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� �� f� c @ s% iM d d 6d d 6d d 6d d 6d d 6d d 6d d 6d d 6d d 6d d 6d d 6d d 6d d 6d d 6d d 6d d 6d d! 6d" d# 6d$ d% 6d& d' 6d( d) 6d* d+ 6d, d- 6d. d/ 6d0 d1 6d2 d3 6d4 d5 6d6 d7 6d8 d9 6d: d; 6d< d= 6d> d? 6d@ dA 6dB dC 6dD dE 6dF dG 6dH dI 6dJ dK 6dL dM 6d8 dN 6dO dP 6dQ dR 6d$ dS 6dT dU 6dV dW 6dX dY 6dZ d[ 6d\ d] 6d^ d_ 6d` da 6db dc 6dd de 6df dg 6dh di 6dj dk 6dl dm 6dn do 6dp dq 6dr ds 6dt du 6dv dw 6dx dy 6dz d{ 6d| d} 6d~ d 6d� d� 6d� d� 6d� d� 6d� d� 6d� d� 6d� d� 6d� d� 6d� d� 6d� d� 6d� d� 6d� d� 6d� d� 6Z d� S(� ur 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. u assertu�# 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 "=")+ (expression_list | yield_expression) target_list ::= target ("," target)* [","] target ::= identifier | "(" target_list ")" | "[" target_list "]" | attributeref | subscription | slicing | "*" target (See section *Primaries* for the syntax definitions for the last three symbols.) 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: The object is assigned to that target. * If the target list is a comma-separated list of targets: 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. * If the target list contains one target prefixed with an asterisk, called a "starred" target: The object must be a sequence with at least as many items as there are targets in the target list, minus one. The first items of the sequence are assigned, from left to right, to the targets before the starred target. The final items of the sequence are assigned to the targets after the starred target. A list of the remaining items in the sequence is then assigned to the starred target (the list can be empty). * Else: The object must be a sequence 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 a target list enclosed in parentheses or in square brackets: The object must be an iterable with the same number of items as there are targets in the target list, and its items are assigned, from left to right, to the corresponding targets. * 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 RHS expression, "a.x" can access either an instance attribute or (if no instance attribute exists) a class attribute. The LHS 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 RHS expression refers to a class attribute, the LHS 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 object 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. WARNING: Although the definition of assignment implies that overlaps between the left-hand side and the right-hand side are 'safe' (for example "a, b = b, a" swaps two variables), overlaps *within* the collection of assigned-to variables are not safe! For instance, the following program prints "[0, 2]": x = [0, 1] i = 0 i, x[i] = 1, 2 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 for 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. 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. u assignmentu� 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. u atom-identifiersu 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. u atom-literalsu�& 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 an attribute lookup has not found the attribute in the usual places (i.e. it is not an instance attribute nor is it found in the class tree for "self"). "name" is the attribute name. This method should 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*. 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)". 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. 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. 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) Called to get the attribute of the owner class (class attribute access) or of an instance of that class (instance attribute access). *owner* is always 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. object.__set__(self, instance, value) Called to set the attribute on an instance *instance* of the owner class to a new value, *value*. object.__delete__(self, instance) Called to delete the attribute on an instance *instance* of the owner class. 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 If "a" is an instance of "super", then the binding "super(B, obj).m()" searches "obj.__class__.__mro__" for the base class "A" immediately preceding "B" and then invokes the descriptor with the call: "A.__dict__['m'].__get__(obj, obj.__class__)". For instance bindings, the precedence of descriptor invocation depends on the 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 "__set__()" and "__get__()" defined always override a redefinition in an instance dictionary. In contrast, non-data descriptors can be overridden by instances. Python methods (including "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__ ========= By default, instances of classes have a dictionary for attribute storage. This wastes space for objects having very few instance variables. The space consumption can become acute when creating large numbers of instances. The default can be overridden by defining *__slots__* in a class definition. The *__slots__* declaration takes a sequence of instance variables and reserves just enough space in each instance to hold a value for each variable. Space is saved because *__dict__* is not created for each instance. object.__slots__ This class variable can be assigned a string, iterable, or sequence of strings with variable names used by instances. If defined in a class, *__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__* attribute of that class will always be accessible, so a *__slots__* definition in the subclass is meaningless. * 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 (*Implementing 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 limited to the class where it is defined. As a result, subclasses will have a *__dict__* unless they also define *__slots__* (which must 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__*. Mappings may also be used; however, in the future, special meaning may be assigned to the values corresponding to each key. * *__class__* assignment works only if both classes have the same *__slots__*. u attribute-accessu� 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 (which 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. u attribute-referencesu� 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 for 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. 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. u augassignu� 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 "//" 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. 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. 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 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 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. The "-" (subtraction) operator yields the difference of its arguments. The numeric arguments are first converted to a common type. u binaryu% 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. The "^" operator yields the bitwise XOR (exclusive OR) of its arguments, which must be integers. The "|" operator yields the bitwise (inclusive) OR of its arguments, which must be integers. u bitwiseuu 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. 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. u bltin-code-objectsu1 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 "...". u bltin-ellipsis-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". u bltin-null-objectu4 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'>". u bltin-type-objectsu� 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 invent a value anyway, it does not bother to return a value of the same type as its argument, so e.g., "not 'foo'" yields "False", not "''".) u booleansu% 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. u breaku� Emulating callable objects ************************** object.__call__(self[, args...]) Called when the instance is "called" as a function; if this method is defined, "x(arg1, arg2, ...)" is a shorthand for "x.__call__(arg1, arg2, ...)". u callable-typesuC 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 ["," keyword_arguments] ["," "*" expression] ["," keyword_arguments] ["," "**" expression] | keyword_arguments ["," "*" expression] ["," keyword_arguments] ["," "**" expression] | "*" expression ["," keyword_arguments] ["," "**" expression] | "**" expression positional_arguments ::= expression ("," expression)* keyword_arguments ::= keyword_item ("," keyword_item)* keyword_item ::= identifier "=" expression A 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 value of 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 this iterable are treated as if they were additional positional arguments; if there are positional arguments *x1*, ..., *xN*, and "expression" evaluates to a sequence *y1*, ..., *yM*, this is equivalent to a call with M+N positional arguments *x1*, ..., *xN*, *y1*, ..., *yM*. A consequence of this is that although the "*expression" syntax may appear *after* some keyword arguments, it is processed *before* the keyword arguments (and the "**expression" argument, if any -- 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 ? 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 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. In the case of a keyword appearing in both "expression" and as an explicit keyword argument, 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. 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. u callsu� Class definitions ***************** A class definition defines a class object (see section *The standard type hierarchy*): classdef ::= [decorators] "class" classname [inheritance] ":" suite inheritance ::= "(" [parameter_list] ")" classname ::= identifier A class definition is an executable statement. The inheritance list usually gives a list of base classes (see *Customizing class creation* 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. [4] 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. 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 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 must be a class object, which is then bound to the class name. **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 3 **PEP 3129** - Class Decorators -[ 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] Currently, control "flows off the end" except in the case of an exception or the execution of a "return", "continue", or "break" statement. [3] 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*. [4] 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*. u classu� 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". 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). The operators "<", ">", "==", ">=", "<=", and "!=" compare the values of two objects. The objects need not have the same type. If both are numbers, they are converted to a common type. Otherwise, the "==" and "!=" operators *always* consider objects of different types to be unequal, while the "<", ">", ">=" and "<=" operators raise a "TypeError" when comparing objects of different types that do not implement these operators for the given pair of types. You can control comparison behavior of objects of non-built-in types by defining rich comparison methods like "__gt__()", described in section *Basic customization*. Comparison of objects of the same type depends on the type: * Numbers are compared arithmetically. * The values "float('NaN')" and "Decimal('NaN')" are special. The are identical to themselves, "x is x" but are not equal to themselves, "x != x". Additionally, comparing any value to a not-a-number value will return "False". For example, both "3 < float('NaN')" and "float('NaN') < 3" will return "False". * Bytes objects are compared lexicographically using the numeric values of their elements. * Strings are compared lexicographically using the numeric equivalents (the result of the built-in function "ord()") of their characters. [3] String and bytes object can't be compared! * Tuples and lists are compared lexicographically using comparison of corresponding elements. This means that to compare equal, each element must compare equal and the two sequences must be of the same type and have the same length. If not equal, the sequences are ordered the same as their first differing elements. For example, "[1,2,x] <= [1,2,y]" has the same value as "x <= y". If the corresponding element does not exist, the shorter sequence is ordered first (for example, "[1,2] < [1,2,3]"). * Mappings (dictionaries) compare equal if and only if they have the same "(key, value)" pairs. Order comparisons "('<', '<=', '>=', '>')" raise "TypeError". * Sets and frozensets define comparison operators to mean subset and superset tests. Those relations do not define total orderings (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. * Most other objects of built-in types compare unequal unless they are the same object; the choice whether one object is considered smaller or larger than another one is made arbitrarily but consistently within one execution of a program. Comparison of objects of the differing types depends on whether either of the types provide explicit support for the comparison. Most numeric types can be compared with one another. When cross-type comparison is not supported, the comparison method returns "NotImplemented". 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 a 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" is true if and only if "y.__contains__(x)" is true. For user-defined classes which do not define "__contains__()" but do define "__iter__()", "x in y" is true if some value "z" with "x == z" 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 == y[i]", and all lower integer indices do not raise "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 true value of "in". The operators "is" and "is not" test for object identity: "x is y" is true if and only if *x* and *y* are the same object. "x is not y" yields the inverse truth value. [4] u comparisonsu�T 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. Compound statements consist 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 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 | funcdef | classdef 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" expression ":" suite ( "elif" 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" 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" expression_list ":" suite ["else" ":" suite] The expression list is evaluated once; it should yield an iterable object. An iterator is created for the result of the "expression_list". The suite is then executed once for each item provided by the iterator, in the order of ascending indices. Each item in turn is assigned to the target list using the standard rules for assignments (see *Assignment statements*), and then the suite is executed. When the items are exhausted (which is immediately when the sequence is empty or an iterator raises a "StopIteration" exception), 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 was no next item. The suite may assign to the variable(s) in the target list; this does not affect the next item assigned to it. Names in the target list are not deleted when the loop is finished, but if the sequence is empty, it will not have been assigned to at all by the loop. Hint: the built-in function "range()" returns an iterator of integers suitable to emulate the effect of Pascal's "for i := a to b do"; e.g., "list(range(3))" returns the list "[0, 1, 2]". Note: There is a subtlety when the sequence is being modified by the loop (this can only occur for mutable sequences, i.e. lists). An internal counter is used to keep track of which item is used next, and this is incremented on each iteration. When this counter has reached the length of the sequence the loop terminates. This means that if the suite deletes the current (or a previous) item from the sequence, the next item will be skipped (since it gets the index of the current item which has already been treated). Likewise, if the suite inserts an item in the sequence before the current item, the current item will be treated again the next time through the loop. This can lead to nasty bugs that can be avoided by making a temporary copy using a slice of the whole sequence, e.g., for x in a[:]: if x < 0: a.remove(x) The "try" statement =================== The "try" statement specifies exception handlers and/or cleanup code for a group of statements: try_stmt ::= try1_stmt | try2_stmt try1_stmt ::= "try" ":" suite ("except" [expression ["as" target]] ":" suite)+ ["else" ":" suite] ["finally" ":" suite] try2_stmt ::= "try" ":" suite "finally" ":" suite 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 it is the class or a base class of the exception object or a tuple containing an item compatible with the exception. 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 access 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. "sys.exc_info()" values are restored to their previous values (before the call) when returning from a function that handled an exception. The optional "else" clause is executed if and when control flows off the end of the "try" clause. [2] Exceptions in the "else" clause are not handled by the preceding "except" clauses. 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" or "break" 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.' A "continue" statement is illegal in the "finally" clause. (The reason is a problem with the current implementation --- this restriction may be lifted in the future). 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*. 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_item ("," with_item)* ":" suite 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 "__exit__()" is loaded for later use. 3. The context manager's "__enter__()" method is invoked. 4. 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 6 below. 5. The suite is executed. 6. 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. 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 equivalent to with A() as a: with B() as b: suite Changed in version 3.1: Support for multiple context expressions. See also: **PEP 0343** - The "with" statement The specification, background, and examples for the Python "with" statement. 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 ::= "@" dotted_name ["(" [parameter_list [","]] ")"] NEWLINE dotted_name ::= identifier ("." identifier)* parameter_list ::= (defparameter ",")* ( "*" [parameter] ("," defparameter)* ["," "**" parameter] | "**" parameter | defparameter [","] ) 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. [3] 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 equivalent to def func(): pass func = f1(arg)(f2(func)) 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 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 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 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 position 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 dictionary receiving any excess keyword arguments, defaulting to a new empty dictionary. Parameters after ""*"" or ""*identifier"" are keyword-only parameters and may only be passed used keyword arguments. Parameters may have annotations 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 and are evaluated when the function definition is executed. Annotations may be evaluated in a different order than they appear in the source code. 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. 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. Class definitions ================= A class definition defines a class object (see section *The standard type hierarchy*): classdef ::= [decorators] "class" classname [inheritance] ":" suite inheritance ::= "(" [parameter_list] ")" classname ::= identifier A class definition is an executable statement. The inheritance list usually gives a list of base classes (see *Customizing class creation* 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. [4] 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. 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 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 must be a class object, which is then bound to the class name. **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 3 **PEP 3129** - Class Decorators -[ 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] Currently, control "flows off the end" except in the case of an exception or the execution of a "return", "continue", or "break" statement. [3] 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*. [4] 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*. u compoundu� 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 0343** - The "with" statement The specification, background, and examples for the Python "with" statement. u context-managersu� 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 or "finally" clause 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. u continueu� 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 that way: * 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 left argument to the '%' operator). Extensions must define their own conversion behavior. u conversionsu93 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(currentclass, cls).__new__(cls[, ...])" with appropriate arguments and then modifying the newly-created instance as necessary before returning it. If "__new__()" 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 "__new__()". 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: "BaseClass.__init__(self, [args...])". Because "__new__()" and "__init__()" work together in constructing objects ("__new__()" to create it, and "__init__()" to customise 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 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. Note that it is possible (though not recommended!) for the "__del__()" method to postpone destruction of the instance by creating a new reference to it. It may then be called at a later time when this new reference is deleted. 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. Some common situations that may prevent the reference count of an object from going to zero include: circular references between objects (e.g., a doubly-linked list or a tree data structure with parent and child pointers); a reference to the object on the stack frame of a function that caught an exception (the traceback stored in "sys.exc_info()[2]" keeps the stack frame alive); or a reference to the object on the stack frame that raised an unhandled exception in interactive mode (the traceback stored in "sys.last_traceback" keeps the stack frame alive). The first situation can only be remedied by explicitly breaking the cycles; the latter two situations can be resolved by storing "None" in "sys.last_traceback". Circular references which are garbage are detected when the option cycle detector is enabled (it's on by default), but can only be cleaned up if there are no Python- level "__del__()" methods involved. Refer to the documentation for the "gc" module for more information about how "__del__()" methods are handled by the cycle detector, particularly the description of the "garbage" value. 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. Also, when "__del__()" is invoked in response to a module being deleted (e.g., when execution of the program is done), other globals referenced by the "__del__()" method may already have been deleted or in the process of being torn down (e.g. the import machinery shutting down). For this reason, "__del__()" methods should do the absolute minimum needed to maintain external invariants. Starting with version 1.5, 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, the "str.format()" method of class "str") 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. 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. There are no implied relationships among the comparison operators. The truth of "x==y" does not imply that "x!=y" is false. Accordingly, when defining "__eq__()", one should also define "__ne__()" so that the operators will behave as expected. 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. Arguments to rich comparison methods are never coerced. To automatically generate ordering operations from a single root operation, see "functools.total_ordering()". object.__hash__(self) Called by built-in function "hash()" and for operations on members of hashed collections including "set", "frozenset", and "dict". "__hash__()" should return an integer. The only required property is that objects which compare equal have the same hash value; it is advised to somehow mix together (e.g. using exclusive or) the hash values for the components of the object that also play a part in comparison of objects. 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.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.Hashable)" call. Note: By default, the "__hash__()" values of str, bytes and datetime 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 dicts, sets and other mappings. 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. u customizationu�D "pdb" --- The Python Debugger ***************************** 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". 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 "print" 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*. The typical usage to break into the debugger from a running program is to insert import pdb; pdb.set_trace() at the location you want to break into the debugger. You can then step through the code following this statement, and continue running without the debugger using the "continue" command. 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 ? 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() 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). 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 class pdb.Pdb(completekey='tab', stdin=None, stdout=None, skip=None, nosigint=False) "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* tot true. Example call to enable tracing with *skip*: import pdb; pdb.Pdb(skip=['django.*']).set_trace() 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. 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. If a file ".pdbrc" exists in the user's home directory or in the current directory, it is read in 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 3.2: ".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 [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 [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 [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) print 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(rint) expression Evaluate the *expression* in the current context and print its value. pp expression Like the "print" 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 interative 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. -[ Footnotes ]- [1] Whether a frame is considered to originate in a certain module is determined by the "__name__" in the frame globals. u debuggeru� 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. u delu� 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 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 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. u dictu� Interaction with dynamic features ********************************* There are several cases where Python statements are illegal when used in conjunction with nested scopes that contain free variables. If a variable is referenced in an enclosing scope, it is illegal to delete the name. An error will be reported at compile time. If the wild card form of import --- "import *" --- is used in a function and the function contains or is a nested block with free variables, the compiler will raise a "SyntaxError". 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. u dynamic-featuresuG The "if" statement ****************** The "if" statement is used for conditional execution: if_stmt ::= "if" expression ":" suite ( "elif" 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. u elseu� 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 backtrace, 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 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. u exceptionsu�" Execution model *************** Naming and binding ================== *Names* refer to objects. Names are introduced by name binding operations. Each occurrence of a name in the program text refers to the *binding* of that name established in the innermost function block containing the use. 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 on the interpreter command line the first argument) is a code block. A script command (a command specified on the interpreter command line with the '**-c**' option) is 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. 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. 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)) 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*. If a name is bound in a block, it is a local variable of that block, unless declared as "nonlocal". 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*. When a name is not found at all, a "NameError" exception is raised. If the name refers to a local variable that has not been bound, a "UnboundLocalError" exception is raised. "UnboundLocalError" is a subclass of "NameError". The following constructs bind names: formal parameters to functions, "import" statements, class and function definitions (these bind the class or function name in the defining block), and targets that are identifiers if occurring in an assignment, "for" loop header, or after "as" in a "with" statement or "except" clause. 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 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. If the "global" statement occurs within a block, all uses of the name specified in the statement refer to the binding of that name 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 name is not found there, the builtins namespace is searched. The global statement must precede all uses of the name. 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. "__builtins__" can be set to a user-created dictionary to create a weak form of 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 namespace for a module is automatically created the first time a module is imported. The main module for a script is always called "__main__". 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. A class definition is an executable statement that may use and define names. These references follow the normal rules for name resolution. The namespace of the class definition becomes the attribute dictionary of the class. Names defined at the class scope are not visible in methods. Interaction with dynamic features --------------------------------- There are several cases where Python statements are illegal when used in conjunction with nested scopes that contain free variables. If a variable is referenced in an enclosing scope, it is illegal to delete the name. An error will be reported at compile time. If the wild card form of import --- "import *" --- is used in a function and the function contains or is a nested block with free variables, the compiler will raise a "SyntaxError". 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 backtrace, 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 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. u execmodeluK Expression lists **************** expression_list ::= expression ( "," expression )* [","] 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. 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: "()".) u exprlistsud Floating point literals *********************** Floating point literals are described by the following lexical definitions: floatnumber ::= pointfloat | exponentfloat pointfloat ::= [intpart] fraction | intpart "." exponentfloat ::= (intpart | pointfloat) exponent intpart ::= digit+ fraction ::= "." digit+ exponent ::= ("e" | "E") ["+" | "-"] digit+ 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. Some examples of floating point literals: 3.14 10. .001 1e100 3.14e-10 0e0 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". u floatingu� 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" expression_list ":" suite ["else" ":" suite] The expression list is evaluated once; it should yield an iterable object. An iterator is created for the result of the "expression_list". The suite is then executed once for each item provided by the iterator, in the order of ascending indices. Each item in turn is assigned to the target list using the standard rules for assignments (see *Assignment statements*), and then the suite is executed. When the items are exhausted (which is immediately when the sequence is empty or an iterator raises a "StopIteration" exception), 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 was no next item. The suite may assign to the variable(s) in the target list; this does not affect the next item assigned to it. Names in the target list are not deleted when the loop is finished, but if the sequence is empty, it will not have been assigned to at all by the loop. Hint: the built-in function "range()" returns an iterator of integers suitable to emulate the effect of Pascal's "for i := a to b do"; e.g., "list(range(3))" returns the list "[0, 1, 2]". Note: There is a subtlety when the sequence is being modified by the loop (this can only occur for mutable sequences, i.e. lists). An internal counter is used to keep track of which item is used next, and this is incremented on each iteration. When this counter has reached the length of the sequence the loop terminates. This means that if the suite deletes the current (or a previous) item from the sequence, the next item will be skipped (since it gets the index of the current item which has already been treated). Likewise, if the suite inserts an item in the sequence before the current item, the current item will be treated again the next time through the loop. This can lead to nasty bugs that can be avoided by making a temporary copy using a slice of the whole sequence, e.g., for x in a[:]: if x < 0: a.remove(x) u foru�F 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). 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 | integer] attribute_name ::= identifier element_index ::= integer | 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, so "'{} {}'" is equivalent to "'{0} {1}'". 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 can contain only a field name; conversion flags and format specifications are 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*). 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 string ("""") produces the same result as if you had called "str()" on the value. A non-empty format string typically modifies the result. The general form of a *standard format specifier* is: format_spec ::= [[fill]align][sign][#][0][width][,][.precision][type] fill ::= <any character> align ::= "<" | ">" | "=" | "^" sign ::= "+" | "-" | " " width ::= integer precision ::= integer 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. Note that it is not possible to use "{" and "}" as *fill* char while using the "str.format()" method; this limitation however 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 | +-----------+------------------------------------------------------------+ | "'>'" | Forces the field to be right-aligned within the available | +-----------+------------------------------------------------------------+ | "'='" | Forces the padding to be placed after the sign (if any) | +-----------+------------------------------------------------------------+ | "'^'" | Forces the field to be centered within the available | +-----------+------------------------------------------------------------+ 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 | +-----------+------------------------------------------------------------+ | "'-'" | indicates that a sign should be used only for negative | +-----------+------------------------------------------------------------+ | space | indicates that a leading space should be used on positive | +-----------+------------------------------------------------------------+ 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, complex and Decimal types. For integers, when binary, octal, or hexadecimal output is used, this option adds the prefix respective "'0b'", "'0o'", or "'0x'" to the output value. For floats, complex and Decimal 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**). *width* is a decimal integer defining the minimum field width. If not specified, then the field width will be determined by the content. 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 "'='". The *precision* is a decimal number indicating how many digits should be displayed after the decimal point for a floating point value formatted with "'f'" and "'F'", or before and after the decimal point for a floating point value formatted with "'g'" or "'G'". For non- number 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 values. 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 | +-----------+------------------------------------------------------------+ | 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 | +-----------+------------------------------------------------------------+ | "'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- | +-----------+------------------------------------------------------------+ | "'X'" | Hex format. Outputs the number in base 16, using upper- | +-----------+------------------------------------------------------------+ | "'n'" | Number. This is the same as "'d'", except that it uses the | +-----------+------------------------------------------------------------+ | 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 floating point and decimal values are: +-----------+------------------------------------------------------------+ | Type | Meaning | +===========+============================================================+ | "'e'" | Exponent notation. Prints the number in scientific | +-----------+------------------------------------------------------------+ | "'E'" | Exponent notation. Same as "'e'" except it uses an upper | +-----------+------------------------------------------------------------+ | "'f'" | Fixed point. Displays the number as a fixed-point number. | +-----------+------------------------------------------------------------+ | "'F'" | Fixed point. Same as "'f'", but converts "nan" to "NAN" | +-----------+------------------------------------------------------------+ | "'g'" | General format. For a given precision "p >= 1", this | +-----------+------------------------------------------------------------+ | "'G'" | General format. Same as "'g'" except switches to "'E'" if | +-----------+------------------------------------------------------------+ | "'n'" | Number. This is the same as "'g'", except that it uses the | +-----------+------------------------------------------------------------+ | "'%'" | Percentage. Multiplies the number by 100 and displays in | +-----------+------------------------------------------------------------+ | None | Similar to "'g'", except with at least one digit past the | +-----------+------------------------------------------------------------+ Format examples =============== This section contains examples of the new 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 follow 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): #doctest: +NORMALIZE_WHITESPACE ... 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 u formatstringsu 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 ::= "@" dotted_name ["(" [parameter_list [","]] ")"] NEWLINE dotted_name ::= identifier ("." identifier)* parameter_list ::= (defparameter ",")* ( "*" [parameter] ("," defparameter)* ["," "**" parameter] | "**" parameter | defparameter [","] ) 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. [3] 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 equivalent to def func(): pass func = f1(arg)(f2(func)) 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 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 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 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 position 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 dictionary receiving any excess keyword arguments, defaulting to a new empty dictionary. Parameters after ""*"" or ""*identifier"" are keyword-only parameters and may only be passed used keyword arguments. Parameters may have annotations 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 and are evaluated when the function definition is executed. Annotations may be evaluated in a different order than they appear in the source code. 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. 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. u functionu� 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 in a "for" loop control target, "class" definition, function definition, or "import" statement. **CPython implementation detail:** The current implementation does not enforce the latter two 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:** the "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. u globalu� 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 *". The special identifier "_" is used in the interactive interpreter to store the result of the last evaluation; it is stored in the "builtins" module. When not in interactive mode, "_" has no special meaning and is not defined. See section *The import statement*. Note: The name "_" is often used in conjunction with internationalization; refer to the documentation for the "gettext" module for more information on this convention. "__*__" System-defined 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)*. u id-classesu* 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 4.1 can be found at http://www.dcl.hpi.uni- potsdam.de/home/loewis/table-3131.html. 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 class finally is return None continue for lambda try True def from nonlocal while and del global not with as elif if or yield assert else import pass break except in raise 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 *". The special identifier "_" is used in the interactive interpreter to store the result of the last evaluation; it is stored in the "builtins" module. When not in interactive mode, "_" has no special meaning and is not defined. See section *The import statement*. Note: The name "_" is often used in conjunction with internationalization; refer to the documentation for the "gettext" module for more information on this convention. "__*__" System-defined 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)*. u identifiersu ifu% Imaginary literals ****************** Imaginary literals are described by the following lexical definitions: imagnumber ::= (floatnumber | intpart) ("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 u imaginaryu(! The "import" statement ********************** import_stmt ::= "import" module ["as" name] ( "," module ["as" name] )* | "from" relative_module "import" identifier ["as" name] ( "," identifier ["as" name] )* | "from" relative_module "import" "(" identifier ["as" name] ( "," identifier ["as" name] )* [","] ")" | "from" module "import" "*" module ::= (identifier ".")* identifier relative_module ::= "."* module | "."+ name ::= identifier 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 individiual import statements. The details of the first step, finding and loading modules is 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 bound 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.bar.baz imported, foo bound locally import foo.bar.baz as fbb # foo.bar.baz imported and bound as fbb from foo.bar import baz # foo.bar.baz imported and 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 "from" form with "*" may only occur in a module scope. The wild card form of import --- "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 within **PEP 328**. "importlib.import_module()" is provided to support applications that determine which modules need to be loaded dynamically. 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. 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_statement ::= "from" "__future__" "import" feature ["as" name] ("," feature ["as" name])* | "from" "__future__" "import" "(" feature ["as" name] ("," feature ["as" name])* [","] ")" feature ::= identifier name ::= 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 features recognized by Python 3.0 are "absolute_import", "division", "generators", "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. u importu inu Integer literals **************** Integer literals are described by the following lexical definitions: integer ::= decimalinteger | octinteger | hexinteger | bininteger decimalinteger ::= nonzerodigit digit* | "0"+ nonzerodigit ::= "1"..."9" digit ::= "0"..."9" octinteger ::= "0" ("o" | "O") octdigit+ hexinteger ::= "0" ("x" | "X") hexdigit+ bininteger ::= "0" ("b" | "B") bindigit+ octdigit ::= "0"..."7" hexdigit ::= digit | "a"..."f" | "A"..."F" bindigit ::= "0" | "1" There is no limit for the length of integer literals apart from what can be stored in available memory. 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 0x100000000 79228162514264337593543950336 0xdeadbeef u integersu� Lambdas ******* lambda_expr ::= "lambda" [parameter_list]: expression lambda_expr_nocond ::= "lambda" [parameter_list]: expression_nocond Lambda expressions (sometimes called lambda forms) have the same syntactic position as expressions. They are a shorthand to create anonymous functions; the expression "lambda arguments: expression" yields a function object. The unnamed object behaves like a function object defined with def <lambda>(arguments): return expression See section *Function definitions* for the syntax of parameter lists. Note that functions created with lambda expressions cannot contain statements or annotations. u lambdau3 List displays ************* A list display is a possibly empty series of expressions enclosed in square brackets: list_display ::= "[" [expression_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. u listsu� Naming and binding ****************** *Names* refer to objects. Names are introduced by name binding operations. Each occurrence of a name in the program text refers to the *binding* of that name established in the innermost function block containing the use. 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 on the interpreter command line the first argument) is a code block. A script command (a command specified on the interpreter command line with the '**-c**' option) is 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. 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. 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)) 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*. If a name is bound in a block, it is a local variable of that block, unless declared as "nonlocal". 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*. When a name is not found at all, a "NameError" exception is raised. If the name refers to a local variable that has not been bound, a "UnboundLocalError" exception is raised. "UnboundLocalError" is a subclass of "NameError". The following constructs bind names: formal parameters to functions, "import" statements, class and function definitions (these bind the class or function name in the defining block), and targets that are identifiers if occurring in an assignment, "for" loop header, or after "as" in a "with" statement or "except" clause. 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 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. If the "global" statement occurs within a block, all uses of the name specified in the statement refer to the binding of that name 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 name is not found there, the builtins namespace is searched. The global statement must precede all uses of the name. 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. "__builtins__" can be set to a user-created dictionary to create a weak form of 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 namespace for a module is automatically created the first time a module is imported. The main module for a script is always called "__main__". 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. A class definition is an executable statement that may use and define names. These references follow the normal rules for name resolution. The namespace of the class definition becomes the attribute dictionary of the class. Names defined at the class scope are not visible in methods. Interaction with dynamic features ================================= There are several cases where Python statements are illegal when used in conjunction with nested scopes that contain free variables. If a variable is referenced in an enclosing scope, it is illegal to delete the name. An error will be reported at compile time. If the wild card form of import --- "import *" --- is used in a function and the function contains or is a nested block with free variables, the compiler will raise a "SyntaxError". 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. u namingu� 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. 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 to 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. u nonlocalu� 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". u numbersuz 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.__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, "x.__add__(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.__rtruediv__(self, other) object.__rfloordiv__(self, other) object.__rmod__(self, other) object.__rdivmod__(self, other) object.__rpow__(self, other) 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 and the operands are of different types. [2] For instance, to evaluate the expression "x - y", where *y* is an instance of a class that has an "__rsub__()" method, "y.__rsub__(x)" is called if "x.__sub__(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 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.__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) object.__round__(self[, n]) Called to implement the built-in functions "complex()", "int()", "float()" and "round()". Should return a value of the appropriate type. object.__index__(self) Called to implement "operator.index()". Also called whenever Python needs an integer object (such as in slicing, or in the built-in "bin()", "hex()" and "oct()" functions). Must return an integer. u numeric-typesu� 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 (ex: always close files). 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".) u objectsu� Operator precedence ******************* The following table summarizes the operator precedences in Python, from lowest precedence (least binding) to highest precedence (most 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 comparisons, including tests, which all have the same precedence and chain from left to right --- see section *Comparisons* --- and exponentiation, which groups from right to left). +-------------------------------------------------+---------------------------------------+ | Operator | Description | +=================================================+=======================================+ | "lambda" | Lambda expression | +-------------------------------------------------+---------------------------------------+ | "if" -- "else" | Conditional expression | +-------------------------------------------------+---------------------------------------+ | "or" | Boolean OR | +-------------------------------------------------+---------------------------------------+ | "and" | Boolean AND | +-------------------------------------------------+---------------------------------------+ | "not" "x" | Boolean NOT | +-------------------------------------------------+---------------------------------------+ | "in", "not in", "is", "is not", "<", "<=", ">", | Comparisons, including membership | | ">=", "!=", "==" | tests and identity tests | +-------------------------------------------------+---------------------------------------+ | "|" | Bitwise OR | +-------------------------------------------------+---------------------------------------+ | "^" | Bitwise XOR | +-------------------------------------------------+---------------------------------------+ | "&" | Bitwise AND | +-------------------------------------------------+---------------------------------------+ | "<<", ">>" | Shifts | +-------------------------------------------------+---------------------------------------+ | "+", "-" | Addition and subtraction | +-------------------------------------------------+---------------------------------------+ | "*", "/", "//", "%" | Multiplication, division, remainder | +-------------------------------------------------+---------------------------------------+ | "+x", "-x", "~x" | Positive, negative, bitwise NOT | +-------------------------------------------------+---------------------------------------+ | "**" | Exponentiation [6] | +-------------------------------------------------+---------------------------------------+ | "x[index]", "x[index:index]", | Subscription, slicing, call, | | "x(arguments...)", "x.attribute" | attribute reference | +-------------------------------------------------+---------------------------------------+ | "(expressions...)", "[expressions...]", "{key: | Binding or tuple display, list | | value...}", "{expressions...}" | display, dictionary display, set | +-------------------------------------------------+---------------------------------------+ -[ 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] While comparisons between strings make sense at the byte level, they may be counter-intuitive to users. For example, the strings ""\u00C7"" and ""\u0327\u0043"" compare differently, even though they both represent the same unicode character (LATIN CAPITAL LETTER C WITH CEDILLA). To compare strings in a human recognizable way, compare using "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 "%" operator is also used for string formatting; the same precedence applies. [6] The power operator "**" binds less tightly than an arithmetic or bitwise unary operator on its right, that is, "2**-1" is "0.5". u operator-summaryux 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) u passu| 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 ::= 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".) u powerud The "raise" statement ********************* raise_stmt ::= "raise" [expression ["from" expression]] If no expressions are present, "raise" re-raises the last exception that was active in the current scope. If no exception is active in the current scope, 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, which will then be attached to the raised exception as the "__cause__" attribute (which is writable). 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: int division or modulo 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 an exception is raised inside an exception handler: 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: int division or modulo 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 Additional information on exceptions can be found in section *Exceptions*, and information about handling exceptions is in section *The try statement*. u raiseu 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. u returnu� 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" 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 be the same as "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. 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. 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.__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, and should also be made available as the method "keys()". Iterator objects also need to implement this method; they are required to return themselves. For more information on iterator objects, see *Iterator Types*. 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 sequence. However, container objects can supply the following special method with a more efficient implementation, which also does not require the object be a sequence. 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*. u sequence-typesu� 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. 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)". Note: In the current implementation, the right-hand operand is required to be at most "sys.maxsize". If the right-hand operand is larger than "sys.maxsize" an "OverflowError" exception is raised. u shiftingu� 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 must evaluate to a mapping object, and it 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. u slicingsu 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. class.__name__ The name of the class or type. class.__qualname__ The *qualified name* of the class or type. 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. Example: >>> int.__subclasses__() [<class 'bool'>] -[ Footnotes ]- [1] Additional information on these special methods may be found in the Python Reference Manual (*Basic customization*). [2] As a consequence, the list "[1, 2]" is considered equal to "[1.0, 2.0]", and similarly for tuples. [3] They must have since the parser can't tell the type of the operands. [4] Cased characters are those with general category property being one of "Lu" (Letter, uppercase), "Ll" (Letter, lowercase), or "Lt" (Letter, titlecase). [5] To format only a tuple you should therefore provide a singleton tuple whose only element is the tuple to be formatted. u specialattrsu�� 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"). 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(currentclass, cls).__new__(cls[, ...])" with appropriate arguments and then modifying the newly-created instance as necessary before returning it. If "__new__()" 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 "__new__()". 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: "BaseClass.__init__(self, [args...])". Because "__new__()" and "__init__()" work together in constructing objects ("__new__()" to create it, and "__init__()" to customise 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 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. Note that it is possible (though not recommended!) for the "__del__()" method to postpone destruction of the instance by creating a new reference to it. It may then be called at a later time when this new reference is deleted. 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. Some common situations that may prevent the reference count of an object from going to zero include: circular references between objects (e.g., a doubly-linked list or a tree data structure with parent and child pointers); a reference to the object on the stack frame of a function that caught an exception (the traceback stored in "sys.exc_info()[2]" keeps the stack frame alive); or a reference to the object on the stack frame that raised an unhandled exception in interactive mode (the traceback stored in "sys.last_traceback" keeps the stack frame alive). The first situation can only be remedied by explicitly breaking the cycles; the latter two situations can be resolved by storing "None" in "sys.last_traceback". Circular references which are garbage are detected when the option cycle detector is enabled (it's on by default), but can only be cleaned up if there are no Python- level "__del__()" methods involved. Refer to the documentation for the "gc" module for more information about how "__del__()" methods are handled by the cycle detector, particularly the description of the "garbage" value. 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. Also, when "__del__()" is invoked in response to a module being deleted (e.g., when execution of the program is done), other globals referenced by the "__del__()" method may already have been deleted or in the process of being torn down (e.g. the import machinery shutting down). For this reason, "__del__()" methods should do the absolute minimum needed to maintain external invariants. Starting with version 1.5, 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, the "str.format()" method of class "str") 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. 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. There are no implied relationships among the comparison operators. The truth of "x==y" does not imply that "x!=y" is false. Accordingly, when defining "__eq__()", one should also define "__ne__()" so that the operators will behave as expected. 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. Arguments to rich comparison methods are never coerced. To automatically generate ordering operations from a single root operation, see "functools.total_ordering()". object.__hash__(self) Called by built-in function "hash()" and for operations on members of hashed collections including "set", "frozenset", and "dict". "__hash__()" should return an integer. The only required property is that objects which compare equal have the same hash value; it is advised to somehow mix together (e.g. using exclusive or) the hash values for the components of the object that also play a part in comparison of objects. 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.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.Hashable)" call. Note: By default, the "__hash__()" values of str, bytes and datetime 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 dicts, sets and other mappings. 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 an attribute lookup has not found the attribute in the usual places (i.e. it is not an instance attribute nor is it found in the class tree for "self"). "name" is the attribute name. This method should 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*. 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)". 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. 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. 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) Called to get the attribute of the owner class (class attribute access) or of an instance of that class (instance attribute access). *owner* is always 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. object.__set__(self, instance, value) Called to set the attribute on an instance *instance* of the owner class to a new value, *value*. object.__delete__(self, instance) Called to delete the attribute on an instance *instance* of the owner class. 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 If "a" is an instance of "super", then the binding "super(B, obj).m()" searches "obj.__class__.__mro__" for the base class "A" immediately preceding "B" and then invokes the descriptor with the call: "A.__dict__['m'].__get__(obj, obj.__class__)". For instance bindings, the precedence of descriptor invocation depends on the 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 "__set__()" and "__get__()" defined always override a redefinition in an instance dictionary. In contrast, non-data descriptors can be overridden by instances. Python methods (including "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__ --------- By default, instances of classes have a dictionary for attribute storage. This wastes space for objects having very few instance variables. The space consumption can become acute when creating large numbers of instances. The default can be overridden by defining *__slots__* in a class definition. The *__slots__* declaration takes a sequence of instance variables and reserves just enough space in each instance to hold a value for each variable. Space is saved because *__dict__* is not created for each instance. object.__slots__ This class variable can be assigned a string, iterable, or sequence of strings with variable names used by instances. If defined in a class, *__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__* attribute of that class will always be accessible, so a *__slots__* definition in the subclass is meaningless. * 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 (*Implementing 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 limited to the class where it is defined. As a result, subclasses will have a *__dict__* unless they also define *__slots__* (which must 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__*. Mappings may also be used; however, in the future, special meaning may be assigned to the values corresponding to each key. * *__class__* assignment works only if both classes have the same *__slots__*. Customizing class creation ========================== 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 customised 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: * the appropriate metaclass is determined * the class namespace is prepared * the class body is executed * the class object is created 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). If the metaclass has no "__prepare__" attribute, then the class namespace is initialised as an empty "dict()" instance. 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, and cannot be accessed at all from static methods. 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. 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. See also: **PEP 3135** - New super Describes the implicit "__class__" closure reference Metaclass example ----------------- The potential uses for metaclasses are boundless. Some ideas that have been explored include logging, interface checking, automatic delegation, automatic property creation, proxies, frameworks, and automatic resource locking/synchronization. Here is an example of a metaclass that uses an "collections.OrderedDict" to remember the order that class members were defined: class OrderedClass(type): @classmethod def __prepare__(metacls, name, bases, **kwds): return collections.OrderedDict() def __new__(cls, name, bases, namespace, **kwds): result = type.__new__(cls, name, bases, dict(namespace)) result.members = tuple(namespace) return result class A(metaclass=OrderedClass): def one(self): pass def two(self): pass def three(self): pass def four(self): pass >>> A.members ('__module__', 'one', 'two', 'three', 'four') When the class definition for *A* gets executed, the process begins with calling the metaclass's "__prepare__()" method which returns an empty "collections.OrderedDict". That mapping records the methods and attributes of *A* as they are defined within the body of the class statement. Once those definitions are executed, the ordered dictionary is fully populated and the metaclass's "__new__()" method gets invoked. That method builds the new type and it saves the ordered dictionary keys in an attribute called "members". 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 callable objects ========================== object.__call__(self[, args...]) Called when the instance is "called" as a function; if this method is defined, "x(arg1, arg2, ...)" is a shorthand for "x.__call__(arg1, arg2, ...)". 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" 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 be the same as "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. 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. 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.__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, and should also be made available as the method "keys()". Iterator objects also need to implement this method; they are required to return themselves. For more information on iterator objects, see *Iterator Types*. 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 sequence. However, container objects can supply the following special method with a more efficient implementation, which also does not require the object be a sequence. 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.__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, "x.__add__(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.__rtruediv__(self, other) object.__rfloordiv__(self, other) object.__rmod__(self, other) object.__rdivmod__(self, other) object.__rpow__(self, other) 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 and the operands are of different types. [2] For instance, to evaluate the expression "x - y", where *y* is an instance of a class that has an "__rsub__()" method, "y.__rsub__(x)" is called if "x.__sub__(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 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.__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) object.__round__(self[, n]) Called to implement the built-in functions "complex()", "int()", "float()" and "round()". Should return a value of the appropriate type. object.__index__(self) Called to implement "operator.index()". Also called whenever Python needs an integer object (such as in slicing, or in the built-in "bin()", "hex()" and "oct()" functions). Must return an integer. 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 0343** - The "with" statement The specification, background, and examples for the Python "with" 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). -[ Footnotes ]- [1] It *is* possible in some cases to change an object's type, under certain controlled conditions. It generally isn't a good idea though, since it can lead to some very strange behaviour if it is handled incorrectly. [2] For operands of the same type, it is assumed that if the non- reflected method (such as "__add__()") fails the operation is not supported, which is why the reflected method is not called. u specialnamesu�G 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 *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. 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 "'Ã'" is equivalent to ""ss"". Since it is already lowercase, "lower()" would do nothing to "'Ã'"; "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 a space). 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. str.encode(encoding="utf-8", errors="strict") Return an encoded version of the string as a bytes object. Default encoding is "'utf-8'". *errors* may be given to set a different error handling scheme. The default for *errors* is "'strict'", meaning that encoding errors raise a "UnicodeError". Other possible values are "'ignore'", "'replace'", "'xmlcharrefreplace'", "'backslashreplace'" and any other name registered via "codecs.register_error()", see section *Codec Base Classes*. For a list of possible encodings, see section *Standard Encodings*. Changed in version 3.1: Support for keyword arguments added. 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]) 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, such that *sub* is contained in 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. 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.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 from general category "Nd". This category includes digit characters, and all characters that can be used to form decimal-radix numbers, e.g. U+0660, ARABIC-INDIC DIGIT ZERO. 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. 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*. Use "keyword.iskeyword()" to test for reserved identifiers such as "def" and "class". 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. Whitespace characters are those characters defined in the Unicode character database as "Other" or "Separator" and those with bidirectional property being 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. str.join(iterable) Return a string which is the concatenation of the strings in the *iterable* *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 a 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' 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.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 a 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' 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 "['']". 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()" returns "['1', '2', '3']", and "' 1 2 3 '.split(None, 1)" returns "['1', '2 3 ']". str.splitlines([keepends]) Return a list of the lines in the string, breaking at line boundaries. This method uses the *universal newlines* approach to splitting lines. Line breaks are not included in the resulting list unless *keepends* is given and true. For example, "'ab c\n\nde fg\rkl\r\n'.splitlines()" returns "['ab c', '', 'de fg', 'kl']", while the same call with "splitlines(True)" returns "['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. 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' 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. 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" 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)[0].upper() + ... mo.group(0)[1:].lower(), ... s) ... >>> titlecase("they're bill's friends.") "They're Bill's Friends." str.translate(map) Return a copy of the *s* where all characters have been mapped through the *map* which must be a dictionary of Unicode ordinals (integers) to Unicode ordinals, strings or "None". Unmapped characters are left untouched. Characters mapped to "None" are deleted. You can use "str.maketrans()" to create a translation map from character-to-character mappings in different formats. Note: An even more flexible approach is to create a custom character mapping codec using the "codecs" module (see "encodings.cp1251" for an example). str.upper() Return a copy of the string with all the cased characters [4] converted to uppercase. Note that "str.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 the numeric string left filled with zeros in a string of length *width*. A sign prefix is handled correctly. The original string is returned if *width* is less than or equal to "len(s)". u string-methodsu� String and Bytes literals ************************* String literals are described by the following lexical definitions: stringliteral ::= [stringprefix](shortstring | longstring) stringprefix ::= "r" | "u" | "R" | "U" 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. As of Python 3.3 it is possible again to prefix unicode strings with a "u" prefix to simplify maintenance of dual 2.x and 3.x codebases. 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. In triple-quoted strings, unescaped newlines and quotes are allowed (and are retained), except that three unescaped quotes in a row terminate the string. (A "quote" is the character used to open the string, i.e. either "'" or """.) Unless an "'r'" or "'R'" prefix is present, escape sequences in strings are interpreted according to rules similar to those used by Standard C. The recognized escape sequences are: +-------------------+-----------------------------------+---------+ | Escape Sequence | Meaning | Notes | +===================+===================================+=========+ +-------------------+-----------------------------------+---------+ +-------------------+-----------------------------------+---------+ +-------------------+-----------------------------------+---------+ +-------------------+-----------------------------------+---------+ +-------------------+-----------------------------------+---------+ +-------------------+-----------------------------------+---------+ +-------------------+-----------------------------------+---------+ +-------------------+-----------------------------------+---------+ +-------------------+-----------------------------------+---------+ +-------------------+-----------------------------------+---------+ +-------------------+-----------------------------------+---------+ | "\ooo" | Character with octal value *ooo* | (1,3) | +-------------------+-----------------------------------+---------+ | "\xhh" | Character with hex value *hh* | (2,3) | +-------------------+-----------------------------------+---------+ Escape sequences only recognized in string literals are: +-------------------+-----------------------------------+---------+ | Escape Sequence | Meaning | Notes | +===================+===================================+=========+ | "\N{name}" | Character named *name* in the | (4) | +-------------------+-----------------------------------+---------+ | "\uxxxx" | Character with 16-bit hex value | (5) | +-------------------+-----------------------------------+---------+ | "\Uxxxxxxxx" | Character with 32-bit hex value | (6) | +-------------------+-----------------------------------+---------+ Notes: 1. As in Standard C, up to three octal digits are accepted. 2. Unlike in Standard C, exactly two hex digits are required. 3. 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. 4. Changed in version 3.3: Support for name aliases [1] has been added. 5. Individual code units which form parts of a surrogate pair can be encoded using this escape sequence. Exactly four hex digits are required. 6. 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 string*. (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. Even in a raw string, string quotes can be escaped with a backslash, but the backslash remains in the string; 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 string 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 string, *not* as a line continuation. u stringsuC Subscriptions ************* A subscription selects an item of a sequence (string, tuple or list) or mapping (dictionary) object: subscription ::= primary "[" expression_list "]" The primary must evaluate to an object that supports subscription, e.g. a list or dictionary. User-defined objects can support subscription by defining a "__getitem__()" method. For built-in objects, there are two types of objects that support subscription: 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. (The expression list is a tuple except if it has exactly one item.) If the primary is a sequence, the expression (list) must evaluate to an integer or a slice (as discussed in the following section). 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 "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's items are characters. A character is not a separate data type but a string of exactly one character. u subscriptionsu� 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. The following values are considered false: * "None" * "False" * zero of any numeric type, for example, "0", "0.0", "0j". * any empty sequence, for example, "''", "()", "[]". * any empty mapping, for example, "{}". * instances of user-defined classes, if the class defines a "__bool__()" or "__len__()" method, when that method returns the integer zero or "bool" value "False". [1] All other values are considered true --- so objects of many types are always true. 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.) u truthuY The "try" statement ******************* The "try" statement specifies exception handlers and/or cleanup code for a group of statements: try_stmt ::= try1_stmt | try2_stmt try1_stmt ::= "try" ":" suite ("except" [expression ["as" target]] ":" suite)+ ["else" ":" suite] ["finally" ":" suite] try2_stmt ::= "try" ":" suite "finally" ":" suite 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 it is the class or a base class of the exception object or a tuple containing an item compatible with the exception. 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 access 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. "sys.exc_info()" values are restored to their previous values (before the call) when returning from a function that handled an exception. The optional "else" clause is executed if and when control flows off the end of the "try" clause. [2] Exceptions in the "else" clause are not handled by the preceding "except" clauses. 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" or "break" 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.' A "continue" statement is illegal in the "finally" clause. (The reason is a problem with the current implementation --- this restriction may be lifted in the future). 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*. u tryu�} 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 may 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.) Its truth value is true. 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. 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 is 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 codepoints. All the codepoints in range "U+0000 - U+10FFFF" can be represented in a string. Python doesn't have a "chr" type, and every character in the string is represented as a string object with length "1". The built-in function "ord()" converts a character to its codepoint (as an integer); "chr()" converts an integer in range "0 - 10FFFF" to the corresponding character. "str.encode()" can be used to convert a "str" to "bytes" using the given 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 function "bytes()" can be used to construct 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 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. 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: +---------------------------+---------------------------------+-------------+ +===========================+=================================+=============+ | "__doc__" | The function's documentation | Writable | +---------------------------+---------------------------------+-------------+ | "__name__" | The function's name | Writable | +---------------------------+---------------------------------+-------------+ | "__qualname__" | The function's *qualified name* | Writable | +---------------------------+---------------------------------+-------------+ | "__module__" | The name of the module the | Writable | +---------------------------+---------------------------------+-------------+ | "__defaults__" | A tuple containing default | Writable | +---------------------------+---------------------------------+-------------+ | "__code__" | The code object representing | Writable | +---------------------------+---------------------------------+-------------+ | "__globals__" | A reference to the dictionary | Read-only | +---------------------------+---------------------------------+-------------+ | "__dict__" | The namespace supporting | Writable | +---------------------------+---------------------------------+-------------+ | "__closure__" | "None" or a tuple of cells that | Read-only | +---------------------------+---------------------------------+-------------+ | "__annotations__" | A dict containing annotations | Writable | +---------------------------+---------------------------------+-------------+ | "__kwdefaults__" | A dict containing defaults for | Writable | +---------------------------+---------------------------------+-------------+ 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.* Additional information about a function's definition can be retrieved from its code object; see the description of internal types below. 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 a user-defined method object is created by retrieving another method object from a class or instance, the behaviour is the same as for a function object, except that the "__func__" attribute of the new instance is not the original method object but its "__func__" attribute. 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. 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 (see "import"), 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". 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. Predefined (writable) attributes: "__name__" is the module's name; "__doc__" is the module's documentation string, or "None" if unavailable; "__file__" is 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 is the pathname of the shared library file. 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 http://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__" attributes 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__" is the class name; "__module__" is the module name in which the class was defined; "__dict__" is the dictionary containing the class's namespace; "__bases__" is a tuple (possibly empty or a singleton) containing the base classes, in the order of their occurrence in the base class list; "__doc__" is the class's documentation string, or None if undefined. 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_argcount" is the number of positional 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 (including local variables); "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. Frame objects Frame objects represent execution frames. They may occur in traceback objects (see below). 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). Special writable attributes: "f_trace", if not "None", is a function called at the start of each source code line (this is used by the debugger); "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. Traceback objects Traceback objects represent a stack trace of an exception. A traceback object is created when an exception occurs. 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()". 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". Special read-only attributes: "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; "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. 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 not themselves callable, although the objects they wrap usually are. 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. u typesu� 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 typesfunctionsu�# 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. Numeric types used for keys obey the normal rules for numeric comparison: if two numbers compare equal (such as "1" and "1.0") then they can be used interchangeably to index the same dictionary entry. (Note however, that since computers store floating-point numbers as approximations it is usually unwise to use them as dictionary keys.) Dictionaries can be created by placing a comma-separated list of "key: value" pairs within braces, for example: "{'jack': 4098, 'sjoerd': 4127}" or "{4098: 'jack', 4127: 'sjoerd'}", or by the "dict" constructor. class class dict(**kwarg) class class dict(mapping, **kwarg) class class dict(iterable, **kwarg) Return a new dictionary initialized from an optional positional argument and a possibly empty set of keyword arguments. 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 *iterator* object. Each item in the iterable must itself be an iterator 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}) >>> a == b == c == d == e 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): 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__()", if the key *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 if the key is not present. 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 See "collections.Counter" for a complete implementation including other methods helpful for accumulating and managing tallies. 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(seq[, value]) Create a new dictionary with keys from *seq* and values set to *value*. "fromkeys()" is a class method that returns a new dictionary. *value* defaults to "None". 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 an arbitrary "(key, value)" pair from the dictionary. "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". 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*. 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 an arbitrary order which is non-random, varies across Python implementations, and depends on the dictionary's history of insertions and deletions. If keys, values and items views are iterated over with no intervening modifications to the dictionary, the order of items will directly correspond. 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. 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). 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 >>> list(keys) ['eggs', 'bacon', 'sausage', 'spam'] >>> list(values) [2, 1, 1, 500] >>> # view objects are dynamic and reflect dict changes >>> del dishes['eggs'] >>> del dishes['sausage'] >>> list(keys) ['spam', 'bacon'] >>> # set operations >>> keys & {'eggs', 'bacon', 'salad'} {'bacon'} >>> keys ^ {'sausage', 'juice'} {'juice', 'sausage', 'bacon', 'spam'} u typesmappingu� 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 typesmethodsu 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 typesmodulesu�I 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 (operations in the same box have the same 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. +----------------------------+----------------------------------+------------+ | Operation | Result | Notes | +============================+==================================+============+ | "x in s" | "True" if an item of *s* is | (1) | +----------------------------+----------------------------------+------------+ | "x not in s" | "False" if an item of *s* is | (1) | +----------------------------+----------------------------------+------------+ | "s + t" | the concatenation of *s* and *t* | (6)(7) | +----------------------------+----------------------------------+------------+ | "s * n" or "n * s" | *n* shallow copies of *s* | (2)(7) | +----------------------------+----------------------------------+------------+ | "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) | +----------------------------+----------------------------------+------------+ +----------------------------+----------------------------------+------------+ +----------------------------+----------------------------------+------------+ +----------------------------+----------------------------------+------------+ | "s.index(x[, i[, j]])" | index of the first occurrence of | (8) | +----------------------------+----------------------------------+------------+ +----------------------------+----------------------------------+------------+ 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.) 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 also that the copies are shallow; nested structures are not copied. 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 (pointers 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]] 3. If *i* or *j* is negative, the index is relative to the end of the string: "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*). If *i* or *j* is greater than "len(s)", use "len(s)". 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 a "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*. When supported, the additional arguments to the index method 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:j:k] = t" | the elements of "s[i:j:k]" are | (1) | +--------------------------------+----------------------------------+-----------------------+ +--------------------------------+----------------------------------+-----------------------+ +--------------------------------+----------------------------------+-----------------------+ | "s.clear()" | removes all items from "s" (same | (5) | +--------------------------------+----------------------------------+-----------------------+ | "s.copy()" | creates a shallow copy of "s" | (5) | +--------------------------------+----------------------------------+-----------------------+ +--------------------------------+----------------------------------+-----------------------+ +--------------------------------+----------------------------------+-----------------------+ | "s.pop([i])" | retrieves the item at *i* and | (2) | +--------------------------------+----------------------------------+-----------------------+ | "s.remove(x)" | remove the first item from *s* | (3) | +--------------------------------+----------------------------------+-----------------------+ | "s.reverse()" | reverses the items of *s* in | (4) | +--------------------------------+----------------------------------+-----------------------+ 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") New in version 3.3: "clear()" and "copy()" methods. Lists ===== Lists are mutable sequences, typically used to store collections of homogeneous items (where the precise degree of similarity will vary by application). class 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=None) 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). **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 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 class range(stop) class 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). 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. u typessequ� 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:j:k] = t" | the elements of "s[i:j:k]" are | (1) | +--------------------------------+----------------------------------+-----------------------+ +--------------------------------+----------------------------------+-----------------------+ +--------------------------------+----------------------------------+-----------------------+ | "s.clear()" | removes all items from "s" (same | (5) | +--------------------------------+----------------------------------+-----------------------+ | "s.copy()" | creates a shallow copy of "s" | (5) | +--------------------------------+----------------------------------+-----------------------+ +--------------------------------+----------------------------------+-----------------------+ +--------------------------------+----------------------------------+-----------------------+ | "s.pop([i])" | retrieves the item at *i* and | (2) | +--------------------------------+----------------------------------+-----------------------+ | "s.remove(x)" | remove the first item from *s* | (3) | +--------------------------------+----------------------------------+-----------------------+ | "s.reverse()" | reverses the items of *s* in | (4) | +--------------------------------+----------------------------------+-----------------------+ 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") New in version 3.3: "clear()" and "copy()" methods. u typesseq-mutableu 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 unary "+" (plus) operator yields its numeric argument unchanged. 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. In all three cases, if the argument does not have the proper type, a "TypeError" exception is raised. u unaryu� The "while" statement ********************* The "while" statement is used for repeated execution as long as an expression is true: while_stmt ::= "while" 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 whileu 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_item ("," with_item)* ":" suite 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 "__exit__()" is loaded for later use. 3. The context manager's "__enter__()" method is invoked. 4. 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 6 below. 5. The suite is executed. 6. 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. 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 equivalent to with A() as a: with B() as b: suite Changed in version 3.1: Support for multiple context expressions. See also: **PEP 0343** - The "with" statement The specification, background, and examples for the Python "with" statement. u withu1 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. u yieldN( u topics( ( ( u6 /opt/alt/python33/lib64/python3.3/pydoc_data/topics.pyu <module> s�
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