str.strip([chars]) Return a copy of the string with the leading and trailing characters removed. The *chars* argument is a string specifying the set of characters to be removed. If omitted or "None", the *chars* argument defaults to removing whitespace. The *chars* argument is not a prefix or suffix; rather, all combinations of its values are stripped: >>> ' spacious '.strip() 'spacious' >>> 'www.example.com'.strip('cmowz.') 'example' The outermost leading and trailing *chars* argument values are stripped from the string. Characters are removed from the leading end until reaching a string character that is not contained in the set of characters in *chars*. A similar action takes place on the trailing end. For example: >>> comment_string = '#....... Section 3.2.1 Issue #32 .......' >>> comment_string.strip('.#! ') 'Section 3.2.1 Issue #32' str.swapcase() Return a copy of the string with uppercase characters converted to lowercase and vice versa. Note that it is not necessarily true that "s.swapcase().swapcase() == s". str.title() Return a titlecased version of the string where words start with an uppercase character and the remaining characters are lowercase. For example: >>> 'Hello world'.title() 'Hello World' The algorithm uses a simple language-independent definition of a word as groups of consecutive letters. The definition works in many contexts but it means that apostrophes in contractions and possessives form word boundaries, which may not be the desired result: >>> "they're bill's friends from the UK".title() "They'Re Bill'S Friends From The Uk" The "string.capwords()" function does not have this problem, as it splits words on spaces only. Alternatively, a workaround for apostrophes can be constructed using regular expressions: >>> import re >>> def titlecase(s): ... return re.sub(r"[A-Za-z]+('[A-Za-z]+)?", ... lambda mo: mo.group(0).capitalize(), ... s) ... >>> titlecase("they're bill's friends.") "They're Bill's Friends." str.translate(table) Return a copy of the string in which each character has been mapped through the given translation table. The table must be an object that implements indexing via "__getitem__()", typically a *mapping* or *sequence*. When indexed by a Unicode ordinal (an integer), the table object can do any of the following: return a Unicode ordinal or a string, to map the character to one or more other characters; return "None", to delete the character from the return string; or raise a "LookupError" exception, to map the character to itself. You can use "str.maketrans()" to create a translation map from character-to-character mappings in different formats. See also the "codecs" module for a more flexible approach to custom character mappings. str.upper() Return a copy of the string with all the cased characters [4] converted to uppercase. Note that "s.upper().isupper()" might be "False" if "s" contains uncased characters or if the Unicode category of the resulting character(s) is not “Lu” (Letter, uppercase), but e.g. “Lt” (Letter, titlecase). The uppercasing algorithm used is described in section 3.13 of the Unicode Standard. str.zfill(width) Return a copy of the string left filled with ASCII "'0'" digits to make a string of length *width*. A leading sign prefix ("'+'"/"'-'") is handled by inserting the padding *after* the sign character rather than before. The original string is returned if *width* is less than or equal to "len(s)". For example: >>> "42".zfill(5) '00042' >>> "-42".zfill(5) '-0042' �stringsu�" String and Bytes literals ************************* String literals are described by the following lexical definitions: stringliteral ::= [stringprefix](shortstring | longstring) stringprefix ::= "r" | "u" | "R" | "U" | "f" | "F" | "fr" | "Fr" | "fR" | "FR" | "rf" | "rF" | "Rf" | "RF" shortstring ::= "'" shortstringitem* "'" | '"' shortstringitem* '"' longstring ::= "'''" longstringitem* "'''" | '"""' longstringitem* '"""' shortstringitem ::= shortstringchar | stringescapeseq longstringitem ::= longstringchar | stringescapeseq shortstringchar ::= <any source character except "\" or newline or the quote> longstringchar ::= <any source character except "\"> stringescapeseq ::= "\" <any source character> bytesliteral ::= bytesprefix(shortbytes | longbytes) bytesprefix ::= "b" | "B" | "br" | "Br" | "bR" | "BR" | "rb" | "rB" | "Rb" | "RB" shortbytes ::= "'" shortbytesitem* "'" | '"' shortbytesitem* '"' longbytes ::= "'''" longbytesitem* "'''" | '"""' longbytesitem* '"""' shortbytesitem ::= shortbyteschar | bytesescapeseq longbytesitem ::= longbyteschar | bytesescapeseq shortbyteschar ::= <any ASCII character except "\" or newline or the quote> longbyteschar ::= <any ASCII character except "\"> bytesescapeseq ::= "\" <any ASCII character> One syntactic restriction not indicated by these productions is that whitespace is not allowed between the "stringprefix" or "bytesprefix" and the rest of the literal. The source character set is defined by the encoding declaration; it is UTF-8 if no encoding declaration is given in the source file; see section Encoding declarations. In plain English: Both types of literals can be enclosed in matching single quotes ("'") or double quotes ("""). They can also be enclosed in matching groups of three single or double quotes (these are generally referred to as *triple-quoted strings*). The backslash ("\") character is used to escape characters that otherwise have a special meaning, such as newline, backslash itself, or the quote character. Bytes literals are always prefixed with "'b'" or "'B'"; they produce an instance of the "bytes" type instead of the "str" type. They may only contain ASCII characters; bytes with a numeric value of 128 or greater must be expressed with escapes. Both string and bytes literals may optionally be prefixed with a letter "'r'" or "'R'"; such strings are called *raw strings* and treat backslashes as literal characters. As a result, in string literals, "'\U'" and "'\u'" escapes in raw strings are not treated specially. Given that Python 2.x’s raw unicode literals behave differently than Python 3.x’s the "'ur'" syntax is not supported. New in version 3.3: The "'rb'" prefix of raw bytes literals has been added as a synonym of "'br'". New in version 3.3: Support for the unicode legacy literal ("u'value'") was reintroduced to simplify the maintenance of dual Python 2.x and 3.x codebases. See **PEP 414** for more information. A string literal with "'f'" or "'F'" in its prefix is a *formatted string literal*; see Formatted string literals. The "'f'" may be combined with "'r'", but not with "'b'" or "'u'", therefore raw formatted strings are possible, but formatted bytes literals are not. In triple-quoted literals, unescaped newlines and quotes are allowed (and are retained), except that three unescaped quotes in a row terminate the literal. (A “quote” is the character used to open the literal, i.e. either "'" or """.) Unless an "'r'" or "'R'" prefix is present, escape sequences in string and bytes literals are interpreted according to rules similar to those used by Standard C. The recognized escape sequences are: +-------------------+-----------------------------------+---------+ | Escape Sequence | Meaning | Notes | |===================|===================================|=========| | "\"<newline> | Backslash and newline ignored | (1) | +-------------------+-----------------------------------+---------+ | "\\" | Backslash ("\") | | +-------------------+-----------------------------------+---------+ | "\'" | Single quote ("'") | | +-------------------+-----------------------------------+---------+ | "\"" | Double quote (""") | | +-------------------+-----------------------------------+---------+ | "\a" | ASCII Bell (BEL) | | +-------------------+-----------------------------------+---------+ | "\b" | ASCII Backspace (BS) | | +-------------------+-----------------------------------+---------+ | "\f" | ASCII Formfeed (FF) | | +-------------------+-----------------------------------+---------+ | "\n" | ASCII Linefeed (LF) | | +-------------------+-----------------------------------+---------+ | "\r" | ASCII Carriage Return (CR) | | +-------------------+-----------------------------------+---------+ | "\t" | ASCII Horizontal Tab (TAB) | | +-------------------+-----------------------------------+---------+ | "\v" | ASCII Vertical Tab (VT) | | +-------------------+-----------------------------------+---------+ | "\ooo" | Character with octal value *ooo* | (2,4) | +-------------------+-----------------------------------+---------+ | "\xhh" | Character with hex value *hh* | (3,4) | +-------------------+-----------------------------------+---------+ Escape sequences only recognized in string literals are: +-------------------+-----------------------------------+---------+ | Escape Sequence | Meaning | Notes | |===================|===================================|=========| | "\N{name}" | Character named *name* in the | (5) | | | Unicode database | | +-------------------+-----------------------------------+---------+ | "\uxxxx" | Character with 16-bit hex value | (6) | | | *xxxx* | | +-------------------+-----------------------------------+---------+ | "\Uxxxxxxxx" | Character with 32-bit hex value | (7) | | | *xxxxxxxx* | | +-------------------+-----------------------------------+---------+ Notes: 1. A backslash can be added at the end of a line to ignore the newline: >>> 'This string will not include \ ... backslashes or newline characters.' 'This string will not include backslashes or newline characters.' The same result can be achieved using triple-quoted strings, or parentheses and string literal concatenation. 2. As in Standard C, up to three octal digits are accepted. Changed in version 3.11: Octal escapes with value larger than "0o377" produce a "DeprecationWarning". In a future Python version they will be a "SyntaxWarning" and eventually a "SyntaxError". 3. Unlike in Standard C, exactly two hex digits are required. 4. In a bytes literal, hexadecimal and octal escapes denote the byte with the given value. In a string literal, these escapes denote a Unicode character with the given value. 5. Changed in version 3.3: Support for name aliases [1] has been added. 6. Exactly four hex digits are required. 7. Any Unicode character can be encoded this way. Exactly eight hex digits are required. Unlike Standard C, all unrecognized escape sequences are left in the string unchanged, i.e., *the backslash is left in the result*. (This behavior is useful when debugging: if an escape sequence is mistyped, the resulting output is more easily recognized as broken.) It is also important to note that the escape sequences only recognized in string literals fall into the category of unrecognized escapes for bytes literals. Changed in version 3.6: Unrecognized escape sequences produce a "DeprecationWarning". In a future Python version they will be a "SyntaxWarning" and eventually a "SyntaxError". Even in a raw literal, quotes can be escaped with a backslash, but the backslash remains in the result; for example, "r"\""" is a valid string literal consisting of two characters: a backslash and a double quote; "r"\"" is not a valid string literal (even a raw string cannot end in an odd number of backslashes). Specifically, *a raw literal cannot end in a single backslash* (since the backslash would escape the following quote character). Note also that a single backslash followed by a newline is interpreted as those two characters as part of the literal, *not* as a line continuation. � subscriptionsu� Subscriptions ************* The subscription of an instance of a container class will generally select an element from the container. The subscription of a *generic class* will generally return a GenericAlias object. subscription ::= primary "[" expression_list "]" When an object is subscripted, the interpreter will evaluate the primary and the expression list. The primary must evaluate to an object that supports subscription. An object may support subscription through defining one or both of "__getitem__()" and "__class_getitem__()". When the primary is subscripted, the evaluated result of the expression list will be passed to one of these methods. For more details on when "__class_getitem__" is called instead of "__getitem__", see __class_getitem__ versus __getitem__. If the expression list contains at least one comma, it will evaluate to a "tuple" containing the items of the expression list. Otherwise, the expression list will evaluate to the value of the list’s sole member. For built-in objects, there are two types of objects that support subscription via "__getitem__()": 1. Mappings. If the primary is a *mapping*, the expression list must evaluate to an object whose value is one of the keys of the mapping, and the subscription selects the value in the mapping that corresponds to that key. An example of a builtin mapping class is the "dict" class. 2. Sequences. If the primary is a *sequence*, the expression list must evaluate to an "int" or a "slice" (as discussed in the following section). Examples of builtin sequence classes include the "str", "list" and "tuple" classes. The formal syntax makes no special provision for negative indices in *sequences*. However, built-in sequences all provide a "__getitem__()" method that interprets negative indices by adding the length of the sequence to the index so that, for example, "x[-1]" selects the last item of "x". The resulting value must be a nonnegative integer less than the number of items in the sequence, and the subscription selects the item whose index is that value (counting from zero). Since the support for negative indices and slicing occurs in the object’s "__getitem__()" method, subclasses overriding this method will need to explicitly add that support. A "string" is a special kind of sequence whose items are *characters*. A character is not a separate data type but a string of exactly one character. �truthax Truth Value Testing ******************* Any object can be tested for truth value, for use in an "if" or "while" condition or as operand of the Boolean operations below. By default, an object is considered true unless its class defines either a "__bool__()" method that returns "False" or a "__len__()" method that returns zero, when called with the object. [1] Here are most of the built-in objects considered false: * constants defined to be false: "None" and "False". * zero of any numeric type: "0", "0.0", "0j", "Decimal(0)", "Fraction(0, 1)" * empty sequences and collections: "''", "()", "[]", "{}", "set()", "range(0)" Operations and built-in functions that have a Boolean result always return "0" or "False" for false and "1" or "True" for true, unless otherwise stated. (Important exception: the Boolean operations "or" and "and" always return one of their operands.) �tryu� The "try" statement ******************* The "try" statement specifies exception handlers and/or cleanup code for a group of statements: try_stmt ::= try1_stmt | try2_stmt | try3_stmt try1_stmt ::= "try" ":" suite ("except" [expression ["as" identifier]] ":" suite)+ ["else" ":" suite] ["finally" ":" suite] try2_stmt ::= "try" ":" suite ("except" "*" expression ["as" identifier] ":" suite)+ ["else" ":" suite] ["finally" ":" suite] try3_stmt ::= "try" ":" suite "finally" ":" suite Additional information on exceptions can be found in section Exceptions, and information on using the "raise" statement to generate exceptions may be found in section The raise statement. "except" clause =============== The "except" clause(s) specify one or more exception handlers. When no exception occurs in the "try" clause, no exception handler is executed. When an exception occurs in the "try" suite, a search for an exception handler is started. This search inspects the "except" clauses in turn until one is found that matches the exception. An expression-less "except" clause, if present, must be last; it matches any exception. For an "except" clause with an expression, that expression is evaluated, and the clause matches the exception if the resulting object is “compatible” with the exception. An object is compatible with an exception if the object is the class or a *non- virtual base class* of the exception object, or a tuple containing an item that is the class or a non-virtual base class of the exception object. If no "except" clause matches the exception, the search for an exception handler continues in the surrounding code and on the invocation stack. [1] If the evaluation of an expression in the header of an "except" clause raises an exception, the original search for a handler is canceled and a search starts for the new exception in the surrounding code and on the call stack (it is treated as if the entire "try" statement raised the exception). When a matching "except" clause is found, the exception is assigned to the target specified after the "as" keyword in that "except" clause, if present, and the "except" clause’s suite is executed. All "except" clauses must have an executable block. When the end of this block is reached, execution continues normally after the entire "try" statement. (This means that if two nested handlers exist for the same exception, and the exception occurs in the "try" clause of the inner handler, the outer handler will not handle the exception.) When an exception has been assigned using "as target", it is cleared at the end of the "except" clause. This is as if except E as N: foo was translated to except E as N: try: foo finally: del N This means the exception must be assigned to a different name to be able to refer to it after the "except" clause. Exceptions are cleared because with the traceback attached to them, they form a reference cycle with the stack frame, keeping all locals in that frame alive until the next garbage collection occurs. Before an "except" clause’s suite is executed, details about the exception are stored in the "sys" module and can be accessed via "sys.exc_info()". "sys.exc_info()" returns a 3-tuple consisting of the exception class, the exception instance and a traceback object (see section The standard type hierarchy) identifying the point in the program where the exception occurred. The details about the exception accessed via "sys.exc_info()" are restored to their previous values when leaving an exception handler: >>> print(sys.exc_info()) (None, None, None) >>> try: ... raise TypeError ... except: ... print(sys.exc_info()) ... try: ... raise ValueError ... except: ... print(sys.exc_info()) ... print(sys.exc_info()) ... (<class 'TypeError'>, TypeError(), <traceback object at 0x10efad080>) (<class 'ValueError'>, ValueError(), <traceback object at 0x10efad040>) (<class 'TypeError'>, TypeError(), <traceback object at 0x10efad080>) >>> print(sys.exc_info()) (None, None, None) "except*" clause ================ The "except*" clause(s) are used for handling "ExceptionGroup"s. The exception type for matching is interpreted as in the case of "except", but in the case of exception groups we can have partial matches when the type matches some of the exceptions in the group. This means that multiple "except*" clauses can execute, each handling part of the exception group. Each clause executes at most once and handles an exception group of all matching exceptions. Each exception in the group is handled by at most one "except*" clause, the first that matches it. >>> try: ... raise ExceptionGroup("eg", ... [ValueError(1), TypeError(2), OSError(3), OSError(4)]) ... except* TypeError as e: ... print(f'caught {type(e)} with nested {e.exceptions}') ... except* OSError as e: ... print(f'caught {type(e)} with nested {e.exceptions}') ... caught <class 'ExceptionGroup'> with nested (TypeError(2),) caught <class 'ExceptionGroup'> with nested (OSError(3), OSError(4)) + Exception Group Traceback (most recent call last): | File "<stdin>", line 2, in <module> | ExceptionGroup: eg +-+---------------- 1 ---------------- | ValueError: 1 +------------------------------------ Any remaining exceptions that were not handled by any "except*" clause are re-raised at the end, combined into an exception group along with all exceptions that were raised from within "except*" clauses. If the raised exception is not an exception group and its type matches one of the "except*" clauses, it is caught and wrapped by an exception group with an empty message string. >>> try: ... raise BlockingIOError ... except* BlockingIOError as e: ... print(repr(e)) ... ExceptionGroup('', (BlockingIOError())) An "except*" clause must have a matching type, and this type cannot be a subclass of "BaseExceptionGroup". It is not possible to mix "except" and "except*" in the same "try". "break", "continue" and "return" cannot appear in an "except*" clause. "else" clause ============= The optional "else" clause is executed if the control flow leaves the "try" suite, no exception was raised, and no "return", "continue", or "break" statement was executed. Exceptions in the "else" clause are not handled by the preceding "except" clauses. "finally" clause ================ If "finally" is present, it specifies a ‘cleanup’ handler. The "try" clause is executed, including any "except" and "else" clauses. If an exception occurs in any of the clauses and is not handled, the exception is temporarily saved. The "finally" clause is executed. If there is a saved exception it is re-raised at the end of the "finally" clause. If the "finally" clause raises another exception, the saved exception is set as the context of the new exception. If the "finally" clause executes a "return", "break" or "continue" statement, the saved exception is discarded: >>> def f(): ... try: ... 1/0 ... finally: ... return 42 ... >>> f() 42 The exception information is not available to the program during execution of the "finally" clause. When a "return", "break" or "continue" statement is executed in the "try" suite of a "try"…"finally" statement, the "finally" clause is also executed ‘on the way out.’ The return value of a function is determined by the last "return" statement executed. Since the "finally" clause always executes, a "return" statement executed in the "finally" clause will always be the last one executed: >>> def foo(): ... try: ... return 'try' ... finally: ... return 'finally' ... >>> foo() 'finally' Changed in version 3.8: Prior to Python 3.8, a "continue" statement was illegal in the "finally" clause due to a problem with the implementation. uա The standard type hierarchy *************************** 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. None This type has a single value. There is a single object with this value. This object is accessed through the built-in name "None". It is used to signify the absence of a value in many situations, e.g., it is returned from functions that don’t explicitly return anything. Its truth value is false. NotImplemented This type has a single value. There is a single object with this value. This object is accessed through the built-in name "NotImplemented". Numeric methods and rich comparison methods should return this value if they do not implement the operation for the operands provided. (The interpreter will then try the reflected operation, or some other fallback, depending on the operator.) It should not be evaluated in a boolean context. See Implementing the arithmetic operations for more details. Changed in version 3.9: Evaluating "NotImplemented" in a boolean context is deprecated. While it currently evaluates as true, it will emit a "DeprecationWarning". It will raise a "TypeError" in a future version of Python. Ellipsis This type has a single value. There is a single object with this value. This object is accessed through the literal "..." or the built-in name "Ellipsis". Its truth value is true. "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: "numbers.Integral" These represent elements from the mathematical set of integers (positive and negative). 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. The rules for integer representation are intended to give the most meaningful interpretation of shift and mask operations involving negative integers. "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. "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". 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]". Sequences also support slicing: "a[i:j]" selects all items with index *k* such that *i* "<=" *k* "<" *j*. When used as an expression, a slice is a sequence of the same type. This implies that the index set is renumbered so that it starts at 0. 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: 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: Strings A string is a sequence of values that represent Unicode code points. All the code points in the range "U+0000 - U+10FFFF" can be represented in a string. Python doesn’t have a char 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 - 10FFFF"; "chr()" converts an integer in the range "0 - 10FFFF" 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. Tuples 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. Bytes 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. Mutable sequences 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. There are currently two intrinsic mutable sequence types: Lists 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.) Byte Arrays 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. The extension module "array" provides an additional example of a mutable sequence type, as does the "collections" module. Set types 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: Sets 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 sets 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. Mappings 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: Dictionaries 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. Changed in version 3.7: 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. Callable types These are the types to which the function call operation (see section Calls) can be applied: User-defined functions A user-defined function object is created by a function definition (see section Function definitions). It should be called with an argument list containing the same number of items as the function’s formal parameter list. Special attributes: +---------------------------+---------------------------------+-------------+ | Attribute | Meaning | | |===========================|=================================|=============| | "__doc__" | The function’s documentation | Writable | | | string, or "None" if | | | | unavailable; not inherited by | | | | subclasses. | | +---------------------------+---------------------------------+-------------+ | "__name__" | The function’s name. | Writable | +---------------------------+---------------------------------+-------------+ | "__qualname__" | The function’s *qualified | Writable | | | name*. New in version 3.3. | | +---------------------------+---------------------------------+-------------+ | "__module__" | The name of the module the | Writable | | | function was defined in, or | | | | "None" if unavailable. | | +---------------------------+---------------------------------+-------------+ | "__defaults__" | A tuple containing default | Writable | | | argument values for those | | | | arguments that have defaults, | | | | or "None" if no arguments have | | | | a default value. | | +---------------------------+---------------------------------+-------------+ | "__code__" | The code object representing | Writable | | | the compiled function body. | | +---------------------------+---------------------------------+-------------+ | "__globals__" | A reference to the dictionary | Read-only | | | that holds the function’s | | | | global variables — the global | | | | namespace of the module in | | | | which the function was defined. | | +---------------------------+---------------------------------+-------------+ | "__dict__" | The namespace supporting | Writable | | | arbitrary function attributes. | | +---------------------------+---------------------------------+-------------+ | "__closure__" | "None" or a tuple of cells that | Read-only | | | contain bindings for the | | | | function’s free variables. See | | | | below for information on the | | | | "cell_contents" attribute. | | +---------------------------+---------------------------------+-------------+ | "__annotations__" | A dict containing annotations | Writable | | | of parameters. The keys of the | | | | dict are the parameter names, | | | | and "'return'" for the return | | | | annotation, if provided. For | | | | more information on working | | | | with this attribute, see | | | | Annotations Best Practices. | | +---------------------------+---------------------------------+-------------+ | "__kwdefaults__" | A dict containing defaults for | Writable | | | keyword-only parameters. | | +---------------------------+---------------------------------+-------------+ Most of the attributes labelled “Writable” check the type of the assigned value. 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. *Note that the current implementation only supports function attributes on user-defined functions. Function attributes on built-in functions may be supported in the future.* 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. Additional information about a function’s definition can be retrieved from its code object; see the description of internal types below. The "cell" type can be accessed in the "types" module. Instance methods An instance method object combines a class, a class instance and any callable object (normally a user-defined function). Special read-only attributes: "__self__" is the class instance object, "__func__" is the function object; "__doc__" is the method’s documentation (same as "__func__.__doc__"); "__name__" is the method name (same as "__func__.__name__"); "__module__" is 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 class method 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 class method 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 class method 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. Note that the transformation from function object to instance method object happens each time the attribute is retrieved from the instance. In some cases, a fruitful optimization is to assign the attribute to a local variable and call that local variable. Also notice that this transformation only happens for user-defined functions; other callable objects (and all non- callable objects) are retrieved without transformation. It is also 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. Generator functions A function or method which uses the "yield" statement (see section The yield statement) 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. Coroutine functions 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 Coroutine Objects section. Asynchronous generator functions 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. Built-in functions A built-in function object is a wrapper around a C function. Examples of built-in functions are "len()" and "math.sin()" ("math" is a standard built-in module). The number and type of the arguments are determined by the C function. Special read- only attributes: "__doc__" is the function’s documentation string, or "None" if unavailable; "__name__" is the function’s 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. Built-in methods 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*. 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. Class Instances Instances of arbitrary classes can be made callable by defining a "__call__()" method in their class. Modules 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". Predefined (writable) attributes: "__name__" The module’s name. "__doc__" The module’s documentation string, or "None" if unavailable. "__file__" The pathname of the file from which the module was loaded, if it was loaded from a file. The "__file__" attribute may be missing for certain types of modules, such as C modules that are statically linked into the interpreter. For extension modules loaded dynamically from a shared library, it’s the pathname of the shared library file. "__annotations__" A dictionary containing *variable annotations* collected during module body execution. For best practices on working with "__annotations__", please see Annotations Best Practices. Special read-only attribute: "__dict__" is the module’s namespace as a dictionary object. **CPython implementation detail:** 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. Custom classes Custom class types are typically created by class definitions (see section Class definitions). 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 in the documentation accompanying the 2.3 release at https://www.python.org/download/releases/2.3/mro/. 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 static method object, it is transformed into the object wrapped by the static method object. See section Implementing Descriptors 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). Special attributes: "__name__" The class name. "__module__" The name of the module in which the class was defined. "__dict__" The dictionary containing the class’s namespace. "__bases__" A tuple containing the base classes, in the order of their occurrence in the base class list. "__doc__" The class’s documentation string, or "None" if undefined. "__annotations__" A dictionary containing *variable annotations* collected during class body execution. For best practices on working with "__annotations__", please see Annotations Best Practices. Class instances 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 Implementing Descriptors 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. Special attributes: "__dict__" is the attribute dictionary; "__class__" is the instance’s class. 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). 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. 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. Code objects 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. Special read-only attributes: "co_name" gives the function name; "co_qualname" gives the fully qualified function name; "co_argcount" is the total number of positional arguments (including positional-only arguments and arguments with default values); "co_posonlyargcount" is the number of positional-only arguments (including arguments with default values); "co_kwonlyargcount" is the number of keyword-only arguments (including arguments with default values); "co_nlocals" is the number of local variables used by the function (including arguments); "co_varnames" is a tuple containing the names of the local variables (starting with the argument names); "co_cellvars" is a tuple containing the names of local variables that are referenced by nested functions; "co_freevars" is a tuple containing the names of free variables; "co_code" is a string representing the sequence of bytecode instructions; "co_consts" is a tuple containing the literals used by the bytecode; "co_names" is a tuple containing the names used by the bytecode; "co_filename" is the filename from which the code was compiled; "co_firstlineno" is the first line number of the function; "co_lnotab" is a string encoding the mapping from bytecode offsets to line numbers (for details see the source code of the interpreter); "co_stacksize" is the required stack size; "co_flags" is 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. Future feature declarations ("from __future__ import division") also use bits in "co_flags" to indicate whether a code object was compiled with a particular feature enabled: bit "0x2000" is set if the function was compiled with future division enabled; bits "0x10" and "0x1000" were used in earlier versions of Python. Other bits in "co_flags" are reserved for internal use. If a code object represents a function, the first item in "co_consts" is the documentation string of the function, or "None" if undefined. 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* instruction. 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". New in version 3.11. Note: 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. Frame objects Frame objects represent execution frames. They may occur in traceback objects (see below), and are also passed to registered trace functions. Special read-only attributes: "f_back" is to the previous stack frame (towards the caller), or "None" if this is the bottom stack frame; "f_code" is the code object being executed in this frame; "f_locals" is the dictionary used to look up local variables; "f_globals" is used for global variables; "f_builtins" is used for built-in (intrinsic) names; "f_lasti" gives the precise instruction (this is an index into the bytecode string of the code object). Accessing "f_code" raises an auditing event "object.__getattr__" with arguments "obj" and ""f_code"". Special writable attributes: "f_trace", if not "None", is a function called for various events during code execution (this is used by the debugger). Normally an event is triggered for each new source line - this can be disabled by setting "f_trace_lines" to "False". Implementations *may* allow per-opcode events to be requested by setting "f_trace_opcodes" to "True". Note that this may lead to undefined interpreter behaviour if exceptions raised by the trace function escape to the function being traced. "f_lineno" is 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 f_lineno. 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. New in version 3.4. Traceback objects Traceback objects represent a 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". 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 The try statement.) 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. Special read-only attributes: "tb_frame" points to the execution frame of the current level; "tb_lineno" gives the line number where the exception occurred; "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. Accessing "tb_frame" raises an auditing event "object.__getattr__" with arguments "obj" and ""tb_frame"". 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. Changed in version 3.7: Traceback objects can now be explicitly instantiated from Python code, and the "tb_next" attribute of existing instances can be updated. 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. 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. 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 “User-defined methods”. Class method objects are created by the built-in "classmethod()" constructor. a� Functions ********* Function objects are created by function definitions. The only operation on a function object is to call it: "func(argument-list)". There are really two flavors of function objects: built-in functions and user-defined functions. Both support the same operation (to call the function), but the implementation is different, hence the different object types. See Function definitions for more information. u�/ Mapping Types — "dict" ********************** A *mapping* object maps *hashable* values to arbitrary objects. Mappings are mutable objects. There is currently only one standard mapping type, the *dictionary*. (For other containers see the built- in "list", "set", and "tuple" classes, and the "collections" module.) A dictionary’s keys are *almost* arbitrary values. Values that are not *hashable*, that is, values containing lists, dictionaries or other mutable types (that are compared by value rather than by object identity) may not be used as keys. Values that compare equal (such as "1", "1.0", and "True") can be used interchangeably to index the same dictionary entry. class dict(**kwargs) class dict(mapping, **kwargs) class dict(iterable, **kwargs) Return a new dictionary initialized from an optional positional argument and a possibly empty set of keyword arguments. Dictionaries can be created by several means: * Use a comma-separated list of "key: value" pairs within braces: "{'jack': 4098, 'sjoerd': 4127}" or "{4098: 'jack', 4127: 'sjoerd'}" * Use a dict comprehension: "{}", "{x: x ** 2 for x in range(10)}" * Use the type constructor: "dict()", "dict([('foo', 100), ('bar', 200)])", "dict(foo=100, bar=200)" If no positional argument is given, an empty dictionary is created. If a positional argument is given and it is a mapping object, a dictionary is created with the same key-value pairs as the mapping object. Otherwise, the positional argument must be an *iterable* object. Each item in the iterable must itself be an iterable with exactly two objects. The first object of each item becomes a key in the new dictionary, and the second object the corresponding value. If a key occurs more than once, the last value for that key becomes the corresponding value in the new dictionary. If keyword arguments are given, the keyword arguments and their values are added to the dictionary created from the positional argument. If a key being added is already present, the value from the keyword argument replaces the value from the positional argument. To illustrate, the following examples all return a dictionary equal to "{"one": 1, "two": 2, "three": 3}": >>> a = dict(one=1, two=2, three=3) >>> b = {'one': 1, 'two': 2, 'three': 3} >>> c = dict(zip(['one', 'two', 'three'], [1, 2, 3])) >>> d = dict([('two', 2), ('one', 1), ('three', 3)]) >>> e = dict({'three': 3, 'one': 1, 'two': 2}) >>> f = dict({'one': 1, 'three': 3}, two=2) >>> a == b == c == d == e == f True Providing keyword arguments as in the first example only works for keys that are valid Python identifiers. Otherwise, any valid keys can be used. These are the operations that dictionaries support (and therefore, custom mapping types should support too): list(d) Return a list of all the keys used in the dictionary *d*. len(d) Return the number of items in the dictionary *d*. d[key] Return the item of *d* with key *key*. Raises a "KeyError" if *key* is not in the map. If a subclass of dict defines a method "__missing__()" and *key* is not present, the "d[key]" operation calls that method with the key *key* as argument. The "d[key]" operation then returns or raises whatever is returned or raised by the "__missing__(key)" call. No other operations or methods invoke "__missing__()". If "__missing__()" is not defined, "KeyError" is raised. "__missing__()" must be a method; it cannot be an instance variable: >>> class Counter(dict): ... def __missing__(self, key): ... return 0 >>> c = Counter() >>> c['red'] 0 >>> c['red'] += 1 >>> c['red'] 1 The example above shows part of the implementation of "collections.Counter". A different "__missing__" method is used by "collections.defaultdict". d[key] = value Set "d[key]" to *value*. del d[key] Remove "d[key]" from *d*. Raises a "KeyError" if *key* is not in the map. key in d Return "True" if *d* has a key *key*, else "False". key not in d Equivalent to "not key in d". iter(d) Return an iterator over the keys of the dictionary. This is a shortcut for "iter(d.keys())". clear() Remove all items from the dictionary. copy() Return a shallow copy of the dictionary. classmethod fromkeys(iterable[, value]) Create a new dictionary with keys from *iterable* and values set to *value*. "fromkeys()" is a class method that returns a new dictionary. *value* defaults to "None". All of the values refer to just a single instance, so it generally doesn’t make sense for *value* to be a mutable object such as an empty list. To get distinct values, use a dict comprehension instead. get(key[, default]) Return the value for *key* if *key* is in the dictionary, else *default*. If *default* is not given, it defaults to "None", so that this method never raises a "KeyError". items() Return a new view of the dictionary’s items ("(key, value)" pairs). See the documentation of view objects. keys() Return a new view of the dictionary’s keys. See the documentation of view objects. pop(key[, default]) If *key* is in the dictionary, remove it and return its value, else return *default*. If *default* is not given and *key* is not in the dictionary, a "KeyError" is raised. popitem() Remove and return a "(key, value)" pair from the dictionary. Pairs are returned in LIFO (last-in, first-out) order. "popitem()" is useful to destructively iterate over a dictionary, as often used in set algorithms. If the dictionary is empty, calling "popitem()" raises a "KeyError". Changed in version 3.7: LIFO order is now guaranteed. In prior versions, "popitem()" would return an arbitrary key/value pair. reversed(d) Return a reverse iterator over the keys of the dictionary. This is a shortcut for "reversed(d.keys())". New in version 3.8. setdefault(key[, default]) If *key* is in the dictionary, return its value. If not, insert *key* with a value of *default* and return *default*. *default* defaults to "None". update([other]) Update the dictionary with the key/value pairs from *other*, overwriting existing keys. Return "None". "update()" accepts either another dictionary object or an iterable of key/value pairs (as tuples or other iterables of length two). If keyword arguments are specified, the dictionary is then updated with those key/value pairs: "d.update(red=1, blue=2)". values() Return a new view of the dictionary’s values. See the documentation of view objects. An equality comparison between one "dict.values()" view and another will always return "False". This also applies when comparing "dict.values()" to itself: >>> d = {'a': 1} >>> d.values() == d.values() False d | other Create a new dictionary with the merged keys and values of *d* and *other*, which must both be dictionaries. The values of *other* take priority when *d* and *other* share keys. New in version 3.9. d |= other Update the dictionary *d* with keys and values from *other*, which may be either a *mapping* or an *iterable* of key/value pairs. The values of *other* take priority when *d* and *other* share keys. New in version 3.9. Dictionaries compare equal if and only if they have the same "(key, value)" pairs (regardless of ordering). Order comparisons (‘<’, ‘<=’, ‘>=’, ‘>’) raise "TypeError". Dictionaries preserve insertion order. Note that updating a key does not affect the order. Keys added after deletion are inserted at the end. >>> d = {"one": 1, "two": 2, "three": 3, "four": 4} >>> d {'one': 1, 'two': 2, 'three': 3, 'four': 4} >>> list(d) ['one', 'two', 'three', 'four'] >>> list(d.values()) [1, 2, 3, 4] >>> d["one"] = 42 >>> d {'one': 42, 'two': 2, 'three': 3, 'four': 4} >>> del d["two"] >>> d["two"] = None >>> d {'one': 42, 'three': 3, 'four': 4, 'two': None} Changed in version 3.7: Dictionary order is guaranteed to be insertion order. This behavior was an implementation detail of CPython from 3.6. Dictionaries and dictionary views are reversible. >>> d = {"one": 1, "two": 2, "three": 3, "four": 4} >>> d {'one': 1, 'two': 2, 'three': 3, 'four': 4} >>> list(reversed(d)) ['four', 'three', 'two', 'one'] >>> list(reversed(d.values())) [4, 3, 2, 1] >>> list(reversed(d.items())) [('four', 4), ('three', 3), ('two', 2), ('one', 1)] Changed in version 3.8: Dictionaries are now reversible. See also: "types.MappingProxyType" can be used to create a read-only view of a "dict". Dictionary view objects ======================= The objects returned by "dict.keys()", "dict.values()" and "dict.items()" are *view objects*. They provide a dynamic view on the dictionary’s entries, which means that when the dictionary changes, the view reflects these changes. Dictionary views can be iterated over to yield their respective data, and support membership tests: len(dictview) Return the number of entries in the dictionary. iter(dictview) Return an iterator over the keys, values or items (represented as tuples of "(key, value)") in the dictionary. Keys and values are iterated over in insertion order. This allows the creation of "(value, key)" pairs using "zip()": "pairs = zip(d.values(), d.keys())". Another way to create the same list is "pairs = [(v, k) for (k, v) in d.items()]". Iterating views while adding or deleting entries in the dictionary may raise a "RuntimeError" or fail to iterate over all entries. Changed in version 3.7: Dictionary order is guaranteed to be insertion order. x in dictview Return "True" if *x* is in the underlying dictionary’s keys, values or items (in the latter case, *x* should be a "(key, value)" tuple). reversed(dictview) Return a reverse iterator over the keys, values or items of the dictionary. The view will be iterated in reverse order of the insertion. Changed in version 3.8: Dictionary views are now reversible. dictview.mapping Return a "types.MappingProxyType" that wraps the original dictionary to which the view refers. New in version 3.10. Keys views are set-like since their entries are unique and hashable. If all values are hashable, so that "(key, value)" pairs are unique and hashable, then the items view is also set-like. (Values views are not treated as set-like since the entries are generally not unique.) For set-like views, all of the operations defined for the abstract base class "collections.abc.Set" are available (for example, "==", "<", or "^"). An example of dictionary view usage: >>> dishes = {'eggs': 2, 'sausage': 1, 'bacon': 1, 'spam': 500} >>> keys = dishes.keys() >>> values = dishes.values() >>> # iteration >>> n = 0 >>> for val in values: ... n += val >>> print(n) 504 >>> # keys and values are iterated over in the same order (insertion order) >>> list(keys) ['eggs', 'sausage', 'bacon', 'spam'] >>> list(values) [2, 1, 1, 500] >>> # view objects are dynamic and reflect dict changes >>> del dishes['eggs'] >>> del dishes['sausage'] >>> list(keys) ['bacon', 'spam'] >>> # set operations >>> keys & {'eggs', 'bacon', 'salad'} {'bacon'} >>> keys ^ {'sausage', 'juice'} {'juice', 'sausage', 'bacon', 'spam'} >>> # get back a read-only proxy for the original dictionary >>> values.mapping mappingproxy({'bacon': 1, 'spam': 500}) >>> values.mapping['spam'] 500 a� Methods ******* Methods are functions that are called using the attribute notation. There are two flavors: built-in methods (such as "append()" on lists) and class instance methods. Built-in methods are described with the types that support them. If you access a method (a function defined in a class namespace) through an instance, you get a special object: a *bound method* (also called *instance method*) object. When called, it will add the "self" argument to the argument list. Bound methods have two special read- only attributes: "m.__self__" is the object on which the method operates, and "m.__func__" is the function implementing the method. Calling "m(arg-1, arg-2, ..., arg-n)" is completely equivalent to calling "m.__func__(m.__self__, arg-1, arg-2, ..., arg-n)". Like function objects, bound method objects support getting arbitrary attributes. However, since method attributes are actually stored on the underlying function object ("meth.__func__"), setting method attributes on bound methods is disallowed. Attempting to set an attribute on a method results in an "AttributeError" being raised. In order to set a method attribute, you need to explicitly set it on the underlying function object: >>> class C: ... def method(self): ... pass ... >>> c = C() >>> c.method.whoami = 'my name is method' # can't set on the method Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: 'method' object has no attribute 'whoami' >>> c.method.__func__.whoami = 'my name is method' >>> c.method.whoami 'my name is method' See The standard type hierarchy for more information. u$ Modules ******* The only special operation on a module is attribute access: "m.name", where *m* is a module and *name* accesses a name defined in *m*’s symbol table. Module attributes can be assigned to. (Note that the "import" statement is not, strictly speaking, an operation on a module object; "import foo" does not require a module object named *foo* to exist, rather it requires an (external) *definition* for a module named *foo* somewhere.) A special attribute of every module is "__dict__". This is the dictionary containing the module’s symbol table. Modifying this dictionary will actually change the module’s symbol table, but direct assignment to the "__dict__" attribute is not possible (you can write "m.__dict__['a'] = 1", which defines "m.a" to be "1", but you can’t write "m.__dict__ = {}"). Modifying "__dict__" directly is not recommended. Modules built into the interpreter are written like this: "<module 'sys' (built-in)>". If loaded from a file, they are written as "<module 'os' from '/usr/local/lib/pythonX.Y/os.pyc'>". u�[ Sequence Types — "list", "tuple", "range" ***************************************** There are three basic sequence types: lists, tuples, and range objects. Additional sequence types tailored for processing of binary data and text strings are described in dedicated sections. Common Sequence Operations ========================== The operations in the following table are supported by most sequence types, both mutable and immutable. The "collections.abc.Sequence" ABC is provided to make it easier to correctly implement these operations on custom sequence types. This table lists the sequence operations sorted in ascending priority. In the table, *s* and *t* are sequences of the same type, *n*, *i*, *j* and *k* are integers and *x* is an arbitrary object that meets any type and value restrictions imposed by *s*. The "in" and "not in" operations have the same priorities as the comparison operations. The "+" (concatenation) and "*" (repetition) operations have the same priority as the corresponding numeric operations. [3] +----------------------------+----------------------------------+------------+ | Operation | Result | Notes | |============================|==================================|============| | "x in s" | "True" if an item of *s* is | (1) | | | equal to *x*, else "False" | | +----------------------------+----------------------------------+------------+ | "x not in s" | "False" if an item of *s* is | (1) | | | equal to *x*, else "True" | | +----------------------------+----------------------------------+------------+ | "s + t" | the concatenation of *s* and *t* | (6)(7) | +----------------------------+----------------------------------+------------+ | "s * n" or "n * s" | equivalent to adding *s* to | (2)(7) | | | itself *n* times | | +----------------------------+----------------------------------+------------+ | "s[i]" | *i*th item of *s*, origin 0 | (3) | +----------------------------+----------------------------------+------------+ | "s[i:j]" | slice of *s* from *i* to *j* | (3)(4) | +----------------------------+----------------------------------+------------+ | "s[i:j:k]" | slice of *s* from *i* to *j* | (3)(5) | | | with step *k* | | +----------------------------+----------------------------------+------------+ | "len(s)" | length of *s* | | +----------------------------+----------------------------------+------------+ | "min(s)" | smallest item of *s* | | +----------------------------+----------------------------------+------------+ | "max(s)" | largest item of *s* | | +----------------------------+----------------------------------+------------+ | "s.index(x[, i[, j]])" | index of the first occurrence of | (8) | | | *x* in *s* (at or after index | | | | *i* and before index *j*) | | +----------------------------+----------------------------------+------------+ | "s.count(x)" | total number of occurrences of | | | | *x* in *s* | | +----------------------------+----------------------------------+------------+ Sequences of the same type also support comparisons. In particular, tuples and lists are compared lexicographically by comparing corresponding elements. This means that to compare equal, every element must compare equal and the two sequences must be of the same type and have the same length. (For full details see Comparisons in the language reference.) Forward and reversed iterators over mutable sequences access values using an index. That index will continue to march forward (or backward) even if the underlying sequence is mutated. The iterator terminates only when an "IndexError" or a "StopIteration" is encountered (or when the index drops below zero). Notes: 1. While the "in" and "not in" operations are used only for simple containment testing in the general case, some specialised sequences (such as "str", "bytes" and "bytearray") also use them for subsequence testing: >>> "gg" in "eggs" True 2. Values of *n* less than "0" are treated as "0" (which yields an empty sequence of the same type as *s*). Note that items in the sequence *s* are not copied; they are referenced multiple times. This often haunts new Python programmers; consider: >>> lists = [[]] * 3 >>> lists [[], [], []] >>> lists[0].append(3) >>> lists [[3], [3], [3]] What has happened is that "[[]]" is a one-element list containing an empty list, so all three elements of "[[]] * 3" are references to this single empty list. Modifying any of the elements of "lists" modifies this single list. You can create a list of different lists this way: >>> lists = [[] for i in range(3)] >>> lists[0].append(3) >>> lists[1].append(5) >>> lists[2].append(7) >>> lists [[3], [5], [7]] Further explanation is available in the FAQ entry How do I create a multidimensional list?. 3. If *i* or *j* is negative, the index is relative to the end of sequence *s*: "len(s) + i" or "len(s) + j" is substituted. But note that "-0" is still "0". 4. The slice of *s* from *i* to *j* is defined as the sequence of items with index *k* such that "i <= k < j". If *i* or *j* is greater than "len(s)", use "len(s)". If *i* is omitted or "None", use "0". If *j* is omitted or "None", use "len(s)". If *i* is greater than or equal to *j*, the slice is empty. 5. The slice of *s* from *i* to *j* with step *k* is defined as the sequence of items with index "x = i + n*k" such that "0 <= n < (j-i)/k". In other words, the indices are "i", "i+k", "i+2*k", "i+3*k" and so on, stopping when *j* is reached (but never including *j*). When *k* is positive, *i* and *j* are reduced to "len(s)" if they are greater. When *k* is negative, *i* and *j* are reduced to "len(s) - 1" if they are greater. If *i* or *j* are omitted or "None", they become “end” values (which end depends on the sign of *k*). Note, *k* cannot be zero. If *k* is "None", it is treated like "1". 6. Concatenating immutable sequences always results in a new object. This means that building up a sequence by repeated concatenation will have a quadratic runtime cost in the total sequence length. To get a linear runtime cost, you must switch to one of the alternatives below: * if concatenating "str" objects, you can build a list and use "str.join()" at the end or else write to an "io.StringIO" instance and retrieve its value when complete * if concatenating "bytes" objects, you can similarly use "bytes.join()" or "io.BytesIO", or you can do in-place concatenation with a "bytearray" object. "bytearray" objects are mutable and have an efficient overallocation mechanism * if concatenating "tuple" objects, extend a "list" instead * for other types, investigate the relevant class documentation 7. Some sequence types (such as "range") only support item sequences that follow specific patterns, and hence don’t support sequence concatenation or repetition. 8. "index" raises "ValueError" when *x* is not found in *s*. Not all implementations support passing the additional arguments *i* and *j*. These arguments allow efficient searching of subsections of the sequence. Passing the extra arguments is roughly equivalent to using "s[i:j].index(x)", only without copying any data and with the returned index being relative to the start of the sequence rather than the start of the slice. Immutable Sequence Types ======================== The only operation that immutable sequence types generally implement that is not also implemented by mutable sequence types is support for the "hash()" built-in. This support allows immutable sequences, such as "tuple" instances, to be used as "dict" keys and stored in "set" and "frozenset" instances. Attempting to hash an immutable sequence that contains unhashable values will result in "TypeError". Mutable Sequence Types ====================== The operations in the following table are defined on mutable sequence types. The "collections.abc.MutableSequence" ABC is provided to make it easier to correctly implement these operations on custom sequence types. In the table *s* is an instance of a mutable sequence type, *t* is any iterable object and *x* is an arbitrary object that meets any type and value restrictions imposed by *s* (for example, "bytearray" only accepts integers that meet the value restriction "0 <= x <= 255"). +--------------------------------+----------------------------------+-----------------------+ | Operation | Result | Notes | |================================|==================================|=======================| | "s[i] = x" | item *i* of *s* is replaced by | | | | *x* | | +--------------------------------+----------------------------------+-----------------------+ | "s[i:j] = t" | slice of *s* from *i* to *j* is | | | | replaced by the contents of the | | | | iterable *t* | | +--------------------------------+----------------------------------+-----------------------+ | "del s[i:j]" | same as "s[i:j] = []" | | +--------------------------------+----------------------------------+-----------------------+ | "s[i:j:k] = t" | the elements of "s[i:j:k]" are | (1) | | | replaced by those of *t* | | +--------------------------------+----------------------------------+-----------------------+ | "del s[i:j:k]" | removes the elements of | | | | "s[i:j:k]" from the list | | +--------------------------------+----------------------------------+-----------------------+ | "s.append(x)" | appends *x* to the end of the | | | | sequence (same as | | | | "s[len(s):len(s)] = [x]") | | +--------------------------------+----------------------------------+-----------------------+ | "s.clear()" | removes all items from *s* (same | (5) | | | as "del s[:]") | | +--------------------------------+----------------------------------+-----------------------+ | "s.copy()" | creates a shallow copy of *s* | (5) | | | (same as "s[:]") | | +--------------------------------+----------------------------------+-----------------------+ | "s.extend(t)" or "s += t" | extends *s* with the contents of | | | | *t* (for the most part the same | | | | as "s[len(s):len(s)] = t") | | +--------------------------------+----------------------------------+-----------------------+ | "s *= n" | updates *s* with its contents | (6) | | | repeated *n* times | | +--------------------------------+----------------------------------+-----------------------+ | "s.insert(i, x)" | inserts *x* into *s* at the | | | | index given by *i* (same as | | | | "s[i:i] = [x]") | | +--------------------------------+----------------------------------+-----------------------+ | "s.pop()" or "s.pop(i)" | retrieves the item at *i* and | (2) | | | also removes it from *s* | | +--------------------------------+----------------------------------+-----------------------+ | "s.remove(x)" | remove the first item from *s* | (3) | | | where "s[i]" is equal to *x* | | +--------------------------------+----------------------------------+-----------------------+ | "s.reverse()" | reverses the items of *s* in | (4) | | | place | | +--------------------------------+----------------------------------+-----------------------+ Notes: 1. *t* must have the same length as the slice it is replacing. 2. The optional argument *i* defaults to "-1", so that by default the last item is removed and returned. 3. "remove()" raises "ValueError" when *x* is not found in *s*. 4. The "reverse()" method modifies the sequence in place for economy of space when reversing a large sequence. To remind users that it operates by side effect, it does not return the reversed sequence. 5. "clear()" and "copy()" are included for consistency with the interfaces of mutable containers that don’t support slicing operations (such as "dict" and "set"). "copy()" is not part of the "collections.abc.MutableSequence" ABC, but most concrete mutable sequence classes provide it. New in version 3.3: "clear()" and "copy()" methods. 6. The value *n* is an integer, or an object implementing "__index__()". Zero and negative values of *n* clear the sequence. Items in the sequence are not copied; they are referenced multiple times, as explained for "s * n" under Common Sequence Operations. Lists ===== Lists are mutable sequences, typically used to store collections of homogeneous items (where the precise degree of similarity will vary by application). class list([iterable]) Lists may be constructed in several ways: * Using a pair of square brackets to denote the empty list: "[]" * Using square brackets, separating items with commas: "[a]", "[a, b, c]" * Using a list comprehension: "[x for x in iterable]" * Using the type constructor: "list()" or "list(iterable)" The constructor builds a list whose items are the same and in the same order as *iterable*’s items. *iterable* may be either a sequence, a container that supports iteration, or an iterator object. If *iterable* is already a list, a copy is made and returned, similar to "iterable[:]". For example, "list('abc')" returns "['a', 'b', 'c']" and "list( (1, 2, 3) )" returns "[1, 2, 3]". If no argument is given, the constructor creates a new empty list, "[]". Many other operations also produce lists, including the "sorted()" built-in. Lists implement all of the common and mutable sequence operations. Lists also provide the following additional method: sort(*, key=None, reverse=False) This method sorts the list in place, using only "<" comparisons between items. Exceptions are not suppressed - if any comparison operations fail, the entire sort operation will fail (and the list will likely be left in a partially modified state). "sort()" accepts two arguments that can only be passed by keyword (keyword-only arguments): *key* specifies a function of one argument that is used to extract a comparison key from each list element (for example, "key=str.lower"). The key corresponding to each item in the list is calculated once and then used for the entire sorting process. The default value of "None" means that list items are sorted directly without calculating a separate key value. The "functools.cmp_to_key()" utility is available to convert a 2.x style *cmp* function to a *key* function. *reverse* is a boolean value. If set to "True", then the list elements are sorted as if each comparison were reversed. This method modifies the sequence in place for economy of space when sorting a large sequence. To remind users that it operates by side effect, it does not return the sorted sequence (use "sorted()" to explicitly request a new sorted list instance). The "sort()" method is guaranteed to be stable. A sort is stable if it guarantees not to change the relative order of elements that compare equal — this is helpful for sorting in multiple passes (for example, sort by department, then by salary grade). For sorting examples and a brief sorting tutorial, see Sorting HOW TO. **CPython implementation detail:** While a list is being sorted, the effect of attempting to mutate, or even inspect, the list is undefined. The C implementation of Python makes the list appear empty for the duration, and raises "ValueError" if it can detect that the list has been mutated during a sort. Tuples ====== Tuples are immutable sequences, typically used to store collections of heterogeneous data (such as the 2-tuples produced by the "enumerate()" built-in). Tuples are also used for cases where an immutable sequence of homogeneous data is needed (such as allowing storage in a "set" or "dict" instance). class tuple([iterable]) Tuples may be constructed in a number of ways: * Using a pair of parentheses to denote the empty tuple: "()" * Using a trailing comma for a singleton tuple: "a," or "(a,)" * Separating items with commas: "a, b, c" or "(a, b, c)" * Using the "tuple()" built-in: "tuple()" or "tuple(iterable)" The constructor builds a tuple whose items are the same and in the same order as *iterable*’s items. *iterable* may be either a sequence, a container that supports iteration, or an iterator object. If *iterable* is already a tuple, it is returned unchanged. For example, "tuple('abc')" returns "('a', 'b', 'c')" and "tuple( [1, 2, 3] )" returns "(1, 2, 3)". If no argument is given, the constructor creates a new empty tuple, "()". Note that it is actually the comma which makes a tuple, not the parentheses. The parentheses are optional, except in the empty tuple case, or when they are needed to avoid syntactic ambiguity. For example, "f(a, b, c)" is a function call with three arguments, while "f((a, b, c))" is a function call with a 3-tuple as the sole argument. Tuples implement all of the common sequence operations. For heterogeneous collections of data where access by name is clearer than access by index, "collections.namedtuple()" may be a more appropriate choice than a simple tuple object. Ranges ====== The "range" type represents an immutable sequence of numbers and is commonly used for looping a specific number of times in "for" loops. class range(stop) class range(start, stop[, step]) The arguments to the range constructor must be integers (either built-in "int" or any object that implements the "__index__()" special method). If the *step* argument is omitted, it defaults to "1". If the *start* argument is omitted, it defaults to "0". If *step* is zero, "ValueError" is raised. For a positive *step*, the contents of a range "r" are determined by the formula "r[i] = start + step*i" where "i >= 0" and "r[i] < stop". For a negative *step*, the contents of the range are still determined by the formula "r[i] = start + step*i", but the constraints are "i >= 0" and "r[i] > stop". A range object will be empty if "r[0]" does not meet the value constraint. Ranges do support negative indices, but these are interpreted as indexing from the end of the sequence determined by the positive indices. Ranges containing absolute values larger than "sys.maxsize" are permitted but some features (such as "len()") may raise "OverflowError". Range examples: >>> list(range(10)) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] >>> list(range(1, 11)) [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] >>> list(range(0, 30, 5)) [0, 5, 10, 15, 20, 25] >>> list(range(0, 10, 3)) [0, 3, 6, 9] >>> list(range(0, -10, -1)) [0, -1, -2, -3, -4, -5, -6, -7, -8, -9] >>> list(range(0)) [] >>> list(range(1, 0)) [] Ranges implement all of the common sequence operations except concatenation and repetition (due to the fact that range objects can only represent sequences that follow a strict pattern and repetition and concatenation will usually violate that pattern). start The value of the *start* parameter (or "0" if the parameter was not supplied) stop The value of the *stop* parameter step The value of the *step* parameter (or "1" if the parameter was not supplied) The advantage of the "range" type over a regular "list" or "tuple" is that a "range" object will always take the same (small) amount of memory, no matter the size of the range it represents (as it only stores the "start", "stop" and "step" values, calculating individual items and subranges as needed). Range objects implement the "collections.abc.Sequence" ABC, and provide features such as containment tests, element index lookup, slicing and support for negative indices (see Sequence Types — list, tuple, range): >>> r = range(0, 20, 2) >>> r range(0, 20, 2) >>> 11 in r False >>> 10 in r True >>> r.index(10) 5 >>> r[5] 10 >>> r[:5] range(0, 10, 2) >>> r[-1] 18 Testing range objects for equality with "==" and "!=" compares them as sequences. That is, two range objects are considered equal if they represent the same sequence of values. (Note that two range objects that compare equal might have different "start", "stop" and "step" attributes, for example "range(0) == range(2, 1, 3)" or "range(0, 3, 2) == range(0, 4, 2)".) Changed in version 3.2: Implement the Sequence ABC. Support slicing and negative indices. Test "int" objects for membership in constant time instead of iterating through all items. Changed in version 3.3: Define ‘==’ and ‘!=’ to compare range objects based on the sequence of values they define (instead of comparing based on object identity). New in version 3.3: The "start", "stop" and "step" attributes. See also: * The linspace recipe shows how to implement a lazy version of range suitable for floating point applications. u� Mutable Sequence Types ********************** The operations in the following table are defined on mutable sequence types. The "collections.abc.MutableSequence" ABC is provided to make it easier to correctly implement these operations on custom sequence types. In the table *s* is an instance of a mutable sequence type, *t* is any iterable object and *x* is an arbitrary object that meets any type and value restrictions imposed by *s* (for example, "bytearray" only accepts integers that meet the value restriction "0 <= x <= 255"). +--------------------------------+----------------------------------+-----------------------+ | Operation | Result | Notes | |================================|==================================|=======================| | "s[i] = x" | item *i* of *s* is replaced by | | | | *x* | | +--------------------------------+----------------------------------+-----------------------+ | "s[i:j] = t" | slice of *s* from *i* to *j* is | | | | replaced by the contents of the | | | | iterable *t* | | +--------------------------------+----------------------------------+-----------------------+ | "del s[i:j]" | same as "s[i:j] = []" | | +--------------------------------+----------------------------------+-----------------------+ | "s[i:j:k] = t" | the elements of "s[i:j:k]" are | (1) | | | replaced by those of *t* | | +--------------------------------+----------------------------------+-----------------------+ | "del s[i:j:k]" | removes the elements of | | | | "s[i:j:k]" from the list | | +--------------------------------+----------------------------------+-----------------------+ | "s.append(x)" | appends *x* to the end of the | | | | sequence (same as | | | | "s[len(s):len(s)] = [x]") | | +--------------------------------+----------------------------------+-----------------------+ | "s.clear()" | removes all items from *s* (same | (5) | | | as "del s[:]") | | +--------------------------------+----------------------------------+-----------------------+ | "s.copy()" | creates a shallow copy of *s* | (5) | | | (same as "s[:]") | | +--------------------------------+----------------------------------+-----------------------+ | "s.extend(t)" or "s += t" | extends *s* with the contents of | | | | *t* (for the most part the same | | | | as "s[len(s):len(s)] = t") | | +--------------------------------+----------------------------------+-----------------------+ | "s *= n" | updates *s* with its contents | (6) | | | repeated *n* times | | +--------------------------------+----------------------------------+-----------------------+ | "s.insert(i, x)" | inserts *x* into *s* at the | | | | index given by *i* (same as | | | | "s[i:i] = [x]") | | +--------------------------------+----------------------------------+-----------------------+ | "s.pop()" or "s.pop(i)" | retrieves the item at *i* and | (2) | | | also removes it from *s* | | +--------------------------------+----------------------------------+-----------------------+ | "s.remove(x)" | remove the first item from *s* | (3) | | | where "s[i]" is equal to *x* | | +--------------------------------+----------------------------------+-----------------------+ | "s.reverse()" | reverses the items of *s* in | (4) | | | place | | +--------------------------------+----------------------------------+-----------------------+ Notes: 1. *t* must have the same length as the slice it is replacing. 2. The optional argument *i* defaults to "-1", so that by default the last item is removed and returned. 3. "remove()" raises "ValueError" when *x* is not found in *s*. 4. The "reverse()" method modifies the sequence in place for economy of space when reversing a large sequence. To remind users that it operates by side effect, it does not return the reversed sequence. 5. "clear()" and "copy()" are included for consistency with the interfaces of mutable containers that don’t support slicing operations (such as "dict" and "set"). "copy()" is not part of the "collections.abc.MutableSequence" ABC, but most concrete mutable sequence classes provide it. New in version 3.3: "clear()" and "copy()" methods. 6. The value *n* is an integer, or an object implementing "__index__()". Zero and negative values of *n* clear the sequence. Items in the sequence are not copied; they are referenced multiple times, as explained for "s * n" under Common Sequence Operations. aM Unary arithmetic and bitwise operations *************************************** All unary arithmetic and bitwise operations have the same priority: u_expr ::= power | "-" u_expr | "+" u_expr | "~" u_expr The unary "-" (minus) operator yields the negation of its numeric argument; the operation can be overridden with the "__neg__()" special method. The unary "+" (plus) operator yields its numeric argument unchanged; the operation can be overridden with the "__pos__()" special method. The unary "~" (invert) operator yields the bitwise inversion of its integer argument. The bitwise inversion of "x" is defined as "-(x+1)". It only applies to integral numbers or to custom objects that override the "__invert__()" special method. In all three cases, if the argument does not have the proper type, a "TypeError" exception is raised. u� The "while" statement ********************* The "while" statement is used for repeated execution as long as an expression is true: while_stmt ::= "while" assignment_expression ":" suite ["else" ":" suite] This repeatedly tests the expression and, if it is true, executes the first suite; if the expression is false (which may be the first time it is tested) the suite of the "else" clause, if present, is executed and the loop terminates. A "break" statement executed in the first suite terminates the loop without executing the "else" clause’s suite. A "continue" statement executed in the first suite skips the rest of the suite and goes back to testing the expression. u� The "with" statement ******************** The "with" statement is used to wrap the execution of a block with methods defined by a context manager (see section With Statement Context Managers). This allows common "try"…"except"…"finally" usage patterns to be encapsulated for convenient reuse. with_stmt ::= "with" ( "(" with_stmt_contents ","? ")" | with_stmt_contents ) ":" suite with_stmt_contents ::= with_item ("," with_item)* with_item ::= expression ["as" target] The execution of the "with" statement with one “item” proceeds as follows: 1. The context expression (the expression given in the "with_item") is evaluated to obtain a context manager. 2. The context manager’s "__enter__()" is loaded for later use. 3. The context manager’s "__exit__()" is loaded for later use. 4. The context manager’s "__enter__()" method is invoked. 5. If a target was included in the "with" statement, the return value from "__enter__()" is assigned to it. Note: The "with" statement guarantees that if the "__enter__()" method returns without an error, then "__exit__()" will always be called. Thus, if an error occurs during the assignment to the target list, it will be treated the same as an error occurring within the suite would be. See step 7 below. 6. The suite is executed. 7. The context manager’s "__exit__()" method is invoked. If an exception caused the suite to be exited, its type, value, and traceback are passed as arguments to "__exit__()". Otherwise, three "None" arguments are supplied. If the suite was exited due to an exception, and the return value from the "__exit__()" method was false, the exception is reraised. If the return value was true, the exception is suppressed, and execution continues with the statement following the "with" statement. If the suite was exited for any reason other than an exception, the return value from "__exit__()" is ignored, and execution proceeds at the normal location for the kind of exit that was taken. The following code: with EXPRESSION as TARGET: SUITE is semantically equivalent to: manager = (EXPRESSION) enter = type(manager).__enter__ exit = type(manager).__exit__ value = enter(manager) hit_except = False try: TARGET = value SUITE except: hit_except = True if not exit(manager, *sys.exc_info()): raise finally: if not hit_except: exit(manager, None, None, None) With more than one item, the context managers are processed as if multiple "with" statements were nested: with A() as a, B() as b: SUITE is semantically equivalent to: with A() as a: with B() as b: SUITE You can also write multi-item context managers in multiple lines if the items are surrounded by parentheses. For example: with ( A() as a, B() as b, ): SUITE Changed in version 3.1: Support for multiple context expressions. Changed in version 3.10: Support for using grouping parentheses to break the statement in multiple lines. See also: **PEP 343** - The “with” statement The specification, background, and examples for the Python "with" statement. a, The "yield" statement ********************* yield_stmt ::= yield_expression A "yield" statement is semantically equivalent to a yield expression. The yield statement can be used to omit the parentheses that would otherwise be required in the equivalent yield expression statement. For example, the yield statements yield <expr> yield from <expr> are equivalent to the yield expression statements (yield <expr>) (yield from <expr>) Yield expressions and statements are only used when defining a *generator* function, and are only used in the body of the generator function. Using yield in a function definition is sufficient to cause that definition to create a generator function instead of a normal function. For full details of "yield" semantics, refer to the Yield expressions section. ) �types�typesfunctions� typesmapping� typesmethods� typesmodules�typesseqztypesseq-mutable�unary�while�with�yieldN)�topics� � �(/usr/lib/python3.11/pydoc_data/topics.py�<module>rD s) ��ss �(� &C� ss �N � w!�Oss �~ � A 7� ss �B � '1�Css �R � �Sss �L � i@�Mss �`$ � D�a$ss �N% � ? �O%ss �N' � "�O'ss �`' � j �a'ss �v* � F�w*ss �f+ � (�g+ss �\, � D�],ss �t, � 3�u,ss �L- � K�M-ss �d- � 0 �e-ss �F/ � �G/ss � ss �p/ � <�q/ss �B0 � J 4�C0ss �X6 � c >�Y6ss �`9 � c;�a9ss �hD � G :�iDss �x~ � =)�y~ss �t@ � /�u@ss �TA � )�UAss �HB � H*�IBss �ZP � G H�[Pss �jf � �kfss �dg � 2 P�egss �Ji � 9�Kiss �~i � #�iss �`j � L �ajss �zl � T �{lss �dw � $)�ewss � ss � ss �nx � '�oxss �jy � - �kyss �F{ � U 1�G{ss �rS � J"�sSss �HZ � , *�IZss �b[ � 9H�c[ss �V] � tI�W]ss �@c � !�Acss �`c � �acss �Hd � K H�Idss �`l � %�alss �lm � -'�mmss �Ho � *�Ioss �ro � 9�soss �Tp � U /�Upss �@w � F�Awss �~w � �wss � ss � ss �Zx � F(�[xss �h~ � &�i~ss �hB � r1�iBss �NH � D�OHss �jH � $ �kHss �tI � x �uIss �fM � " E�gMss �lN � t.�mNss �VV � �WVss �BW � 21�CWss �hX � <,�iXss �bZ � \"?�cZss �\_ � g%�]_ss �l| � y >�m|ss �`D � R!�aDss �FG � >�GGss �|G � z �}Gss � ss �rOF A�N$F�"` �B8K�r$<�JK ?�XN%�^ /�8 )�0u �l �kfss � ss � ss ���rB