# python-clinic / Lib / pydoc_data / topics.py

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79  # -*- coding: utf-8 -*- # Autogenerated by Sphinx on Sat Aug 3 12:46:08 2013 topics = {'assert': '\nThe assert statement\n************************\n\nAssert statements are a convenient way to insert debugging assertions\ninto a program:\n\n assert_stmt ::= "assert" expression ["," expression]\n\nThe simple form, assert expression, is equivalent to\n\n if __debug__:\n if not expression: raise AssertionError\n\nThe extended form, assert expression1, expression2, is equivalent\nto\n\n if __debug__:\n if not expression1: raise AssertionError(expression2)\n\nThese equivalences assume that __debug__ and AssertionError\nrefer to the built-in variables with those names. In the current\nimplementation, the built-in variable __debug__ is True under\nnormal circumstances, False when optimization is requested\n(command line option -O). The current code generator emits no code\nfor an assert statement when optimization is requested at compile\ntime. Note that it is unnecessary to include the source code for the\nexpression that failed in the error message; it will be displayed as\npart of the stack trace.\n\nAssignments to __debug__ are illegal. The value for the built-in\nvariable is determined when the interpreter starts.\n', 'assignment': '\nAssignment statements\n*********************\n\nAssignment statements are used to (re)bind names to values and to\nmodify attributes or items of mutable objects:\n\n assignment_stmt ::= (target_list "=")+ (expression_list | yield_expression)\n target_list ::= target ("," target)* [","]\n target ::= identifier\n | "(" target_list ")"\n | "[" target_list "]"\n | attributeref\n | subscription\n | slicing\n | "*" target\n\n(See section *Primaries* for the syntax definitions for the last three\nsymbols.)\n\nAn assignment statement evaluates the expression list (remember that\nthis can be a single expression or a comma-separated list, the latter\nyielding a tuple) and assigns the single resulting object to each of\nthe target lists, from left to right.\n\nAssignment is defined recursively depending on the form of the target\n(list). When a target is part of a mutable object (an attribute\nreference, subscription or slicing), the mutable object must\nultimately perform the assignment and decide about its validity, and\nmay raise an exception if the assignment is unacceptable. The rules\nobserved by various types and the exceptions raised are given with the\ndefinition of the object types (see section *The standard type\nhierarchy*).\n\nAssignment of an object to a target list, optionally enclosed in\nparentheses or square brackets, is recursively defined as follows.\n\n* If the target list is a single target: The object is assigned to\n that target.\n\n* If the target list is a comma-separated list of targets: The object\n must be an iterable with the same number of items as there are\n targets in the target list, and the items are assigned, from left to\n right, to the corresponding targets.\n\n * If the target list contains one target prefixed with an asterisk,\n called a "starred" target: The object must be a sequence with at\n least as many items as there are targets in the target list, minus\n one. The first items of the sequence are assigned, from left to\n right, to the targets before the starred target. The final items\n of the sequence are assigned to the targets after the starred\n target. A list of the remaining items in the sequence is then\n assigned to the starred target (the list can be empty).\n\n * Else: The object must be a sequence with the same number of items\n as there are targets in the target list, and the items are\n assigned, from left to right, to the corresponding targets.\n\nAssignment of an object to a single target is recursively defined as\nfollows.\n\n* If the target is an identifier (name):\n\n * If the name does not occur in a global or nonlocal\n statement in the current code block: the name is bound to the\n object in the current local namespace.\n\n * Otherwise: the name is bound to the object in the global namespace\n or the outer namespace determined by nonlocal, respectively.\n\n The name is rebound if it was already bound. This may cause the\n reference count for the object previously bound to the name to reach\n zero, causing the object to be deallocated and its destructor (if it\n has one) to be called.\n\n* If the target is a target list enclosed in parentheses or in square\n brackets: The object must be an iterable with the same number of\n items as there are targets in the target list, and its items are\n assigned, from left to right, to the corresponding targets.\n\n* If the target is an attribute reference: The primary expression in\n the reference is evaluated. It should yield an object with\n assignable attributes; if this is not the case, TypeError is\n raised. That object is then asked to assign the assigned object to\n the given attribute; if it cannot perform the assignment, it raises\n an exception (usually but not necessarily AttributeError).\n\n Note: If the object is a class instance and the attribute reference\n occurs on both sides of the assignment operator, the RHS expression,\n a.x can access either an instance attribute or (if no instance\n attribute exists) a class attribute. The LHS target a.x is\n always set as an instance attribute, creating it if necessary.\n Thus, the two occurrences of a.x do not necessarily refer to the\n same attribute: if the RHS expression refers to a class attribute,\n the LHS creates a new instance attribute as the target of the\n assignment:\n\n class Cls:\n x = 3 # class variable\n inst = Cls()\n inst.x = inst.x + 1 # writes inst.x as 4 leaving Cls.x as 3\n\n This description does not necessarily apply to descriptor\n attributes, such as properties created with property().\n\n* If the target is a subscription: The primary expression in the\n reference is evaluated. It should yield either a mutable sequence\n object (such as a list) or a mapping object (such as a dictionary).\n Next, the subscript expression is evaluated.\n\n If the primary is a mutable sequence object (such as a list), the\n subscript must yield an integer. If it is negative, the sequence\'s\n length is added to it. The resulting value must be a nonnegative\n integer less than the sequence\'s length, and the sequence is asked\n to assign the assigned object to its item with that index. If the\n index is out of range, IndexError is raised (assignment to a\n subscripted sequence cannot add new items to a list).\n\n If the primary is a mapping object (such as a dictionary), the\n subscript must have a type compatible with the mapping\'s key type,\n and the mapping is then asked to create a key/datum pair which maps\n the subscript to the assigned object. This can either replace an\n existing key/value pair with the same key value, or insert a new\n key/value pair (if no key with the same value existed).\n\n For user-defined objects, the __setitem__() method is called\n with appropriate arguments.\n\n* If the target is a slicing: The primary expression in the reference\n is evaluated. It should yield a mutable sequence object (such as a\n list). The assigned object should be a sequence object of the same\n type. Next, the lower and upper bound expressions are evaluated,\n insofar they are present; defaults are zero and the sequence\'s\n length. The bounds should evaluate to integers. If either bound is\n negative, the sequence\'s length is added to it. The resulting\n bounds are clipped to lie between zero and the sequence\'s length,\n inclusive. Finally, the sequence object is asked to replace the\n slice with the items of the assigned sequence. The length of the\n slice may be different from the length of the assigned sequence,\n thus changing the length of the target sequence, if the object\n allows it.\n\n**CPython implementation detail:** In the current implementation, the\nsyntax for targets is taken to be the same as for expressions, and\ninvalid syntax is rejected during the code generation phase, causing\nless detailed error messages.\n\nWARNING: Although the definition of assignment implies that overlaps\nbetween the left-hand side and the right-hand side are \'safe\' (for\nexample a, b = b, a swaps two variables), overlaps *within* the\ncollection of assigned-to variables are not safe! For instance, the\nfollowing program prints [0, 2]:\n\n x = [0, 1]\n i = 0\n i, x[i] = 1, 2\n print(x)\n\nSee also:\n\n **PEP 3132** - Extended Iterable Unpacking\n The specification for the *target feature.\n\n\nAugmented assignment statements\n===============================\n\nAugmented assignment is the combination, in a single statement, of a\nbinary operation and an assignment statement:\n\n augmented_assignment_stmt ::= augtarget augop (expression_list | yield_expression)\n augtarget ::= identifier | attributeref | subscription | slicing\n augop ::= "+=" | "-=" | "*=" | "/=" | "//=" | "%=" | "**="\n | ">>=" | "<<=" | "&=" | "^=" | "|="\n\n(See section *Primaries* for the syntax definitions for the last three\nsymbols.)\n\nAn augmented assignment evaluates the target (which, unlike normal\nassignment statements, cannot be an unpacking) and the expression\nlist, performs the binary operation specific to the type of assignment\non the two operands, and assigns the result to the original target.\nThe target is only evaluated once.\n\nAn augmented assignment expression like x += 1 can be rewritten as\nx = x + 1 to achieve a similar, but not exactly equal effect. In\nthe augmented version, x is only evaluated once. Also, when\npossible, the actual operation is performed *in-place*, meaning that\nrather than creating a new object and assigning that to the target,\nthe old object is modified instead.\n\nWith the exception of assigning to tuples and multiple targets in a\nsingle statement, the assignment done by augmented assignment\nstatements is handled the same way as normal assignments. Similarly,\nwith the exception of the possible *in-place* behavior, the binary\noperation performed by augmented assignment is the same as the normal\nbinary operations.\n\nFor targets which are attribute references, the same *caveat about\nclass and instance attributes* applies as for regular assignments.\n', 'atom-identifiers': '\nIdentifiers (Names)\n*******************\n\nAn identifier occurring as an atom is a name. See section\n*Identifiers and keywords* for lexical definition and section *Naming\nand binding* for documentation of naming and binding.\n\nWhen the name is bound to an object, evaluation of the atom yields\nthat object. When a name is not bound, an attempt to evaluate it\nraises a NameError exception.\n\n**Private name mangling:** When an identifier that textually occurs in\na class definition begins with two or more underscore characters and\ndoes not end in two or more underscores, it is considered a *private\nname* of that class. Private names are transformed to a longer form\nbefore code is generated for them. The transformation inserts the\nclass name, with leading underscores removed and a single underscore\ninserted, in front of the name. For example, the identifier\n__spam occurring in a class named Ham will be transformed to\n_Ham__spam. This transformation is independent of the syntactical\ncontext in which the identifier is used. If the transformed name is\nextremely long (longer than 255 characters), implementation defined\ntruncation may happen. If the class name consists only of underscores,\nno transformation is done.\n', 'atom-literals': "\nLiterals\n********\n\nPython supports string and bytes literals and various numeric\nliterals:\n\n literal ::= stringliteral | bytesliteral\n | integer | floatnumber | imagnumber\n\nEvaluation of a literal yields an object of the given type (string,\nbytes, integer, floating point number, complex number) with the given\nvalue. The value may be approximated in the case of floating point\nand imaginary (complex) literals. See section *Literals* for details.\n\nAll literals correspond to immutable data types, and hence the\nobject's identity is less important than its value. Multiple\nevaluations of literals with the same value (either the same\noccurrence in the program text or a different occurrence) may obtain\nthe same object or a different object with the same value.\n", 'attribute-access': '\nCustomizing attribute access\n****************************\n\nThe following methods can be defined to customize the meaning of\nattribute access (use of, assignment to, or deletion of x.name)\nfor class instances.\n\nobject.__getattr__(self, name)\n\n Called when an attribute lookup has not found the attribute in the\n usual places (i.e. it is not an instance attribute nor is it found\n in the class tree for self). name is the attribute name.\n This method should return the (computed) attribute value or raise\n an AttributeError exception.\n\n Note that if the attribute is found through the normal mechanism,\n __getattr__() is not called. (This is an intentional asymmetry\n between __getattr__() and __setattr__().) This is done both\n for efficiency reasons and because otherwise __getattr__()\n would have no way to access other attributes of the instance. Note\n that at least for instance variables, you can fake total control by\n not inserting any values in the instance attribute dictionary (but\n instead inserting them in another object). See the\n __getattribute__() method below for a way to actually get total\n control over attribute access.\n\nobject.__getattribute__(self, name)\n\n Called unconditionally to implement attribute accesses for\n instances of the class. If the class also defines\n __getattr__(), the latter will not be called unless\n __getattribute__() either calls it explicitly or raises an\n AttributeError. This method should return the (computed)\n attribute value or raise an AttributeError exception. In order\n to avoid infinite recursion in this method, its implementation\n should always call the base class method with the same name to\n access any attributes it needs, for example,\n object.__getattribute__(self, name).\n\n Note: This method may still be bypassed when looking up special methods\n as the result of implicit invocation via language syntax or\n built-in functions. See *Special method lookup*.\n\nobject.__setattr__(self, name, value)\n\n Called when an attribute assignment is attempted. This is called\n instead of the normal mechanism (i.e. store the value in the\n instance dictionary). *name* is the attribute name, *value* is the\n value to be assigned to it.\n\n If __setattr__() wants to assign to an instance attribute, it\n should call the base class method with the same name, for example,\n object.__setattr__(self, name, value).\n\nobject.__delattr__(self, name)\n\n Like __setattr__() but for attribute deletion instead of\n assignment. This should only be implemented if del obj.name is\n meaningful for the object.\n\nobject.__dir__(self)\n\n Called when dir() is called on the object. A sequence must be\n returned. dir() converts the returned sequence to a list and\n sorts it.\n\n\nImplementing Descriptors\n========================\n\nThe following methods only apply when an instance of the class\ncontaining the method (a so-called *descriptor* class) appears in an\n*owner* class (the descriptor must be in either the owner\'s class\ndictionary or in the class dictionary for one of its parents). In the\nexamples below, "the attribute" refers to the attribute whose name is\nthe key of the property in the owner class\' __dict__.\n\nobject.__get__(self, instance, owner)\n\n Called to get the attribute of the owner class (class attribute\n access) or of an instance of that class (instance attribute\n access). *owner* is always the owner class, while *instance* is the\n instance that the attribute was accessed through, or None when\n the attribute is accessed through the *owner*. This method should\n return the (computed) attribute value or raise an\n AttributeError exception.\n\nobject.__set__(self, instance, value)\n\n Called to set the attribute on an instance *instance* of the owner\n class to a new value, *value*.\n\nobject.__delete__(self, instance)\n\n Called to delete the attribute on an instance *instance* of the\n owner class.\n\n\nInvoking Descriptors\n====================\n\nIn general, a descriptor is an object attribute with "binding\nbehavior", one whose attribute access has been overridden by methods\nin the descriptor protocol: __get__(), __set__(), and\n__delete__(). If any of those methods are defined for an object,\nit is said to be a descriptor.\n\nThe default behavior for attribute access is to get, set, or delete\nthe attribute from an object\'s dictionary. For instance, a.x has a\nlookup chain starting with a.__dict__[\'x\'], then\ntype(a).__dict__[\'x\'], and continuing through the base classes of\ntype(a) excluding metaclasses.\n\nHowever, if the looked-up value is an object defining one of the\ndescriptor methods, then Python may override the default behavior and\ninvoke the descriptor method instead. Where this occurs in the\nprecedence chain depends on which descriptor methods were defined and\nhow they were called.\n\nThe starting point for descriptor invocation is a binding, a.x.\nHow the arguments are assembled depends on a:\n\nDirect Call\n The simplest and least common call is when user code directly\n invokes a descriptor method: x.__get__(a).\n\nInstance Binding\n If binding to an object instance, a.x is transformed into the\n call: type(a).__dict__[\'x\'].__get__(a, type(a)).\n\nClass Binding\n If binding to a class, A.x is transformed into the call:\n A.__dict__[\'x\'].__get__(None, A).\n\nSuper Binding\n If a is an instance of super, then the binding super(B,\n obj).m() searches obj.__class__.__mro__ for the base class\n A immediately preceding B and then invokes the descriptor\n with the call: A.__dict__[\'m\'].__get__(obj, obj.__class__).\n\nFor instance bindings, the precedence of descriptor invocation depends\non the which descriptor methods are defined. A descriptor can define\nany combination of __get__(), __set__() and __delete__().\nIf it does not define __get__(), then accessing the attribute will\nreturn the descriptor object itself unless there is a value in the\nobject\'s instance dictionary. If the descriptor defines __set__()\nand/or __delete__(), it is a data descriptor; if it defines\nneither, it is a non-data descriptor. Normally, data descriptors\ndefine both __get__() and __set__(), while non-data\ndescriptors have just the __get__() method. Data descriptors with\n__set__() and __get__() defined always override a redefinition\nin an instance dictionary. In contrast, non-data descriptors can be\noverridden by instances.\n\nPython methods (including staticmethod() and classmethod())\nare implemented as non-data descriptors. Accordingly, instances can\nredefine and override methods. This allows individual instances to\nacquire behaviors that differ from other instances of the same class.\n\nThe property() function is implemented as a data descriptor.\nAccordingly, instances cannot override the behavior of a property.\n\n\n__slots__\n=========\n\nBy default, instances of classes have a dictionary for attribute\nstorage. This wastes space for objects having very few instance\nvariables. The space consumption can become acute when creating large\nnumbers of instances.\n\nThe default can be overridden by defining *__slots__* in a class\ndefinition. The *__slots__* declaration takes a sequence of instance\nvariables and reserves just enough space in each instance to hold a\nvalue for each variable. Space is saved because *__dict__* is not\ncreated for each instance.\n\nobject.__slots__\n\n This class variable can be assigned a string, iterable, or sequence\n of strings with variable names used by instances. If defined in a\n class, *__slots__* reserves space for the declared variables and\n prevents the automatic creation of *__dict__* and *__weakref__* for\n each instance.\n\n\nNotes on using *__slots__*\n--------------------------\n\n* When inheriting from a class without *__slots__*, the *__dict__*\n attribute of that class will always be accessible, so a *__slots__*\n definition in the subclass is meaningless.\n\n* Without a *__dict__* variable, instances cannot be assigned new\n variables not listed in the *__slots__* definition. Attempts to\n assign to an unlisted variable name raises AttributeError. If\n dynamic assignment of new variables is desired, then add\n \'__dict__\' to the sequence of strings in the *__slots__*\n declaration.\n\n* Without a *__weakref__* variable for each instance, classes defining\n *__slots__* do not support weak references to its instances. If weak\n reference support is needed, then add \'__weakref__\' to the\n sequence of strings in the *__slots__* declaration.\n\n* *__slots__* are implemented at the class level by creating\n descriptors (*Implementing Descriptors*) for each variable name. As\n a result, class attributes cannot be used to set default values for\n instance variables defined by *__slots__*; otherwise, the class\n attribute would overwrite the descriptor assignment.\n\n* The action of a *__slots__* declaration is limited to the class\n where it is defined. As a result, subclasses will have a *__dict__*\n unless they also define *__slots__* (which must only contain names\n of any *additional* slots).\n\n* If a class defines a slot also defined in a base class, the instance\n variable defined by the base class slot is inaccessible (except by\n retrieving its descriptor directly from the base class). This\n renders the meaning of the program undefined. In the future, a\n check may be added to prevent this.\n\n* Nonempty *__slots__* does not work for classes derived from\n "variable-length" built-in types such as int, str and\n tuple.\n\n* Any non-string iterable may be assigned to *__slots__*. Mappings may\n also be used; however, in the future, special meaning may be\n assigned to the values corresponding to each key.\n\n* *__class__* assignment works only if both classes have the same\n *__slots__*.\n', 'attribute-references': '\nAttribute references\n********************\n\nAn attribute reference is a primary followed by a period and a name:\n\n attributeref ::= primary "." identifier\n\nThe primary must evaluate to an object of a type that supports\nattribute references, which most objects do. This object is then\nasked to produce the attribute whose name is the identifier (which can\nbe customized by overriding the __getattr__() method). If this\nattribute is not available, the exception AttributeError is\nraised. Otherwise, the type and value of the object produced is\ndetermined by the object. Multiple evaluations of the same attribute\nreference may yield different objects.\n', 'augassign': '\nAugmented assignment statements\n*******************************\n\nAugmented assignment is the combination, in a single statement, of a\nbinary operation and an assignment statement:\n\n augmented_assignment_stmt ::= augtarget augop (expression_list | yield_expression)\n augtarget ::= identifier | attributeref | subscription | slicing\n augop ::= "+=" | "-=" | "*=" | "/=" | "//=" | "%=" | "**="\n | ">>=" | "<<=" | "&=" | "^=" | "|="\n\n(See section *Primaries* for the syntax definitions for the last three\nsymbols.)\n\nAn augmented assignment evaluates the target (which, unlike normal\nassignment statements, cannot be an unpacking) and the expression\nlist, performs the binary operation specific to the type of assignment\non the two operands, and assigns the result to the original target.\nThe target is only evaluated once.\n\nAn augmented assignment expression like x += 1 can be rewritten as\nx = x + 1 to achieve a similar, but not exactly equal effect. In\nthe augmented version, x is only evaluated once. Also, when\npossible, the actual operation is performed *in-place*, meaning that\nrather than creating a new object and assigning that to the target,\nthe old object is modified instead.\n\nWith the exception of assigning to tuples and multiple targets in a\nsingle statement, the assignment done by augmented assignment\nstatements is handled the same way as normal assignments. Similarly,\nwith the exception of the possible *in-place* behavior, the binary\noperation performed by augmented assignment is the same as the normal\nbinary operations.\n\nFor targets which are attribute references, the same *caveat about\nclass and instance attributes* applies as for regular assignments.\n', 'binary': '\nBinary arithmetic operations\n****************************\n\nThe binary arithmetic operations have the conventional priority\nlevels. Note that some of these operations also apply to certain non-\nnumeric types. Apart from the power operator, there are only two\nlevels, one for multiplicative operators and one for additive\noperators:\n\n m_expr ::= u_expr | m_expr "*" u_expr | m_expr "//" u_expr | m_expr "/" u_expr\n | m_expr "%" u_expr\n a_expr ::= m_expr | a_expr "+" m_expr | a_expr "-" m_expr\n\nThe * (multiplication) operator yields the product of its\narguments. The arguments must either both be numbers, or one argument\nmust be an integer and the other must be a sequence. In the former\ncase, the numbers are converted to a common type and then multiplied\ntogether. In the latter case, sequence repetition is performed; a\nnegative repetition factor yields an empty sequence.\n\nThe / (division) and // (floor division) operators yield the\nquotient of their arguments. The numeric arguments are first\nconverted to a common type. Integer division yields a float, while\nfloor division of integers results in an integer; the result is that\nof mathematical division with the \'floor\' function applied to the\nresult. Division by zero raises the ZeroDivisionError exception.\n\nThe % (modulo) operator yields the remainder from the division of\nthe first argument by the second. The numeric arguments are first\nconverted to a common type. A zero right argument raises the\nZeroDivisionError exception. The arguments may be floating point\nnumbers, e.g., 3.14%0.7 equals 0.34 (since 3.14 equals\n4*0.7 + 0.34.) The modulo operator always yields a result with\nthe same sign as its second operand (or zero); the absolute value of\nthe result is strictly smaller than the absolute value of the second\noperand [1].\n\nThe floor division and modulo operators are connected by the following\nidentity: x == (x//y)*y + (x%y). Floor division and modulo are\nalso connected with the built-in function divmod(): divmod(x, y)\n== (x//y, x%y). [2].\n\nIn addition to performing the modulo operation on numbers, the %\noperator is also overloaded by string objects to perform old-style\nstring formatting (also known as interpolation). The syntax for\nstring formatting is described in the Python Library Reference,\nsection *printf-style String Formatting*.\n\nThe floor division operator, the modulo operator, and the divmod()\nfunction are not defined for complex numbers. Instead, convert to a\nfloating point number using the abs() function if appropriate.\n\nThe + (addition) operator yields the sum of its arguments. The\narguments must either both be numbers or both sequences of the same\ntype. In the former case, the numbers are converted to a common type\nand then added together. In the latter case, the sequences are\nconcatenated.\n\nThe - (subtraction) operator yields the difference of its\narguments. The numeric arguments are first converted to a common\ntype.\n', 'bitwise': '\nBinary bitwise operations\n*************************\n\nEach of the three bitwise operations has a different priority level:\n\n and_expr ::= shift_expr | and_expr "&" shift_expr\n xor_expr ::= and_expr | xor_expr "^" and_expr\n or_expr ::= xor_expr | or_expr "|" xor_expr\n\nThe & operator yields the bitwise AND of its arguments, which must\nbe integers.\n\nThe ^ operator yields the bitwise XOR (exclusive OR) of its\narguments, which must be integers.\n\nThe | operator yields the bitwise (inclusive) OR of its arguments,\nwhich must be integers.\n', 'bltin-code-objects': '\nCode Objects\n************\n\nCode objects are used by the implementation to represent "pseudo-\ncompiled" executable Python code such as a function body. They differ\nfrom function objects because they don\'t contain a reference to their\nglobal execution environment. Code objects are returned by the built-\nin compile() function and can be extracted from function objects\nthrough their __code__ attribute. See also the code module.\n\nA code object can be executed or evaluated by passing it (instead of a\nsource string) to the exec() or eval() built-in functions.\n\nSee *The standard type hierarchy* for more information.\n', 'bltin-ellipsis-object': '\nThe Ellipsis Object\n*******************\n\nThis object is commonly used by slicing (see *Slicings*). It supports\nno special operations. There is exactly one ellipsis object, named\nEllipsis (a built-in name). type(Ellipsis)() produces the\nEllipsis singleton.\n\nIt is written as Ellipsis or ....\n', 'bltin-null-object': "\nThe Null Object\n***************\n\nThis object is returned by functions that don't explicitly return a\nvalue. It supports no special operations. There is exactly one null\nobject, named None (a built-in name). type(None)() produces\nthe same singleton.\n\nIt is written as None.\n", 'bltin-type-objects': "\nType Objects\n************\n\nType objects represent the various object types. An object's type is\naccessed by the built-in function type(). There are no special\noperations on types. The standard module types defines names for\nall standard built-in types.\n\nTypes are written like this: .\n", 'booleans': '\nBoolean operations\n******************\n\n or_test ::= and_test | or_test "or" and_test\n and_test ::= not_test | and_test "and" not_test\n not_test ::= comparison | "not" not_test\n\nIn the context of Boolean operations, and also when expressions are\nused by control flow statements, the following values are interpreted\nas false: False, None, numeric zero of all types, and empty\nstrings and containers (including strings, tuples, lists,\ndictionaries, sets and frozensets). All other values are interpreted\nas true. User-defined objects can customize their truth value by\nproviding a __bool__() method.\n\nThe operator not yields True if its argument is false,\nFalse otherwise.\n\nThe expression x and y first evaluates *x*; if *x* is false, its\nvalue is returned; otherwise, *y* is evaluated and the resulting value\nis returned.\n\nThe expression x or y first evaluates *x*; if *x* is true, its\nvalue is returned; otherwise, *y* is evaluated and the resulting value\nis returned.\n\n(Note that neither and nor or restrict the value and type they\nreturn to False and True, but rather return the last evaluated\nargument. This is sometimes useful, e.g., if s is a string that\nshould be replaced by a default value if it is empty, the expression\ns or \'foo\' yields the desired value. Because not has to\ninvent a value anyway, it does not bother to return a value of the\nsame type as its argument, so e.g., not \'foo\' yields False,\nnot \'\'.)\n', 'break': '\nThe break statement\n***********************\n\n break_stmt ::= "break"\n\nbreak may only occur syntactically nested in a for or\nwhile loop, but not nested in a function or class definition\nwithin that loop.\n\nIt terminates the nearest enclosing loop, skipping the optional\nelse clause if the loop has one.\n\nIf a for loop is terminated by break, the loop control target\nkeeps its current value.\n\nWhen break passes control out of a try statement with a\nfinally clause, that finally clause is executed before really\nleaving the loop.\n', 'callable-types': '\nEmulating callable objects\n**************************\n\nobject.__call__(self[, args...])\n\n Called when the instance is "called" as a function; if this method\n is defined, x(arg1, arg2, ...) is a shorthand for\n x.__call__(arg1, arg2, ...).\n', 'calls': '\nCalls\n*****\n\nA call calls a callable object (e.g., a *function*) with a possibly\nempty series of *arguments*:\n\n call ::= primary "(" [argument_list [","] | comprehension] ")"\n argument_list ::= positional_arguments ["," keyword_arguments]\n ["," "*" expression] ["," keyword_arguments]\n ["," "**" expression]\n | keyword_arguments ["," "*" expression]\n ["," keyword_arguments] ["," "**" expression]\n | "*" expression ["," keyword_arguments] ["," "**" expression]\n | "**" expression\n positional_arguments ::= expression ("," expression)*\n keyword_arguments ::= keyword_item ("," keyword_item)*\n keyword_item ::= identifier "=" expression\n\nA trailing comma may be present after the positional and keyword\narguments but does not affect the semantics.\n\nThe primary must evaluate to a callable object (user-defined\nfunctions, built-in functions, methods of built-in objects, class\nobjects, methods of class instances, and all objects having a\n__call__() method are callable). All argument expressions are\nevaluated before the call is attempted. Please refer to section\n*Function definitions* for the syntax of formal *parameter* lists.\n\nIf keyword arguments are present, they are first converted to\npositional arguments, as follows. First, a list of unfilled slots is\ncreated for the formal parameters. If there are N positional\narguments, they are placed in the first N slots. Next, for each\nkeyword argument, the identifier is used to determine the\ncorresponding slot (if the identifier is the same as the first formal\nparameter name, the first slot is used, and so on). If the slot is\nalready filled, a TypeError exception is raised. Otherwise, the\nvalue of the argument is placed in the slot, filling it (even if the\nexpression is None, it fills the slot). When all arguments have\nbeen processed, the slots that are still unfilled are filled with the\ncorresponding default value from the function definition. (Default\nvalues are calculated, once, when the function is defined; thus, a\nmutable object such as a list or dictionary used as default value will\nbe shared by all calls that don\'t specify an argument value for the\ncorresponding slot; this should usually be avoided.) If there are any\nunfilled slots for which no default value is specified, a\nTypeError exception is raised. Otherwise, the list of filled\nslots is used as the argument list for the call.\n\n**CPython implementation detail:** An implementation may provide\nbuilt-in functions whose positional parameters do not have names, even\nif they are \'named\' for the purpose of documentation, and which\ntherefore cannot be supplied by keyword. In CPython, this is the case\nfor functions implemented in C that use PyArg_ParseTuple() to\nparse their arguments.\n\nIf there are more positional arguments than there are formal parameter\nslots, a TypeError exception is raised, unless a formal parameter\nusing the syntax *identifier is present; in this case, that formal\nparameter receives a tuple containing the excess positional arguments\n(or an empty tuple if there were no excess positional arguments).\n\nIf any keyword argument does not correspond to a formal parameter\nname, a TypeError exception is raised, unless a formal parameter\nusing the syntax **identifier is present; in this case, that\nformal parameter receives a dictionary containing the excess keyword\narguments (using the keywords as keys and the argument values as\ncorresponding values), or a (new) empty dictionary if there were no\nexcess keyword arguments.\n\nIf the syntax *expression appears in the function call,\nexpression must evaluate to an iterable. Elements from this\niterable are treated as if they were additional positional arguments;\nif there are positional arguments *x1*, ..., *xN*, and expression\nevaluates to a sequence *y1*, ..., *yM*, this is equivalent to a call\nwith M+N positional arguments *x1*, ..., *xN*, *y1*, ..., *yM*.\n\nA consequence of this is that although the *expression syntax may\nappear *after* some keyword arguments, it is processed *before* the\nkeyword arguments (and the **expression argument, if any -- see\nbelow). So:\n\n >>> def f(a, b):\n ... print(a, b)\n ...\n >>> f(b=1, *(2,))\n 2 1\n >>> f(a=1, *(2,))\n Traceback (most recent call last):\n File "", line 1, in ?\n TypeError: f() got multiple values for keyword argument \'a\'\n >>> f(1, *(2,))\n 1 2\n\nIt is unusual for both keyword arguments and the *expression\nsyntax to be used in the same call, so in practice this confusion does\nnot arise.\n\nIf the syntax **expression appears in the function call,\nexpression must evaluate to a mapping, the contents of which are\ntreated as additional keyword arguments. In the case of a keyword\nappearing in both expression and as an explicit keyword argument,\na TypeError exception is raised.\n\nFormal parameters using the syntax *identifier or **identifier\ncannot be used as positional argument slots or as keyword argument\nnames.\n\nA call always returns some value, possibly None, unless it raises\nan exception. How this value is computed depends on the type of the\ncallable object.\n\nIf it is---\n\na user-defined function:\n The code block for the function is executed, passing it the\n argument list. The first thing the code block will do is bind the\n formal parameters to the arguments; this is described in section\n *Function definitions*. When the code block executes a return\n statement, this specifies the return value of the function call.\n\na built-in function or method:\n The result is up to the interpreter; see *Built-in Functions* for\n the descriptions of built-in functions and methods.\n\na class object:\n A new instance of that class is returned.\n\na class instance method:\n The corresponding user-defined function is called, with an argument\n list that is one longer than the argument list of the call: the\n instance becomes the first argument.\n\na class instance:\n The class must define a __call__() method; the effect is then\n the same as if that method was called.\n', 'class': '\nClass definitions\n*****************\n\nA class definition defines a class object (see section *The standard\ntype hierarchy*):\n\n classdef ::= [decorators] "class" classname [inheritance] ":" suite\n inheritance ::= "(" [parameter_list] ")"\n classname ::= identifier\n\nA class definition is an executable statement. The inheritance list\nusually gives a list of base classes (see *Customizing class creation*\nfor more advanced uses), so each item in the list should evaluate to a\nclass object which allows subclassing. Classes without an inheritance\nlist inherit, by default, from the base class object; hence,\n\n class Foo:\n pass\n\nis equivalent to\n\n class Foo(object):\n pass\n\nThe class\'s suite is then executed in a new execution frame (see\n*Naming and binding*), using a newly created local namespace and the\noriginal global namespace. (Usually, the suite contains mostly\nfunction definitions.) When the class\'s suite finishes execution, its\nexecution frame is discarded but its local namespace is saved. [4] A\nclass object is then created using the inheritance list for the base\nclasses and the saved local namespace for the attribute dictionary.\nThe class name is bound to this class object in the original local\nnamespace.\n\nClass creation can be customized heavily using *metaclasses*.\n\nClasses can also be decorated: just like when decorating functions,\n\n @f1(arg)\n @f2\n class Foo: pass\n\nis equivalent to\n\n class Foo: pass\n Foo = f1(arg)(f2(Foo))\n\nThe evaluation rules for the decorator expressions are the same as for\nfunction decorators. The result must be a class object, which is then\nbound to the class name.\n\n**Programmer\'s note:** Variables defined in the class definition are\nclass attributes; they are shared by instances. Instance attributes\ncan be set in a method with self.name = value. Both class and\ninstance attributes are accessible through the notation\n"self.name", and an instance attribute hides a class attribute\nwith the same name when accessed in this way. Class attributes can be\nused as defaults for instance attributes, but using mutable values\nthere can lead to unexpected results. *Descriptors* can be used to\ncreate instance variables with different implementation details.\n\nSee also:\n\n **PEP 3115** - Metaclasses in Python 3 **PEP 3129** - Class\n Decorators\n\n-[ Footnotes ]-\n\n[1] The exception is propagated to the invocation stack unless there\n is a finally clause which happens to raise another exception.\n That new exception causes the old one to be lost.\n\n[2] Currently, control "flows off the end" except in the case of an\n exception or the execution of a return, continue, or\n break statement.\n\n[3] A string literal appearing as the first statement in the function\n body is transformed into the function\'s __doc__ attribute and\n therefore the function\'s *docstring*.\n\n[4] A string literal appearing as the first statement in the class\n body is transformed into the namespace\'s __doc__ item and\n therefore the class\'s *docstring*.\n', 'comparisons': '\nComparisons\n***********\n\nUnlike C, all comparison operations in Python have the same priority,\nwhich is lower than that of any arithmetic, shifting or bitwise\noperation. Also unlike C, expressions like a < b < c have the\ninterpretation that is conventional in mathematics:\n\n comparison ::= or_expr ( comp_operator or_expr )*\n comp_operator ::= "<" | ">" | "==" | ">=" | "<=" | "!="\n | "is" ["not"] | ["not"] "in"\n\nComparisons yield boolean values: True or False.\n\nComparisons can be chained arbitrarily, e.g., x < y <= z is\nequivalent to x < y and y <= z, except that y is evaluated\nonly once (but in both cases z is not evaluated at all when x <\ny is found to be false).\n\nFormally, if *a*, *b*, *c*, ..., *y*, *z* are expressions and *op1*,\n*op2*, ..., *opN* are comparison operators, then a op1 b op2 c ... y\nopN z is equivalent to a op1 b and b op2 c and ... y opN z,\nexcept that each expression is evaluated at most once.\n\nNote that a op1 b op2 c doesn\'t imply any kind of comparison\nbetween *a* and *c*, so that, e.g., x < y > z is perfectly legal\n(though perhaps not pretty).\n\nThe operators <, >, ==, >=, <=, and != compare\nthe values of two objects. The objects need not have the same type.\nIf both are numbers, they are converted to a common type. Otherwise,\nthe == and != operators *always* consider objects of different\ntypes to be unequal, while the <, >, >= and <=\noperators raise a TypeError when comparing objects of different\ntypes that do not implement these operators for the given pair of\ntypes. You can control comparison behavior of objects of non-built-in\ntypes by defining rich comparison methods like __gt__(), described\nin section *Basic customization*.\n\nComparison of objects of the same type depends on the type:\n\n* Numbers are compared arithmetically.\n\n* The values float(\'NaN\') and Decimal(\'NaN\') are special. The\n are identical to themselves, x is x but are not equal to\n themselves, x != x. Additionally, comparing any value to a\n not-a-number value will return False. For example, both 3 <\n float(\'NaN\') and float(\'NaN\') < 3 will return False.\n\n* Bytes objects are compared lexicographically using the numeric\n values of their elements.\n\n* Strings are compared lexicographically using the numeric equivalents\n (the result of the built-in function ord()) of their characters.\n [3] String and bytes object can\'t be compared!\n\n* Tuples and lists are compared lexicographically using comparison of\n corresponding elements. This means that to compare equal, each\n element must compare equal and the two sequences must be of the same\n type and have the same length.\n\n If not equal, the sequences are ordered the same as their first\n differing elements. For example, [1,2,x] <= [1,2,y] has the\n same value as x <= y. If the corresponding element does not\n exist, the shorter sequence is ordered first (for example, [1,2] <\n [1,2,3]).\n\n* Mappings (dictionaries) compare equal if and only if they have the\n same (key, value) pairs. Order comparisons (\'<\', \'<=\', \'>=\',\n \'>\') raise TypeError.\n\n* Sets and frozensets define comparison operators to mean subset and\n superset tests. Those relations do not define total orderings (the\n two sets {1,2} and {2,3} are not equal, nor subsets of one\n another, nor supersets of one another). Accordingly, sets are not\n appropriate arguments for functions which depend on total ordering.\n For example, min(), max(), and sorted() produce\n undefined results given a list of sets as inputs.\n\n* Most other objects of built-in types compare unequal unless they are\n the same object; the choice whether one object is considered smaller\n or larger than another one is made arbitrarily but consistently\n within one execution of a program.\n\nComparison of objects of the differing types depends on whether either\nof the types provide explicit support for the comparison. Most\nnumeric types can be compared with one another. When cross-type\ncomparison is not supported, the comparison method returns\nNotImplemented.\n\nThe operators in and not in test for membership. x in s\nevaluates to true if *x* is a member of *s*, and false otherwise. x\nnot in s returns the negation of x in s. All built-in sequences\nand set types support this as well as dictionary, for which in\ntests whether a the dictionary has a given key. For container types\nsuch as list, tuple, set, frozenset, dict, or collections.deque, the\nexpression x in y is equivalent to any(x is e or x == e for e in\ny).\n\nFor the string and bytes types, x in y is true if and only if *x*\nis a substring of *y*. An equivalent test is y.find(x) != -1.\nEmpty strings are always considered to be a substring of any other\nstring, so "" in "abc" will return True.\n\nFor user-defined classes which define the __contains__() method,\nx in y is true if and only if y.__contains__(x) is true.\n\nFor user-defined classes which do not define __contains__() but do\ndefine __iter__(), x in y is true if some value z with x\n== z is produced while iterating over y. If an exception is\nraised during the iteration, it is as if in raised that exception.\n\nLastly, the old-style iteration protocol is tried: if a class defines\n__getitem__(), x in y is true if and only if there is a non-\nnegative integer index *i* such that x == y[i], and all lower\ninteger indices do not raise IndexError exception. (If any other\nexception is raised, it is as if in raised that exception).\n\nThe operator not in is defined to have the inverse true value of\nin.\n\nThe operators is and is not test for object identity: x is\ny is true if and only if *x* and *y* are the same object. x is\nnot y yields the inverse truth value. [4]\n', 'compound': '\nCompound statements\n*******************\n\nCompound statements contain (groups of) other statements; they affect\nor control the execution of those other statements in some way. In\ngeneral, compound statements span multiple lines, although in simple\nincarnations a whole compound statement may be contained in one line.\n\nThe if, while and for statements implement traditional\ncontrol flow constructs. try specifies exception handlers and/or\ncleanup code for a group of statements, while the with statement\nallows the execution of initialization and finalization code around a\nblock of code. Function and class definitions are also syntactically\ncompound statements.\n\nCompound statements consist of one or more \'clauses.\' A clause\nconsists of a header and a \'suite.\' The clause headers of a\nparticular compound statement are all at the same indentation level.\nEach clause header begins with a uniquely identifying keyword and ends\nwith a colon. A suite is a group of statements controlled by a\nclause. A suite can be one or more semicolon-separated simple\nstatements on the same line as the header, following the header\'s\ncolon, or it can be one or more indented statements on subsequent\nlines. Only the latter form of suite can contain nested compound\nstatements; the following is illegal, mostly because it wouldn\'t be\nclear to which if clause a following else clause would belong:\n\n if test1: if test2: print(x)\n\nAlso note that the semicolon binds tighter than the colon in this\ncontext, so that in the following example, either all or none of the\nprint() calls are executed:\n\n if x < y < z: print(x); print(y); print(z)\n\nSummarizing:\n\n compound_stmt ::= if_stmt\n | while_stmt\n | for_stmt\n | try_stmt\n | with_stmt\n | funcdef\n | classdef\n suite ::= stmt_list NEWLINE | NEWLINE INDENT statement+ DEDENT\n statement ::= stmt_list NEWLINE | compound_stmt\n stmt_list ::= simple_stmt (";" simple_stmt)* [";"]\n\nNote that statements always end in a NEWLINE possibly followed by\na DEDENT. Also note that optional continuation clauses always\nbegin with a keyword that cannot start a statement, thus there are no\nambiguities (the \'dangling else\' problem is solved in Python by\nrequiring nested if statements to be indented).\n\nThe formatting of the grammar rules in the following sections places\neach clause on a separate line for clarity.\n\n\nThe if statement\n====================\n\nThe if statement is used for conditional execution:\n\n if_stmt ::= "if" expression ":" suite\n ( "elif" expression ":" suite )*\n ["else" ":" suite]\n\nIt selects exactly one of the suites by evaluating the expressions one\nby one until one is found to be true (see section *Boolean operations*\nfor the definition of true and false); then that suite is executed\n(and no other part of the if statement is executed or evaluated).\nIf all expressions are false, the suite of the else clause, if\npresent, is executed.\n\n\nThe while statement\n=======================\n\nThe while statement is used for repeated execution as long as an\nexpression is true:\n\n while_stmt ::= "while" expression ":" suite\n ["else" ":" suite]\n\nThis repeatedly tests the expression and, if it is true, executes the\nfirst suite; if the expression is false (which may be the first time\nit is tested) the suite of the else clause, if present, is\nexecuted and the loop terminates.\n\nA break statement executed in the first suite terminates the loop\nwithout executing the else clause\'s suite. A continue\nstatement executed in the first suite skips the rest of the suite and\ngoes back to testing the expression.\n\n\nThe for statement\n=====================\n\nThe for statement is used to iterate over the elements of a\nsequence (such as a string, tuple or list) or other iterable object:\n\n for_stmt ::= "for" target_list "in" expression_list ":" suite\n ["else" ":" suite]\n\nThe expression list is evaluated once; it should yield an iterable\nobject. An iterator is created for the result of the\nexpression_list. The suite is then executed once for each item\nprovided by the iterator, in the order of ascending indices. Each\nitem in turn is assigned to the target list using the standard rules\nfor assignments (see *Assignment statements*), and then the suite is\nexecuted. When the items are exhausted (which is immediately when the\nsequence is empty or an iterator raises a StopIteration\nexception), the suite in the else clause, if present, is executed,\nand the loop terminates.\n\nA break statement executed in the first suite terminates the loop\nwithout executing the else clause\'s suite. A continue\nstatement executed in the first suite skips the rest of the suite and\ncontinues with the next item, or with the else clause if there was\nno next item.\n\nThe suite may assign to the variable(s) in the target list; this does\nnot affect the next item assigned to it.\n\nNames in the target list are not deleted when the loop is finished,\nbut if the sequence is empty, it will not have been assigned to at all\nby the loop. Hint: the built-in function range() returns an\niterator of integers suitable to emulate the effect of Pascal\'s for\ni := a to b do; e.g., list(range(3)) returns the list [0, 1,\n2].\n\nNote: There is a subtlety when the sequence is being modified by the loop\n (this can only occur for mutable sequences, i.e. lists). An\n internal counter is used to keep track of which item is used next,\n and this is incremented on each iteration. When this counter has\n reached the length of the sequence the loop terminates. This means\n that if the suite deletes the current (or a previous) item from the\n sequence, the next item will be skipped (since it gets the index of\n the current item which has already been treated). Likewise, if the\n suite inserts an item in the sequence before the current item, the\n current item will be treated again the next time through the loop.\n This can lead to nasty bugs that can be avoided by making a\n temporary copy using a slice of the whole sequence, e.g.,\n\n for x in a[:]:\n if x < 0: a.remove(x)\n\n\nThe try statement\n=====================\n\nThe try statement specifies exception handlers and/or cleanup code\nfor a group of statements:\n\n try_stmt ::= try1_stmt | try2_stmt\n try1_stmt ::= "try" ":" suite\n ("except" [expression ["as" target]] ":" suite)+\n ["else" ":" suite]\n ["finally" ":" suite]\n try2_stmt ::= "try" ":" suite\n "finally" ":" suite\n\nThe except clause(s) specify one or more exception handlers. When\nno exception occurs in the try clause, no exception handler is\nexecuted. When an exception occurs in the try suite, a search for\nan exception handler is started. This search inspects the except\nclauses in turn until one is found that matches the exception. An\nexpression-less except clause, if present, must be last; it matches\nany exception. For an except clause with an expression, that\nexpression is evaluated, and the clause matches the exception if the\nresulting object is "compatible" with the exception. An object is\ncompatible with an exception if it is the class or a base class of the\nexception object or a tuple containing an item compatible with the\nexception.\n\nIf no except clause matches the exception, the search for an exception\nhandler continues in the surrounding code and on the invocation stack.\n[1]\n\nIf the evaluation of an expression in the header of an except clause\nraises an exception, the original search for a handler is canceled and\na search starts for the new exception in the surrounding code and on\nthe call stack (it is treated as if the entire try statement\nraised the exception).\n\nWhen a matching except clause is found, the exception is assigned to\nthe target specified after the as keyword in that except clause,\nif present, and the except clause\'s suite is executed. All except\nclauses must have an executable block. When the end of this block is\nreached, execution continues normally after the entire try statement.\n(This means that if two nested handlers exist for the same exception,\nand the exception occurs in the try clause of the inner handler, the\nouter handler will not handle the exception.)\n\nWhen an exception has been assigned using as target, it is cleared\nat the end of the except clause. This is as if\n\n except E as N:\n foo\n\nwas translated to\n\n except E as N:\n try:\n foo\n finally:\n del N\n\nThis means the exception must be assigned to a different name to be\nable to refer to it after the except clause. Exceptions are cleared\nbecause with the traceback attached to them, they form a reference\ncycle with the stack frame, keeping all locals in that frame alive\nuntil the next garbage collection occurs.\n\nBefore an except clause\'s suite is executed, details about the\nexception are stored in the sys module and can be access via\nsys.exc_info(). sys.exc_info() returns a 3-tuple consisting of\nthe exception class, the exception instance and a traceback object\n(see section *The standard type hierarchy*) identifying the point in\nthe program where the exception occurred. sys.exc_info() values\nare restored to their previous values (before the call) when returning\nfrom a function that handled an exception.\n\nThe optional else clause is executed if and when control flows off\nthe end of the try clause. [2] Exceptions in the else clause\nare not handled by the preceding except clauses.\n\nIf finally is present, it specifies a \'cleanup\' handler. The\ntry clause is executed, including any except and else\nclauses. If an exception occurs in any of the clauses and is not\nhandled, the exception is temporarily saved. The finally clause is\nexecuted. If there is a saved exception it is re-raised at the end of\nthe finally clause. If the finally clause raises another\nexception, the saved exception is set as the context of the new\nexception. If the finally clause executes a return or\nbreak statement, the saved exception is discarded:\n\n def f():\n try:\n 1/0\n finally:\n return 42\n\n >>> f()\n 42\n\nThe exception information is not available to the program during\nexecution of the finally clause.\n\nWhen a return, break or continue statement is executed in\nthe try suite of a try...finally statement, the\nfinally clause is also executed \'on the way out.\' A continue\nstatement is illegal in the finally clause. (The reason is a\nproblem with the current implementation --- this restriction may be\nlifted in the future).\n\nAdditional information on exceptions can be found in section\n*Exceptions*, and information on using the raise statement to\ngenerate exceptions may be found in section *The raise statement*.\n\n\nThe with statement\n======================\n\nThe with statement is used to wrap the execution of a block with\nmethods defined by a context manager (see section *With Statement\nContext Managers*). This allows common\ntry...except...finally usage patterns to be encapsulated\nfor convenient reuse.\n\n with_stmt ::= "with" with_item ("," with_item)* ":" suite\n with_item ::= expression ["as" target]\n\nThe execution of the with statement with one "item" proceeds as\nfollows:\n\n1. The context expression (the expression given in the with_item)\n is evaluated to obtain a context manager.\n\n2. The context manager\'s __exit__() is loaded for later use.\n\n3. The context manager\'s __enter__() method is invoked.\n\n4. If a target was included in the with statement, the return\n value from __enter__() is assigned to it.\n\n Note: The with statement guarantees that if the __enter__()\n method returns without an error, then __exit__() will always\n be called. Thus, if an error occurs during the assignment to the\n target list, it will be treated the same as an error occurring\n within the suite would be. See step 6 below.\n\n5. The suite is executed.\n\n6. The context manager\'s __exit__() method is invoked. If an\n exception caused the suite to be exited, its type, value, and\n traceback are passed as arguments to __exit__(). Otherwise,\n three None arguments are supplied.\n\n If the suite was exited due to an exception, and the return value\n from the __exit__() method was false, the exception is\n reraised. If the return value was true, the exception is\n suppressed, and execution continues with the statement following\n the with statement.\n\n If the suite was exited for any reason other than an exception, the\n return value from __exit__() is ignored, and execution proceeds\n at the normal location for the kind of exit that was taken.\n\nWith more than one item, the context managers are processed as if\nmultiple with statements were nested:\n\n with A() as a, B() as b:\n suite\n\nis equivalent to\n\n with A() as a:\n with B() as b:\n suite\n\nChanged in version 3.1: Support for multiple context expressions.\n\nSee also:\n\n **PEP 0343** - The "with" statement\n The specification, background, and examples for the Python\n with statement.\n\n\nFunction definitions\n====================\n\nA function definition defines a user-defined function object (see\nsection *The standard type hierarchy*):\n\n funcdef ::= [decorators] "def" funcname "(" [parameter_list] ")" ["->" expression] ":" suite\n decorators ::= decorator+\n decorator ::= "@" dotted_name ["(" [parameter_list [","]] ")"] NEWLINE\n dotted_name ::= identifier ("." identifier)*\n parameter_list ::= (defparameter ",")*\n ( "*" [parameter] ("," defparameter)* ["," "**" parameter]\n | "**" parameter\n | defparameter [","] )\n parameter ::= identifier [":" expression]\n defparameter ::= parameter ["=" expression]\n funcname ::= identifier\n\nA function definition is an executable statement. Its execution binds\nthe function name in the current local namespace to a function object\n(a wrapper around the executable code for the function). This\nfunction object contains a reference to the current global namespace\nas the global namespace to be used when the function is called.\n\nThe function definition does not execute the function body; this gets\nexecuted only when the function is called. [3]\n\nA function definition may be wrapped by one or more *decorator*\nexpressions. Decorator expressions are evaluated when the function is\ndefined, in the scope that contains the function definition. The\nresult must be a callable, which is invoked with the function object\nas the only argument. The returned value is bound to the function name\ninstead of the function object. Multiple decorators are applied in\nnested fashion. For example, the following code\n\n @f1(arg)\n @f2\n def func(): pass\n\nis equivalent to\n\n def func(): pass\n func = f1(arg)(f2(func))\n\nWhen one or more *parameters* have the form *parameter* =\n*expression*, the function is said to have "default parameter values."\nFor a parameter with a default value, the corresponding *argument* may\nbe omitted from a call, in which case the parameter\'s default value is\nsubstituted. If a parameter has a default value, all following\nparameters up until the "*" must also have a default value ---\nthis is a syntactic restriction that is not expressed by the grammar.\n\n**Default parameter values are evaluated from left to right when the\nfunction definition is executed.** This means that the expression is\nevaluated once, when the function is defined, and that the same "pre-\ncomputed" value is used for each call. This is especially important\nto understand when a default parameter is a mutable object, such as a\nlist or a dictionary: if the function modifies the object (e.g. by\nappending an item to a list), the default value is in effect modified.\nThis is generally not what was intended. A way around this is to use\nNone as the default, and explicitly test for it in the body of the\nfunction, e.g.:\n\n def whats_on_the_telly(penguin=None):\n if penguin is None:\n penguin = []\n penguin.append("property of the zoo")\n return penguin\n\nFunction call semantics are described in more detail in section\n*Calls*. A function call always assigns values to all parameters\nmentioned in the parameter list, either from position arguments, from\nkeyword arguments, or from default values. If the form\n"*identifier" is present, it is initialized to a tuple receiving\nany excess positional parameters, defaulting to the empty tuple. If\nthe form "**identifier" is present, it is initialized to a new\ndictionary receiving any excess keyword arguments, defaulting to a new\nempty dictionary. Parameters after "*" or "*identifier" are\nkeyword-only parameters and may only be passed used keyword arguments.\n\nParameters may have annotations of the form ": expression"\nfollowing the parameter name. Any parameter may have an annotation\neven those of the form *identifier or **identifier. Functions\nmay have "return" annotation of the form "-> expression" after the\nparameter list. These annotations can be any valid Python expression\nand are evaluated when the function definition is executed.\nAnnotations may be evaluated in a different order than they appear in\nthe source code. The presence of annotations does not change the\nsemantics of a function. The annotation values are available as\nvalues of a dictionary keyed by the parameters\' names in the\n__annotations__ attribute of the function object.\n\nIt is also possible to create anonymous functions (functions not bound\nto a name), for immediate use in expressions. This uses lambda forms,\ndescribed in section *Lambdas*. Note that the lambda form is merely a\nshorthand for a simplified function definition; a function defined in\na "def" statement can be passed around or assigned to another name\njust like a function defined by a lambda form. The "def" form is\nactually more powerful since it allows the execution of multiple\nstatements and annotations.\n\n**Programmer\'s note:** Functions are first-class objects. A "def"\nform executed inside a function definition defines a local function\nthat can be returned or passed around. Free variables used in the\nnested function can access the local variables of the function\ncontaining the def. See section *Naming and binding* for details.\n\nSee also:\n\n **PEP 3107** - Function Annotations\n The original specification for function annotations.\n\n\nClass definitions\n=================\n\nA class definition defines a class object (see section *The standard\ntype hierarchy*):\n\n classdef ::= [decorators] "class" classname [inheritance] ":" suite\n inheritance ::= "(" [parameter_list] ")"\n classname ::= identifier\n\nA class definition is an executable statement. The inheritance list\nusually gives a list of base classes (see *Customizing class creation*\nfor more advanced uses), so each item in the list should evaluate to a\nclass object which allows subclassing. Classes without an inheritance\nlist inherit, by default, from the base class object; hence,\n\n class Foo:\n pass\n\nis equivalent to\n\n class Foo(object):\n pass\n\nThe class\'s suite is then executed in a new execution frame (see\n*Naming and binding*), using a newly created local namespace and the\noriginal global namespace. (Usually, the suite contains mostly\nfunction definitions.) When the class\'s suite finishes execution, its\nexecution frame is discarded but its local namespace is saved. [4] A\nclass object is then created using the inheritance list for the base\nclasses and the saved local namespace for the attribute dictionary.\nThe class name is bound to this class object in the original local\nnamespace.\n\nClass creation can be customized heavily using *metaclasses*.\n\nClasses can also be decorated: just like when decorating functions,\n\n @f1(arg)\n @f2\n class Foo: pass\n\nis equivalent to\n\n class Foo: pass\n Foo = f1(arg)(f2(Foo))\n\nThe evaluation rules for the decorator expressions are the same as for\nfunction decorators. The result must be a class object, which is then\nbound to the class name.\n\n**Programmer\'s note:** Variables defined in the class definition are\nclass attributes; they are shared by instances. Instance attributes\ncan be set in a method with self.name = value. Both class and\ninstance attributes are accessible through the notation\n"self.name", and an instance attribute hides a class attribute\nwith the same name when accessed in this way. Class attributes can be\nused as defaults for instance attributes, but using mutable values\nthere can lead to unexpected results. *Descriptors* can be used to\ncreate instance variables with different implementation details.\n\nSee also:\n\n **PEP 3115** - Metaclasses in Python 3 **PEP 3129** - Class\n Decorators\n\n-[ Footnotes ]-\n\n[1] The exception is propagated to the invocation stack unless there\n is a finally clause which happens to raise another exception.\n That new exception causes the old one to be lost.\n\n[2] Currently, control "flows off the end" except in the case of an\n exception or the execution of a return, continue, or\n break statement.\n\n[3] A string literal appearing as the first statement in the function\n body is transformed into the function\'s __doc__ attribute and\n therefore the function\'s *docstring*.\n\n[4] A string literal appearing as the first statement in the class\n body is transformed into the namespace\'s __doc__ item and\n therefore the class\'s *docstring*.\n', 'context-managers': '\nWith Statement Context Managers\n*******************************\n\nA *context manager* is an object that defines the runtime context to\nbe established when executing a with statement. The context\nmanager handles the entry into, and the exit from, the desired runtime\ncontext for the execution of the block of code. Context managers are\nnormally invoked using the with statement (described in section\n*The with statement*), but can also be used by directly invoking their\nmethods.\n\nTypical uses of context managers include saving and restoring various\nkinds of global state, locking and unlocking resources, closing opened\nfiles, etc.\n\nFor more information on context managers, see *Context Manager Types*.\n\nobject.__enter__(self)\n\n Enter the runtime context related to this object. The with\n statement will bind this method\'s return value to the target(s)\n specified in the as clause of the statement, if any.\n\nobject.__exit__(self, exc_type, exc_value, traceback)\n\n Exit the runtime context related to this object. The parameters\n describe the exception that caused the context to be exited. If the\n context was exited without an exception, all three arguments will\n be None.\n\n If an exception is supplied, and the method wishes to suppress the\n exception (i.e., prevent it from being propagated), it should\n return a true value. Otherwise, the exception will be processed\n normally upon exit from this method.\n\n Note that __exit__() methods should not reraise the passed-in\n exception; this is the caller\'s responsibility.\n\nSee also:\n\n **PEP 0343** - The "with" statement\n The specification, background, and examples for the Python\n with statement.\n', 'continue': '\nThe continue statement\n**************************\n\n continue_stmt ::= "continue"\n\ncontinue may only occur syntactically nested in a for or\nwhile loop, but not nested in a function or class definition or\nfinally clause within that loop. It continues with the next cycle\nof the nearest enclosing loop.\n\nWhen continue passes control out of a try statement with a\nfinally clause, that finally clause is executed before really\nstarting the next loop cycle.\n', 'conversions': '\nArithmetic conversions\n**********************\n\nWhen a description of an arithmetic operator below uses the phrase\n"the numeric arguments are converted to a common type," this means\nthat the operator implementation for built-in types works that way:\n\n* If either argument is a complex number, the other is converted to\n complex;\n\n* otherwise, if either argument is a floating point number, the other\n is converted to floating point;\n\n* otherwise, both must be integers and no conversion is necessary.\n\nSome additional rules apply for certain operators (e.g., a string left\nargument to the \'%\' operator). Extensions must define their own\nconversion behavior.\n', 'customization': '\nBasic customization\n*******************\n\nobject.__new__(cls[, ...])\n\n Called to create a new instance of class *cls*. __new__() is a\n static method (special-cased so you need not declare it as such)\n that takes the class of which an instance was requested as its\n first argument. The remaining arguments are those passed to the\n object constructor expression (the call to the class). The return\n value of __new__() should be the new object instance (usually\n an instance of *cls*).\n\n Typical implementations create a new instance of the class by\n invoking the superclass\'s __new__() method using\n super(currentclass, cls).__new__(cls[, ...]) with appropriate\n arguments and then modifying the newly-created instance as\n necessary before returning it.\n\n If __new__() returns an instance of *cls*, then the new\n instance\'s __init__() method will be invoked like\n __init__(self[, ...]), where *self* is the new instance and the\n remaining arguments are the same as were passed to __new__().\n\n If __new__() does not return an instance of *cls*, then the new\n instance\'s __init__() method will not be invoked.\n\n __new__() is intended mainly to allow subclasses of immutable\n types (like int, str, or tuple) to customize instance creation. It\n is also commonly overridden in custom metaclasses in order to\n customize class creation.\n\nobject.__init__(self[, ...])\n\n Called when the instance is created. The arguments are those\n passed to the class constructor expression. If a base class has an\n __init__() method, the derived class\'s __init__() method,\n if any, must explicitly call it to ensure proper initialization of\n the base class part of the instance; for example:\n BaseClass.__init__(self, [args...]). As a special constraint\n on constructors, no value may be returned; doing so will cause a\n TypeError to be raised at runtime.\n\nobject.__del__(self)\n\n Called when the instance is about to be destroyed. This is also\n called a destructor. If a base class has a __del__() method,\n the derived class\'s __del__() method, if any, must explicitly\n call it to ensure proper deletion of the base class part of the\n instance. Note that it is possible (though not recommended!) for\n the __del__() method to postpone destruction of the instance by\n creating a new reference to it. It may then be called at a later\n time when this new reference is deleted. It is not guaranteed that\n __del__() methods are called for objects that still exist when\n the interpreter exits.\n\n Note: del x doesn\'t directly call x.__del__() --- the former\n decrements the reference count for x by one, and the latter\n is only called when x\'s reference count reaches zero. Some\n common situations that may prevent the reference count of an\n object from going to zero include: circular references between\n objects (e.g., a doubly-linked list or a tree data structure with\n parent and child pointers); a reference to the object on the\n stack frame of a function that caught an exception (the traceback\n stored in sys.exc_info()[2] keeps the stack frame alive); or\n a reference to the object on the stack frame that raised an\n unhandled exception in interactive mode (the traceback stored in\n sys.last_traceback keeps the stack frame alive). The first\n situation can only be remedied by explicitly breaking the cycles;\n the latter two situations can be resolved by storing None in\n sys.last_traceback. Circular references which are garbage are\n detected and cleaned up when the cyclic garbage collector is\n enabled (it\'s on by default). Refer to the documentation for the\n gc module for more information about this topic.\n\n Warning: Due to the precarious circumstances under which __del__()\n methods are invoked, exceptions that occur during their execution\n are ignored, and a warning is printed to sys.stderr instead.\n Also, when __del__() is invoked in response to a module being\n deleted (e.g., when execution of the program is done), other\n globals referenced by the __del__() method may already have\n been deleted or in the process of being torn down (e.g. the\n import machinery shutting down). For this reason, __del__()\n methods should do the absolute minimum needed to maintain\n external invariants. Starting with version 1.5, Python\n guarantees that globals whose name begins with a single\n underscore are deleted from their module before other globals are\n deleted; if no other references to such globals exist, this may\n help in assuring that imported modules are still available at the\n time when the __del__() method is called.\n\nobject.__repr__(self)\n\n Called by the repr() built-in function to compute the\n "official" string representation of an object. If at all possible,\n this should look like a valid Python expression that could be used\n to recreate an object with the same value (given an appropriate\n environment). If this is not possible, a string of the form\n <...some useful description...> should be returned. The return\n value must be a string object. If a class defines __repr__()\n but not __str__(), then __repr__() is also used when an\n "informal" string representation of instances of that class is\n required.\n\n This is typically used for debugging, so it is important that the\n representation is information-rich and unambiguous.\n\nobject.__str__(self)\n\n Called by str(object) and the built-in functions format()\n and print() to compute the "informal" or nicely printable\n string representation of an object. The return value must be a\n *string* object.\n\n This method differs from object.__repr__() in that there is no\n expectation that __str__() return a valid Python expression: a\n more convenient or concise representation can be used.\n\n The default implementation defined by the built-in type object\n calls object.__repr__().\n\nobject.__bytes__(self)\n\n Called by bytes() to compute a byte-string representation of an\n object. This should return a bytes object.\n\nobject.__format__(self, format_spec)\n\n Called by the format() built-in function (and by extension, the\n str.format() method of class str) to produce a "formatted"\n string representation of an object. The format_spec argument is\n a string that contains a description of the formatting options\n desired. The interpretation of the format_spec argument is up\n to the type implementing __format__(), however most classes\n will either delegate formatting to one of the built-in types, or\n use a similar formatting option syntax.\n\n See *Format Specification Mini-Language* for a description of the\n standard formatting syntax.\n\n The return value must be a string object.\n\nobject.__lt__(self, other)\nobject.__le__(self, other)\nobject.__eq__(self, other)\nobject.__ne__(self, other)\nobject.__gt__(self, other)\nobject.__ge__(self, other)\n\n These are the so-called "rich comparison" methods. The\n correspondence between operator symbols and method names is as\n follows: xy calls x.__gt__(y), and x>=y calls\n x.__ge__(y).\n\n A rich comparison method may return the singleton\n NotImplemented if it does not implement the operation for a\n given pair of arguments. By convention, False and True are\n returned for a successful comparison. However, these methods can\n return any value, so if the comparison operator is used in a\n Boolean context (e.g., in the condition of an if statement),\n Python will call bool() on the value to determine if the result\n is true or false.\n\n There are no implied relationships among the comparison operators.\n The truth of x==y does not imply that x!=y is false.\n Accordingly, when defining __eq__(), one should also define\n __ne__() so that the operators will behave as expected. See\n the paragraph on __hash__() for some important notes on\n creating *hashable* objects which support custom comparison\n operations and are usable as dictionary keys.\n\n There are no swapped-argument versions of these methods (to be used\n when the left argument does not support the operation but the right\n argument does); rather, __lt__() and __gt__() are each\n other\'s reflection, __le__() and __ge__() are each other\'s\n reflection, and __eq__() and __ne__() are their own\n reflection.\n\n Arguments to rich comparison methods are never coerced.\n\n To automatically generate ordering operations from a single root\n operation, see functools.total_ordering().\n\nobject.__hash__(self)\n\n Called by built-in function hash() and for operations on\n members of hashed collections including set, frozenset, and\n dict. __hash__() should return an integer. The only\n required property is that objects which compare equal have the same\n hash value; it is advised to somehow mix together (e.g. using\n exclusive or) the hash values for the components of the object that\n also play a part in comparison of objects.\n\n Note: hash() truncates the value returned from an object\'s custom\n __hash__() method to the size of a Py_ssize_t. This is\n typically 8 bytes on 64-bit builds and 4 bytes on 32-bit builds.\n If an object\'s __hash__() must interoperate on builds of\n different bit sizes, be sure to check the width on all supported\n builds. An easy way to do this is with python -c "import sys;\n print(sys.hash_info.width)"\n\n If a class does not define an __eq__() method it should not\n define a __hash__() operation either; if it defines\n __eq__() but not __hash__(), its instances will not be\n usable as items in hashable collections. If a class defines\n mutable objects and implements an __eq__() method, it should\n not implement __hash__(), since the implementation of hashable\n collections requires that a key\'s hash value is immutable (if the\n object\'s hash value changes, it will be in the wrong hash bucket).\n\n User-defined classes have __eq__() and __hash__() methods\n by default; with them, all objects compare unequal (except with\n themselves) and x.__hash__() returns an appropriate value such\n that x == y implies both that x is y and hash(x) ==\n hash(y).\n\n A class that overrides __eq__() and does not define\n __hash__() will have its __hash__() implicitly set to\n None. When the __hash__() method of a class is None,\n instances of the class will raise an appropriate TypeError when\n a program attempts to retrieve their hash value, and will also be\n correctly identified as unhashable when checking isinstance(obj,\n collections.Hashable).\n\n If a class that overrides __eq__() needs to retain the\n implementation of __hash__() from a parent class, the\n interpreter must be told this explicitly by setting __hash__ =\n .__hash__.\n\n If a class that does not override __eq__() wishes to suppress\n hash support, it should include __hash__ = None in the class\n definition. A class which defines its own __hash__() that\n explicitly raises a TypeError would be incorrectly identified\n as hashable by an isinstance(obj, collections.Hashable) call.\n\n Note: By default, the __hash__() values of str, bytes and datetime\n objects are "salted" with an unpredictable random value.\n Although they remain constant within an individual Python\n process, they are not predictable between repeated invocations of\n Python.This is intended to provide protection against a denial-\n of-service caused by carefully-chosen inputs that exploit the\n worst case performance of a dict insertion, O(n^2) complexity.\n See http://www.ocert.org/advisories/ocert-2011-003.html for\n details.Changing hash values affects the iteration order of\n dicts, sets and other mappings. Python has never made guarantees\n about this ordering (and it typically varies between 32-bit and\n 64-bit builds).See also PYTHONHASHSEED.\n\n Changed in version 3.3: Hash randomization is enabled by default.\n\nobject.__bool__(self)\n\n Called to implement truth value testing and the built-in operation\n bool(); should return False or True. When this method\n is not defined, __len__() is called, if it is defined, and the\n object is considered true if its result is nonzero. If a class\n defines neither __len__() nor __bool__(), all its instances\n are considered true.\n', 'debugger': '\npdb --- The Python Debugger\n*******************************\n\nThe module pdb defines an interactive source code debugger for\nPython programs. It supports setting (conditional) breakpoints and\nsingle stepping at the source line level, inspection of stack frames,\nsource code listing, and evaluation of arbitrary Python code in the\ncontext of any stack frame. It also supports post-mortem debugging\nand can be called under program control.\n\nThe debugger is extensible -- it is actually defined as the class\nPdb. This is currently undocumented but easily understood by\nreading the source. The extension interface uses the modules bdb\nand cmd.\n\nThe debugger\'s prompt is (Pdb). Typical usage to run a program\nunder control of the debugger is:\n\n >>> import pdb\n >>> import mymodule\n >>> pdb.run(\'mymodule.test()\')\n > (0)?()\n (Pdb) continue\n > (1)?()\n (Pdb) continue\n NameError: \'spam\'\n > (1)?()\n (Pdb)\n\nChanged in version 3.3: Tab-completion via the readline module is\navailable for commands and command arguments, e.g. the current global\nand local names are offered as arguments of the print command.\n\npdb.py can also be invoked as a script to debug other scripts.\nFor example:\n\n python3 -m pdb myscript.py\n\nWhen invoked as a script, pdb will automatically enter post-mortem\ndebugging if the program being debugged exits abnormally. After post-\nmortem debugging (or after normal exit of the program), pdb will\nrestart the program. Automatic restarting preserves pdb\'s state (such\nas breakpoints) and in most cases is more useful than quitting the\ndebugger upon program\'s exit.\n\nNew in version 3.2: pdb.py now accepts a -c option that\nexecutes commands as if given in a .pdbrc file, see *Debugger\nCommands*.\n\nThe typical usage to break into the debugger from a running program is\nto insert\n\n import pdb; pdb.set_trace()\n\nat the location you want to break into the debugger. You can then\nstep through the code following this statement, and continue running\nwithout the debugger using the continue command.\n\nThe typical usage to inspect a crashed program is:\n\n >>> import pdb\n >>> import mymodule\n >>> mymodule.test()\n Traceback (most recent call last):\n File "", line 1, in ?\n File "./mymodule.py", line 4, in test\n test2()\n File "./mymodule.py", line 3, in test2\n print(spam)\n NameError: spam\n >>> pdb.pm()\n > ./mymodule.py(3)test2()\n -> print(spam)\n (Pdb)\n\nThe module defines the following functions; each enters the debugger\nin a slightly different way:\n\npdb.run(statement, globals=None, locals=None)\n\n Execute the *statement* (given as a string or a code object) under\n debugger control. The debugger prompt appears before any code is\n executed; you can set breakpoints and type continue, or you can\n step through the statement using step or next (all these\n commands are explained below). The optional *globals* and *locals*\n arguments specify the environment in which the code is executed; by\n default the dictionary of the module __main__ is used. (See\n the explanation of the built-in exec() or eval()\n functions.)\n\npdb.runeval(expression, globals=None, locals=None)\n\n Evaluate the *expression* (given as a string or a code object)\n under debugger control. When runeval() returns, it returns the\n value of the expression. Otherwise this function is similar to\n run().\n\npdb.runcall(function, *args, **kwds)\n\n Call the *function* (a function or method object, not a string)\n with the given arguments. When runcall() returns, it returns\n whatever the function call returned. The debugger prompt appears\n as soon as the function is entered.\n\npdb.set_trace()\n\n Enter the debugger at the calling stack frame. This is useful to\n hard-code a breakpoint at a given point in a program, even if the\n code is not otherwise being debugged (e.g. when an assertion\n fails).\n\npdb.post_mortem(traceback=None)\n\n Enter post-mortem debugging of the given *traceback* object. If no\n *traceback* is given, it uses the one of the exception that is\n currently being handled (an exception must be being handled if the\n default is to be used).\n\npdb.pm()\n\n Enter post-mortem debugging of the traceback found in\n sys.last_traceback.\n\nThe run* functions and set_trace() are aliases for\ninstantiating the Pdb class and calling the method of the same\nname. If you want to access further features, you have to do this\nyourself:\n\nclass class pdb.Pdb(completekey=\'tab\', stdin=None, stdout=None, skip=None, nosigint=False)\n\n Pdb is the debugger class.\n\n The *completekey*, *stdin* and *stdout* arguments are passed to the\n underlying cmd.Cmd class; see the description there.\n\n The *skip* argument, if given, must be an iterable of glob-style\n module name patterns. The debugger will not step into frames that\n originate in a module that matches one of these patterns. [1]\n\n By default, Pdb sets a handler for the SIGINT signal (which is sent\n when the user presses Ctrl-C on the console) when you give a\n continue command. This allows you to break into the debugger\n again by pressing Ctrl-C. If you want Pdb not to touch the SIGINT\n handler, set *nosigint* tot true.\n\n Example call to enable tracing with *skip*:\n\n import pdb; pdb.Pdb(skip=[\'django.*\']).set_trace()\n\n New in version 3.1: The *skip* argument.\n\n New in version 3.2: The *nosigint* argument. Previously, a SIGINT\n handler was never set by Pdb.\n\n run(statement, globals=None, locals=None)\n runeval(expression, globals=None, locals=None)\n runcall(function, *args, **kwds)\n set_trace()\n\n See the documentation for the functions explained above.\n\n\nDebugger Commands\n=================\n\nThe commands recognized by the debugger are listed below. Most\ncommands can be abbreviated to one or two letters as indicated; e.g.\nh(elp) means that either h or help can be used to enter\nthe help command (but not he or hel, nor H or Help or\nHELP). Arguments to commands must be separated by whitespace\n(spaces or tabs). Optional arguments are enclosed in square brackets\n([]) in the command syntax; the square brackets must not be typed.\nAlternatives in the command syntax are separated by a vertical bar\n(|).\n\nEntering a blank line repeats the last command entered. Exception: if\nthe last command was a list command, the next 11 lines are listed.\n\nCommands that the debugger doesn\'t recognize are assumed to be Python\nstatements and are executed in the context of the program being\ndebugged. Python statements can also be prefixed with an exclamation\npoint (!). This is a powerful way to inspect the program being\ndebugged; it is even possible to change a variable or call a function.\nWhen an exception occurs in such a statement, the exception name is\nprinted but the debugger\'s state is not changed.\n\nThe debugger supports *aliases*. Aliases can have parameters which\nallows one a certain level of adaptability to the context under\nexamination.\n\nMultiple commands may be entered on a single line, separated by\n;;. (A single ; is not used as it is the separator for\nmultiple commands in a line that is passed to the Python parser.) No\nintelligence is applied to separating the commands; the input is split\nat the first ;; pair, even if it is in the middle of a quoted\nstring.\n\nIf a file .pdbrc exists in the user\'s home directory or in the\ncurrent directory, it is read in and executed as if it had been typed\nat the debugger prompt. This is particularly useful for aliases. If\nboth files exist, the one in the home directory is read first and\naliases defined there can be overridden by the local file.\n\nChanged in version 3.2: .pdbrc can now contain commands that\ncontinue debugging, such as continue or next. Previously,\nthese commands had no effect.\n\nh(elp) [command]\n\n Without argument, print the list of available commands. With a\n *command* as argument, print help about that command. help pdb\n displays the full documentation (the docstring of the pdb\n module). Since the *command* argument must be an identifier,\n help exec must be entered to get help on the ! command.\n\nw(here)\n\n Print a stack trace, with the most recent frame at the bottom. An\n arrow indicates the current frame, which determines the context of\n most commands.\n\nd(own) [count]\n\n Move the current frame *count* (default one) levels down in the\n stack trace (to a newer frame).\n\nu(p) [count]\n\n Move the current frame *count* (default one) levels up in the stack\n trace (to an older frame).\n\nb(reak) [([filename:]lineno | function) [, condition]]\n\n With a *lineno* argument, set a break there in the current file.\n With a *function* argument, set a break at the first executable\n statement within that function. The line number may be prefixed\n with a filename and a colon, to specify a breakpoint in another\n file (probably one that hasn\'t been loaded yet). The file is\n searched on sys.path. Note that each breakpoint is assigned a\n number to which all the other breakpoint commands refer.\n\n If a second argument is present, it is an expression which must\n evaluate to true before the breakpoint is honored.\n\n Without argument, list all breaks, including for each breakpoint,\n the number of times that breakpoint has been hit, the current\n ignore count, and the associated condition if any.\n\ntbreak [([filename:]lineno | function) [, condition]]\n\n Temporary breakpoint, which is removed automatically when it is\n first hit. The arguments are the same as for break.\n\ncl(ear) [filename:lineno | bpnumber [bpnumber ...]]\n\n With a *filename:lineno* argument, clear all the breakpoints at\n this line. With a space separated list of breakpoint numbers, clear\n those breakpoints. Without argument, clear all breaks (but first\n ask confirmation).\n\ndisable [bpnumber [bpnumber ...]]\n\n Disable the breakpoints given as a space separated list of\n breakpoint numbers. Disabling a breakpoint means it cannot cause\n the program to stop execution, but unlike clearing a breakpoint, it\n remains in the list of breakpoints and can be (re-)enabled.\n\nenable [bpnumber [bpnumber ...]]\n\n Enable the breakpoints specified.\n\nignore bpnumber [count]\n\n Set the ignore count for the given breakpoint number. If count is\n omitted, the ignore count is set to 0. A breakpoint becomes active\n when the ignore count is zero. When non-zero, the count is\n decremented each time the breakpoint is reached and the breakpoint\n is not disabled and any associated condition evaluates to true.\n\ncondition bpnumber [condition]\n\n Set a new *condition* for the breakpoint, an expression which must\n evaluate to true before the breakpoint is honored. If *condition*\n is absent, any existing condition is removed; i.e., the breakpoint\n is made unconditional.\n\ncommands [bpnumber]\n\n Specify a list of commands for breakpoint number *bpnumber*. The\n commands themselves appear on the following lines. Type a line\n containing just end to terminate the commands. An example:\n\n (Pdb) commands 1\n (com) print some_variable\n (com) end\n (Pdb)\n\n To remove all commands from a breakpoint, type commands and follow\n it immediately with end; that is, give no commands.\n\n With no *bpnumber* argument, commands refers to the last breakpoint\n set.\n\n You can use breakpoint commands to start your program up again.\n Simply use the continue command, or step, or any other command that\n resumes execution.\n\n Specifying any command resuming execution (currently continue,\n step, next, return, jump, quit and their abbreviations) terminates\n the command list (as if that command was immediately followed by\n end). This is because any time you resume execution (even with a\n simple next or step), you may encounter another breakpoint--which\n could have its own command list, leading to ambiguities about which\n list to execute.\n\n If you use the \'silent\' command in the command list, the usual\n message about stopping at a breakpoint is not printed. This may be\n desirable for breakpoints that are to print a specific message and\n then continue. If none of the other commands print anything, you\n see no sign that the breakpoint was reached.\n\ns(tep)\n\n Execute the current line, stop at the first possible occasion\n (either in a function that is called or on the next line in the\n current function).\n\nn(ext)\n\n Continue execution until the next line in the current function is\n reached or it returns. (The difference between next and\n step is that step stops inside a called function, while\n next executes called functions at (nearly) full speed, only\n stopping at the next line in the current function.)\n\nunt(il) [lineno]\n\n Without argument, continue execution until the line with a number\n greater than the current one is reached.\n\n With a line number, continue execution until a line with a number\n greater or equal to that is reached. In both cases, also stop when\n the current frame returns.\n\n Changed in version 3.2: Allow giving an explicit line number.\n\nr(eturn)\n\n Continue execution until the current function returns.\n\nc(ont(inue))\n\n Continue execution, only stop when a breakpoint is encountered.\n\nj(ump) lineno\n\n Set the next line that will be executed. Only available in the\n bottom-most frame. This lets you jump back and execute code again,\n or jump forward to skip code that you don\'t want to run.\n\n It should be noted that not all jumps are allowed -- for instance\n it is not possible to jump into the middle of a for loop or out\n of a finally clause.\n\nl(ist) [first[, last]]\n\n List source code for the current file. Without arguments, list 11\n lines around the current line or continue the previous listing.\n With . as argument, list 11 lines around the current line.\n With one argument, list 11 lines around at that line. With two\n arguments, list the given range; if the second argument is less\n than the first, it is interpreted as a count.\n\n The current line in the current frame is indicated by ->. If\n an exception is being debugged, the line where the exception was\n originally raised or propagated is indicated by >>, if it\n differs from the current line.\n\n New in version 3.2: The >> marker.\n\nll | longlist\n\n List all source code for the current function or frame.\n Interesting lines are marked as for list.\n\n New in version 3.2.\n\na(rgs)\n\n Print the argument list of the current function.\n\np(rint) expression\n\n Evaluate the *expression* in the current context and print its\n value.\n\npp expression\n\n Like the print command, except the value of the expression is\n pretty-printed using the pprint module.\n\nwhatis expression\n\n Print the type of the *expression*.\n\nsource expression\n\n Try to get source code for the given object and display it.\n\n New in version 3.2.\n\ndisplay [expression]\n\n Display the value of the expression if it changed, each time\n execution stops in the current frame.\n\n Without expression, list all display expressions for the current\n frame.\n\n New in version 3.2.\n\nundisplay [expression]\n\n Do not display the expression any more in the current frame.\n Without expression, clear all display expressions for the current\n frame.\n\n New in version 3.2.\n\ninteract\n\n Start an interative interpreter (using the code module) whose\n global namespace contains all the (global and local) names found in\n the current scope.\n\n New in version 3.2.\n\nalias [name [command]]\n\n Create an alias called *name* that executes *command*. The command\n must *not* be enclosed in quotes. Replaceable parameters can be\n indicated by %1, %2, and so on, while %* is replaced by\n all the parameters. If no command is given, the current alias for\n *name* is shown. If no arguments are given, all aliases are listed.\n\n Aliases may be nested and can contain anything that can be legally\n typed at the pdb prompt. Note that internal pdb commands *can* be\n overridden by aliases. Such a command is then hidden until the\n alias is removed. Aliasing is recursively applied to the first\n word of the command line; all other words in the line are left\n alone.\n\n As an example, here are two useful aliases (especially when placed\n in the .pdbrc file):\n\n # Print instance variables (usage "pi classInst")\n alias pi for k in %1.__dict__.keys(): print("%1.",k,"=",%1.__dict__[k])\n # Print instance variables in self\n alias ps pi self\n\nunalias name\n\n Delete the specified alias.\n\n! statement\n\n Execute the (one-line) *statement* in the context of the current\n stack frame. The exclamation point can be omitted unless the first\n word of the statement resembles a debugger command. To set a\n global variable, you can prefix the assignment command with a\n global statement on the same line, e.g.:\n\n (Pdb) global list_options; list_options = [\'-l\']\n (Pdb)\n\nrun [args ...]\nrestart [args ...]\n\n Restart the debugged Python program. If an argument is supplied,\n it is split with shlex and the result is used as the new\n sys.argv. History, breakpoints, actions and debugger options\n are preserved. restart is an alias for run.\n\nq(uit)\n\n Quit from the debugger. The program being executed is aborted.\n\n-[ Footnotes ]-\n\n[1] Whether a frame is considered to originate in a certain module is\n determined by the __name__ in the frame globals.\n', 'del': '\nThe del statement\n*********************\n\n del_stmt ::= "del" target_list\n\nDeletion is recursively defined very similar to the way assignment is\ndefined. Rather than spelling it out in full details, here are some\nhints.\n\nDeletion of a target list recursively deletes each target, from left\nto right.\n\nDeletion of a name removes the binding of that name from the local or\nglobal namespace, depending on whether the name occurs in a global\nstatement in the same code block. If the name is unbound, a\nNameError exception will be raised.\n\nDeletion of attribute references, subscriptions and slicings is passed\nto the primary object involved; deletion of a slicing is in general\nequivalent to assignment of an empty slice of the right type (but even\nthis is determined by the sliced object).\n\nChanged in version 3.2: Previously it was illegal to delete a name\nfrom the local namespace if it occurs as a free variable in a nested\nblock.\n', 'dict': '\nDictionary displays\n*******************\n\nA dictionary display is a possibly empty series of key/datum pairs\nenclosed in curly braces:\n\n dict_display ::= "{" [key_datum_list | dict_comprehension] "}"\n key_datum_list ::= key_datum ("," key_datum)* [","]\n key_datum ::= expression ":" expression\n dict_comprehension ::= expression ":" expression comp_for\n\nA dictionary display yields a new dictionary object.\n\nIf a comma-separated sequence of key/datum pairs is given, they are\nevaluated from left to right to define the entries of the dictionary:\neach key object is used as a key into the dictionary to store the\ncorresponding datum. This means that you can specify the same key\nmultiple times in the key/datum list, and the final dictionary\'s value\nfor that key will be the last one given.\n\nA dict comprehension, in contrast to list and set comprehensions,\nneeds two expressions separated with a colon followed by the usual\n"for" and "if" clauses. When the comprehension is run, the resulting\nkey and value elements are inserted in the new dictionary in the order\nthey are produced.\n\nRestrictions on the types of the key values are listed earlier in\nsection *The standard type hierarchy*. (To summarize, the key type\nshould be *hashable*, which excludes all mutable objects.) Clashes\nbetween duplicate keys are not detected; the last datum (textually\nrightmost in the display) stored for a given key value prevails.\n', 'dynamic-features': '\nInteraction with dynamic features\n*********************************\n\nThere are several cases where Python statements are illegal when used\nin conjunction with nested scopes that contain free variables.\n\nIf a variable is referenced in an enclosing scope, it is illegal to\ndelete the name. An error will be reported at compile time.\n\nIf the wild card form of import --- import * --- is used in a\nfunction and the function contains or is a nested block with free\nvariables, the compiler will raise a SyntaxError.\n\nThe eval() and exec() functions do not have access to the full\nenvironment for resolving names. Names may be resolved in the local\nand global namespaces of the caller. Free variables are not resolved\nin the nearest enclosing namespace, but in the global namespace. [1]\nThe exec() and eval() functions have optional arguments to\noverride the global and local namespace. If only one namespace is\nspecified, it is used for both.\n', 'else': '\nThe if statement\n********************\n\nThe if statement is used for conditional execution:\n\n if_stmt ::= "if" expression ":" suite\n ( "elif" expression ":" suite )*\n ["else" ":" suite]\n\nIt selects exactly one of the suites by evaluating the expressions one\nby one until one is found to be true (see section *Boolean operations*\nfor the definition of true and false); then that suite is executed\n(and no other part of the if statement is executed or evaluated).\nIf all expressions are false, the suite of the else clause, if\npresent, is executed.\n', 'exceptions': '\nExceptions\n**********\n\nExceptions are a means of breaking out of the normal flow of control\nof a code block in order to handle errors or other exceptional\nconditions. An exception is *raised* at the point where the error is\ndetected; it may be *handled* by the surrounding code block or by any\ncode block that directly or indirectly invoked the code block where\nthe error occurred.\n\nThe Python interpreter raises an exception when it detects a run-time\nerror (such as division by zero). A Python program can also\nexplicitly raise an exception with the raise statement. Exception\nhandlers are specified with the try ... except statement. The\nfinally clause of such a statement can be used to specify cleanup\ncode which does not handle the exception, but is executed whether an\nexception occurred or not in the preceding code.\n\nPython uses the "termination" model of error handling: an exception\nhandler can find out what happened and continue execution at an outer\nlevel, but it cannot repair the cause of the error and retry the\nfailing operation (except by re-entering the offending piece of code\nfrom the top).\n\nWhen an exception is not handled at all, the interpreter terminates\nexecution of the program, or returns to its interactive main loop. In\neither case, it prints a stack backtrace, except when the exception is\nSystemExit.\n\nExceptions are identified by class instances. The except clause\nis selected depending on the class of the instance: it must reference\nthe class of the instance or a base class thereof. The instance can\nbe received by the handler and can carry additional information about\nthe exceptional condition.\n\nNote: Exception messages are not part of the Python API. Their contents\n may change from one version of Python to the next without warning\n and should not be relied on by code which will run under multiple\n versions of the interpreter.\n\nSee also the description of the try statement in section *The try\nstatement* and raise statement in section *The raise statement*.\n\n-[ Footnotes ]-\n\n[1] This limitation occurs because the code that is executed by these\n operations is not available at the time the module is compiled.\n', 'execmodel': '\nExecution model\n***************\n\n\nNaming and binding\n==================\n\n*Names* refer to objects. Names are introduced by name binding\noperations. Each occurrence of a name in the program text refers to\nthe *binding* of that name established in the innermost function block\ncontaining the use.\n\nA *block* is a piece of Python program text that is executed as a\nunit. The following are blocks: a module, a function body, and a class\ndefinition. Each command typed interactively is a block. A script\nfile (a file given as standard input to the interpreter or specified\non the interpreter command line the first argument) is a code block.\nA script command (a command specified on the interpreter command line\nwith the \'**-c**\' option) is a code block. The string argument passed\nto the built-in functions eval() and exec() is a code block.\n\nA code block is executed in an *execution frame*. A frame contains\nsome administrative information (used for debugging) and determines\nwhere and how execution continues after the code block\'s execution has\ncompleted.\n\nA *scope* defines the visibility of a name within a block. If a local\nvariable is defined in a block, its scope includes that block. If the\ndefinition occurs in a function block, the scope extends to any blocks\ncontained within the defining one, unless a contained block introduces\na different binding for the name. The scope of names defined in a\nclass block is limited to the class block; it does not extend to the\ncode blocks of methods -- this includes comprehensions and generator\nexpressions since they are implemented using a function scope. This\nmeans that the following will fail:\n\n class A:\n a = 42\n b = list(a + i for i in range(10))\n\nWhen a name is used in a code block, it is resolved using the nearest\nenclosing scope. The set of all such scopes visible to a code block\nis called the block\'s *environment*.\n\nIf a name is bound in a block, it is a local variable of that block,\nunless declared as nonlocal. If a name is bound at the module\nlevel, it is a global variable. (The variables of the module code\nblock are local and global.) If a variable is used in a code block\nbut not defined there, it is a *free variable*.\n\nWhen a name is not found at all, a NameError exception is raised.\nIf the name refers to a local variable that has not been bound, a\nUnboundLocalError exception is raised. UnboundLocalError is a\nsubclass of NameError.\n\nThe following constructs bind names: formal parameters to functions,\nimport statements, class and function definitions (these bind the\nclass or function name in the defining block), and targets that are\nidentifiers if occurring in an assignment, for loop header, or\nafter as in a with statement or except clause. The\nimport statement of the form from ... import * binds all names\ndefined in the imported module, except those beginning with an\nunderscore. This form may only be used at the module level.\n\nA target occurring in a del statement is also considered bound for\nthis purpose (though the actual semantics are to unbind the name).\n\nEach assignment or import statement occurs within a block defined by a\nclass or function definition or at the module level (the top-level\ncode block).\n\nIf a name binding operation occurs anywhere within a code block, all\nuses of the name within the block are treated as references to the\ncurrent block. This can lead to errors when a name is used within a\nblock before it is bound. This rule is subtle. Python lacks\ndeclarations and allows name binding operations to occur anywhere\nwithin a code block. The local variables of a code block can be\ndetermined by scanning the entire text of the block for name binding\noperations.\n\nIf the global statement occurs within a block, all uses of the\nname specified in the statement refer to the binding of that name in\nthe top-level namespace. Names are resolved in the top-level\nnamespace by searching the global namespace, i.e. the namespace of the\nmodule containing the code block, and the builtins namespace, the\nnamespace of the module builtins. The global namespace is\nsearched first. If the name is not found there, the builtins\nnamespace is searched. The global statement must precede all uses of\nthe name.\n\nThe builtins namespace associated with the execution of a code block\nis actually found by looking up the name __builtins__ in its\nglobal namespace; this should be a dictionary or a module (in the\nlatter case the module\'s dictionary is used). By default, when in the\n__main__ module, __builtins__ is the built-in module\nbuiltins; when in any other module, __builtins__ is an alias\nfor the dictionary of the builtins module itself.\n__builtins__ can be set to a user-created dictionary to create a\nweak form of restricted execution.\n\n**CPython implementation detail:** Users should not touch\n__builtins__; it is strictly an implementation detail. Users\nwanting to override values in the builtins namespace should import\nthe builtins module and modify its attributes appropriately.\n\nThe namespace for a module is automatically created the first time a\nmodule is imported. The main module for a script is always called\n__main__.\n\nThe global statement has the same scope as a name binding\noperation in the same block. If the nearest enclosing scope for a\nfree variable contains a global statement, the free variable is\ntreated as a global.\n\nA class definition is an executable statement that may use and define\nnames. These references follow the normal rules for name resolution.\nThe namespace of the class definition becomes the attribute dictionary\nof the class. Names defined at the class scope are not visible in\nmethods.\n\n\nInteraction with dynamic features\n---------------------------------\n\nThere are several cases where Python statements are illegal when used\nin conjunction with nested scopes that contain free variables.\n\nIf a variable is referenced in an enclosing scope, it is illegal to\ndelete the name. An error will be reported at compile time.\n\nIf the wild card form of import --- import * --- is used in a\nfunction and the function contains or is a nested block with free\nvariables, the compiler will raise a SyntaxError.\n\nThe eval() and exec() functions do not have access to the full\nenvironment for resolving names. Names may be resolved in the local\nand global namespaces of the caller. Free variables are not resolved\nin the nearest enclosing namespace, but in the global namespace. [1]\nThe exec() and eval() functions have optional arguments to\noverride the global and local namespace. If only one namespace is\nspecified, it is used for both.\n\n\nExceptions\n==========\n\nExceptions are a means of breaking out of the normal flow of control\nof a code block in order to handle errors or other exceptional\nconditions. An exception is *raised* at the point where the error is\ndetected; it may be *handled* by the surrounding code block or by any\ncode block that directly or indirectly invoked the code block where\nthe error occurred.\n\nThe Python interpreter raises an exception when it detects a run-time\nerror (such as division by zero). A Python program can also\nexplicitly raise an exception with the raise statement. Exception\nhandlers are specified with the try ... except statement. The\nfinally clause of such a statement can be used to specify cleanup\ncode which does not handle the exception, but is executed whether an\nexception occurred or not in the preceding code.\n\nPython uses the "termination" model of error handling: an exception\nhandler can find out what happened and continue execution at an outer\nlevel, but it cannot repair the cause of the error and retry the\nfailing operation (except by re-entering the offending piece of code\nfrom the top).\n\nWhen an exception is not handled at all, the interpreter terminates\nexecution of the program, or returns to its interactive main loop. In\neither case, it prints a stack backtrace, except when the exception is\nSystemExit.\n\nExceptions are identified by class instances. The except clause\nis selected depending on the class of the instance: it must reference\nthe class of the instance or a base class thereof. The instance can\nbe received by the handler and can carry additional information about\nthe exceptional condition.\n\nNote: Exception messages are not part of the Python API. Their contents\n may change from one version of Python to the next without warning\n and should not be relied on by code which will run under multiple\n versions of the interpreter.\n\nSee also the description of the try statement in section *The try\nstatement* and raise statement in section *The raise statement*.\n\n-[ Footnotes ]-\n\n[1] This limitation occurs because the code that is executed by these\n operations is not available at the time the module is compiled.\n', 'exprlists': '\nExpression lists\n****************\n\n expression_list ::= expression ( "," expression )* [","]\n\nAn expression list containing at least one comma yields a tuple. The\nlength of the tuple is the number of expressions in the list. The\nexpressions are evaluated from left to right.\n\nThe trailing comma is required only to create a single tuple (a.k.a. a\n*singleton*); it is optional in all other cases. A single expression\nwithout a trailing comma doesn\'t create a tuple, but rather yields the\nvalue of that expression. (To create an empty tuple, use an empty pair\nof parentheses: ().)\n', 'floating': '\nFloating point literals\n***********************\n\nFloating point literals are described by the following lexical\ndefinitions:\n\n floatnumber ::= pointfloat | exponentfloat\n pointfloat ::= [intpart] fraction | intpart "."\n exponentfloat ::= (intpart | pointfloat) exponent\n intpart ::= digit+\n fraction ::= "." digit+\n exponent ::= ("e" | "E") ["+" | "-"] digit+\n\nNote that the integer and exponent parts are always interpreted using\nradix 10. For example, 077e010 is legal, and denotes the same\nnumber as 77e10. The allowed range of floating point literals is\nimplementation-dependent. Some examples of floating point literals:\n\n 3.14 10. .001 1e100 3.14e-10 0e0\n\nNote that numeric literals do not include a sign; a phrase like -1\nis actually an expression composed of the unary operator - and the\nliteral 1.\n', 'for': '\nThe for statement\n*********************\n\nThe for statement is used to iterate over the elements of a\nsequence (such as a string, tuple or list) or other iterable object:\n\n for_stmt ::= "for" target_list "in" expression_list ":" suite\n ["else" ":" suite]\n\nThe expression list is evaluated once; it should yield an iterable\nobject. An iterator is created for the result of the\nexpression_list. The suite is then executed once for each item\nprovided by the iterator, in the order of ascending indices. Each\nitem in turn is assigned to the target list using the standard rules\nfor assignments (see *Assignment statements*), and then the suite is\nexecuted. When the items are exhausted (which is immediately when the\nsequence is empty or an iterator raises a StopIteration\nexception), the suite in the else clause, if present, is executed,\nand the loop terminates.\n\nA break statement executed in the first suite terminates the loop\nwithout executing the else clause\'s suite. A continue\nstatement executed in the first suite skips the rest of the suite and\ncontinues with the next item, or with the else clause if there was\nno next item.\n\nThe suite may assign to the variable(s) in the target list; this does\nnot affect the next item assigned to it.\n\nNames in the target list are not deleted when the loop is finished,\nbut if the sequence is empty, it will not have been assigned to at all\nby the loop. Hint: the built-in function range() returns an\niterator of integers suitable to emulate the effect of Pascal\'s for\ni := a to b do; e.g., list(range(3)) returns the list [0, 1,\n2].\n\nNote: There is a subtlety when the sequence is being modified by the loop\n (this can only occur for mutable sequences, i.e. lists). An\n internal counter is used to keep track of which item is used next,\n and this is incremented on each iteration. When this counter has\n reached the length of the sequence the loop terminates. This means\n that if the suite deletes the current (or a previous) item from the\n sequence, the next item will be skipped (since it gets the index of\n the current item which has already been treated). Likewise, if the\n suite inserts an item in the sequence before the current item, the\n current item will be treated again the next time through the loop.\n This can lead to nasty bugs that can be avoided by making a\n temporary copy using a slice of the whole sequence, e.g.,\n\n for x in a[:]:\n if x < 0: a.remove(x)\n', 'formatstrings': '\nFormat String Syntax\n********************\n\nThe str.format() method and the Formatter class share the same\nsyntax for format strings (although in the case of Formatter,\nsubclasses can define their own format string syntax).\n\nFormat strings contain "replacement fields" surrounded by curly braces\n{}. Anything that is not contained in braces is considered literal\ntext, which is copied unchanged to the output. If you need to include\na brace character in the literal text, it can be escaped by doubling:\n{{ and }}.\n\nThe grammar for a replacement field is as follows:\n\n replacement_field ::= "{" [field_name] ["!" conversion] [":" format_spec] "}"\n field_name ::= arg_name ("." attribute_name | "[" element_index "]")*\n arg_name ::= [identifier | integer]\n attribute_name ::= identifier\n element_index ::= integer | index_string\n index_string ::= +\n conversion ::= "r" | "s" | "a"\n format_spec ::= \n\nIn less formal terms, the replacement field can start with a\n*field_name* that specifies the object whose value is to be formatted\nand inserted into the output instead of the replacement field. The\n*field_name* is optionally followed by a *conversion* field, which is\npreceded by an exclamation point \'!\', and a *format_spec*, which\nis preceded by a colon \':\'. These specify a non-default format\nfor the replacement value.\n\nSee also the *Format Specification Mini-Language* section.\n\nThe *field_name* itself begins with an *arg_name* that is either a\nnumber or a keyword. If it\'s a number, it refers to a positional\nargument, and if it\'s a keyword, it refers to a named keyword\nargument. If the numerical arg_names in a format string are 0, 1, 2,\n... in sequence, they can all be omitted (not just some) and the\nnumbers 0, 1, 2, ... will be automatically inserted in that order.\nBecause *arg_name* is not quote-delimited, it is not possible to\nspecify arbitrary dictionary keys (e.g., the strings \'10\' or\n\':-]\') within a format string. The *arg_name* can be followed by\nany number of index or attribute expressions. An expression of the\nform \'.name\' selects the named attribute using getattr(),\nwhile an expression of the form \'[index]\' does an index lookup\nusing __getitem__().\n\nChanged in version 3.1: The positional argument specifiers can be\nomitted, so \'{} {}\' is equivalent to \'{0} {1}\'.\n\nSome simple format string examples:\n\n "First, thou shalt count to {0}" # References first positional argument\n "Bring me a {}" # Implicitly references the first positional argument\n "From {} to {}" # Same as "From {0} to {1}"\n "My quest is {name}" # References keyword argument \'name\'\n "Weight in tons {0.weight}" # \'weight\' attribute of first positional arg\n "Units destroyed: {players[0]}" # First element of keyword argument \'players\'.\n\nThe *conversion* field causes a type coercion before formatting.\nNormally, the job of formatting a value is done by the\n__format__() method of the value itself. However, in some cases\nit is desirable to force a type to be formatted as a string,\noverriding its own definition of formatting. By converting the value\nto a string before calling __format__(), the normal formatting\nlogic is bypassed.\n\nThree conversion flags are currently supported: \'!s\' which calls\nstr() on the value, \'!r\' which calls repr() and \'!a\'\nwhich calls ascii().\n\nSome examples:\n\n "Harold\'s a clever {0!s}" # Calls str() on the argument first\n "Bring out the holy {name!r}" # Calls repr() on the argument first\n "More {!a}" # Calls ascii() on the argument first\n\nThe *format_spec* field contains a specification of how the value\nshould be presented, including such details as field width, alignment,\npadding, decimal precision and so on. Each value type can define its\nown "formatting mini-language" or interpretation of the *format_spec*.\n\nMost built-in types support a common formatting mini-language, which\nis described in the next section.\n\nA *format_spec* field can also include nested replacement fields\nwithin it. These nested replacement fields can contain only a field\nname; conversion flags and format specifications are not allowed. The\nreplacement fields within the format_spec are substituted before the\n*format_spec* string is interpreted. This allows the formatting of a\nvalue to be dynamically specified.\n\nSee the *Format examples* section for some examples.\n\n\nFormat Specification Mini-Language\n==================================\n\n"Format specifications" are used within replacement fields contained\nwithin a format string to define how individual values are presented\n(see *Format String Syntax*). They can also be passed directly to the\nbuilt-in format() function. Each formattable type may define how\nthe format specification is to be interpreted.\n\nMost built-in types implement the following options for format\nspecifications, although some of the formatting options are only\nsupported by the numeric types.\n\nA general convention is that an empty format string ("") produces\nthe same result as if you had called str() on the value. A non-\nempty format string typically modifies the result.\n\nThe general form of a *standard format specifier* is:\n\n format_spec ::= [[fill]align][sign][#][0][width][,][.precision][type]\n fill ::= \n align ::= "<" | ">" | "=" | "^"\n sign ::= "+" | "-" | " "\n width ::= integer\n precision ::= integer\n type ::= "b" | "c" | "d" | "e" | "E" | "f" | "F" | "g" | "G" | "n" | "o" | "s" | "x" | "X" | "%"\n\nThe *fill* character can be any character other than \'{\' or \'}\'. The\npresence of a fill character is signaled by the character following\nit, which must be one of the alignment options. If the second\ncharacter of *format_spec* is not a valid alignment option, then it is\nassumed that both the fill character and the alignment option are\nabsent.\n\nThe meaning of the various alignment options is as follows:\n\n +-----------+------------------------------------------------------------+\n | Option | Meaning |\n +===========+============================================================+\n | \'<\' | Forces the field to be left-aligned within the available |\n | | space (this is the default for most objects). |\n +-----------+------------------------------------------------------------+\n | \'>\' | Forces the field to be right-aligned within the available |\n | | space (this is the default for numbers). |\n +-----------+------------------------------------------------------------+\n | \'=\' | Forces the padding to be placed after the sign (if any) |\n | | but before the digits. This is used for printing fields |\n | | in the form \'+000000120\'. This alignment option is only |\n | | valid for numeric types. |\n +-----------+------------------------------------------------------------+\n | \'^\' | Forces the field to be centered within the available |\n | | space. |\n +-----------+------------------------------------------------------------+\n\nNote that unless a minimum field width is defined, the field width\nwill always be the same size as the data to fill it, so that the\nalignment option has no meaning in this case.\n\nThe *sign* option is only valid for number types, and can be one of\nthe following:\n\n +-----------+------------------------------------------------------------+\n | Option | Meaning |\n +===========+============================================================+\n | \'+\' | indicates that a sign should be used for both positive as |\n | | well as negative numbers. |\n +-----------+------------------------------------------------------------+\n | \'-\' | indicates that a sign should be used only for negative |\n | | numbers (this is the default behavior). |\n +-----------+------------------------------------------------------------+\n | space | indicates that a leading space should be used on positive |\n | | numbers, and a minus sign on negative numbers. |\n +-----------+------------------------------------------------------------+\n\nThe \'#\' option causes the "alternate form" to be used for the\nconversion. The alternate form is defined differently for different\ntypes. This option is only valid for integer, float, complex and\nDecimal types. For integers, when binary, octal, or hexadecimal output\nis used, this option adds the prefix respective \'0b\', \'0o\', or\n\'0x\' to the output value. For floats, complex and Decimal the\nalternate form causes the result of the conversion to always contain a\ndecimal-point character, even if no digits follow it. Normally, a\ndecimal-point character appears in the result of these conversions\nonly if a digit follows it. In addition, for \'g\' and \'G\'\nconversions, trailing zeros are not removed from the result.\n\nThe \',\' option signals the use of a comma for a thousands\nseparator. For a locale aware separator, use the \'n\' integer\npresentation type instead.\n\nChanged in version 3.1: Added the \',\' option (see also **PEP\n378**).\n\n*width* is a decimal integer defining the minimum field width. If not\nspecified, then the field width will be determined by the content.\n\nPreceding the *width* field by a zero (\'0\') character enables\nsign-aware zero-padding for numeric types. This is equivalent to a\n*fill* character of \'0\' with an *alignment* type of \'=\'.\n\nThe *precision* is a decimal number indicating how many digits should\nbe displayed after the decimal point for a floating point value\nformatted with \'f\' and \'F\', or before and after the decimal\npoint for a floating point value formatted with \'g\' or \'G\'.\nFor non-number types the field indicates the maximum field size - in\nother words, how many characters will be used from the field content.\nThe *precision* is not allowed for integer values.\n\nFinally, the *type* determines how the data should be presented.\n\nThe available string presentation types are:\n\n +-----------+------------------------------------------------------------+\n | Type | Meaning |\n +===========+============================================================+\n | \'s\' | String format. This is the default type for strings and |\n | | may be omitted. |\n +-----------+------------------------------------------------------------+\n | None | The same as \'s\'. |\n +-----------+------------------------------------------------------------+\n\nThe available integer presentation types are:\n\n +-----------+------------------------------------------------------------+\n | Type | Meaning |\n +===========+============================================================+\n | \'b\' | Binary format. Outputs the number in base 2. |\n +-----------+------------------------------------------------------------+\n | \'c\' | Character. Converts the integer to the corresponding |\n | | unicode character before printing. |\n +-----------+------------------------------------------------------------+\n | \'d\' | Decimal Integer. Outputs the number in base 10. |\n +-----------+------------------------------------------------------------+\n | \'o\' | Octal format. Outputs the number in base 8. |\n +-----------+------------------------------------------------------------+\n | \'x\' | Hex format. Outputs the number in base 16, using lower- |\n | | case letters for the digits above 9. |\n +-----------+------------------------------------------------------------+\n | \'X\' | Hex format. Outputs the number in base 16, using upper- |\n | | case letters for the digits above 9. |\n +-----------+------------------------------------------------------------+\n | \'n\' | Number. This is the same as \'d\', except that it uses |\n | | the current locale setting to insert the appropriate |\n | | number separator characters. |\n +-----------+------------------------------------------------------------+\n | None | The same as \'d\'. |\n +-----------+------------------------------------------------------------+\n\nIn addition to the above presentation types, integers can be formatted\nwith the floating point presentation types listed below (except\n\'n\' and None). When doing so, float() is used to convert the\ninteger to a floating point number before formatting.\n\nThe available presentation types for floating point and decimal values\nare:\n\n +-----------+------------------------------------------------------------+\n | Type | Meaning |\n +===========+============================================================+\n | \'e\' | Exponent notation. Prints the number in scientific |\n | | notation using the letter \'e\' to indicate the exponent. |\n | | The default precision is 6. |\n +-----------+------------------------------------------------------------+\n | \'E\' | Exponent notation. Same as \'e\' except it uses an upper |\n | | case \'E\' as the separator character. |\n +-----------+------------------------------------------------------------+\n | \'f\' | Fixed point. Displays the number as a fixed-point number. |\n | | The default precision is 6. |\n +-----------+------------------------------------------------------------+\n | \'F\' | Fixed point. Same as \'f\', but converts nan to |\n | | NAN and inf to INF. |\n +-----------+------------------------------------------------------------+\n | \'g\' | General format. For a given precision p >= 1, this |\n | | rounds the number to p significant digits and then |\n | | formats the result in either fixed-point format or in |\n | | scientific notation, depending on its magnitude. The |\n | | precise rules are as follows: suppose that the result |\n | | formatted with presentation type \'e\' and precision |\n | | p-1 would have exponent exp. Then if -4 <= exp |\n | | < p, the number is formatted with presentation type |\n | | \'f\' and precision p-1-exp. Otherwise, the number |\n | | is formatted with presentation type \'e\' and precision |\n | | p-1. In both cases insignificant trailing zeros are |\n | | removed from the significand, and the decimal point is |\n | | also removed if there are no remaining digits following |\n | | it. Positive and negative infinity, positive and negative |\n | | zero, and nans, are formatted as inf, -inf, 0, |\n | | -0 and nan respectively, regardless of the |\n | | precision. A precision of 0 is treated as equivalent |\n | | to a precision of 1. The default precision is 6. |\n +-----------+------------------------------------------------------------+\n | \'G\' | General format. Same as \'g\' except switches to \'E\' |\n | | if the number gets too large. The representations of |\n | | infinity and NaN are uppercased, too. |\n +-----------+------------------------------------------------------------+\n | \'n\' | Number. This is the same as \'g\', except that it uses |\n | | the current locale setting to insert the appropriate |\n | | number separator characters. |\n +-----------+------------------------------------------------------------+\n | \'%\' | Percentage. Multiplies the number by 100 and displays in |\n | | fixed (\'f\') format, followed by a percent sign. |\n +-----------+------------------------------------------------------------+\n | None | Similar to \'g\', except with at least one digit past |\n | | the decimal point and a default precision of 12. This is |\n | | intended to match str(), except you can add the other |\n | | format modifiers. |\n +-----------+------------------------------------------------------------+\n\n\nFormat examples\n===============\n\nThis section contains examples of the new format syntax and comparison\nwith the old %-formatting.\n\nIn most of the cases the syntax is similar to the old\n%-formatting, with the addition of the {} and with : used\ninstead of %. For example, \'%03.2f\' can be translated to\n\'{:03.2f}\'.\n\nThe new format syntax also supports new and different options, shown\nin the follow examples.\n\nAccessing arguments by position:\n\n >>> \'{0}, {1}, {2}\'.format(\'a\', \'b\', \'c\')\n \'a, b, c\'\n >>> \'{}, {}, {}\'.format(\'a\', \'b\', \'c\') # 3.1+ only\n \'a, b, c\'\n >>> \'{2}, {1}, {0}\'.format(\'a\', \'b\', \'c\')\n \'c, b, a\'\n >>> \'{2}, {1}, {0}\'.format(*\'abc\') # unpacking argument sequence\n \'c, b, a\'\n >>> \'{0}{1}{0}\'.format(\'abra\', \'cad\') # arguments\' indices can be repeated\n \'abracadabra\'\n\nAccessing arguments by name:\n\n >>> \'Coordinates: {latitude}, {longitude}\'.format(latitude=\'37.24N\', longitude=\'-115.81W\')\n \'Coordinates: 37.24N, -115.81W\'\n >>> coord = {\'latitude\': \'37.24N\', \'longitude\': \'-115.81W\'}\n >>> \'Coordinates: {latitude}, {longitude}\'.format(**coord)\n \'Coordinates: 37.24N, -115.81W\'\n\nAccessing arguments\' attributes:\n\n >>> c = 3-5j\n >>> (\'The complex number {0} is formed from the real part {0.real} \'\n ... \'and the imaginary part {0.imag}.\').format(c)\n \'The complex number (3-5j) is formed from the real part 3.0 and the imaginary part -5.0.\'\n >>> class Point:\n ... def __init__(self, x, y):\n ... self.x, self.y = x, y\n ... def __str__(self):\n ... return \'Point({self.x}, {self.y})\'.format(self=self)\n ...\n >>> str(Point(4, 2))\n \'Point(4, 2)\'\n\nAccessing arguments\' items:\n\n >>> coord = (3, 5)\n >>> \'X: {0[0]}; Y: {0[1]}\'.format(coord)\n \'X: 3; Y: 5\'\n\nReplacing %s and %r:\n\n >>> "repr() shows quotes: {!r}; str() doesn\'t: {!s}".format(\'test1\', \'test2\')\n "repr() shows quotes: \'test1\'; str() doesn\'t: test2"\n\nAligning the text and specifying a width:\n\n >>> \'{:<30}\'.format(\'left aligned\')\n \'left aligned \'\n >>> \'{:>30}\'.format(\'right aligned\')\n \' right aligned\'\n >>> \'{:^30}\'.format(\'centered\')\n \' centered \'\n >>> \'{:*^30}\'.format(\'centered\') # use \'*\' as a fill char\n \'***********centered***********\'\n\nReplacing %+f, %-f, and % f and specifying a sign:\n\n >>> \'{:+f}; {:+f}\'.format(3.14, -3.14) # show it always\n \'+3.140000; -3.140000\'\n >>> \'{: f}; {: f}\'.format(3.14, -3.14) # show a space for positive numbers\n \' 3.140000; -3.140000\'\n >>> \'{:-f}; {:-f}\'.format(3.14, -3.14) # show only the minus -- same as \'{:f}; {:f}\'\n \'3.140000; -3.140000\'\n\nReplacing %x and %o and converting the value to different\nbases:\n\n >>> # format also supports binary numbers\n >>> "int: {0:d}; hex: {0:x}; oct: {0:o}; bin: {0:b}".format(42)\n \'int: 42; hex: 2a; oct: 52; bin: 101010\'\n >>> # with 0x, 0o, or 0b as prefix:\n >>> "int: {0:d}; hex: {0:#x}; oct: {0:#o}; bin: {0:#b}".format(42)\n \'int: 42; hex: 0x2a; oct: 0o52; bin: 0b101010\'\n\nUsing the comma as a thousands separator:\n\n >>> \'{:,}\'.format(1234567890)\n \'1,234,567,890\'\n\nExpressing a percentage:\n\n >>> points = 19\n >>> total = 22\n >>> \'Correct answers: {:.2%}\'.format(points/total)\n \'Correct answers: 86.36%\'\n\nUsing type-specific formatting:\n\n >>> import datetime\n >>> d = datetime.datetime(2010, 7, 4, 12, 15, 58)\n >>> \'{:%Y-%m-%d %H:%M:%S}\'.format(d)\n \'2010-07-04 12:15:58\'\n\nNesting arguments and more complex examples:\n\n >>> for align, text in zip(\'<^>\', [\'left\', \'center\', \'right\']):\n ... \'{0:{fill}{align}16}\'.format(text, fill=align, align=align)\n ...\n \'left<<<<<<<<<<<<\'\n \'^^^^^center^^^^^\'\n \'>>>>>>>>>>>right\'\n >>>\n >>> octets = [192, 168, 0, 1]\n >>> \'{:02X}{:02X}{:02X}{:02X}\'.format(*octets)\n \'C0A80001\'\n >>> int(_, 16)\n 3232235521\n >>>\n >>> width = 5\n >>> for num in range(5,12): #doctest: +NORMALIZE_WHITESPACE\n ... for base in \'dXob\':\n ... print(\'{0:{width}{base}}\'.format(num, base=base, width=width), end=\' \')\n ... print()\n ...\n 5 5 5 101\n 6 6 6 110\n 7 7 7 111\n 8 8 10 1000\n 9 9 11 1001\n 10 A 12 1010\n 11 B 13 1011\n', 'function': '\nFunction definitions\n********************\n\nA function definition defines a user-defined function object (see\nsection *The standard type hierarchy*):\n\n funcdef ::= [decorators] "def" funcname "(" [parameter_list] ")" ["->" expression] ":" suite\n decorators ::= decorator+\n decorator ::= "@" dotted_name ["(" [parameter_list [","]] ")"] NEWLINE\n dotted_name ::= identifier ("." identifier)*\n parameter_list ::= (defparameter ",")*\n ( "*" [parameter] ("," defparameter)* ["," "**" parameter]\n | "**" parameter\n | defparameter [","] )\n parameter ::= identifier [":" expression]\n defparameter ::= parameter ["=" expression]\n funcname ::= identifier\n\nA function definition is an executable statement. Its execution binds\nthe function name in the current local namespace to a function object\n(a wrapper around the executable code for the function). This\nfunction object contains a reference to the current global namespace\nas the global namespace to be used when the function is called.\n\nThe function definition does not execute the function body; this gets\nexecuted only when the function is called. [3]\n\nA function definition may be wrapped by one or more *decorator*\nexpressions. Decorator expressions are evaluated when the function is\ndefined, in the scope that contains the function definition. The\nresult must be a callable, which is invoked with the function object\nas the only argument. The returned value is bound to the function name\ninstead of the function object. Multiple decorators are applied in\nnested fashion. For example, the following code\n\n @f1(arg)\n @f2\n def func(): pass\n\nis equivalent to\n\n def func(): pass\n func = f1(arg)(f2(func))\n\nWhen one or more *parameters* have the form *parameter* =\n*expression*, the function is said to have "default parameter values."\nFor a parameter with a default value, the corresponding *argument* may\nbe omitted from a call, in which case the parameter\'s default value is\nsubstituted. If a parameter has a default value, all following\nparameters up until the "*" must also have a default value ---\nthis is a syntactic restriction that is not expressed by the grammar.\n\n**Default parameter values are evaluated from left to right when the\nfunction definition is executed.** This means that the expression is\nevaluated once, when the function is defined, and that the same "pre-\ncomputed" value is used for each call. This is especially important\nto understand when a default parameter is a mutable object, such as a\nlist or a dictionary: if the function modifies the object (e.g. by\nappending an item to a list), the default value is in effect modified.\nThis is generally not what was intended. A way around this is to use\nNone as the default, and explicitly test for it in the body of the\nfunction, e.g.:\n\n def whats_on_the_telly(penguin=None):\n if penguin is None:\n penguin = []\n penguin.append("property of the zoo")\n return penguin\n\nFunction call semantics are described in more detail in section\n*Calls*. A function call always assigns values to all parameters\nmentioned in the parameter list, either from position arguments, from\nkeyword arguments, or from default values. If the form\n"*identifier" is present, it is initialized to a tuple receiving\nany excess positional parameters, defaulting to the empty tuple. If\nthe form "**identifier" is present, it is initialized to a new\ndictionary receiving any excess keyword arguments, defaulting to a new\nempty dictionary. Parameters after "*" or "*identifier" are\nkeyword-only parameters and may only be passed used keyword arguments.\n\nParameters may have annotations of the form ": expression"\nfollowing the parameter name. Any parameter may have an annotation\neven those of the form *identifier or **identifier. Functions\nmay have "return" annotation of the form "-> expression" after the\nparameter list. These annotations can be any valid Python expression\nand are evaluated when the function definition is executed.\nAnnotations may be evaluated in a different order than they appear in\nthe source code. The presence of annotations does not change the\nsemantics of a function. The annotation values are available as\nvalues of a dictionary keyed by the parameters\' names in the\n__annotations__ attribute of the function object.\n\nIt is also possible to create anonymous functions (functions not bound\nto a name), for immediate use in expressions. This uses lambda forms,\ndescribed in section *Lambdas*. Note that the lambda form is merely a\nshorthand for a simplified function definition; a function defined in\na "def" statement can be passed around or assigned to another name\njust like a function defined by a lambda form. The "def" form is\nactually more powerful since it allows the execution of multiple\nstatements and annotations.\n\n**Programmer\'s note:** Functions are first-class objects. A "def"\nform executed inside a function definition defines a local function\nthat can be returned or passed around. Free variables used in the\nnested function can access the local variables of the function\ncontaining the def. See section *Naming and binding* for details.\n\nSee also:\n\n **PEP 3107** - Function Annotations\n The original specification for function annotations.\n', 'global': '\nThe global statement\n************************\n\n global_stmt ::= "global" identifier ("," identifier)*\n\nThe global statement is a declaration which holds for the entire\ncurrent code block. It means that the listed identifiers are to be\ninterpreted as globals. It would be impossible to assign to a global\nvariable without global, although free variables may refer to\nglobals without being declared global.\n\nNames listed in a global statement must not be used in the same\ncode block textually preceding that global statement.\n\nNames listed in a global statement must not be defined as formal\nparameters or in a for loop control target, class definition,\nfunction definition, or import statement.\n\n**CPython implementation detail:** The current implementation does not\nenforce the latter two restrictions, but programs should not abuse\nthis freedom, as future implementations may enforce them or silently\nchange the meaning of the program.\n\n**Programmer\'s note:** the global is a directive to the parser.\nIt applies only to code parsed at the same time as the global\nstatement. In particular, a global statement contained in a string\nor code object supplied to the built-in exec() function does not\naffect the code block *containing* the function call, and code\ncontained in such a string is unaffected by global statements in\nthe code containing the function call. The same applies to the\neval() and compile() functions.\n', 'id-classes': '\nReserved classes of identifiers\n*******************************\n\nCertain classes of identifiers (besides keywords) have special\nmeanings. These classes are identified by the patterns of leading and\ntrailing underscore characters:\n\n_*\n Not imported by from module import *. The special identifier\n _ is used in the interactive interpreter to store the result of\n the last evaluation; it is stored in the builtins module. When\n not in interactive mode, _ has no special meaning and is not\n defined. See section *The import statement*.\n\n Note: The name _ is often used in conjunction with\n internationalization; refer to the documentation for the\n gettext module for more information on this convention.\n\n__*__\n System-defined names. These names are defined by the interpreter\n and its implementation (including the standard library). Current\n system names are discussed in the *Special method names* section\n and elsewhere. More will likely be defined in future versions of\n Python. *Any* use of __*__ names, in any context, that does\n not follow explicitly documented use, is subject to breakage\n without warning.\n\n__*\n Class-private names. Names in this category, when used within the\n context of a class definition, are re-written to use a mangled form\n to help avoid name clashes between "private" attributes of base and\n derived classes. See section *Identifiers (Names)*.\n', 'identifiers': '\nIdentifiers and keywords\n************************\n\nIdentifiers (also referred to as *names*) are described by the\nfollowing lexical definitions.\n\nThe syntax of identifiers in Python is based on the Unicode standard\nannex UAX-31, with elaboration and changes as defined below; see also\n**PEP 3131** for further details.\n\nWithin the ASCII range (U+0001..U+007F), the valid characters for\nidentifiers are the same as in Python 2.x: the uppercase and lowercase\nletters A through Z, the underscore _ and, except for the\nfirst character, the digits 0 through 9.\n\nPython 3.0 introduces additional characters from outside the ASCII\nrange (see **PEP 3131**). For these characters, the classification\nuses the version of the Unicode Character Database as included in the\nunicodedata module.\n\nIdentifiers are unlimited in length. Case is significant.\n\n identifier ::= xid_start xid_continue*\n id_start ::= \n id_continue ::= \n xid_start ::= \n xid_continue ::= \n\nThe Unicode category codes mentioned above stand for:\n\n* *Lu* - uppercase letters\n\n* *Ll* - lowercase letters\n\n* *Lt* - titlecase letters\n\n* *Lm* - modifier letters\n\n* *Lo* - other letters\n\n* *Nl* - letter numbers\n\n* *Mn* - nonspacing marks\n\n* *Mc* - spacing combining marks\n\n* *Nd* - decimal numbers\n\n* *Pc* - connector punctuations\n\n* *Other_ID_Start* - explicit list of characters in PropList.txt to\n support backwards compatibility\n\n* *Other_ID_Continue* - likewise\n\nAll identifiers are converted into the normal form NFKC while parsing;\ncomparison of identifiers is based on NFKC.\n\nA non-normative HTML file listing all valid identifier characters for\nUnicode 4.1 can be found at http://www.dcl.hpi.uni-\npotsdam.de/home/loewis/table-3131.html.\n\n\nKeywords\n========\n\nThe following identifiers are used as reserved words, or *keywords* of\nthe language, and cannot be used as ordinary identifiers. They must\nbe spelled exactly as written here:\n\n False class finally is return\n None continue for lambda try\n True def from nonlocal while\n and del global not with\n as elif if or yield\n assert else import pass\n break except in raise\n\n\nReserved classes of identifiers\n===============================\n\nCertain classes of identifiers (besides keywords) have special\nmeanings. These classes are identified by the patterns of leading and\ntrailing underscore characters:\n\n_*\n Not imported by from module import *. The special identifier\n _ is used in the interactive interpreter to store the result of\n the last evaluation; it is stored in the builtins module. When\n not in interactive mode, _ has no special meaning and is not\n defined. See section *The import statement*.\n\n Note: The name _ is often used in conjunction with\n internationalization; refer to the documentation for the\n gettext module for more information on this convention.\n\n__*__\n System-defined names. These names are defined by the interpreter\n and its implementation (including the standard library). Current\n system names are discussed in the *Special method names* section\n and elsewhere. More will likely be defined in future versions of\n Python. *Any* use of __*__ names, in any context, that does\n not follow explicitly documented use, is subject to breakage\n without warning.\n\n__*\n Class-private names. Names in this category, when used within the\n context of a class definition, are re-written to use a mangled form\n to help avoid name clashes between "private" attributes of base and\n derived classes. See section *Identifiers (Names)*.\n', 'if': '\nThe if statement\n********************\n\nThe if statement is used for conditional execution:\n\n if_stmt ::= "if" expression ":" suite\n ( "elif" expression ":" suite )*\n ["else" ":" suite]\n\nIt selects exactly one of the suites by evaluating the expressions one\nby one until one is found to be true (see section *Boolean operations*\nfor the definition of true and false); then that suite is executed\n(and no other part of the if statement is executed or evaluated).\nIf all expressions are false, the suite of the else clause, if\npresent, is executed.\n', 'imaginary': '\nImaginary literals\n******************\n\nImaginary literals are described by the following lexical definitions:\n\n imagnumber ::= (floatnumber | intpart) ("j" | "J")\n\nAn imaginary literal yields a complex number with a real part of 0.0.\nComplex numbers are represented as a pair of floating point numbers\nand have the same restrictions on their range. To create a complex\nnumber with a nonzero real part, add a floating point number to it,\ne.g., (3+4j). Some examples of imaginary literals:\n\n 3.14j 10.j 10j .001j 1e100j 3.14e-10j\n', 'import': '\nThe import statement\n************************\n\n import_stmt ::= "import" module ["as" name] ( "," module ["as" name] )*\n | "from" relative_module "import" identifier ["as" name]\n ( "," identifier ["as" name] )*\n | "from" relative_module "import" "(" identifier ["as" name]\n ( "," identifier ["as" name] )* [","] ")"\n | "from" module "import" "*"\n module ::= (identifier ".")* identifier\n relative_module ::= "."* module | "."+\n name ::= identifier\n\nThe basic import statement (no from clause) is executed in two\nsteps:\n\n1. find a module, loading and initializing it if necessary\n\n2. define a name or names in the local namespace for the scope where\n the import statement occurs.\n\nWhen the statement contains multiple clauses (separated by commas) the\ntwo steps are carried out separately for each clause, just as though\nthe clauses had been separated out into individiual import statements.\n\nThe details of the first step, finding and loading modules is\ndescribed in greater detail in the section on the *import system*,\nwhich also describes the various types of packages and modules that\ncan be imported, as well as all the hooks that can be used to\ncustomize the import system. Note that failures in this step may\nindicate either that the module could not be located, *or* that an\nerror occurred while initializing the module, which includes execution\nof the module\'s code.\n\nIf the requested module is retrieved successfully, it will be made\navailable in the local namespace in one of three ways:\n\n* If the module name is followed by as, then the name following\n as is bound directly to the imported module.\n\n* If no other name is specified, and the module being imported is a\n top level module, the module\'s name is bound in the local namespace\n as a reference to the imported module\n\n* If the module being imported is *not* a top level module, then the\n name of the top level package that contains the module is bound in\n the local namespace as a reference to the top level package. The\n imported module must be accessed using its full qualified name\n rather than directly\n\nThe from form uses a slightly more complex process:\n\n1. find the module specified in the from clause loading and\n initializing it if necessary;\n\n2. for each of the identifiers specified in the import clauses:\n\n 1. check if the imported module has an attribute by that name\n\n 2. if not, attempt to import a submodule with that name and then\n check the imported module again for that attribute\n\n 3. if the attribute is not found, ImportError is raised.\n\n 4. otherwise, a reference to that value is bound in the local\n namespace, using the name in the as clause if it is present,\n otherwise using the attribute name\n\nExamples:\n\n import foo # foo imported and bound locally\n import foo.bar.baz # foo.bar.baz imported, foo bound locally\n import foo.bar.baz as fbb # foo.bar.baz imported and bound as fbb\n from foo.bar import baz # foo.bar.baz imported and bound as baz\n from foo import attr # foo imported and foo.attr bound as attr\n\nIf the list of identifiers is replaced by a star (\'*\'), all public\nnames defined in the module are bound in the local namespace for the\nscope where the import statement occurs.\n\nThe *public names* defined by a module are determined by checking the\nmodule\'s namespace for a variable named __all__; if defined, it\nmust be a sequence of strings which are names defined or imported by\nthat module. The names given in __all__ are all considered public\nand are required to exist. If __all__ is not defined, the set of\npublic names includes all names found in the module\'s namespace which\ndo not begin with an underscore character (\'_\'). __all__\nshould contain the entire public API. It is intended to avoid\naccidentally exporting items that are not part of the API (such as\nlibrary modules which were imported and used within the module).\n\nThe from form with * may only occur in a module scope. The\nwild card form of import --- import * --- is only allowed at the\nmodule level. Attempting to use it in class or function definitions\nwill raise a SyntaxError.\n\nWhen specifying what module to import you do not have to specify the\nabsolute name of the module. When a module or package is contained\nwithin another package it is possible to make a relative import within\nthe same top package without having to mention the package name. By\nusing leading dots in the specified module or package after from\nyou can specify how high to traverse up the current package hierarchy\nwithout specifying exact names. One leading dot means the current\npackage where the module making the import exists. Two dots means up\none package level. Three dots is up two levels, etc. So if you execute\nfrom . import mod from a module in the pkg package then you\nwill end up importing pkg.mod. If you execute from ..subpkg2\nimport mod from within pkg.subpkg1 you will import\npkg.subpkg2.mod. The specification for relative imports is\ncontained within **PEP 328**.\n\nimportlib.import_module() is provided to support applications that\ndetermine which modules need to be loaded dynamically.\n\n\nFuture statements\n=================\n\nA *future statement* is a directive to the compiler that a particular\nmodule should be compiled using syntax or semantics that will be\navailable in a specified future release of Python. The future\nstatement is intended to ease migration to future versions of Python\nthat introduce incompatible changes to the language. It allows use of\nthe new features on a per-module basis before the release in which the\nfeature becomes standard.\n\n future_statement ::= "from" "__future__" "import" feature ["as" name]\n ("," feature ["as" name])*\n | "from" "__future__" "import" "(" feature ["as" name]\n ("," feature ["as" name])* [","] ")"\n feature ::= identifier\n name ::= identifier\n\nA future statement must appear near the top of the module. The only\nlines that can appear before a future statement are:\n\n* the module docstring (if any),\n\n* comments,\n\n* blank lines, and\n\n* other future statements.\n\nThe features recognized by Python 3.0 are absolute_import,\ndivision, generators, unicode_literals,\nprint_function, nested_scopes and with_statement. They\nare all redundant because they are always enabled, and only kept for\nbackwards compatibility.\n\nA future statement is recognized and treated specially at compile\ntime: Changes to the semantics of core constructs are often\nimplemented by generating different code. It may even be the case\nthat a new feature introduces new incompatible syntax (such as a new\nreserved word), in which case the compiler may need to parse the\nmodule differently. Such decisions cannot be pushed off until\nruntime.\n\nFor any given release, the compiler knows which feature names have\nbeen defined, and raises a compile-time error if a future statement\ncontains a feature not known to it.\n\nThe direct runtime semantics are the same as for any import statement:\nthere is a standard module __future__, described later, and it\nwill be imported in the usual way at the time the future statement is\nexecuted.\n\nThe interesting runtime semantics depend on the specific feature\nenabled by the future statement.\n\nNote that there is nothing special about the statement:\n\n import __future__ [as name]\n\nThat is not a future statement; it\'s an ordinary import statement with\nno special semantics or syntax restrictions.\n\nCode compiled by calls to the built-in functions exec() and\ncompile() that occur in a module M containing a future\nstatement will, by default, use the new syntax or semantics associated\nwith the future statement. This can be controlled by optional\narguments to compile() --- see the documentation of that function\nfor details.\n\nA future statement typed at an interactive interpreter prompt will\ntake effect for the rest of the interpreter session. If an\ninterpreter is started with the *-i* option, is passed a script name\nto execute, and the script includes a future statement, it will be in\neffect in the interactive session started after the script is\nexecuted.\n\nSee also:\n\n **PEP 236** - Back to the __future__\n The original proposal for the __future__ mechanism.\n', 'in': '\nComparisons\n***********\n\nUnlike C, all comparison operations in Python have the same priority,\nwhich is lower than that of any arithmetic, shifting or bitwise\noperation. Also unlike C, expressions like a < b < c have the\ninterpretation that is conventional in mathematics:\n\n comparison ::= or_expr ( comp_operator or_expr )*\n comp_operator ::= "<" | ">" | "==" | ">=" | "<=" | "!="\n | "is" ["not"] | ["not"] "in"\n\nComparisons yield boolean values: True or False.\n\nComparisons can be chained arbitrarily, e.g., x < y <= z is\nequivalent to x < y and y <= z, except that y is evaluated\nonly once (but in both cases z is not evaluated at all when x <\ny is found to be false).\n\nFormally, if *a*, *b*, *c*, ..., *y*, *z* are expressions and *op1*,\n*op2*, ..., *opN* are comparison operators, then a op1 b op2 c ... y\nopN z is equivalent to a op1 b and b op2 c and ... y opN z,\nexcept that each expression is evaluated at most once.\n\nNote that a op1 b op2 c doesn\'t imply any kind of comparison\nbetween *a* and *c*, so that, e.g., x < y > z is perfectly legal\n(though perhaps not pretty).\n\nThe operators <, >, ==, >=, <=, and != compare\nthe values of two objects. The objects need not have the same type.\nIf both are numbers, they are converted to a common type. Otherwise,\nthe == and != operators *always* consider objects of different\ntypes to be unequal, while the <, >, >= and <=\noperators raise a TypeError when comparing objects of different\ntypes that do not implement these operators for the given pair of\ntypes. You can control comparison behavior of objects of non-built-in\ntypes by defining rich comparison methods like __gt__(), described\nin section *Basic customization*.\n\nComparison of objects of the same type depends on the type:\n\n* Numbers are compared arithmetically.\n\n* The values float(\'NaN\') and Decimal(\'NaN\') are special. The\n are identical to themselves, x is x but are not equal to\n themselves, x != x. Additionally, comparing any value to a\n not-a-number value will return False. For example, both 3 <\n float(\'NaN\') and float(\'NaN\') < 3 will return False.\n\n* Bytes objects are compared lexicographically using the numeric\n values of their elements.\n\n* Strings are compared lexicographically using the numeric equivalents\n (the result of the built-in function ord()) of their characters.\n [3] String and bytes object can\'t be compared!\n\n* Tuples and lists are compared lexicographically using comparison of\n corresponding elements. This means that to compare equal, each\n element must compare equal and the two sequences must be of the same\n type and have the same length.\n\n If not equal, the sequences are ordered the same as their first\n differing elements. For example, [1,2,x] <= [1,2,y] has the\n same value as x <= y. If the corresponding element does not\n exist, the shorter sequence is ordered first (for example, [1,2] <\n [1,2,3]).\n\n* Mappings (dictionaries) compare equal if and only if they have the\n same (key, value) pairs. Order comparisons (\'<\', \'<=\', \'>=\',\n \'>\') raise TypeError.\n\n* Sets and frozensets define comparison operators to mean subset and\n superset tests. Those relations do not define total orderings (the\n two sets {1,2} and {2,3} are not equal, nor subsets of one\n another, nor supersets of one another). Accordingly, sets are not\n appropriate arguments for functions which depend on total ordering.\n For example, min(), max(), and sorted() produce\n undefined results given a list of sets as inputs.\n\n* Most other objects of built-in types compare unequal unless they are\n the same object; the choice whether one object is considered smaller\n or larger than another one is made arbitrarily but consistently\n within one execution of a program.\n\nComparison of objects of the differing types depends on whether either\nof the types provide explicit support for the comparison. Most\nnumeric types can be compared with one another. When cross-type\ncomparison is not supported, the comparison method returns\nNotImplemented.\n\nThe operators in and not in test for membership. x in s\nevaluates to true if *x* is a member of *s*, and false otherwise. x\nnot in s returns the negation of x in s. All built-in sequences\nand set types support this as well as dictionary, for which in\ntests whether a the dictionary has a given key. For container types\nsuch as list, tuple, set, frozenset, dict, or collections.deque, the\nexpression x in y is equivalent to any(x is e or x == e for e in\ny).\n\nFor the string and bytes types, x in y is true if and only if *x*\nis a substring of *y*. An equivalent test is y.find(x) != -1.\nEmpty strings are always considered to be a substring of any other\nstring, so "" in "abc" will return True.\n\nFor user-defined classes which define the __contains__() method,\nx in y is true if and only if y.__contains__(x) is true.\n\nFor user-defined classes which do not define __contains__() but do\ndefine __iter__(), x in y is true if some value z with x\n== z is produced while iterating over y. If an exception is\nraised during the iteration, it is as if in raised that exception.\n\nLastly, the old-style iteration protocol is tried: if a class defines\n__getitem__(), x in y is true if and only if there is a non-\nnegative integer index *i* such that x == y[i], and all lower\ninteger indices do not raise IndexError exception. (If any other\nexception is raised, it is as if in raised that exception).\n\nThe operator not in is defined to have the inverse true value of\nin.\n\nThe operators is and is not test for object identity: x is\ny is true if and only if *x* and *y* are the same object. x is\nnot y yields the inverse truth value. [4]\n', 'integers': '\nInteger literals\n****************\n\nInteger literals are described by the following lexical definitions:\n\n integer ::= decimalinteger | octinteger | hexinteger | bininteger\n decimalinteger ::= nonzerodigit digit* | "0"+\n nonzerodigit ::= "1"..."9"\n digit ::= "0"..."9"\n octinteger ::= "0" ("o" | "O") octdigit+\n hexinteger ::= "0" ("x" | "X") hexdigit+\n bininteger ::= "0" ("b" | "B") bindigit+\n octdigit ::= "0"..."7"\n hexdigit ::= digit | "a"..."f" | "A"..."F"\n bindigit ::= "0" | "1"\n\nThere is no limit for the length of integer literals apart from what\ncan be stored in available memory.\n\nNote that leading zeros in a non-zero decimal number are not allowed.\nThis is for disambiguation with C-style octal literals, which Python\nused before version 3.0.\n\nSome examples of integer literals:\n\n 7 2147483647 0o177 0b100110111\n 3 79228162514264337593543950336 0o377 0x100000000\n 79228162514264337593543950336 0xdeadbeef\n', 'lambda': '\nLambdas\n*******\n\n lambda_form ::= "lambda" [parameter_list]: expression\n lambda_form_nocond ::= "lambda" [parameter_list]: expression_nocond\n\nLambda forms (lambda expressions) have the same syntactic position as\nexpressions. They are a shorthand to create anonymous functions; the\nexpression lambda arguments: expression yields a function object.\nThe unnamed object behaves like a function object defined with\n\n def (arguments):\n return expression\n\nSee section *Function definitions* for the syntax of parameter lists.\nNote that functions created with lambda forms cannot contain\nstatements or annotations.\n', 'lists': '\nList displays\n*************\n\nA list display is a possibly empty series of expressions enclosed in\nsquare brackets:\n\n list_display ::= "[" [expression_list | comprehension] "]"\n\nA list display yields a new list object, the contents being specified\nby either a list of expressions or a comprehension. When a comma-\nseparated list of expressions is supplied, its elements are evaluated\nfrom left to right and placed into the list object in that order.\nWhen a comprehension is supplied, the list is constructed from the\nelements resulting from the comprehension.\n', 'naming': "\nNaming and binding\n******************\n\n*Names* refer to objects. Names are introduced by name binding\noperations. Each occurrence of a name in the program text refers to\nthe *binding* of that name established in the innermost function block\ncontaining the use.\n\nA *block* is a piece of Python program text that is executed as a\nunit. The following are blocks: a module, a function body, and a class\ndefinition. Each command typed interactively is a block. A script\nfile (a file given as standard input to the interpreter or specified\non the interpreter command line the first argument) is a code block.\nA script command (a command specified on the interpreter command line\nwith the '**-c**' option) is a code block. The string argument passed\nto the built-in functions eval() and exec() is a code block.\n\nA code block is executed in an *execution frame*. A frame contains\nsome administrative information (used for debugging) and determines\nwhere and how execution continues after the code block's execution has\ncompleted.\n\nA *scope* defines the visibility of a name within a block. If a local\nvariable is defined in a block, its scope includes that block. If the\ndefinition occurs in a function block, the scope extends to any blocks\ncontained within the defining one, unless a contained block introduces\na different binding for the name. The scope of names defined in a\nclass block is limited to the class block; it does not extend to the\ncode blocks of methods -- this includes comprehensions and generator\nexpressions since they are implemented using a function scope. This\nmeans that the following will fail:\n\n class A:\n a = 42\n b = list(a + i for i in range(10))\n\nWhen a name is used in a code block, it is resolved using the nearest\nenclosing scope. The set of all such scopes visible to a code block\nis called the block's *environment*.\n\nIf a name is bound in a block, it is a local variable of that block,\nunless declared as nonlocal. If a name is bound at the module\nlevel, it is a global variable. (The variables of the module code\nblock are local and global.) If a variable is used in a code block\nbut not defined there, it is a *free variable*.\n\nWhen a name is not found at all, a NameError exception is raised.\nIf the name refers to a local variable that has not been bound, a\nUnboundLocalError exception is raised. UnboundLocalError is a\nsubclass of NameError.\n\nThe following constructs bind names: formal parameters to functions,\nimport statements, class and function definitions (these bind the\nclass or function name in the defining block), and targets that are\nidentifiers if occurring in an assignment, for loop header, or\nafter as in a with statement or except clause. The\nimport statement of the form from ... import * binds all names\ndefined in the imported module, except those beginning with an\nunderscore. This form may only be used at the module level.\n\nA target occurring in a del statement is also considered bound for\nthis purpose (though the actual semantics are to unbind the name).\n\nEach assignment or import statement occurs within a block defined by a\nclass or function definition or at the module level (the top-level\ncode block).\n\nIf a name binding operation occurs anywhere within a code block, all\nuses of the name within the block are treated as references to the\ncurrent block. This can lead to errors when a name is used within a\nblock before it is bound. This rule is subtle. Python lacks\ndeclarations and allows name binding operations to occur anywhere\nwithin a code block. The local variables of a code block can be\ndetermined by scanning the entire text of the block for name binding\noperations.\n\nIf the global statement occurs within a block, all uses of the\nname specified in the statement refer to the binding of that name in\nthe top-level namespace. Names are resolved in the top-level\nnamespace by searching the global namespace, i.e. the namespace of the\nmodule containing the code block, and the builtins namespace, the\nnamespace of the module builtins. The global namespace is\nsearched first. If the name is not found there, the builtins\nnamespace is searched. The global statement must precede all uses of\nthe name.\n\nThe builtins namespace associated with the execution of a code block\nis actually found by looking up the name __builtins__ in its\nglobal namespace; this should be a dictionary or a module (in the\nlatter case the module's dictionary is used). By default, when in the\n__main__ module, __builtins__ is the built-in module\nbuiltins; when in any other module, __builtins__ is an alias\nfor the dictionary of the builtins module itself.\n__builtins__ can be set to a user-created dictionary to create a\nweak form of restricted execution.\n\n**CPython implementation detail:** Users should not touch\n__builtins__; it is strictly an implementation detail. Users\nwanting to override values in the builtins namespace should import\nthe builtins module and modify its attributes appropriately.\n\nThe namespace for a module is automatically created the first time a\nmodule is imported. The main module for a script is always called\n__main__.\n\nThe global statement has the same scope as a name binding\noperation in the same block. If the nearest enclosing scope for a\nfree variable contains a global statement, the free variable is\ntreated as a global.\n\nA class definition is an executable statement that may use and define\nnames. These references follow the normal rules for name resolution.\nThe namespace of the class definition becomes the attribute dictionary\nof the class. Names defined at the class scope are not visible in\nmethods.\n\n\nInteraction with dynamic features\n=================================\n\nThere are several cases where Python statements are illegal when used\nin conjunction with nested scopes that contain free variables.\n\nIf a variable is referenced in an enclosing scope, it is illegal to\ndelete the name. An error will be reported at compile time.\n\nIf the wild card form of import --- import * --- is used in a\nfunction and the function contains or is a nested block with free\nvariables, the compiler will raise a SyntaxError.\n\nThe eval() and exec() functions do not have access to the full\nenvironment for resolving names. Names may be resolved in the local\nand global namespaces of the caller. Free variables are not resolved\nin the nearest enclosing namespace, but in the global namespace. [1]\nThe exec() and eval() functions have optional arguments to\noverride the global and local namespace. If only one namespace is\nspecified, it is used for both.\n", 'nonlocal': '\nThe nonlocal statement\n**************************\n\n nonlocal_stmt ::= "nonlocal" identifier ("," identifier)*\n\nThe nonlocal statement causes the listed identifiers to refer to\npreviously bound variables in the nearest enclosing scope. This is\nimportant because the default behavior for binding is to search the\nlocal namespace first. The statement allows encapsulated code to\nrebind variables outside of the local scope besides the global\n(module) scope.\n\nNames listed in a nonlocal statement, unlike to those listed in a\nglobal statement, must refer to pre-existing bindings in an\nenclosing scope (the scope in which a new binding should be created\ncannot be determined unambiguously).\n\nNames listed in a nonlocal statement must not collide with pre-\nexisting bindings in the local scope.\n\nSee also:\n\n **PEP 3104** - Access to Names in Outer Scopes\n The specification for the nonlocal statement.\n', 'numbers': "\nNumeric literals\n****************\n\nThere are three types of numeric literals: integers, floating point\nnumbers, and imaginary numbers. There are no complex literals\n(complex numbers can be formed by adding a real number and an\nimaginary number).\n\nNote that numeric literals do not include a sign; a phrase like -1\nis actually an expression composed of the unary operator '-' and\nthe literal 1.\n", 'numeric-types': "\nEmulating numeric types\n***********************\n\nThe following methods can be defined to emulate numeric objects.\nMethods corresponding to operations that are not supported by the\nparticular kind of number implemented (e.g., bitwise operations for\nnon-integral numbers) should be left undefined.\n\nobject.__add__(self, other)\nobject.__sub__(self, other)\nobject.__mul__(self, other)\nobject.__truediv__(self, other)\nobject.__floordiv__(self, other)\nobject.__mod__(self, other)\nobject.__divmod__(self, other)\nobject.__pow__(self, other[, modulo])\nobject.__lshift__(self, other)\nobject.__rshift__(self, other)\nobject.__and__(self, other)\nobject.__xor__(self, other)\nobject.__or__(self, other)\n\n These methods are called to implement the binary arithmetic\n operations (+, -, *, /, //, %,\n divmod(), pow(), **, <<, >>, &, ^,\n |). For instance, to evaluate the expression x + y, where\n *x* is an instance of a class that has an __add__() method,\n x.__add__(y) is called. The __divmod__() method should be\n the equivalent to using __floordiv__() and __mod__(); it\n should not be related to __truediv__(). Note that\n __pow__() should be defined to accept an optional third\n argument if the ternary version of the built-in pow() function\n is to be supported.\n\n If one of those methods does not support the operation with the\n supplied arguments, it should return NotImplemented.\n\nobject.__radd__(self, other)\nobject.__rsub__(self, other)\nobject.__rmul__(self, other)\nobject.__rtruediv__(self, other)\nobject.__rfloordiv__(self, other)\nobject.__rmod__(self, other)\nobject.__rdivmod__(self, other)\nobject.__rpow__(self, other)\nobject.__rlshift__(self, other)\nobject.__rrshift__(self, other)\nobject.__rand__(self, other)\nobject.__rxor__(self, other)\nobject.__ror__(self, other)\n\n These methods are called to implement the binary arithmetic\n operations (+, -, *, /, //, %,\n divmod(), pow(), **, <<, >>, &, ^,\n |) with reflected (swapped) operands. These functions are only\n called if the left operand does not support the corresponding\n operation and the operands are of different types. [2] For\n instance, to evaluate the expression x - y, where *y* is an\n instance of a class that has an __rsub__() method,\n y.__rsub__(x) is called if x.__sub__(y) returns\n *NotImplemented*.\n\n Note that ternary pow() will not try calling __rpow__()\n (the coercion rules would become too complicated).\n\n Note: If the right operand's type is a subclass of the left operand's\n type and that subclass provides the reflected method for the\n operation, this method will be called before the left operand's\n non-reflected method. This behavior allows subclasses to\n override their ancestors' operations.\n\nobject.__iadd__(self, other)\nobject.__isub__(self, other)\nobject.__imul__(self, other)\nobject.__itruediv__(self, other)\nobject.__ifloordiv__(self, other)\nobject.__imod__(self, other)\nobject.__ipow__(self, other[, modulo])\nobject.__ilshift__(self, other)\nobject.__irshift__(self, other)\nobject.__iand__(self, other)\nobject.__ixor__(self, other)\nobject.__ior__(self, other)\n\n These methods are called to implement the augmented arithmetic\n assignments (+=, -=, *=, /=, //=, %=,\n **=, <<=, >>=, &=, ^=, |=). These methods\n should attempt to do the operation in-place (modifying *self*) and\n return the result (which could be, but does not have to be,\n *self*). If a specific method is not defined, the augmented\n assignment falls back to the normal methods. For instance, to\n execute the statement x += y, where *x* is an instance of a\n class that has an __iadd__() method, x.__iadd__(y) is\n called. If *x* is an instance of a class that does not define a\n __iadd__() method, x.__add__(y) and y.__radd__(x) are\n considered, as with the evaluation of x + y.\n\nobject.__neg__(self)\nobject.__pos__(self)\nobject.__abs__(self)\nobject.__invert__(self)\n\n Called to implement the unary arithmetic operations (-, +,\n abs() and ~).\n\nobject.__complex__(self)\nobject.__int__(self)\nobject.__float__(self)\nobject.__round__(self[, n])\n\n Called to implement the built-in functions complex(),\n int(), float() and round(). Should return a value of\n the appropriate type.\n\nobject.__index__(self)\n\n Called to implement operator.index(). Also called whenever\n Python needs an integer object (such as in slicing, or in the\n built-in bin(), hex() and oct() functions). Must return\n an integer.\n", 'objects': '\nObjects, values and types\n*************************\n\n*Objects* are Python\'s abstraction for data. All data in a Python\nprogram is represented by objects or by relations between objects. (In\na sense, and in conformance to Von Neumann\'s model of a "stored\nprogram computer," code is also represented by objects.)\n\nEvery object has an identity, a type and a value. An object\'s\n*identity* never changes once it has been created; you may think of it\nas the object\'s address in memory. The \'is\' operator compares the\nidentity of two objects; the id() function returns an integer\nrepresenting its identity.\n\n**CPython implementation detail:** For CPython, id(x) is the\nmemory address where x is stored.\n\nAn object\'s type determines the operations that the object supports\n(e.g., "does it have a length?") and also defines the possible values\nfor objects of that type. The type() function returns an object\'s\ntype (which is an object itself). Like its identity, an object\'s\n*type* is also unchangeable. [1]\n\nThe *value* of some objects can change. Objects whose value can\nchange are said to be *mutable*; objects whose value is unchangeable\nonce they are created are called *immutable*. (The value of an\nimmutable container object that contains a reference to a mutable\nobject can change when the latter\'s value is changed; however the\ncontainer is still considered immutable, because the collection of\nobjects it contains cannot be changed. So, immutability is not\nstrictly the same as having an unchangeable value, it is more subtle.)\nAn object\'s mutability is determined by its type; for instance,\nnumbers, strings and tuples are immutable, while dictionaries and\nlists are mutable.\n\nObjects are never explicitly destroyed; however, when they become\nunreachable they may be garbage-collected. An implementation is\nallowed to postpone garbage collection or omit it altogether --- it is\na matter of implementation quality how garbage collection is\nimplemented, as long as no objects are collected that are still\nreachable.\n\n**CPython implementation detail:** CPython currently uses a reference-\ncounting scheme with (optional) delayed detection of cyclically linked\ngarbage, which collects most objects as soon as they become\nunreachable, but is not guaranteed to collect garbage containing\ncircular references. See the documentation of the gc module for\ninformation on controlling the collection of cyclic garbage. Other\nimplementations act differently and CPython may change. Do not depend\non immediate finalization of objects when they become unreachable (ex:\nalways close files).\n\nNote that the use of the implementation\'s tracing or debugging\nfacilities may keep objects alive that would normally be collectable.\nAlso note that catching an exception with a \'try...except\'\nstatement may keep objects alive.\n\nSome objects contain references to "external" resources such as open\nfiles or windows. It is understood that these resources are freed\nwhen the object is garbage-collected, but since garbage collection is\nnot guaranteed to happen, such objects also provide an explicit way to\nrelease the external resource, usually a close() method. Programs\nare strongly recommended to explicitly close such objects. The\n\'try...finally\' statement and the \'with\' statement provide\nconvenient ways to do this.\n\nSome objects contain references to other objects; these are called\n*containers*. Examples of containers are tuples, lists and\ndictionaries. The references are part of a container\'s value. In\nmost cases, when we talk about the value of a container, we imply the\nvalues, not the identities of the contained objects; however, when we\ntalk about the mutability of a container, only the identities of the\nimmediately contained objects are implied. So, if an immutable\ncontainer (like a tuple) contains a reference to a mutable object, its\nvalue changes if that mutable object is changed.\n\nTypes affect almost all aspects of object behavior. Even the\nimportance of object identity is affected in some sense: for immutable\ntypes, operations that compute new values may actually return a\nreference to any existing object with the same type and value, while\nfor mutable objects this is not allowed. E.g., after a = 1; b =\n1, a and b may or may not refer to the same object with the\nvalue one, depending on the implementation, but after c = []; d =\n[], c and d are guaranteed to refer to two different,\nunique, newly created empty lists. (Note that c = d = [] assigns\nthe same object to both c and d.)\n', 'operator-summary': '\nOperator precedence\n*******************\n\nThe following table summarizes the operator precedences in Python,\nfrom lowest precedence (least binding) to highest precedence (most\nbinding). Operators in the same box have the same precedence. Unless\nthe syntax is explicitly given, operators are binary. Operators in\nthe same box group left to right (except for comparisons, including\ntests, which all have the same precedence and chain from left to right\n--- see section *Comparisons* --- and exponentiation, which groups\nfrom right to left).\n\n+-------------------------------------------------+---------------------------------------+\n| Operator | Description |\n+=================================================+=======================================+\n| lambda | Lambda expression |\n+-------------------------------------------------+---------------------------------------+\n| if -- else | Conditional expression |\n+-------------------------------------------------+---------------------------------------+\n| or | Boolean OR |\n+-------------------------------------------------+---------------------------------------+\n| and | Boolean AND |\n+-------------------------------------------------+---------------------------------------+\n| not x | Boolean NOT |\n+-------------------------------------------------+---------------------------------------+\n| in, not in, is, is not, <, | Comparisons, including membership |\n| <=, >, >=, !=, == | tests and identity tests |\n+-------------------------------------------------+---------------------------------------+\n| | | Bitwise OR |\n+-------------------------------------------------+---------------------------------------+\n| ^ | Bitwise XOR |\n+-------------------------------------------------+---------------------------------------+\n| & | Bitwise AND |\n+-------------------------------------------------+---------------------------------------+\n| <<, >> | Shifts |\n+-------------------------------------------------+---------------------------------------+\n| +, - | Addition and subtraction |\n+-------------------------------------------------+---------------------------------------+\n| *, /, //, % | Multiplication, division, remainder |\n| | [5] |\n+-------------------------------------------------+---------------------------------------+\n| +x, -x, ~x | Positive, negative, bitwise NOT |\n+-------------------------------------------------+---------------------------------------+\n| ** | Exponentiation [6] |\n+-------------------------------------------------+---------------------------------------+\n| x[index], x[index:index], | Subscription, slicing, call, |\n| x(arguments...), x.attribute | attribute reference |\n+-------------------------------------------------+---------------------------------------+\n| (expressions...), [expressions...], | Binding or tuple display, list |\n| {key: value...}, {expressions...} | display, dictionary display, set |\n| | display |\n+-------------------------------------------------+---------------------------------------+\n\n-[ Footnotes ]-\n\n[1] While abs(x%y) < abs(y) is true mathematically, for floats it\n may not be true numerically due to roundoff. For example, and\n assuming a platform on which a Python float is an IEEE 754 double-\n precision number, in order that -1e-100 % 1e100 have the same\n sign as 1e100, the computed result is -1e-100 + 1e100,\n which is numerically exactly equal to 1e100. The function\n math.fmod() returns a result whose sign matches the sign of\n the first argument instead, and so returns -1e-100 in this\n case. Which approach is more appropriate depends on the\n application.\n\n[2] If x is very close to an exact integer multiple of y, it\'s\n possible for x//y to be one larger than (x-x%y)//y due to\n rounding. In such cases, Python returns the latter result, in\n order to preserve that divmod(x,y)[0] * y + x % y be very\n close to x.\n\n[3] While comparisons between strings make sense at the byte level,\n they may be counter-intuitive to users. For example, the strings\n "\\u00C7" and "\\u0327\\u0043" compare differently, even\n though they both represent the same unicode character (LATIN\n CAPITAL LETTER C WITH CEDILLA). To compare strings in a human\n recognizable way, compare using unicodedata.normalize().\n\n[4] Due to automatic garbage-collection, free lists, and the dynamic\n nature of descriptors, you may notice seemingly unusual behaviour\n in certain uses of the is operator, like those involving\n comparisons between instance methods, or constants. Check their\n documentation for more info.\n\n[5] The % operator is also used for string formatting; the same\n precedence applies.\n\n[6] The power operator ** binds less tightly than an arithmetic or\n bitwise unary operator on its right, that is, 2**-1 is\n 0.5.\n', 'pass': '\nThe pass statement\n**********************\n\n pass_stmt ::= "pass"\n\npass is a null operation --- when it is executed, nothing happens.\nIt is useful as a placeholder when a statement is required\nsyntactically, but no code needs to be executed, for example:\n\n def f(arg): pass # a function that does nothing (yet)\n\n class C: pass # a class with no methods (yet)\n', 'power': '\nThe power operator\n******************\n\nThe power operator binds more tightly than unary operators on its\nleft; it binds less tightly than unary operators on its right. The\nsyntax is:\n\n power ::= primary ["**" u_expr]\n\nThus, in an unparenthesized sequence of power and unary operators, the\noperators are evaluated from right to left (this does not constrain\nthe evaluation order for the operands): -1**2 results in -1.\n\nThe power operator has the same semantics as the built-in pow()\nfunction, when called with two arguments: it yields its left argument\nraised to the power of its right argument. The numeric arguments are\nfirst converted to a common type, and the result is of that type.\n\nFor int operands, the result has the same type as the operands unless\nthe second argument is negative; in that case, all arguments are\nconverted to float and a float result is delivered. For example,\n10**2 returns 100, but 10**-2 returns 0.01.\n\nRaising 0.0 to a negative power results in a\nZeroDivisionError. Raising a negative number to a fractional power\nresults in a complex number. (In earlier versions it raised a\nValueError.)\n', 'raise': '\nThe raise statement\n***********************\n\n raise_stmt ::= "raise" [expression ["from" expression]]\n\nIf no expressions are present, raise re-raises the last exception\nthat was active in the current scope. If no exception is active in\nthe current scope, a RuntimeError exception is raised indicating\nthat this is an error.\n\nOtherwise, raise evaluates the first expression as the exception\nobject. It must be either a subclass or an instance of\nBaseException. If it is a class, the exception instance will be\nobtained when needed by instantiating the class with no arguments.\n\nThe *type* of the exception is the exception instance\'s class, the\n*value* is the instance itself.\n\nA traceback object is normally created automatically when an exception\nis raised and attached to it as the __traceback__ attribute, which\nis writable. You can create an exception and set your own traceback in\none step using the with_traceback() exception method (which\nreturns the same exception instance, with its traceback set to its\nargument), like so:\n\n raise Exception("foo occurred").with_traceback(tracebackobj)\n\nThe from clause is used for exception chaining: if given, the\nsecond *expression* must be another exception class or instance, which\nwill then be attached to the raised exception as the __cause__\nattribute (which is writable). If the raised exception is not\nhandled, both exceptions will be printed:\n\n >>> try:\n ... print(1 / 0)\n ... except Exception as exc:\n ... raise RuntimeError("Something bad happened") from exc\n ...\n Traceback (most recent call last):\n File "", line 2, in \n ZeroDivisionError: int division or modulo by zero\n\n The above exception was the direct cause of the following exception:\n\n Traceback (most recent call last):\n File "", line 4, in \n RuntimeError: Something bad happened\n\nA similar mechanism works implicitly if an exception is raised inside\nan exception handler: the previous exception is then attached as the\nnew exception\'s __context__ attribute:\n\n >>> try:\n ... print(1 / 0)\n ... except:\n ... raise RuntimeError("Something bad happened")\n ...\n Traceback (most recent call last):\n File "", line 2, in \n ZeroDivisionError: int division or modulo by zero\n\n During handling of the above exception, another exception occurred:\n\n Traceback (most recent call last):\n File "", line 4, in \n RuntimeError: Something bad happened\n\nAdditional information on exceptions can be found in section\n*Exceptions*, and information about handling exceptions is in section\n*The try statement*.\n', 'return': '\nThe return statement\n************************\n\n return_stmt ::= "return" [expression_list]\n\nreturn may only occur syntactically nested in a function\ndefinition, not within a nested class definition.\n\nIf an expression list is present, it is evaluated, else None is\nsubstituted.\n\nreturn leaves the current function call with the expression list\n(or None) as return value.\n\nWhen return passes control out of a try statement with a\nfinally clause, that finally clause is executed before really\nleaving the function.\n\nIn a generator function, the return statement indicates that the\ngenerator is done and will cause StopIteration to be raised. The\nreturned value (if any) is used as an argument to construct\nStopIteration and becomes the StopIteration.value attribute.\n', 'sequence-types': "\nEmulating container types\n*************************\n\nThe following methods can be defined to implement container objects.\nContainers usually are sequences (such as lists or tuples) or mappings\n(like dictionaries), but can represent other containers as well. The\nfirst set of methods is used either to emulate a sequence or to\nemulate a mapping; the difference is that for a sequence, the\nallowable keys should be the integers *k* for which 0 <= k < N\nwhere *N* is the length of the sequence, or slice objects, which\ndefine a range of items. It is also recommended that mappings provide\nthe methods keys(), values(), items(), get(),\nclear(), setdefault(), pop(), popitem(), copy(),\nand update() behaving similar to those for Python's standard\ndictionary objects. The collections module provides a\nMutableMapping abstract base class to help create those methods\nfrom a base set of __getitem__(), __setitem__(),\n__delitem__(), and keys(). Mutable sequences should provide\nmethods append(), count(), index(), extend(),\ninsert(), pop(), remove(), reverse() and sort(),\nlike Python standard list objects. Finally, sequence types should\nimplement addition (meaning concatenation) and multiplication (meaning\nrepetition) by defining the methods __add__(), __radd__(),\n__iadd__(), __mul__(), __rmul__() and __imul__()\ndescribed below; they should not define other numerical operators. It\nis recommended that both mappings and sequences implement the\n__contains__() method to allow efficient use of the in\noperator; for mappings, in should search the mapping's keys; for\nsequences, it should search through the values. It is further\nrecommended that both mappings and sequences implement the\n__iter__() method to allow efficient iteration through the\ncontainer; for mappings, __iter__() should be the same as\nkeys(); for sequences, it should iterate through the values.\n\nobject.__len__(self)\n\n Called to implement the built-in function len(). Should return\n the length of the object, an integer >= 0. Also, an object\n that doesn't define a __bool__() method and whose __len__()\n method returns zero is considered to be false in a Boolean context.\n\nobject.__length_hint__(self)\n\n Called to implement operator.length_hint(). Should return an\n estimated length for the object (which may be greater or less than\n the actual length). The length must be an integer >= 0. This\n method is purely an optimization and is never required for\n correctness.\n\n New in version 3.4.\n\nNote: Slicing is done exclusively with the following three methods. A\n call like\n\n a[1:2] = b\n\n is translated to\n\n a[slice(1, 2, None)] = b\n\n and so forth. Missing slice items are always filled in with\n None.\n\nobject.__getitem__(self, key)\n\n Called to implement evaluation of self[key]. For sequence\n types, the accepted keys should be integers and slice objects.\n Note that the special interpretation of negative indexes (if the\n class wishes to emulate a sequence type) is up to the\n __getitem__() method. If *key* is of an inappropriate type,\n TypeError may be raised; if of a value outside the set of\n indexes for the sequence (after any special interpretation of\n negative values), IndexError should be raised. For mapping\n types, if *key* is missing (not in the container), KeyError\n should be raised.\n\n Note: for loops expect that an IndexError will be raised for\n illegal indexes to allow proper detection of the end of the\n sequence.\n\nobject.__setitem__(self, key, value)\n\n Called to implement assignment to self[key]. Same note as for\n __getitem__(). This should only be implemented for mappings if\n the objects support changes to the values for keys, or if new keys\n can be added, or for sequences if elements can be replaced. The\n same exceptions should be raised for improper *key* values as for\n the __getitem__() method.\n\nobject.__delitem__(self, key)\n\n Called to implement deletion of self[key]. Same note as for\n __getitem__(). This should only be implemented for mappings if\n the objects support removal of keys, or for sequences if elements\n can be removed from the sequence. The same exceptions should be\n raised for improper *key* values as for the __getitem__()\n method.\n\nobject.__iter__(self)\n\n This method is called when an iterator is required for a container.\n This method should return a new iterator object that can iterate\n over all the objects in the container. For mappings, it should\n iterate over the keys of the container, and should also be made\n available as the method keys().\n\n Iterator objects also need to implement this method; they are\n required to return themselves. For more information on iterator\n objects, see *Iterator Types*.\n\nobject.__reversed__(self)\n\n Called (if present) by the reversed() built-in to implement\n reverse iteration. It should return a new iterator object that\n iterates over all the objects in the container in reverse order.\n\n If the __reversed__() method is not provided, the\n reversed() built-in will fall back to using the sequence\n protocol (__len__() and __getitem__()). Objects that\n support the sequence protocol should only provide\n __reversed__() if they can provide an implementation that is\n more efficient than the one provided by reversed().\n\nThe membership test operators (in and not in) are normally\nimplemented as an iteration through a sequence. However, container\nobjects can supply the following special method with a more efficient\nimplementation, which also does not require the object be a sequence.\n\nobject.__contains__(self, item)\n\n Called to implement membership test operators. Should return true\n if *item* is in *self*, false otherwise. For mapping objects, this\n should consider the keys of the mapping rather than the values or\n the key-item pairs.\n\n For objects that don't define __contains__(), the membership\n test first tries iteration via __iter__(), then the old\n sequence iteration protocol via __getitem__(), see *this\n section in the language reference*.\n", 'shifting': '\nShifting operations\n*******************\n\nThe shifting operations have lower priority than the arithmetic\noperations:\n\n shift_expr ::= a_expr | shift_expr ( "<<" | ">>" ) a_expr\n\nThese operators accept integers as arguments. They shift the first\nargument to the left or right by the number of bits given by the\nsecond argument.\n\nA right shift by *n* bits is defined as division by pow(2,n). A\nleft shift by *n* bits is defined as multiplication with pow(2,n).\n\nNote: In the current implementation, the right-hand operand is required to\n be at most sys.maxsize. If the right-hand operand is larger\n than sys.maxsize an OverflowError exception is raised.\n', 'slicings': '\nSlicings\n********\n\nA slicing selects a range of items in a sequence object (e.g., a\nstring, tuple or list). Slicings may be used as expressions or as\ntargets in assignment or del statements. The syntax for a\nslicing:\n\n slicing ::= primary "[" slice_list "]"\n slice_list ::= slice_item ("," slice_item)* [","]\n slice_item ::= expression | proper_slice\n proper_slice ::= [lower_bound] ":" [upper_bound] [ ":" [stride] ]\n lower_bound ::= expression\n upper_bound ::= expression\n stride ::= expression\n\nThere is ambiguity in the formal syntax here: anything that looks like\nan expression list also looks like a slice list, so any subscription\ncan be interpreted as a slicing. Rather than further complicating the\nsyntax, this is disambiguated by defining that in this case the\ninterpretation as a subscription takes priority over the\ninterpretation as a slicing (this is the case if the slice list\ncontains no proper slice).\n\nThe semantics for a slicing are as follows. The primary must evaluate\nto a mapping object, and it is indexed (using the same\n__getitem__() method as normal subscription) with a key that is\nconstructed from the slice list, as follows. If the slice list\ncontains at least one comma, the key is a tuple containing the\nconversion of the slice items; otherwise, the conversion of the lone\nslice item is the key. The conversion of a slice item that is an\nexpression is that expression. The conversion of a proper slice is a\nslice object (see section *The standard type hierarchy*) whose\nstart, stop and step attributes are the values of the\nexpressions given as lower bound, upper bound and stride,\nrespectively, substituting None for missing expressions.\n', 'specialattrs': '\nSpecial Attributes\n******************\n\nThe implementation adds a few special read-only attributes to several\nobject types, where they are relevant. Some of these are not reported\nby the dir() built-in function.\n\nobject.__dict__\n\n A dictionary or other mapping object used to store an object\'s\n (writable) attributes.\n\ninstance.__class__\n\n The class to which a class instance belongs.\n\nclass.__bases__\n\n The tuple of base classes of a class object.\n\nclass.__name__\n\n The name of the class or type.\n\nclass.__qualname__\n\n The *qualified name* of the class or type.\n\n New in version 3.3.\n\nclass.__mro__\n\n This attribute is a tuple of classes that are considered when\n looking for base classes during method resolution.\n\nclass.mro()\n\n This method can be overridden by a metaclass to customize the\n method resolution order for its instances. It is called at class\n instantiation, and its result is stored in __mro__.\n\nclass.__subclasses__()\n\n Each class keeps a list of weak references to its immediate\n subclasses. This method returns a list of all those references\n still alive. Example:\n\n >>> int.__subclasses__()\n []\n\n-[ Footnotes ]-\n\n[1] Additional information on these special methods may be found in\n the Python Reference Manual (*Basic customization*).\n\n[2] As a consequence, the list [1, 2] is considered equal to\n [1.0, 2.0], and similarly for tuples.\n\n[3] They must have since the parser can\'t tell the type of the\n operands.\n\n[4] Cased characters are those with general category property being\n one of "Lu" (Letter, uppercase), "Ll" (Letter, lowercase), or "Lt"\n (Letter, titlecase).\n\n[5] To format only a tuple you should therefore provide a singleton\n tuple whose only element is the tuple to be formatted.\n', 'specialnames': '\nSpecial method names\n********************\n\nA class can implement certain operations that are invoked by special\nsyntax (such as arithmetic operations or subscripting and slicing) by\ndefining methods with special names. This is Python\'s approach to\n*operator overloading*, allowing classes to define their own behavior\nwith respect to language operators. For instance, if a class defines\na method named __getitem__(), and x is an instance of this\nclass, then x[i] is roughly equivalent to type(x).__getitem__(x,\ni). Except where mentioned, attempts to execute an operation raise\nan exception when no appropriate method is defined (typically\nAttributeError or TypeError).\n\nWhen implementing a class that emulates any built-in type, it is\nimportant that the emulation only be implemented to the degree that it\nmakes sense for the object being modelled. For example, some\nsequences may work well with retrieval of individual elements, but\nextracting a slice may not make sense. (One example of this is the\nNodeList interface in the W3C\'s Document Object Model.)\n\n\nBasic customization\n===================\n\nobject.__new__(cls[, ...])\n\n Called to create a new instance of class *cls*. __new__() is a\n static method (special-cased so you need not declare it as such)\n that takes the class of which an instance was requested as its\n first argument. The remaining arguments are those passed to the\n object constructor expression (the call to the class). The return\n value of __new__() should be the new object instance (usually\n an instance of *cls*).\n\n Typical implementations create a new instance of the class by\n invoking the superclass\'s __new__() method using\n super(currentclass, cls).__new__(cls[, ...]) with appropriate\n arguments and then modifying the newly-created instance as\n necessary before returning it.\n\n If __new__() returns an instance of *cls*, then the new\n instance\'s __init__() method will be invoked like\n __init__(self[, ...]), where *self* is the new instance and the\n remaining arguments are the same as were passed to __new__().\n\n If __new__() does not return an instance of *cls*, then the new\n instance\'s __init__() method will not be invoked.\n\n __new__() is intended mainly to allow subclasses of immutable\n types (like int, str, or tuple) to customize instance creation. It\n is also commonly overridden in custom metaclasses in order to\n customize class creation.\n\nobject.__init__(self[, ...])\n\n Called when the instance is created. The arguments are those\n passed to the class constructor expression. If a base class has an\n __init__() method, the derived class\'s __init__() method,\n if any, must explicitly call it to ensure proper initialization of\n the base class part of the instance; for example:\n BaseClass.__init__(self, [args...]). As a special constraint\n on constructors, no value may be returned; doing so will cause a\n TypeError to be raised at runtime.\n\nobject.__del__(self)\n\n Called when the instance is about to be destroyed. This is also\n called a destructor. If a base class has a __del__() method,\n the derived class\'s __del__() method, if any, must explicitly\n call it to ensure proper deletion of the base class part of the\n instance. Note that it is possible (though not recommended!) for\n the __del__() method to postpone destruction of the instance by\n creating a new reference to it. It may then be called at a later\n time when this new reference is deleted. It is not guaranteed that\n __del__() methods are called for objects that still exist when\n the interpreter exits.\n\n Note: del x doesn\'t directly call x.__del__() --- the former\n decrements the reference count for x by one, and the latter\n is only called when x\'s reference count reaches zero. Some\n common situations that may prevent the reference count of an\n object from going to zero include: circular references between\n objects (e.g., a doubly-linked list or a tree data structure with\n parent and child pointers); a reference to the object on the\n stack frame of a function that caught an exception (the traceback\n stored in sys.exc_info()[2] keeps the stack frame alive); or\n a reference to the object on the stack frame that raised an\n unhandled exception in interactive mode (the traceback stored in\n sys.last_traceback keeps the stack frame alive). The first\n situation can only be remedied by explicitly breaking the cycles;\n the latter two situations can be resolved by storing None in\n sys.last_traceback. Circular references which are garbage are\n detected and cleaned up when the cyclic garbage collector is\n enabled (it\'s on by default). Refer to the documentation for the\n gc module for more information about this topic.\n\n Warning: Due to the precarious circumstances under which __del__()\n methods are invoked, exceptions that occur during their execution\n are ignored, and a warning is printed to sys.stderr instead.\n Also, when __del__() is invoked in response to a module being\n deleted (e.g., when execution of the program is done), other\n globals referenced by the __del__() method may already have\n been deleted or in the process of being torn down (e.g. the\n import machinery shutting down). For this reason, __del__()\n methods should do the absolute minimum needed to maintain\n external invariants. Starting with version 1.5, Python\n guarantees that globals whose name begins with a single\n underscore are deleted from their module before other globals are\n deleted; if no other references to such globals exist, this may\n help in assuring that imported modules are still available at the\n time when the __del__() method is called.\n\nobject.__repr__(self)\n\n Called by the repr() built-in function to compute the\n "official" string representation of an object. If at all possible,\n this should look like a valid Python expression that could be used\n to recreate an object with the same value (given an appropriate\n environment). If this is not possible, a string of the form\n <...some useful description...> should be returned. The return\n value must be a string object. If a class defines __repr__()\n but not __str__(), then __repr__() is also used when an\n "informal" string representation of instances of that class is\n required.\n\n This is typically used for debugging, so it is important that the\n representation is information-rich and unambiguous.\n\nobject.__str__(self)\n\n Called by str(object) and the built-in functions format()\n and print() to compute the "informal" or nicely printable\n string representation of an object. The return value must be a\n *string* object.\n\n This method differs from object.__repr__() in that there is no\n expectation that __str__() return a valid Python expression: a\n more convenient or concise representation can be used.\n\n The default implementation defined by the built-in type object\n calls object.__repr__().\n\nobject.__bytes__(self)\n\n Called by bytes() to compute a byte-string representation of an\n object. This should return a bytes object.\n\nobject.__format__(self, format_spec)\n\n Called by the format() built-in function (and by extension, the\n str.format() method of class str) to produce a "formatted"\n string representation of an object. The format_spec argument is\n a string that contains a description of the formatting options\n desired. The interpretation of the format_spec argument is up\n to the type implementing __format__(), however most classes\n will either delegate formatting to one of the built-in types, or\n use a similar formatting option syntax.\n\n See *Format Specification Mini-Language* for a description of the\n standard formatting syntax.\n\n The return value must be a string object.\n\nobject.__lt__(self, other)\nobject.__le__(self, other)\nobject.__eq__(self, other)\nobject.__ne__(self, other)\nobject.__gt__(self, other)\nobject.__ge__(self, other)\n\n These are the so-called "rich comparison" methods. The\n correspondence between operator symbols and method names is as\n follows: xy calls x.__gt__(y), and x>=y calls\n x.__ge__(y).\n\n A rich comparison method may return the singleton\n NotImplemented if it does not implement the operation for a\n given pair of arguments. By convention, False and True are\n returned for a successful comparison. However, these methods can\n return any value, so if the comparison operator is used in a\n Boolean context (e.g., in the condition of an if statement),\n Python will call bool() on the value to determine if the result\n is true or false.\n\n There are no implied relationships among the comparison operators.\n The truth of x==y does not imply that x!=y is false.\n Accordingly, when defining __eq__(), one should also define\n __ne__() so that the operators will behave as expected. See\n the paragraph on __hash__() for some important notes on\n creating *hashable* objects which support custom comparison\n operations and are usable as dictionary keys.\n\n There are no swapped-argument versions of these methods (to be used\n when the left argument does not support the operation but the right\n argument does); rather, __lt__() and __gt__() are each\n other\'s reflection, __le__() and __ge__() are each other\'s\n reflection, and __eq__() and __ne__() are their own\n reflection.\n\n Arguments to rich comparison methods are never coerced.\n\n To automatically generate ordering operations from a single root\n operation, see functools.total_ordering().\n\nobject.__hash__(self)\n\n Called by built-in function hash() and for operations on\n members of hashed collections including set, frozenset, and\n dict. __hash__() should return an integer. The only\n required property is that objects which compare equal have the same\n hash value; it is advised to somehow mix together (e.g. using\n exclusive or) the hash values for the components of the object that\n also play a part in comparison of objects.\n\n Note: hash() truncates the value returned from an object\'s custom\n __hash__() method to the size of a Py_ssize_t. This is\n typically 8 bytes on 64-bit builds and 4 bytes on 32-bit builds.\n If an object\'s __hash__() must interoperate on builds of\n different bit sizes, be sure to check the width on all supported\n builds. An easy way to do this is with python -c "import sys;\n print(sys.hash_info.width)"\n\n If a class does not define an __eq__() method it should not\n define a __hash__() operation either; if it defines\n __eq__() but not __hash__(), its instances will not be\n usable as items in hashable collections. If a class defines\n mutable objects and implements an __eq__() method, it should\n not implement __hash__(), since the implementation of hashable\n collections requires that a key\'s hash value is immutable (if the\n object\'s hash value changes, it will be in the wrong hash bucket).\n\n User-defined classes have __eq__() and __hash__() methods\n by default; with them, all objects compare unequal (except with\n themselves) and x.__hash__() returns an appropriate value such\n that x == y implies both that x is y and hash(x) ==\n hash(y).\n\n A class that overrides __eq__() and does not define\n __hash__() will have its __hash__() implicitly set to\n None. When the __hash__() method of a class is None,\n instances of the class will raise an appropriate TypeError when\n a program attempts to retrieve their hash value, and will also be\n correctly identified as unhashable when checking isinstance(obj,\n collections.Hashable).\n\n If a class that overrides __eq__() needs to retain the\n implementation of __hash__() from a parent class, the\n interpreter must be told this explicitly by setting __hash__ =\n .__hash__.\n\n If a class that does not override __eq__() wishes to suppress\n hash support, it should include __hash__ = None in the class\n definition. A class which defines its own __hash__() that\n explicitly raises a TypeError would be incorrectly identified\n as hashable by an isinstance(obj, collections.Hashable) call.\n\n Note: By default, the __hash__() values of str, bytes and datetime\n objects are "salted" with an unpredictable random value.\n Although they remain constant within an individual Python\n process, they are not predictable between repeated invocations of\n Python.This is intended to provide protection against a denial-\n of-service caused by carefully-chosen inputs that exploit the\n worst case performance of a dict insertion, O(n^2) complexity.\n See http://www.ocert.org/advisories/ocert-2011-003.html for\n details.Changing hash values affects the iteration order of\n dicts, sets and other mappings. Python has never made guarantees\n about this ordering (and it typically varies between 32-bit and\n 64-bit builds).See also PYTHONHASHSEED.\n\n Changed in version 3.3: Hash randomization is enabled by default.\n\nobject.__bool__(self)\n\n Called to implement truth value testing and the built-in operation\n bool(); should return False or True. When this method\n is not defined, __len__() is called, if it is defined, and the\n object is considered true if its result is nonzero. If a class\n defines neither __len__() nor __bool__(), all its instances\n are considered true.\n\n\nCustomizing attribute access\n============================\n\nThe following methods can be defined to customize the meaning of\nattribute access (use of, assignment to, or deletion of x.name)\nfor class instances.\n\nobject.__getattr__(self, name)\n\n Called when an attribute lookup has not found the attribute in the\n usual places (i.e. it is not an instance attribute nor is it found\n in the class tree for self). name is the attribute name.\n This method should return the (computed) attribute value or raise\n an AttributeError exception.\n\n Note that if the attribute is found through the normal mechanism,\n __getattr__() is not called. (This is an intentional asymmetry\n between __getattr__() and __setattr__().) This is done both\n for efficiency reasons and because otherwise __getattr__()\n would have no way to access other attributes of the instance. Note\n that at least for instance variables, you can fake total control by\n not inserting any values in the instance attribute dictionary (but\n instead inserting them in another object). See the\n __getattribute__() method below for a way to actually get total\n control over attribute access.\n\nobject.__getattribute__(self, name)\n\n Called unconditionally to implement attribute accesses for\n instances of the class. If the class also defines\n __getattr__(), the latter will not be called unless\n __getattribute__() either calls it explicitly or raises an\n AttributeError. This method should return the (computed)\n attribute value or raise an AttributeError exception. In order\n to avoid infinite recursion in this method, its implementation\n should always call the base class method with the same name to\n access any attributes it needs, for example,\n object.__getattribute__(self, name).\n\n Note: This method may still be bypassed when looking up special methods\n as the result of implicit invocation via language syntax or\n built-in functions. See *Special method lookup*.\n\nobject.__setattr__(self, name, value)\n\n Called when an attribute assignment is attempted. This is called\n instead of the normal mechanism (i.e. store the value in the\n instance dictionary). *name* is the attribute name, *value* is the\n value to be assigned to it.\n\n If __setattr__() wants to assign to an instance attribute, it\n should call the base class method with the same name, for example,\n object.__setattr__(self, name, value).\n\nobject.__delattr__(self, name)\n\n Like __setattr__() but for attribute deletion instead of\n assignment. This should only be implemented if del obj.name is\n meaningful for the object.\n\nobject.__dir__(self)\n\n Called when dir() is called on the object. A sequence must be\n returned. dir() converts the returned sequence to a list and\n sorts it.\n\n\nImplementing Descriptors\n------------------------\n\nThe following methods only apply when an instance of the class\ncontaining the method (a so-called *descriptor* class) appears in an\n*owner* class (the descriptor must be in either the owner\'s class\ndictionary or in the class dictionary for one of its parents). In the\nexamples below, "the attribute" refers to the attribute whose name is\nthe key of the property in the owner class\' __dict__.\n\nobject.__get__(self, instance, owner)\n\n Called to get the attribute of the owner class (class attribute\n access) or of an instance of that class (instance attribute\n access). *owner* is always the owner class, while *instance* is the\n instance that the attribute was accessed through, or None when\n the attribute is accessed through the *owner*. This method should\n return the (computed) attribute value or raise an\n AttributeError exception.\n\nobject.__set__(self, instance, value)\n\n Called to set the attribute on an instance *instance* of the owner\n class to a new value, *value*.\n\nobject.__delete__(self, instance)\n\n Called to delete the attribute on an instance *instance* of the\n owner class.\n\n\nInvoking Descriptors\n--------------------\n\nIn general, a descriptor is an object attribute with "binding\nbehavior", one whose attribute access has been overridden by methods\nin the descriptor protocol: __get__(), __set__(), and\n__delete__(). If any of those methods are defined for an object,\nit is said to be a descriptor.\n\nThe default behavior for attribute access is to get, set, or delete\nthe attribute from an object\'s dictionary. For instance, a.x has a\nlookup chain starting with a.__dict__[\'x\'], then\ntype(a).__dict__[\'x\'], and continuing through the base classes of\ntype(a) excluding metaclasses.\n\nHowever, if the looked-up value is an object defining one of the\ndescriptor methods, then Python may override the default behavior and\ninvoke the descriptor method instead. Where this occurs in the\nprecedence chain depends on which descriptor methods were defined and\nhow they were called.\n\nThe starting point for descriptor invocation is a binding, a.x.\nHow the arguments are assembled depends on a:\n\nDirect Call\n The simplest and least common call is when user code directly\n invokes a descriptor method: x.__get__(a).\n\nInstance Binding\n If binding to an object instance, a.x is transformed into the\n call: type(a).__dict__[\'x\'].__get__(a, type(a)).\n\nClass Binding\n If binding to a class, A.x is transformed into the call:\n A.__dict__[\'x\'].__get__(None, A).\n\nSuper Binding\n If a is an instance of super, then the binding super(B,\n obj).m() searches obj.__class__.__mro__ for the base class\n A immediately preceding B and then invokes the descriptor\n with the call: A.__dict__[\'m\'].__get__(obj, obj.__class__).\n\nFor instance bindings, the precedence of descriptor invocation depends\non the which descriptor methods are defined. A descriptor can define\nany combination of __get__(), __set__() and __delete__().\nIf it does not define __get__(), then accessing the attribute will\nreturn the descriptor object itself unless there is a value in the\nobject\'s instance dictionary. If the descriptor defines __set__()\nand/or __delete__(), it is a data descriptor; if it defines\nneither, it is a non-data descriptor. Normally, data descriptors\ndefine both __get__() and __set__(), while non-data\ndescriptors have just the __get__() method. Data descriptors with\n__set__() and __get__() defined always override a redefinition\nin an instance dictionary. In contrast, non-data descriptors can be\noverridden by instances.\n\nPython methods (including staticmethod() and classmethod())\nare implemented as non-data descriptors. Accordingly, instances can\nredefine and override methods. This allows individual instances to\nacquire behaviors that differ from other instances of the same class.\n\nThe property() function is implemented as a data descriptor.\nAccordingly, instances cannot override the behavior of a property.\n\n\n__slots__\n---------\n\nBy default, instances of classes have a dictionary for attribute\nstorage. This wastes space for objects having very few instance\nvariables. The space consumption can become acute when creating large\nnumbers of instances.\n\nThe default can be overridden by defining *__slots__* in a class\ndefinition. The *__slots__* declaration takes a sequence of instance\nvariables and reserves just enough space in each instance to hold a\nvalue for each variable. Space is saved because *__dict__* is not\ncreated for each instance.\n\nobject.__slots__\n\n This class variable can be assigned a string, iterable, or sequence\n of strings with variable names used by instances. If defined in a\n class, *__slots__* reserves space for the declared variables and\n prevents the automatic creation of *__dict__* and *__weakref__* for\n each instance.\n\n\nNotes on using *__slots__*\n~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n* When inheriting from a class without *__slots__*, the *__dict__*\n attribute of that class will always be accessible, so a *__slots__*\n definition in the subclass is meaningless.\n\n* Without a *__dict__* variable, instances cannot be assigned new\n variables not listed in the *__slots__* definition. Attempts to\n assign to an unlisted variable name raises AttributeError. If\n dynamic assignment of new variables is desired, then add\n \'__dict__\' to the sequence of strings in the *__slots__*\n declaration.\n\n* Without a *__weakref__* variable for each instance, classes defining\n *__slots__* do not support weak references to its instances. If weak\n reference support is needed, then add \'__weakref__\' to the\n sequence of strings in the *__slots__* declaration.\n\n* *__slots__* are implemented at the class level by creating\n descriptors (*Implementing Descriptors*) for each variable name. As\n a result, class attributes cannot be used to set default values for\n instance variables defined by *__slots__*; otherwise, the class\n attribute would overwrite the descriptor assignment.\n\n* The action of a *__slots__* declaration is limited to the class\n where it is defined. As a result, subclasses will have a *__dict__*\n unless they also define *__slots__* (which must only contain names\n of any *additional* slots).\n\n* If a class defines a slot also defined in a base class, the instance\n variable defined by the base class slot is inaccessible (except by\n retrieving its descriptor directly from the base class). This\n renders the meaning of the program undefined. In the future, a\n check may be added to prevent this.\n\n* Nonempty *__slots__* does not work for classes derived from\n "variable-length" built-in types such as int, str and\n tuple.\n\n* Any non-string iterable may be assigned to *__slots__*. Mappings may\n also be used; however, in the future, special meaning may be\n assigned to the values corresponding to each key.\n\n* *__class__* assignment works only if both classes have the same\n *__slots__*.\n\n\nCustomizing class creation\n==========================\n\nBy default, classes are constructed using type(). The class body\nis executed in a new namespace and the class name is bound locally to\nthe result of type(name, bases, namespace).\n\nThe class creation process can be customised by passing the\nmetaclass keyword argument in the class definition line, or by\ninheriting from an existing class that included such an argument. In\nthe following example, both MyClass and MySubclass are\ninstances of Meta:\n\n class Meta(type):\n pass\n\n class MyClass(metaclass=Meta):\n pass\n\n class MySubclass(MyClass):\n pass\n\nAny other keyword arguments that are specified in the class definition\nare passed through to all metaclass operations described below.\n\nWhen a class definition is executed, the following steps occur:\n\n* the appropriate metaclass is determined\n\n* the class namespace is prepared\n\n* the class body is executed\n\n* the class object is created\n\n\nDetermining the appropriate metaclass\n-------------------------------------\n\nThe appropriate metaclass for a class definition is determined as\nfollows:\n\n* if no bases and no explicit metaclass are given, then type() is\n used\n\n* if an explicit metaclass is given and it is *not* an instance of\n type(), then it is used directly as the metaclass\n\n* if an instance of type() is given as the explicit metaclass, or\n bases are defined, then the most derived metaclass is used\n\nThe most derived metaclass is selected from the explicitly specified\nmetaclass (if any) and the metaclasses (i.e. type(cls)) of all\nspecified base classes. The most derived metaclass is one which is a\nsubtype of *all* of these candidate metaclasses. If none of the\ncandidate metaclasses meets that criterion, then the class definition\nwill fail with TypeError.\n\n\nPreparing the class namespace\n-----------------------------\n\nOnce the appropriate metaclass has been identified, then the class\nnamespace is prepared. If the metaclass has a __prepare__\nattribute, it is called as namespace = metaclass.__prepare__(name,\nbases, **kwds) (where the additional keyword arguments, if any, come\nfrom the class definition).\n\nIf the metaclass has no __prepare__ attribute, then the class\nnamespace is initialised as an empty dict() instance.\n\nSee also:\n\n **PEP 3115** - Metaclasses in Python 3000\n Introduced the __prepare__ namespace hook\n\n\nExecuting the class body\n------------------------\n\nThe class body is executed (approximately) as exec(body, globals(),\nnamespace). The key difference from a normal call to exec() is\nthat lexical scoping allows the class body (including any methods) to\nreference names from the current and outer scopes when the class\ndefinition occurs inside a function.\n\nHowever, even when the class definition occurs inside the function,\nmethods defined inside the class still cannot see names defined at the\nclass scope. Class variables must be accessed through the first\nparameter of instance or class methods, and cannot be accessed at all\nfrom static methods.\n\n\nCreating the class object\n-------------------------\n\nOnce the class namespace has been populated by executing the class\nbody, the class object is created by calling metaclass(name, bases,\nnamespace, **kwds) (the additional keywords passed here are the same\nas those passed to __prepare__).\n\nThis class object is the one that will be referenced by the zero-\nargument form of super(). __class__ is an implicit closure\nreference created by the compiler if any methods in a class body refer\nto either __class__ or super. This allows the zero argument\nform of super() to correctly identify the class being defined\nbased on lexical scoping, while the class or instance that was used to\nmake the current call is identified based on the first argument passed\nto the method.\n\nAfter the class object is created, it is passed to the class\ndecorators included in the class definition (if any) and the resulting\nobject is bound in the local namespace as the defined class.\n\nSee also:\n\n **PEP 3135** - New super\n Describes the implicit __class__ closure reference\n\n\nMetaclass example\n-----------------\n\nThe potential uses for metaclasses are boundless. Some ideas that have\nbeen explored include logging, interface checking, automatic\ndelegation, automatic property creation, proxies, frameworks, and\nautomatic resource locking/synchronization.\n\nHere is an example of a metaclass that uses an\ncollections.OrderedDict to remember the order that class members\nwere defined:\n\n class OrderedClass(type):\n\n @classmethod\n def __prepare__(metacls, name, bases, **kwds):\n return collections.OrderedDict()\n\n def __new__(cls, name, bases, namespace, **kwds):\n result = type.__new__(cls, name, bases, dict(namespace))\n result.members = tuple(namespace)\n return result\n\n class A(metaclass=OrderedClass):\n def one(self): pass\n def two(self): pass\n def three(self): pass\n def four(self): pass\n\n >>> A.members\n (\'__module__\', \'one\', \'two\', \'three\', \'four\')\n\nWhen the class definition for *A* gets executed, the process begins\nwith calling the metaclass\'s __prepare__() method which returns an\nempty collections.OrderedDict. That mapping records the methods\nand attributes of *A* as they are defined within the body of the class\nstatement. Once those definitions are executed, the ordered dictionary\nis fully populated and the metaclass\'s __new__() method gets\ninvoked. That method builds the new type and it saves the ordered\ndictionary keys in an attribute called members.\n\n\nCustomizing instance and subclass checks\n========================================\n\nThe following methods are used to override the default behavior of the\nisinstance() and issubclass() built-in functions.\n\nIn particular, the metaclass abc.ABCMeta implements these methods\nin order to allow the addition of Abstract Base Classes (ABCs) as\n"virtual base classes" to any class or type (including built-in\ntypes), including other ABCs.\n\nclass.__instancecheck__(self, instance)\n\n Return true if *instance* should be considered a (direct or\n indirect) instance of *class*. If defined, called to implement\n isinstance(instance, class).\n\nclass.__subclasscheck__(self, subclass)\n\n Return true if *subclass* should be considered a (direct or\n indirect) subclass of *class*. If defined, called to implement\n issubclass(subclass, class).\n\nNote that these methods are looked up on the type (metaclass) of a\nclass. They cannot be defined as class methods in the actual class.\nThis is consistent with the lookup of special methods that are called\non instances, only in this case the instance is itself a class.\n\nSee also:\n\n **PEP 3119** - Introducing Abstract Base Classes\n Includes the specification for customizing isinstance() and\n issubclass() behavior through __instancecheck__() and\n __subclasscheck__(), with motivation for this functionality\n in the context of adding Abstract Base Classes (see the abc\n module) to the language.\n\n\nEmulating callable objects\n==========================\n\nobject.__call__(self[, args...])\n\n Called when the instance is "called" as a function; if this method\n is defined, x(arg1, arg2, ...) is a shorthand for\n x.__call__(arg1, arg2, ...).\n\n\nEmulating container types\n=========================\n\nThe following methods can be defined to implement container objects.\nContainers usually are sequences (such as lists or tuples) or mappings\n(like dictionaries), but can represent other containers as well. The\nfirst set of methods is used either to emulate a sequence or to\nemulate a mapping; the difference is that for a sequence, the\nallowable keys should be the integers *k* for which 0 <= k < N\nwhere *N* is the length of the sequence, or slice objects, which\ndefine a range of items. It is also recommended that mappings provide\nthe methods keys(), values(), items(), get(),\nclear(), setdefault(), pop(), popitem(), copy(),\nand update() behaving similar to those for Python\'s standard\ndictionary objects. The collections module provides a\nMutableMapping abstract base class to help create those methods\nfrom a base set of __getitem__(), __setitem__(),\n__delitem__(), and keys(). Mutable sequences should provide\nmethods append(), count(), index(), extend(),\ninsert(), pop(), remove(), reverse() and sort(),\nlike Python standard list objects. Finally, sequence types should\nimplement addition (meaning concatenation) and multiplication (meaning\nrepetition) by defining the methods __add__(), __radd__(),\n__iadd__(), __mul__(), __rmul__() and __imul__()\ndescribed below; they should not define other numerical operators. It\nis recommended that both mappings and sequences implement the\n__contains__() method to allow efficient use of the in\noperator; for mappings, in should search the mapping\'s keys; for\nsequences, it should search through the values. It is further\nrecommended that both mappings and sequences implement the\n__iter__() method to allow efficient iteration through the\ncontainer; for mappings, __iter__() should be the same as\nkeys(); for sequences, it should iterate through the values.\n\nobject.__len__(self)\n\n Called to implement the built-in function len(). Should return\n the length of the object, an integer >= 0. Also, an object\n that doesn\'t define a __bool__() method and whose __len__()\n method returns zero is considered to be false in a Boolean context.\n\nobject.__length_hint__(self)\n\n Called to implement operator.length_hint(). Should return an\n estimated length for the object (which may be greater or less than\n the actual length). The length must be an integer >= 0. This\n method is purely an optimization and is never required for\n correctness.\n\n New in version 3.4.\n\nNote: Slicing is done exclusively with the following three methods. A\n call like\n\n a[1:2] = b\n\n is translated to\n\n a[slice(1, 2, None)] = b\n\n and so forth. Missing slice items are always filled in with\n None.\n\nobject.__getitem__(self, key)\n\n Called to implement evaluation of self[key]. For sequence\n types, the accepted keys should be integers and slice objects.\n Note that the special interpretation of negative indexes (if the\n class wishes to emulate a sequence type) is up to the\n __getitem__() method. If *key* is of an inappropriate type,\n TypeError may be raised; if of a value outside the set of\n indexes for the sequence (after any special interpretation of\n negative values), IndexError should be raised. For mapping\n types, if *key* is missing (not in the container), KeyError\n should be raised.\n\n Note: for loops expect that an IndexError will be raised for\n illegal indexes to allow proper detection of the end of the\n sequence.\n\nobject.__setitem__(self, key, value)\n\n Called to implement assignment to self[key]. Same note as for\n __getitem__(). This should only be implemented for mappings if\n the objects support changes to the values for keys, or if new keys\n can be added, or for sequences if elements can be replaced. The\n same exceptions should be raised for improper *key* values as for\n the __getitem__() method.\n\nobject.__delitem__(self, key)\n\n Called to implement deletion of self[key]. Same note as for\n __getitem__(). This should only be implemented for mappings if\n the objects support removal of keys, or for sequences if elements\n can be removed from the sequence. The same exceptions should be\n raised for improper *key* values as for the __getitem__()\n method.\n\nobject.__iter__(self)\n\n This method is called when an iterator is required for a container.\n This method should return a new iterator object that can iterate\n over all the objects in the container. For mappings, it should\n iterate over the keys of the container, and should also be made\n available as the method keys().\n\n Iterator objects also need to implement this method; they are\n required to return themselves. For more information on iterator\n objects, see *Iterator Types*.\n\nobject.__reversed__(self)\n\n Called (if present) by the reversed() built-in to implement\n reverse iteration. It should return a new iterator object that\n iterates over all the objects in the container in reverse order.\n\n If the __reversed__() method is not provided, the\n reversed() built-in will fall back to using the sequence\n protocol (__len__() and __getitem__()). Objects that\n support the sequence protocol should only provide\n __reversed__() if they can provide an implementation that is\n more efficient than the one provided by reversed().\n\nThe membership test operators (in and not in) are normally\nimplemented as an iteration through a sequence. However, container\nobjects can supply the following special method with a more efficient\nimplementation, which also does not require the object be a sequence.\n\nobject.__contains__(self, item)\n\n Called to implement membership test operators. Should return true\n if *item* is in *self*, false otherwise. For mapping objects, this\n should consider the keys of the mapping rather than the values or\n the key-item pairs.\n\n For objects that don\'t define __contains__(), the membership\n test first tries iteration via __iter__(), then the old\n sequence iteration protocol via __getitem__(), see *this\n section in the language reference*.\n\n\nEmulating numeric types\n=======================\n\nThe following methods can be defined to emulate numeric objects.\nMethods corresponding to operations that are not supported by the\nparticular kind of number implemented (e.g., bitwise operations for\nnon-integral numbers) should be left undefined.\n\nobject.__add__(self, other)\nobject.__sub__(self, other)\nobject.__mul__(self, other)\nobject.__truediv__(self, other)\nobject.__floordiv__(self, other)\nobject.__mod__(self, other)\nobject.__divmod__(self, other)\nobject.__pow__(self, other[, modulo])\nobject.__lshift__(self, other)\nobject.__rshift__(self, other)\nobject.__and__(self, other)\nobject.__xor__(self, other)\nobject.__or__(self, other)\n\n These methods are called to implement the binary arithmetic\n operations (+, -, *, /, //, %,\n divmod(), pow(), **, <<, >>, &, ^,\n |). For instance, to evaluate the expression x + y, where\n *x* is an instance of a class that has an __add__() method,\n x.__add__(y) is called. The __divmod__() method should be\n the equivalent to using __floordiv__() and __mod__(); it\n should not be related to __truediv__(). Note that\n __pow__() should be defined to accept an optional third\n argument if the ternary version of the built-in pow() function\n is to be supported.\n\n If one of those methods does not support the operation with the\n supplied arguments, it should return NotImplemented.\n\nobject.__radd__(self, other)\nobject.__rsub__(self, other)\nobject.__rmul__(self, other)\nobject.__rtruediv__(self, other)\nobject.__rfloordiv__(self, other)\nobject.__rmod__(self, other)\nobject.__rdivmod__(self, other)\nobject.__rpow__(self, other)\nobject.__rlshift__(self, other)\nobject.__rrshift__(self, other)\nobject.__rand__(self, other)\nobject.__rxor__(self, other)\nobject.__ror__(self, other)\n\n These methods are called to implement the binary arithmetic\n operations (+, -, *, /, //, %,\n divmod(), pow(), **, <<, >>, &, ^,\n |) with reflected (swapped) operands. These functions are only\n called if the left operand does not support the corresponding\n operation and the operands are of different types. [2] For\n instance, to evaluate the expression x - y, where *y* is an\n instance of a class that has an __rsub__() method,\n y.__rsub__(x) is called if x.__sub__(y) returns\n *NotImplemented*.\n\n Note that ternary pow() will not try calling __rpow__()\n (the coercion rules would become too complicated).\n\n Note: If the right operand\'s type is a subclass of the left operand\'s\n type and that subclass provides the reflected method for the\n operation, this method will be called before the left operand\'s\n non-reflected method. This behavior allows subclasses to\n override their ancestors\' operations.\n\nobject.__iadd__(self, other)\nobject.__isub__(self, other)\nobject.__imul__(self, other)\nobject.__itruediv__(self, other)\nobject.__ifloordiv__(self, other)\nobject.__imod__(self, other)\nobject.__ipow__(self, other[, modulo])\nobject.__ilshift__(self, other)\nobject.__irshift__(self, other)\nobject.__iand__(self, other)\nobject.__ixor__(self, other)\nobject.__ior__(self, other)\n\n These methods are called to implement the augmented arithmetic\n assignments (+=, -=, *=, /=, //=, %=,\n **=, <<=, >>=, &=, ^=, |=). These methods\n should attempt to do the operation in-place (modifying *self*) and\n return the result (which could be, but does not have to be,\n *self*). If a specific method is not defined, the augmented\n assignment falls back to the normal methods. For instance, to\n execute the statement x += y, where *x* is an instance of a\n class that has an __iadd__() method, x.__iadd__(y) is\n called. If *x* is an instance of a class that does not define a\n __iadd__() method, x.__add__(y) and y.__radd__(x) are\n considered, as with the evaluation of x + y.\n\nobject.__neg__(self)\nobject.__pos__(self)\nobject.__abs__(self)\nobject.__invert__(self)\n\n Called to implement the unary arithmetic operations (-, +,\n abs() and ~).\n\nobject.__complex__(self)\nobject.__int__(self)\nobject.__float__(self)\nobject.__round__(self[, n])\n\n Called to implement the built-in functions complex(),\n int(), float() and round(). Should return a value of\n the appropriate type.\n\nobject.__index__(self)\n\n Called to implement operator.index(). Also called whenever\n Python needs an integer object (such as in slicing, or in the\n built-in bin(), hex() and oct() functions). Must return\n an integer.\n\n\nWith Statement Context Managers\n===============================\n\nA *context manager* is an object that defines the runtime context to\nbe established when executing a with statement. The context\nmanager handles the entry into, and the exit from, the desired runtime\ncontext for the execution of the block of code. Context managers are\nnormally invoked using the with statement (described in section\n*The with statement*), but can also be used by directly invoking their\nmethods.\n\nTypical uses of context managers include saving and restoring various\nkinds of global state, locking and unlocking resources, closing opened\nfiles, etc.\n\nFor more information on context managers, see *Context Manager Types*.\n\nobject.__enter__(self)\n\n Enter the runtime context related to this object. The with\n statement will bind this method\'s return value to the target(s)\n specified in the as clause of the statement, if any.\n\nobject.__exit__(self, exc_type, exc_value, traceback)\n\n Exit the runtime context related to this object. The parameters\n describe the exception that caused the context to be exited. If the\n context was exited without an exception, all three arguments will\n be None.\n\n If an exception is supplied, and the method wishes to suppress the\n exception (i.e., prevent it from being propagated), it should\n return a true value. Otherwise, the exception will be processed\n normally upon exit from this method.\n\n Note that __exit__() methods should not reraise the passed-in\n exception; this is the caller\'s responsibility.\n\nSee also:\n\n **PEP 0343** - The "with" statement\n The specification, background, and examples for the Python\n with statement.\n\n\nSpecial method lookup\n=====================\n\nFor custom classes, implicit invocations of special methods are only\nguaranteed to work correctly if defined on an object\'s type, not in\nthe object\'s instance dictionary. That behaviour is the reason why\nthe following code raises an exception:\n\n >>> class C:\n ... pass\n ...\n >>> c = C()\n >>> c.__len__ = lambda: 5\n >>> len(c)\n Traceback (most recent call last):\n File "", line 1, in \n TypeError: object of type \'C\' has no len()\n\nThe rationale behind this behaviour lies with a number of special\nmethods such as __hash__() and __repr__() that are implemented\nby all objects, including type objects. If the implicit lookup of\nthese methods used the conventional lookup process, they would fail\nwhen invoked on the type object itself:\n\n >>> 1 .__hash__() == hash(1)\n True\n >>> int.__hash__() == hash(int)\n Traceback (most recent call last):\n File "", line 1, in \n TypeError: descriptor \'__hash__\' of \'int\' object needs an argument\n\nIncorrectly attempting to invoke an unbound method of a class in this\nway is sometimes referred to as \'metaclass confusion\', and is avoided\nby bypassing the instance when looking up special methods:\n\n >>> type(1).__hash__(1) == hash(1)\n True\n >>> type(int).__hash__(int) == hash(int)\n True\n\nIn addition to bypassing any instance attributes in the interest of\ncorrectness, implicit special method lookup generally also bypasses\nthe __getattribute__() method even of the object\'s metaclass:\n\n >>> class Meta(type):\n ... def __getattribute__(*args):\n ... print("Metaclass getattribute invoked")\n ... return type.__getattribute__(*args)\n ...\n >>> class C(object, metaclass=Meta):\n ... def __len__(self):\n ... return 10\n ... def __getattribute__(*args):\n ... print("Class getattribute invoked")\n ... return object.__getattribute__(*args)\n ...\n >>> c = C()\n >>> c.__len__() # Explicit lookup via instance\n Class getattribute invoked\n 10\n >>> type(c).__len__(c) # Explicit lookup via type\n Metaclass getattribute invoked\n 10\n >>> len(c) # Implicit lookup\n 10\n\nBypassing the __getattribute__() machinery in this fashion\nprovides significant scope for speed optimisations within the\ninterpreter, at the cost of some flexibility in the handling of\nspecial methods (the special method *must* be set on the class object\nitself in order to be consistently invoked by the interpreter).\n\n-[ Footnotes ]-\n\n[1] It *is* possible in some cases to change an object\'s type, under\n certain controlled conditions. It generally isn\'t a good idea\n though, since it can lead to some very strange behaviour if it is\n handled incorrectly.\n\n[2] For operands of the same type, it is assumed that if the non-\n reflected method (such as __add__()) fails the operation is\n not supported, which is why the reflected method is not called.\n', 'string-methods': '\nString Methods\n**************\n\nStrings implement all of the *common* sequence operations, along with\nthe additional methods described below.\n\nStrings also support two styles of string formatting, one providing a\nlarge degree of flexibility and customization (see str.format(),\n*Format String Syntax* and *String Formatting*) and the other based on\nC printf style formatting that handles a narrower range of types\nand is slightly harder to use correctly, but is often faster for the\ncases it can handle (*printf-style String Formatting*).\n\nThe *Text Processing Services* section of the standard library covers\na number of other modules that provide various text related utilities\n(including regular expression support in the re module).\n\nstr.capitalize()\n\n Return a copy of the string with its first character capitalized\n and the rest lowercased.\n\nstr.casefold()\n\n Return a casefolded copy of the string. Casefolded strings may be\n used for caseless matching.\n\n Casefolding is similar to lowercasing but more aggressive because\n it is intended to remove all case distinctions in a string. For\n example, the German lowercase letter \'\xc3\x9f\' is equivalent to\n "ss". Since it is already lowercase, lower() would do\n nothing to \'\xc3\x9f\'; casefold() converts it to "ss".\n\n The casefolding algorithm is described in section 3.13 of the\n Unicode Standard.\n\n New in version 3.3.\n\nstr.center(width[, fillchar])\n\n Return centered in a string of length *width*. Padding is done\n using the specified *fillchar* (default is a space).\n\nstr.count(sub[, start[, end]])\n\n Return the number of non-overlapping occurrences of substring *sub*\n in the range [*start*, *end*]. Optional arguments *start* and\n *end* are interpreted as in slice notation.\n\nstr.encode(encoding="utf-8", errors="strict")\n\n Return an encoded version of the string as a bytes object. Default\n encoding is \'utf-8\'. *errors* may be given to set a different\n error handling scheme. The default for *errors* is \'strict\',\n meaning that encoding errors raise a UnicodeError. Other\n possible values are \'ignore\', \'replace\',\n \'xmlcharrefreplace\', \'backslashreplace\' and any other name\n registered via codecs.register_error(), see section *Codec Base\n Classes*. For a list of possible encodings, see section *Standard\n Encodings*.\n\n Changed in version 3.1: Support for keyword arguments added.\n\nstr.endswith(suffix[, start[, end]])\n\n Return True if the string ends with the specified *suffix*,\n otherwise return False. *suffix* can also be a tuple of\n suffixes to look for. With optional *start*, test beginning at\n that position. With optional *end*, stop comparing at that\n position.\n\nstr.expandtabs([tabsize])\n\n Return a copy of the string where all tab characters are replaced\n by one or more spaces, depending on the current column and the\n given tab size. Tab positions occur every *tabsize* characters\n (default is 8, giving tab positions at columns 0, 8, 16 and so on).\n To expand the string, the current column is set to zero and the\n string is examined character by character. If the character is a\n tab (\\t), one or more space characters are inserted in the\n result until the current column is equal to the next tab position.\n (The tab character itself is not copied.) If the character is a\n newline (\\n) or return (\\r), it is copied and the current\n column is reset to zero. Any other character is copied unchanged\n and the current column is incremented by one regardless of how the\n character is represented when printed.\n\n >>> \'01\\t012\\t0123\\t01234\'.expandtabs()\n \'01 012 0123 01234\'\n >>> \'01\\t012\\t0123\\t01234\'.expandtabs(4)\n \'01 012 0123 01234\'\n\nstr.find(sub[, start[, end]])\n\n Return the lowest index in the string where substring *sub* is\n found, such that *sub* is contained in the slice s[start:end].\n Optional arguments *start* and *end* are interpreted as in slice\n notation. Return -1 if *sub* is not found.\n\n Note: The find() method should be used only if you need to know the\n position of *sub*. To check if *sub* is a substring or not, use\n the in operator:\n\n >>> \'Py\' in \'Python\'\n True\n\nstr.format(*args, **kwargs)\n\n Perform a string formatting operation. The string on which this\n method is called can contain literal text or replacement fields\n delimited by braces {}. Each replacement field contains either\n the numeric index of a positional argument, or the name of a\n keyword argument. Returns a copy of the string where each\n replacement field is replaced with the string value of the\n corresponding argument.\n\n >>> "The sum of 1 + 2 is {0}".format(1+2)\n \'The sum of 1 + 2 is 3\'\n\n See *Format String Syntax* for a description of the various\n formatting options that can be specified in format strings.\n\nstr.format_map(mapping)\n\n Similar to str.format(**mapping), except that mapping is\n used directly and not copied to a dict . This is useful if for\n example mapping is a dict subclass:\n\n >>> class Default(dict):\n ... def __missing__(self, key):\n ... return key\n ...\n >>> \'{name} was born in {country}\'.format_map(Default(name=\'Guido\'))\n \'Guido was born in country\'\n\n New in version 3.2.\n\nstr.index(sub[, start[, end]])\n\n Like find(), but raise ValueError when the substring is not\n found.\n\nstr.isalnum()\n\n Return true if all characters in the string are alphanumeric and\n there is at least one character, false otherwise. A character\n c is alphanumeric if one of the following returns True:\n c.isalpha(), c.isdecimal(), c.isdigit(), or\n c.isnumeric().\n\nstr.isalpha()\n\n Return true if all characters in the string are alphabetic and\n there is at least one character, false otherwise. Alphabetic\n characters are those characters defined in the Unicode character\n database as "Letter", i.e., those with general category property\n being one of "Lm", "Lt", "Lu", "Ll", or "Lo". Note that this is\n different from the "Alphabetic" property defined in the Unicode\n Standard.\n\nstr.isdecimal()\n\n Return true if all characters in the string are decimal characters\n and there is at least one character, false otherwise. Decimal\n characters are those from general category "Nd". This category\n includes digit characters, and all characters that can be used to\n form decimal-radix numbers, e.g. U+0660, ARABIC-INDIC DIGIT ZERO.\n\nstr.isdigit()\n\n Return true if all characters in the string are digits and there is\n at least one character, false otherwise. Digits include decimal\n characters and digits that need special handling, such as the\n compatibility superscript digits. Formally, a digit is a character\n that has the property value Numeric_Type=Digit or\n Numeric_Type=Decimal.\n\nstr.isidentifier()\n\n Return true if the string is a valid identifier according to the\n language definition, section *Identifiers and keywords*.\n\n Use keyword.iskeyword() to test for reserved identifiers such\n as def and class.\n\nstr.islower()\n\n Return true if all cased characters [4] in the string are lowercase\n and there is at least one cased character, false otherwise.\n\nstr.isnumeric()\n\n Return true if all characters in the string are numeric characters,\n and there is at least one character, false otherwise. Numeric\n characters include digit characters, and all characters that have\n the Unicode numeric value property, e.g. U+2155, VULGAR FRACTION\n ONE FIFTH. Formally, numeric characters are those with the\n property value Numeric_Type=Digit, Numeric_Type=Decimal or\n Numeric_Type=Numeric.\n\nstr.isprintable()\n\n Return true if all characters in the string are printable or the\n string is empty, false otherwise. Nonprintable characters are\n those characters defined in the Unicode character database as\n "Other" or "Separator", excepting the ASCII space (0x20) which is\n considered printable. (Note that printable characters in this\n context are those which should not be escaped when repr() is\n invoked on a string. It has no bearing on the handling of strings\n written to sys.stdout or sys.stderr.)\n\nstr.isspace()\n\n Return true if there are only whitespace characters in the string\n and there is at least one character, false otherwise. Whitespace\n characters are those characters defined in the Unicode character\n database as "Other" or "Separator" and those with bidirectional\n property being one of "WS", "B", or "S".\n\nstr.istitle()\n\n Return true if the string is a titlecased string and there is at\n least one character, for example uppercase characters may only\n follow uncased characters and lowercase characters only cased ones.\n Return false otherwise.\n\nstr.isupper()\n\n Return true if all cased characters [4] in the string are uppercase\n and there is at least one cased character, false otherwise.\n\nstr.join(iterable)\n\n Return a string which is the concatenation of the strings in the\n *iterable* *iterable*. A TypeError will be raised if there are\n any non-string values in *iterable*, including bytes objects.\n The separator between elements is the string providing this method.\n\nstr.ljust(width[, fillchar])\n\n Return the string left justified in a string of length *width*.\n Padding is done using the specified *fillchar* (default is a\n space). The original string is returned if *width* is less than or\n equal to len(s).\n\nstr.lower()\n\n Return a copy of the string with all the cased characters [4]\n converted to lowercase.\n\n The lowercasing algorithm used is described in section 3.13 of the\n Unicode Standard.\n\nstr.lstrip([chars])\n\n Return a copy of the string with leading characters removed. The\n *chars* argument is a string specifying the set of characters to be\n removed. If omitted or None, the *chars* argument defaults to\n removing whitespace. The *chars* argument is not a prefix; rather,\n all combinations of its values are stripped:\n\n >>> \' spacious \'.lstrip()\n \'spacious \'\n >>> \'www.example.com\'.lstrip(\'cmowz.\')\n \'example.com\'\n\nstatic str.maketrans(x[, y[, z]])\n\n This static method returns a translation table usable for\n str.translate().\n\n If there is only one argument, it must be a dictionary mapping\n Unicode ordinals (integers) or characters (strings of length 1) to\n Unicode ordinals, strings (of arbitrary lengths) or None.\n Character keys will then be converted to ordinals.\n\n If there are two arguments, they must be strings of equal length,\n and in the resulting dictionary, each character in x will be mapped\n to the character at the same position in y. If there is a third\n argument, it must be a string, whose characters will be mapped to\n None in the result.\n\nstr.partition(sep)\n\n Split the string at the first occurrence of *sep*, and return a\n 3-tuple containing the part before the separator, the separator\n itself, and the part after the separator. If the separator is not\n found, return a 3-tuple containing the string itself, followed by\n two empty strings.\n\nstr.replace(old, new[, count])\n\n Return a copy of the string with all occurrences of substring *old*\n replaced by *new*. If the optional argument *count* is given, only\n the first *count* occurrences are replaced.\n\nstr.rfind(sub[, start[, end]])\n\n Return the highest index in the string where substring *sub* is\n found, such that *sub* is contained within s[start:end].\n Optional arguments *start* and *end* are interpreted as in slice\n notation. Return -1 on failure.\n\nstr.rindex(sub[, start[, end]])\n\n Like rfind() but raises ValueError when the substring *sub*\n is not found.\n\nstr.rjust(width[, fillchar])\n\n Return the string right justified in a string of length *width*.\n Padding is done using the specified *fillchar* (default is a\n space). The original string is returned if *width* is less than or\n equal to len(s).\n\nstr.rpartition(sep)\n\n Split the string at the last occurrence of *sep*, and return a\n 3-tuple containing the part before the separator, the separator\n itself, and the part after the separator. If the separator is not\n found, return a 3-tuple containing two empty strings, followed by\n the string itself.\n\nstr.rsplit(sep=None, maxsplit=-1)\n\n Return a list of the words in the string, using *sep* as the\n delimiter string. If *maxsplit* is given, at most *maxsplit* splits\n are done, the *rightmost* ones. If *sep* is not specified or\n None, any whitespace string is a separator. Except for\n splitting from the right, rsplit() behaves like split()\n which is described in detail below.\n\nstr.rstrip([chars])\n\n Return a copy of the string with trailing characters removed. The\n *chars* argument is a string specifying the set of characters to be\n removed. If omitted or None, the *chars* argument defaults to\n removing whitespace. The *chars* argument is not a suffix; rather,\n all combinations of its values are stripped:\n\n >>> \' spacious \'.rstrip()\n \' spacious\'\n >>> \'mississippi\'.rstrip(\'ipz\')\n \'mississ\'\n\nstr.split(sep=None, maxsplit=-1)\n\n Return a list of the words in the string, using *sep* as the\n delimiter string. If *maxsplit* is given, at most *maxsplit*\n splits are done (thus, the list will have at most maxsplit+1\n elements). If *maxsplit* is not specified or -1, then there is\n no limit on the number of splits (all possible splits are made).\n\n If *sep* is given, consecutive delimiters are not grouped together\n and are deemed to delimit empty strings (for example,\n \'1,,2\'.split(\',\') returns [\'1\', \'\', \'2\']). The *sep*\n argument may consist of multiple characters (for example,\n \'1<>2<>3\'.split(\'<>\') returns [\'1\', \'2\', \'3\']). Splitting\n an empty string with a specified separator returns [\'\'].\n\n If *sep* is not specified or is None, a different splitting\n algorithm is applied: runs of consecutive whitespace are regarded\n as a single separator, and the result will contain no empty strings\n at the start or end if the string has leading or trailing\n whitespace. Consequently, splitting an empty string or a string\n consisting of just whitespace with a None separator returns\n [].\n\n For example, \' 1 2 3 \'.split() returns [\'1\', \'2\', \'3\'],\n and \' 1 2 3 \'.split(None, 1) returns [\'1\', \'2 3 \'].\n\nstr.splitlines([keepends])\n\n Return a list of the lines in the string, breaking at line\n boundaries. This method uses the *universal newlines* approach to\n splitting lines. Line breaks are not included in the resulting list\n unless *keepends* is given and true.\n\n For example, \'ab c\\n\\nde fg\\rkl\\r\\n\'.splitlines() returns\n [\'ab c\', \'\', \'de fg\', \'kl\'], while the same call with\n splitlines(True) returns [\'ab c\\n\', \'\\n\', \'de fg\\r\',\n \'kl\\r\\n\'].\n\n Unlike split() when a delimiter string *sep* is given, this\n method returns an empty list for the empty string, and a terminal\n line break does not result in an extra line.\n\nstr.startswith(prefix[, start[, end]])\n\n Return True if string starts with the *prefix*, otherwise\n return False. *prefix* can also be a tuple of prefixes to look\n for. With optional *start*, test string beginning at that\n position. With optional *end*, stop comparing string at that\n position.\n\nstr.strip([chars])\n\n Return a copy of the string with the leading and trailing\n characters removed. The *chars* argument is a string specifying the\n set of characters to be removed. If omitted or None, the\n *chars* argument defaults to removing whitespace. The *chars*\n argument is not a prefix or suffix; rather, all combinations of its\n values are stripped:\n\n >>> \' spacious \'.strip()\n \'spacious\'\n >>> \'www.example.com\'.strip(\'cmowz.\')\n \'example\'\n\nstr.swapcase()\n\n Return a copy of the string with uppercase characters converted to\n lowercase and vice versa. Note that it is not necessarily true that\n s.swapcase().swapcase() == s.\n\nstr.title()\n\n Return a titlecased version of the string where words start with an\n uppercase character and the remaining characters are lowercase.\n\n The algorithm uses a simple language-independent definition of a\n word as groups of consecutive letters. The definition works in\n many contexts but it means that apostrophes in contractions and\n possessives form word boundaries, which may not be the desired\n result:\n\n >>> "they\'re bill\'s friends from the UK".title()\n "They\'Re Bill\'S Friends From The Uk"\n\n A workaround for apostrophes can be constructed using regular\n expressions:\n\n >>> import re\n >>> def titlecase(s):\n ... return re.sub(r"[A-Za-z]+(\'[A-Za-z]+)?",\n ... lambda mo: mo.group(0)[0].upper() +\n ... mo.group(0)[1:].lower(),\n ... s)\n ...\n >>> titlecase("they\'re bill\'s friends.")\n "They\'re Bill\'s Friends."\n\nstr.translate(map)\n\n Return a copy of the *s* where all characters have been mapped\n through the *map* which must be a dictionary of Unicode ordinals\n (integers) to Unicode ordinals, strings or None. Unmapped\n characters are left untouched. Characters mapped to None are\n deleted.\n\n You can use str.maketrans() to create a translation map from\n character-to-character mappings in different formats.\n\n Note: An even more flexible approach is to create a custom character\n mapping codec using the codecs module (see\n encodings.cp1251 for an example).\n\nstr.upper()\n\n Return a copy of the string with all the cased characters [4]\n converted to uppercase. Note that str.upper().isupper() might\n be False if s contains uncased characters or if the Unicode\n category of the resulting character(s) is not "Lu" (Letter,\n uppercase), but e.g. "Lt" (Letter, titlecase).\n\n The uppercasing algorithm used is described in section 3.13 of the\n Unicode Standard.\n\nstr.zfill(width)\n\n Return the numeric string left filled with zeros in a string of\n length *width*. A sign prefix is handled correctly. The original\n string is returned if *width* is less than or equal to len(s).\n', 'strings': '\nString and Bytes literals\n*************************\n\nString literals are described by the following lexical definitions:\n\n stringliteral ::= [stringprefix](shortstring | longstring)\n stringprefix ::= "r" | "u" | "R" | "U"\n shortstring ::= "\'" shortstringitem* "\'" | \'"\' shortstringitem* \'"\'\n longstring ::= "\'\'\'" longstringitem* "\'\'\'" | \'"""\' longstringitem* \'"""\'\n shortstringitem ::= shortstringchar | stringescapeseq\n longstringitem ::= longstringchar | stringescapeseq\n shortstringchar ::= \n longstringchar ::= \n stringescapeseq ::= "\\" \n\n bytesliteral ::= bytesprefix(shortbytes | longbytes)\n bytesprefix ::= "b" | "B" | "br" | "Br" | "bR" | "BR" | "rb" | "rB" | "Rb" | "RB"\n shortbytes ::= "\'" shortbytesitem* "\'" | \'"\' shortbytesitem* \'"\'\n longbytes ::= "\'\'\'" longbytesitem* "\'\'\'" | \'"""\' longbytesitem* \'"""\'\n shortbytesitem ::= shortbyteschar | bytesescapeseq\n longbytesitem ::= longbyteschar | bytesescapeseq\n shortbyteschar ::= \n longbyteschar ::= \n bytesescapeseq ::= "\\" \n\nOne syntactic restriction not indicated by these productions is that\nwhitespace is not allowed between the stringprefix or\nbytesprefix and the rest of the literal. The source character set\nis defined by the encoding declaration; it is UTF-8 if no encoding\ndeclaration is given in the source file; see section *Encoding\ndeclarations*.\n\nIn plain English: Both types of literals can be enclosed in matching\nsingle quotes (\') or double quotes ("). They can also be\nenclosed in matching groups of three single or double quotes (these\nare generally referred to as *triple-quoted strings*). The backslash\n(\\) character is used to escape characters that otherwise have a\nspecial meaning, such as newline, backslash itself, or the quote\ncharacter.\n\nBytes literals are always prefixed with \'b\' or \'B\'; they\nproduce an instance of the bytes type instead of the str type.\nThey may only contain ASCII characters; bytes with a numeric value of\n128 or greater must be expressed with escapes.\n\nAs of Python 3.3 it is possible again to prefix unicode strings with a\nu prefix to simplify maintenance of dual 2.x and 3.x codebases.\n\nBoth string and bytes literals may optionally be prefixed with a\nletter \'r\' or \'R\'; such strings are called *raw strings* and\ntreat backslashes as literal characters. As a result, in string\nliterals, \'\\U\' and \'\\u\' escapes in raw strings are not treated\nspecially. Given that Python 2.x\'s raw unicode literals behave\ndifferently than Python 3.x\'s the \'ur\' syntax is not supported.\n\n New in version 3.3: The \'rb\' prefix of raw bytes literals has\n been added as a synonym of \'br\'.\n\n New in version 3.3: Support for the unicode legacy literal\n (u\'value\') was reintroduced to simplify the maintenance of dual\n Python 2.x and 3.x codebases. See **PEP 414** for more information.\n\nIn triple-quoted strings, unescaped newlines and quotes are allowed\n(and are retained), except that three unescaped quotes in a row\nterminate the string. (A "quote" is the character used to open the\nstring, i.e. either \' or ".)\n\nUnless an \'r\' or \'R\' prefix is present, escape sequences in\nstrings are interpreted according to rules similar to those used by\nStandard C. The recognized escape sequences are:\n\n+-------------------+-----------------------------------+---------+\n| Escape Sequence | Meaning | Notes |\n+===================+===================================+=========+\n| \\newline | Backslash and newline ignored | |\n+-------------------+-----------------------------------+---------+\n| \\\\ | Backslash (\\) | |\n+-------------------+-----------------------------------+---------+\n| \\\' | Single quote (\') | |\n+-------------------+-----------------------------------+---------+\n| \\" | Double quote (") | |\n+-------------------+-----------------------------------+---------+\n| \\a | ASCII Bell (BEL) | |\n+-------------------+-----------------------------------+---------+\n| \\b | ASCII Backspace (BS) | |\n+-------------------+-----------------------------------+---------+\n| \\f | ASCII Formfeed (FF) | |\n+-------------------+-----------------------------------+---------+\n| \\n | ASCII Linefeed (LF) | |\n+-------------------+-----------------------------------+---------+\n| \\r | ASCII Carriage Return (CR) | |\n+-------------------+-----------------------------------+---------+\n| \\t | ASCII Horizontal Tab (TAB) | |\n+-------------------+-----------------------------------+---------+\n| \\v | ASCII Vertical Tab (VT) | |\n+-------------------+-----------------------------------+---------+\n| \\ooo | Character with octal value *ooo* | (1,3) |\n+-------------------+-----------------------------------+---------+\n| \\xhh | Character with hex value *hh* | (2,3) |\n+-------------------+-----------------------------------+---------+\n\nEscape sequences only recognized in string literals are:\n\n+-------------------+-----------------------------------+---------+\n| Escape Sequence | Meaning | Notes |\n+===================+===================================+=========+\n| \\N{name} | Character named *name* in the | (4) |\n| | Unicode database | |\n+-------------------+-----------------------------------+---------+\n| \\uxxxx | Character with 16-bit hex value | (5) |\n| | *xxxx* | |\n+-------------------+-----------------------------------+---------+\n| \\Uxxxxxxxx | Character with 32-bit hex value | (6) |\n| | *xxxxxxxx* | |\n+-------------------+-----------------------------------+---------+\n\nNotes:\n\n1. As in Standard C, up to three octal digits are accepted.\n\n2. Unlike in Standard C, exactly two hex digits are required.\n\n3. In a bytes literal, hexadecimal and octal escapes denote the byte\n with the given value. In a string literal, these escapes denote a\n Unicode character with the given value.\n\n4. Changed in version 3.3: Support for name aliases [1] has been\n added.\n\n5. Individual code units which form parts of a surrogate pair can be\n encoded using this escape sequence. Exactly four hex digits are\n required.\n\n6. Any Unicode character can be encoded this way. Exactly eight hex\n digits are required.\n\nUnlike Standard C, all unrecognized escape sequences are left in the\nstring unchanged, i.e., *the backslash is left in the string*. (This\nbehavior is useful when debugging: if an escape sequence is mistyped,\nthe resulting output is more easily recognized as broken.) It is also\nimportant to note that the escape sequences only recognized in string\nliterals fall into the category of unrecognized escapes for bytes\nliterals.\n\nEven in a raw string, string quotes can be escaped with a backslash,\nbut the backslash remains in the string; for example, r"\\"" is a\nvalid string literal consisting of two characters: a backslash and a\ndouble quote; r"\\" is not a valid string literal (even a raw\nstring cannot end in an odd number of backslashes). Specifically, *a\nraw string cannot end in a single backslash* (since the backslash\nwould escape the following quote character). Note also that a single\nbackslash followed by a newline is interpreted as those two characters\nas part of the string, *not* as a line continuation.\n', 'subscriptions': '\nSubscriptions\n*************\n\nA subscription selects an item of a sequence (string, tuple or list)\nor mapping (dictionary) object:\n\n subscription ::= primary "[" expression_list "]"\n\nThe primary must evaluate to an object that supports subscription,\ne.g. a list or dictionary. User-defined objects can support\nsubscription by defining a __getitem__() method.\n\nFor built-in objects, there are two types of objects that support\nsubscription:\n\nIf the primary is a mapping, the expression list must evaluate to an\nobject whose value is one of the keys of the mapping, and the\nsubscription selects the value in the mapping that corresponds to that\nkey. (The expression list is a tuple except if it has exactly one\nitem.)\n\nIf the primary is a sequence, the expression (list) must evaluate to\nan integer or a slice (as discussed in the following section).\n\nThe formal syntax makes no special provision for negative indices in\nsequences; however, built-in sequences all provide a __getitem__()\nmethod that interprets negative indices by adding the length of the\nsequence to the index (so that x[-1] selects the last item of\nx). The resulting value must be a nonnegative integer less than\nthe number of items in the sequence, and the subscription selects the\nitem whose index is that value (counting from zero). Since the support\nfor negative indices and slicing occurs in the object\'s\n__getitem__() method, subclasses overriding this method will need\nto explicitly add that support.\n\nA string\'s items are characters. A character is not a separate data\ntype but a string of exactly one character.\n', 'truth': "\nTruth Value Testing\n*******************\n\nAny object can be tested for truth value, for use in an if or\nwhile condition or as operand of the Boolean operations below. The\nfollowing values are considered false:\n\n* None\n\n* False\n\n* zero of any numeric type, for example, 0, 0.0, 0j.\n\n* any empty sequence, for example, '', (), [].\n\n* any empty mapping, for example, {}.\n\n* instances of user-defined classes, if the class defines a\n __bool__() or __len__() method, when that method returns the\n integer zero or bool value False. [1]\n\nAll other values are considered true --- so objects of many types are\nalways true.\n\nOperations and built-in functions that have a Boolean result always\nreturn 0 or False for false and 1 or True for true,\nunless otherwise stated. (Important exception: the Boolean operations\nor and and always return one of their operands.)\n", 'try': '\nThe try statement\n*********************\n\nThe try statement specifies exception handlers and/or cleanup code\nfor a group of statements:\n\n try_stmt ::= try1_stmt | try2_stmt\n try1_stmt ::= "try" ":" suite\n ("except" [expression ["as" target]] ":" suite)+\n ["else" ":" suite]\n ["finally" ":" suite]\n try2_stmt ::= "try" ":" suite\n "finally" ":" suite\n\nThe except clause(s) specify one or more exception handlers. When\nno exception occurs in the try clause, no exception handler is\nexecuted. When an exception occurs in the try suite, a search for\nan exception handler is started. This search inspects the except\nclauses in turn until one is found that matches the exception. An\nexpression-less except clause, if present, must be last; it matches\nany exception. For an except clause with an expression, that\nexpression is evaluated, and the clause matches the exception if the\nresulting object is "compatible" with the exception. An object is\ncompatible with an exception if it is the class or a base class of the\nexception object or a tuple containing an item compatible with the\nexception.\n\nIf no except clause matches the exception, the search for an exception\nhandler continues in the surrounding code and on the invocation stack.\n[1]\n\nIf the evaluation of an expression in the header of an except clause\nraises an exception, the original search for a handler is canceled and\na search starts for the new exception in the surrounding code and on\nthe call stack (it is treated as if the entire try statement\nraised the exception).\n\nWhen a matching except clause is found, the exception is assigned to\nthe target specified after the as keyword in that except clause,\nif present, and the except clause\'s suite is executed. All except\nclauses must have an executable block. When the end of this block is\nreached, execution continues normally after the entire try statement.\n(This means that if two nested handlers exist for the same exception,\nand the exception occurs in the try clause of the inner handler, the\nouter handler will not handle the exception.)\n\nWhen an exception has been assigned using as target, it is cleared\nat the end of the except clause. This is as if\n\n except E as N:\n foo\n\nwas translated to\n\n except E as N:\n try:\n foo\n finally:\n del N\n\nThis means the exception must be assigned to a different name to be\nable to refer to it after the except clause. Exceptions are cleared\nbecause with the traceback attached to them, they form a reference\ncycle with the stack frame, keeping all locals in that frame alive\nuntil the next garbage collection occurs.\n\nBefore an except clause\'s suite is executed, details about the\nexception are stored in the sys module and can be access via\nsys.exc_info(). sys.exc_info() returns a 3-tuple consisting of\nthe exception class, the exception instance and a traceback object\n(see section *The standard type hierarchy*) identifying the point in\nthe program where the exception occurred. sys.exc_info() values\nare restored to their previous values (before the call) when returning\nfrom a function that handled an exception.\n\nThe optional else clause is executed if and when control flows off\nthe end of the try clause. [2] Exceptions in the else clause\nare not handled by the preceding except clauses.\n\nIf finally is present, it specifies a \'cleanup\' handler. The\ntry clause is executed, including any except and else\nclauses. If an exception occurs in any of the clauses and is not\nhandled, the exception is temporarily saved. The finally clause is\nexecuted. If there is a saved exception it is re-raised at the end of\nthe finally clause. If the finally clause raises another\nexception, the saved exception is set as the context of the new\nexception. If the finally clause executes a return or\nbreak statement, the saved exception is discarded:\n\n def f():\n try:\n 1/0\n finally:\n return 42\n\n >>> f()\n 42\n\nThe exception information is not available to the program during\nexecution of the finally clause.\n\nWhen a return, break or continue statement is executed in\nthe try suite of a try...finally statement, the\nfinally clause is also executed \'on the way out.\' A continue\nstatement is illegal in the finally clause. (The reason is a\nproblem with the current implementation --- this restriction may be\nlifted in the future).\n\nAdditional information on exceptions can be found in section\n*Exceptions*, and information on using the raise statement to\ngenerate exceptions may be found in section *The raise statement*.\n', 'types': '\nThe standard type hierarchy\n***************************\n\nBelow is a list of the types that are built into Python. Extension\nmodules (written in C, Java, or other languages, depending on the\nimplementation) can define additional types. Future versions of\nPython may add types to the type hierarchy (e.g., rational numbers,\nefficiently stored arrays of integers, etc.), although such additions\nwill often be provided via the standard library instead.\n\nSome of the type descriptions below contain a paragraph listing\n\'special attributes.\' These are attributes that provide access to the\nimplementation and are not intended for general use. Their definition\nmay change in the future.\n\nNone\n This type has a single value. There is a single object with this\n value. This object is accessed through the built-in name None.\n It is used to signify the absence of a value in many situations,\n e.g., it is returned from functions that don\'t explicitly return\n anything. Its truth value is false.\n\nNotImplemented\n This type has a single value. There is a single object with this\n value. This object is accessed through the built-in name\n NotImplemented. Numeric methods and rich comparison methods may\n return this value if they do not implement the operation for the\n operands provided. (The interpreter will then try the reflected\n operation, or some other fallback, depending on the operator.) Its\n truth value is true.\n\nEllipsis\n This type has a single value. There is a single object with this\n value. This object is accessed through the literal ... or the\n built-in name Ellipsis. Its truth value is true.\n\nnumbers.Number\n These are created by numeric literals and returned as results by\n arithmetic operators and arithmetic built-in functions. Numeric\n objects are immutable; once created their value never changes.\n Python numbers are of course strongly related to mathematical\n numbers, but subject to the limitations of numerical representation\n in computers.\n\n Python distinguishes between integers, floating point numbers, and\n complex numbers:\n\n numbers.Integral\n These represent elements from the mathematical set of integers\n (positive and negative).\n\n There are two types of integers:\n\n Integers (int)\n\n These represent numbers in an unlimited range, subject to\n available (virtual) memory only. For the purpose of shift\n and mask operations, a binary representation is assumed, and\n negative numbers are represented in a variant of 2\'s\n complement which gives the illusion of an infinite string of\n sign bits extending to the left.\n\n Booleans (bool)\n These represent the truth values False and True. The two\n objects representing the values False and True are the only\n Boolean objects. The Boolean type is a subtype of the integer\n type, and Boolean values behave like the values 0 and 1,\n respectively, in almost all contexts, the exception being\n that when converted to a string, the strings "False" or\n "True" are returned, respectively.\n\n The rules for integer representation are intended to give the\n most meaningful interpretation of shift and mask operations\n involving negative integers.\n\n numbers.Real (float)\n These represent machine-level double precision floating point\n numbers. You are at the mercy of the underlying machine\n architecture (and C or Java implementation) for the accepted\n range and handling of overflow. Python does not support single-\n precision floating point numbers; the savings in processor and\n memory usage that are usually the reason for using these is\n dwarfed by the overhead of using objects in Python, so there is\n no reason to complicate the language with two kinds of floating\n point numbers.\n\n numbers.Complex (complex)\n These represent complex numbers as a pair of machine-level\n double precision floating point numbers. The same caveats apply\n as for floating point numbers. The real and imaginary parts of a\n complex number z can be retrieved through the read-only\n attributes z.real and z.imag.\n\nSequences\n These represent finite ordered sets indexed by non-negative\n numbers. The built-in function len() returns the number of\n items of a sequence. When the length of a sequence is *n*, the\n index set contains the numbers 0, 1, ..., *n*-1. Item *i* of\n sequence *a* is selected by a[i].\n\n Sequences also support slicing: a[i:j] selects all items with\n index *k* such that *i* <= *k* < *j*. When used as an\n expression, a slice is a sequence of the same type. This implies\n that the index set is renumbered so that it starts at 0.\n\n Some sequences also support "extended slicing" with a third "step"\n parameter: a[i:j:k] selects all items of *a* with index *x*\n where x = i + n*k, *n* >= 0 and *i* <= *x* <\n *j*.\n\n Sequences are distinguished according to their mutability:\n\n Immutable sequences\n An object of an immutable sequence type cannot change once it is\n created. (If the object contains references to other objects,\n these other objects may be mutable and may be changed; however,\n the collection of objects directly referenced by an immutable\n object cannot change.)\n\n The following types are immutable sequences:\n\n Strings\n A string is a sequence of values that represent Unicode\n codepoints. All the codepoints in range U+0000 - U+10FFFF\n can be represented in a string. Python doesn\'t have a\n chr type, and every character in the string is\n represented as a string object with length 1. The built-\n in function ord() converts a character to its codepoint\n (as an integer); chr() converts an integer in range 0 -\n 10FFFF to the corresponding character. str.encode() can\n be used to convert a str to bytes using the given\n encoding, and bytes.decode() can be used to achieve the\n opposite.\n\n Tuples\n The items of a tuple are arbitrary Python objects. Tuples of\n two or more items are formed by comma-separated lists of\n expressions. A tuple of one item (a \'singleton\') can be\n formed by affixing a comma to an expression (an expression by\n itself does not create a tuple, since parentheses must be\n usable for grouping of expressions). An empty tuple can be\n formed by an empty pair of parentheses.\n\n Bytes\n A bytes object is an immutable array. The items are 8-bit\n bytes, represented by integers in the range 0 <= x < 256.\n Bytes literals (like b\'abc\') and the built-in function\n bytes() can be used to construct bytes objects. Also,\n bytes objects can be decoded to strings via the decode()\n method.\n\n Mutable sequences\n Mutable sequences can be changed after they are created. The\n subscription and slicing notations can be used as the target of\n assignment and del (delete) statements.\n\n There are currently two intrinsic mutable sequence types:\n\n Lists\n The items of a list are arbitrary Python objects. Lists are\n formed by placing a comma-separated list of expressions in\n square brackets. (Note that there are no special cases needed\n to form lists of length 0 or 1.)\n\n Byte Arrays\n A bytearray object is a mutable array. They are created by\n the built-in bytearray() constructor. Aside from being\n mutable (and hence unhashable), byte arrays otherwise provide\n the same interface and functionality as immutable bytes\n objects.\n\n The extension module array provides an additional example of\n a mutable sequence type, as does the collections module.\n\nSet types\n These represent unordered, finite sets of unique, immutable\n objects. As such, they cannot be indexed by any subscript. However,\n they can be iterated over, and the built-in function len()\n returns the number of items in a set. Common uses for sets are fast\n membership testing, removing duplicates from a sequence, and\n computing mathematical operations such as intersection, union,\n difference, and symmetric difference.\n\n For set elements, the same immutability rules apply as for\n dictionary keys. Note that numeric types obey the normal rules for\n numeric comparison: if two numbers compare equal (e.g., 1 and\n 1.0), only one of them can be contained in a set.\n\n There are currently two intrinsic set types:\n\n Sets\n These represent a mutable set. They are created by the built-in\n set() constructor and can be modified afterwards by several\n methods, such as add().\n\n Frozen sets\n These represent an immutable set. They are created by the\n built-in frozenset() constructor. As a frozenset is\n immutable and *hashable*, it can be used again as an element of\n another set, or as a dictionary key.\n\nMappings\n These represent finite sets of objects indexed by arbitrary index\n sets. The subscript notation a[k] selects the item indexed by\n k from the mapping a; this can be used in expressions and\n as the target of assignments or del statements. The built-in\n function len() returns the number of items in a mapping.\n\n There is currently a single intrinsic mapping type:\n\n Dictionaries\n These represent finite sets of objects indexed by nearly\n arbitrary values. The only types of values not acceptable as\n keys are values containing lists or dictionaries or other\n mutable types that are compared by value rather than by object\n identity, the reason being that the efficient implementation of\n dictionaries requires a key\'s hash value to remain constant.\n Numeric types used for keys obey the normal rules for numeric\n comparison: if two numbers compare equal (e.g., 1 and\n 1.0) then they can be used interchangeably to index the same\n dictionary entry.\n\n Dictionaries are mutable; they can be created by the {...}\n notation (see section *Dictionary displays*).\n\n The extension modules dbm.ndbm and dbm.gnu provide\n additional examples of mapping types, as does the\n collections module.\n\nCallable types\n These are the types to which the function call operation (see\n section *Calls*) can be applied:\n\n User-defined functions\n A user-defined function object is created by a function\n definition (see section *Function definitions*). It should be\n called with an argument list containing the same number of items\n as the function\'s formal parameter list.\n\n Special attributes:\n\n +---------------------------+---------------------------------+-------------+\n | Attribute | Meaning | |\n +===========================+=================================+=============+\n | __doc__ | The function\'s documentation | Writable |\n | | string, or None if | |\n | | unavailable | |\n +---------------------------+---------------------------------+-------------+\n | __name__ | The function\'s name | Writable |\n +---------------------------+---------------------------------+-------------+\n | __qualname__ | The function\'s *qualified name* | Writable |\n | | New in version 3.3. | |\n +---------------------------+---------------------------------+-------------+\n | __module__ | The name of the module the | Writable |\n | | function was defined in, or | |\n | | None if unavailable. | |\n +---------------------------+---------------------------------+-------------+\n | __defaults__ | A tuple containing default | Writable |\n | | argument values for those | |\n | | arguments that have defaults, | |\n | | or None if no arguments | |\n | | have a default value | |\n +---------------------------+---------------------------------+-------------+\n | __code__ | The code object representing | Writable |\n | | the compiled function body. | |\n +---------------------------+---------------------------------+-------------+\n | __globals__ | A reference to the dictionary | Read-only |\n | | that holds the function\'s | |\n | | global variables --- the global | |\n | | namespace of the module in | |\n | | which the function was defined. | |\n +---------------------------+---------------------------------+-------------+\n | __dict__ | The namespace supporting | Writable |\n | | arbitrary function attributes. | |\n +---------------------------+---------------------------------+-------------+\n | __closure__ | None or a tuple of cells | Read-only |\n | | that contain bindings for the | |\n | | function\'s free variables. | |\n +---------------------------+---------------------------------+-------------+\n | __annotations__ | A dict containing annotations | Writable |\n | | of parameters. The keys of the | |\n | | dict are the parameter names, | |\n | | or \'return\' for the return | |\n | | annotation, if provided. | |\n +---------------------------+---------------------------------+-------------+\n | __kwdefaults__ | A dict containing defaults for | Writable |\n | | keyword-only parameters. | |\n +---------------------------+---------------------------------+-------------+\n\n Most of the attributes labelled "Writable" check the type of the\n assigned value.\n\n Function objects also support getting and setting arbitrary\n attributes, which can be used, for example, to attach metadata\n to functions. Regular attribute dot-notation is used to get and\n set such attributes. *Note that the current implementation only\n supports function attributes on user-defined functions. Function\n attributes on built-in functions may be supported in the\n future.*\n\n Additional information about a function\'s definition can be\n retrieved from its code object; see the description of internal\n types below.\n\n Instance methods\n An instance method object combines a class, a class instance and\n any callable object (normally a user-defined function).\n\n Special read-only attributes: __self__ is the class instance\n object, __func__ is the function object; __doc__ is the\n method\'s documentation (same as __func__.__doc__);\n __name__ is the method name (same as __func__.__name__);\n __module__ is the name of the module the method was defined\n in, or None if unavailable.\n\n Methods also support accessing (but not setting) the arbitrary\n function attributes on the underlying function object.\n\n User-defined method objects may be created when getting an\n attribute of a class (perhaps via an instance of that class), if\n that attribute is a user-defined function object or a class\n method object.\n\n When an instance method object is created by retrieving a user-\n defined function object from a class via one of its instances,\n its __self__ attribute is the instance, and the method\n object is said to be bound. The new method\'s __func__\n attribute is the original function object.\n\n When a user-defined method object is created by retrieving\n another method object from a class or instance, the behaviour is\n the same as for a function object, except that the __func__\n attribute of the new instance is not the original method object\n but its __func__ attribute.\n\n When an instance method object is created by retrieving a class\n method object from a class or instance, its __self__\n attribute is the class itself, and its __func__ attribute is\n the function object underlying the class method.\n\n When an instance method object is called, the underlying\n function (__func__) is called, inserting the class instance\n (__self__) in front of the argument list. For instance,\n when C is a class which contains a definition for a function\n f(), and x is an instance of C, calling x.f(1)\n is equivalent to calling C.f(x, 1).\n\n When an instance method object is derived from a class method\n object, the "class instance" stored in __self__ will\n actually be the class itself, so that calling either x.f(1)\n or C.f(1) is equivalent to calling f(C,1) where f is\n the underlying function.\n\n Note that the transformation from function object to instance\n method object happens each time the attribute is retrieved from\n the instance. In some cases, a fruitful optimization is to\n assign the attribute to a local variable and call that local\n variable. Also notice that this transformation only happens for\n user-defined functions; other callable objects (and all non-\n callable objects) are retrieved without transformation. It is\n also important to note that user-defined functions which are\n attributes of a class instance are not converted to bound\n methods; this *only* happens when the function is an attribute\n of the class.\n\n Generator functions\n A function or method which uses the yield statement (see\n section *The yield statement*) is called a *generator function*.\n Such a function, when called, always returns an iterator object\n which can be used to execute the body of the function: calling\n the iterator\'s iterator__next__() method will cause the\n function to execute until it provides a value using the\n yield statement. When the function executes a return\n statement or falls off the end, a StopIteration exception is\n raised and the iterator will have reached the end of the set of\n values to be returned.\n\n Built-in functions\n A built-in function object is a wrapper around a C function.\n Examples of built-in functions are len() and math.sin()\n (math is a standard built-in module). The number and type of\n the arguments are determined by the C function. Special read-\n only attributes: __doc__ is the function\'s documentation\n string, or None if unavailable; __name__ is the\n function\'s name; __self__ is set to None (but see the\n next item); __module__ is the name of the module the\n function was defined in or None if unavailable.\n\n Built-in methods\n This is really a different disguise of a built-in function, this\n time containing an object passed to the C function as an\n implicit extra argument. An example of a built-in method is\n alist.append(), assuming *alist* is a list object. In this\n case, the special read-only attribute __self__ is set to the\n object denoted by *alist*.\n\n Classes\n Classes are callable. These objects normally act as factories\n for new instances of themselves, but variations are possible for\n class types that override __new__(). The arguments of the\n call are passed to __new__() and, in the typical case, to\n __init__() to initialize the new instance.\n\n Class Instances\n Instances of arbitrary classes can be made callable by defining\n a __call__() method in their class.\n\nModules\n Modules are a basic organizational unit of Python code, and are\n created by the *import system* as invoked either by the import\n statement (see import), or by calling functions such as\n importlib.import_module() and built-in __import__(). A\n module object has a namespace implemented by a dictionary object\n (this is the dictionary referenced by the __globals__ attribute\n of functions defined in the module). Attribute references are\n translated to lookups in this dictionary, e.g., m.x is\n equivalent to m.__dict__["x"]. A module object does not contain\n the code object used to initialize the module (since it isn\'t\n needed once the initialization is done).\n\n Attribute assignment updates the module\'s namespace dictionary,\n e.g., m.x = 1 is equivalent to m.__dict__["x"] = 1.\n\n Special read-only attribute: __dict__ is the module\'s namespace\n as a dictionary object.\n\n **CPython implementation detail:** Because of the way CPython\n clears module dictionaries, the module dictionary will be cleared\n when the module falls out of scope even if the dictionary still has\n live references. To avoid this, copy the dictionary or keep the\n module around while using its dictionary directly.\n\n Predefined (writable) attributes: __name__ is the module\'s\n name; __doc__ is the module\'s documentation string, or None\n if unavailable; __file__ is the pathname of the file from which\n the module was loaded, if it was loaded from a file. The\n __file__ attribute may be missing for certain types of modules,\n such as C modules that are statically linked into the interpreter;\n for extension modules loaded dynamically from a shared library, it\n is the pathname of the shared library file.\n\nCustom classes\n Custom class types are typically created by class definitions (see\n section *Class definitions*). A class has a namespace implemented\n by a dictionary object. Class attribute references are translated\n to lookups in this dictionary, e.g., C.x is translated to\n C.__dict__["x"] (although there are a number of hooks which\n allow for other means of locating attributes). When the attribute\n name is not found there, the attribute search continues in the base\n classes. This search of the base classes uses the C3 method\n resolution order which behaves correctly even in the presence of\n \'diamond\' inheritance structures where there are multiple\n inheritance paths leading back to a common ancestor. Additional\n details on the C3 MRO used by Python can be found in the\n documentation accompanying the 2.3 release at\n http://www.python.org/download/releases/2.3/mro/.\n\n When a class attribute reference (for class C, say) would yield\n a class method object, it is transformed into an instance method\n object whose __self__ attributes is C. When it would yield\n a static method object, it is transformed into the object wrapped\n by the static method object. See section *Implementing Descriptors*\n for another way in which attributes retrieved from a class may\n differ from those actually contained in its __dict__.\n\n Class attribute assignments update the class\'s dictionary, never\n the dictionary of a base class.\n\n A class object can be called (see above) to yield a class instance\n (see below).\n\n Special attributes: __name__ is the class name; __module__\n is the module name in which the class was defined; __dict__ is\n the dictionary containing the class\'s namespace; __bases__ is a\n tuple (possibly empty or a singleton) containing the base classes,\n in the order of their occurrence in the base class list;\n __doc__ is the class\'s documentation string, or None if\n undefined.\n\nClass instances\n A class instance is created by calling a class object (see above).\n A class instance has a namespace implemented as a dictionary which\n is the first place in which attribute references are searched.\n When an attribute is not found there, and the instance\'s class has\n an attribute by that name, the search continues with the class\n attributes. If a class attribute is found that is a user-defined\n function object, it is transformed into an instance method object\n whose __self__ attribute is the instance. Static method and\n class method objects are also transformed; see above under\n "Classes". See section *Implementing Descriptors* for another way\n in which attributes of a class retrieved via its instances may\n differ from the objects actually stored in the class\'s\n __dict__. If no class attribute is found, and the object\'s\n class has a __getattr__() method, that is called to satisfy the\n lookup.\n\n Attribute assignments and deletions update the instance\'s\n dictionary, never a class\'s dictionary. If the class has a\n __setattr__() or __delattr__() method, this is called\n instead of updating the instance dictionary directly.\n\n Class instances can pretend to be numbers, sequences, or mappings\n if they have methods with certain special names. See section\n *Special method names*.\n\n Special attributes: __dict__ is the attribute dictionary;\n __class__ is the instance\'s class.\n\nI/O objects (also known as file objects)\n A *file object* represents an open file. Various shortcuts are\n available to create file objects: the open() built-in function,\n and also os.popen(), os.fdopen(), and the makefile()\n method of socket objects (and perhaps by other functions or methods\n provided by extension modules).\n\n The objects sys.stdin, sys.stdout and sys.stderr are\n initialized to file objects corresponding to the interpreter\'s\n standard input, output and error streams; they are all open in text\n mode and therefore follow the interface defined by the\n io.TextIOBase abstract class.\n\nInternal types\n A few types used internally by the interpreter are exposed to the\n user. Their definitions may change with future versions of the\n interpreter, but they are mentioned here for completeness.\n\n Code objects\n Code objects represent *byte-compiled* executable Python code,\n or *bytecode*. The difference between a code object and a\n function object is that the function object contains an explicit\n reference to the function\'s globals (the module in which it was\n defined), while a code object contains no context; also the\n default argument values are stored in the function object, not\n in the code object (because they represent values calculated at\n run-time). Unlike function objects, code objects are immutable\n and contain no references (directly or indirectly) to mutable\n objects.\n\n Special read-only attributes: co_name gives the function\n name; co_argcount is the number of positional arguments\n (including arguments with default values); co_nlocals is the\n number of local variables used by the function (including\n arguments); co_varnames is a tuple containing the names of\n the local variables (starting with the argument names);\n co_cellvars is a tuple containing the names of local\n variables that are referenced by nested functions;\n co_freevars is a tuple containing the names of free\n variables; co_code is a string representing the sequence of\n bytecode instructions; co_consts is a tuple containing the\n literals used by the bytecode; co_names is a tuple\n containing the names used by the bytecode; co_filename is\n the filename from which the code was compiled;\n co_firstlineno is the first line number of the function;\n co_lnotab is a string encoding the mapping from bytecode\n offsets to line numbers (for details see the source code of the\n interpreter); co_stacksize is the required stack size\n (including local variables); co_flags is an integer encoding\n a number of flags for the interpreter.\n\n The following flag bits are defined for co_flags: bit\n 0x04 is set if the function uses the *arguments syntax\n to accept an arbitrary number of positional arguments; bit\n 0x08 is set if the function uses the **keywords syntax\n to accept arbitrary keyword arguments; bit 0x20 is set if\n the function is a generator.\n\n Future feature declarations (from __future__ import\n division) also use bits in co_flags to indicate whether a\n code object was compiled with a particular feature enabled: bit\n 0x2000 is set if the function was compiled with future\n division enabled; bits 0x10 and 0x1000 were used in\n earlier versions of Python.\n\n Other bits in co_flags are reserved for internal use.\n\n If a code object represents a function, the first item in\n co_consts is the documentation string of the function, or\n None if undefined.\n\n Frame objects\n Frame objects represent execution frames. They may occur in\n traceback objects (see below).\n\n Special read-only attributes: f_back is to the previous\n stack frame (towards the caller), or None if this is the\n bottom stack frame; f_code is the code object being executed\n in this frame; f_locals is the dictionary used to look up\n local variables; f_globals is used for global variables;\n f_builtins is used for built-in (intrinsic) names;\n f_lasti gives the precise instruction (this is an index into\n the bytecode string of the code object).\n\n Special writable attributes: f_trace, if not None, is a\n function called at the start of each source code line (this is\n used by the debugger); f_lineno is the current line number\n of the frame --- writing to this from within a trace function\n jumps to the given line (only for the bottom-most frame). A\n debugger can implement a Jump command (aka Set Next Statement)\n by writing to f_lineno.\n\n Traceback objects\n Traceback objects represent a stack trace of an exception. A\n traceback object is created when an exception occurs. When the\n search for an exception handler unwinds the execution stack, at\n each unwound level a traceback object is inserted in front of\n the current traceback. When an exception handler is entered,\n the stack trace is made available to the program. (See section\n *The try statement*.) It is accessible as the third item of the\n tuple returned by sys.exc_info(). When the program contains\n no suitable handler, the stack trace is written (nicely\n formatted) to the standard error stream; if the interpreter is\n interactive, it is also made available to the user as\n sys.last_traceback.\n\n Special read-only attributes: tb_next is the next level in\n the stack trace (towards the frame where the exception\n occurred), or None if there is no next level; tb_frame\n points to the execution frame of the current level;\n tb_lineno gives the line number where the exception\n occurred; tb_lasti indicates the precise instruction. The\n line number and last instruction in the traceback may differ\n from the line number of its frame object if the exception\n occurred in a try statement with no matching except clause\n or with a finally clause.\n\n Slice objects\n Slice objects are used to represent slices for __getitem__()\n methods. They are also created by the built-in slice()\n function.\n\n Special read-only attributes: start is the lower bound;\n stop is the upper bound; step is the step value; each is\n None if omitted. These attributes can have any type.\n\n Slice objects support one method:\n\n slice.indices(self, length)\n\n This method takes a single integer argument *length* and\n computes information about the slice that the slice object\n would describe if applied to a sequence of *length* items.\n It returns a tuple of three integers; respectively these are\n the *start* and *stop* indices and the *step* or stride\n length of the slice. Missing or out-of-bounds indices are\n handled in a manner consistent with regular slices.\n\n Static method objects\n Static method objects provide a way of defeating the\n transformation of function objects to method objects described\n above. A static method object is a wrapper around any other\n object, usually a user-defined method object. When a static\n method object is retrieved from a class or a class instance, the\n object actually returned is the wrapped object, which is not\n subject to any further transformation. Static method objects are\n not themselves callable, although the objects they wrap usually\n are. Static method objects are created by the built-in\n staticmethod() constructor.\n\n Class method objects\n A class method object, like a static method object, is a wrapper\n around another object that alters the way in which that object\n is retrieved from classes and class instances. The behaviour of\n class method objects upon such retrieval is described above,\n under "User-defined methods". Class method objects are created\n by the built-in classmethod() constructor.\n', 'typesfunctions': '\nFunctions\n*********\n\nFunction objects are created by function definitions. The only\noperation on a function object is to call it: func(argument-list).\n\nThere are really two flavors of function objects: built-in functions\nand user-defined functions. Both support the same operation (to call\nthe function), but the implementation is different, hence the\ndifferent object types.\n\nSee *Function definitions* for more information.\n', 'typesmapping': '\nMapping Types --- dict\n**************************\n\nA *mapping* object maps *hashable* values to arbitrary objects.\nMappings are mutable objects. There is currently only one standard\nmapping type, the *dictionary*. (For other containers see the built-\nin list, set, and tuple classes, and the collections\nmodule.)\n\nA dictionary\'s keys are *almost* arbitrary values. Values that are\nnot *hashable*, that is, values containing lists, dictionaries or\nother mutable types (that are compared by value rather than by object\nidentity) may not be used as keys. Numeric types used for keys obey\nthe normal rules for numeric comparison: if two numbers compare equal\n(such as 1 and 1.0) then they can be used interchangeably to\nindex the same dictionary entry. (Note however, that since computers\nstore floating-point numbers as approximations it is usually unwise to\nuse them as dictionary keys.)\n\nDictionaries can be created by placing a comma-separated list of\nkey: value pairs within braces, for example: {\'jack\': 4098,\n\'sjoerd\': 4127} or {4098: \'jack\', 4127: \'sjoerd\'}, or by the\ndict constructor.\n\nclass class dict(**kwarg)\nclass class dict(mapping, **kwarg)\nclass class dict(iterable, **kwarg)\n\n Return a new dictionary initialized from an optional positional\n argument and a possibly empty set of keyword arguments.\n\n If no positional argument is given, an empty dictionary is created.\n If a positional argument is given and it is a mapping object, a\n dictionary is created with the same key-value pairs as the mapping\n object. Otherwise, the positional argument must be an *iterator*\n object. Each item in the iterable must itself be an iterator with\n exactly two objects. The first object of each item becomes a key\n in the new dictionary, and the second object the corresponding\n value. If a key occurs more than once, the last value for that key\n becomes the corresponding value in the new dictionary.\n\n If keyword arguments are given, the keyword arguments and their\n values are added to the dictionary created from the positional\n argument. If a key being added is already present, the value from\n the keyword argument replaces the value from the positional\n argument.\n\n To illustrate, the following examples all return a dictionary equal\n to {"one": 1, "two": 2, "three": 3}:\n\n >>> a = dict(one=1, two=2, three=3)\n >>> b = {\'one\': 1, \'two\': 2, \'three\': 3}\n >>> c = dict(zip([\'one\', \'two\', \'three\'], [1, 2, 3]))\n >>> d = dict([(\'two\', 2), (\'one\', 1), (\'three\', 3)])\n >>> e = dict({\'three\': 3, \'one\': 1, \'two\': 2})\n >>> a == b == c == d == e\n True\n\n Providing keyword arguments as in the first example only works for\n keys that are valid Python identifiers. Otherwise, any valid keys\n can be used.\n\n These are the operations that dictionaries support (and therefore,\n custom mapping types should support too):\n\n len(d)\n\n Return the number of items in the dictionary *d*.\n\n d[key]\n\n Return the item of *d* with key *key*. Raises a KeyError if\n *key* is not in the map.\n\n If a subclass of dict defines a method __missing__(), if the\n key *key* is not present, the d[key] operation calls that\n method with the key *key* as argument. The d[key] operation\n then returns or raises whatever is returned or raised by the\n __missing__(key) call if the key is not present. No other\n operations or methods invoke __missing__(). If\n __missing__() is not defined, KeyError is raised.\n __missing__() must be a method; it cannot be an instance\n variable:\n\n >>> class Counter(dict):\n ... def __missing__(self, key):\n ... return 0\n >>> c = Counter()\n >>> c[\'red\']\n 0\n >>> c[\'red\'] += 1\n >>> c[\'red\']\n 1\n\n See collections.Counter for a complete implementation\n including other methods helpful for accumulating and managing\n tallies.\n\n d[key] = value\n\n Set d[key] to *value*.\n\n del d[key]\n\n Remove d[key] from *d*. Raises a KeyError if *key* is\n not in the map.\n\n key in d\n\n Return True if *d* has a key *key*, else False.\n\n key not in d\n\n Equivalent to not key in d.\n\n iter(d)\n\n Return an iterator over the keys of the dictionary. This is a\n shortcut for iter(d.keys()).\n\n clear()\n\n Remove all items from the dictionary.\n\n copy()\n\n Return a shallow copy of the dictionary.\n\n classmethod fromkeys(seq[, value])\n\n Create a new dictionary with keys from *seq* and values set to\n *value*.\n\n fromkeys() is a class method that returns a new dictionary.\n *value* defaults to None.\n\n get(key[, default])\n\n Return the value for *key* if *key* is in the dictionary, else\n *default*. If *default* is not given, it defaults to None,\n so that this method never raises a KeyError.\n\n items()\n\n Return a new view of the dictionary\'s items ((key, value)\n pairs). See the *documentation of view objects*.\n\n keys()\n\n Return a new view of the dictionary\'s keys. See the\n *documentation of view objects*.\n\n pop(key[, default])\n\n If *key* is in the dictionary, remove it and return its value,\n else return *default*. If *default* is not given and *key* is\n not in the dictionary, a KeyError is raised.\n\n popitem()\n\n Remove and return an arbitrary (key, value) pair from the\n dictionary.\n\n popitem() is useful to destructively iterate over a\n dictionary, as often used in set algorithms. If the dictionary\n is empty, calling popitem() raises a KeyError.\n\n setdefault(key[, default])\n\n If *key* is in the dictionary, return its value. If not, insert\n *key* with a value of *default* and return *default*. *default*\n defaults to None.\n\n update([other])\n\n Update the dictionary with the key/value pairs from *other*,\n overwriting existing keys. Return None.\n\n update() accepts either another dictionary object or an\n iterable of key/value pairs (as tuples or other iterables of\n length two). If keyword arguments are specified, the dictionary\n is then updated with those key/value pairs: d.update(red=1,\n blue=2).\n\n values()\n\n Return a new view of the dictionary\'s values. See the\n *documentation of view objects*.\n\nSee also:\n\n types.MappingProxyType can be used to create a read-only view\n of a dict.\n\n\nDictionary view objects\n=======================\n\nThe objects returned by dict.keys(), dict.values() and\ndict.items() are *view objects*. They provide a dynamic view on\nthe dictionary\'s entries, which means that when the dictionary\nchanges, the view reflects these changes.\n\nDictionary views can be iterated over to yield their respective data,\nand support membership tests:\n\nlen(dictview)\n\n Return the number of entries in the dictionary.\n\niter(dictview)\n\n Return an iterator over the keys, values or items (represented as\n tuples of (key, value)) in the dictionary.\n\n Keys and values are iterated over in an arbitrary order which is\n non-random, varies across Python implementations, and depends on\n the dictionary\'s history of insertions and deletions. If keys,\n values and items views are iterated over with no intervening\n modifications to the dictionary, the order of items will directly\n correspond. This allows the creation of (value, key) pairs\n using zip(): pairs = zip(d.values(), d.keys()). Another\n way to create the same list is pairs = [(v, k) for (k, v) in\n d.items()].\n\n Iterating views while adding or deleting entries in the dictionary\n may raise a RuntimeError or fail to iterate over all entries.\n\nx in dictview\n\n Return True if *x* is in the underlying dictionary\'s keys,\n values or items (in the latter case, *x* should be a (key,\n value) tuple).\n\nKeys views are set-like since their entries are unique and hashable.\nIf all values are hashable, so that (key, value) pairs are unique\nand hashable, then the items view is also set-like. (Values views are\nnot treated as set-like since the entries are generally not unique.)\nFor set-like views, all of the operations defined for the abstract\nbase class collections.abc.Set are available (for example, ==,\n<, or ^).\n\nAn example of dictionary view usage:\n\n >>> dishes = {\'eggs\': 2, \'sausage\': 1, \'bacon\': 1, \'spam\': 500}\n >>> keys = dishes.keys()\n >>> values = dishes.values()\n\n >>> # iteration\n >>> n = 0\n >>> for val in values:\n ... n += val\n >>> print(n)\n 504\n\n >>> # keys and values are iterated over in the same order\n >>> list(keys)\n [\'eggs\', \'bacon\', \'sausage\', \'spam\']\n >>> list(values)\n [2, 1, 1, 500]\n\n >>> # view objects are dynamic and reflect dict changes\n >>> del dishes[\'eggs\']\n >>> del dishes[\'sausage\']\n >>> list(keys)\n [\'spam\', \'bacon\']\n\n >>> # set operations\n >>> keys & {\'eggs\', \'bacon\', \'salad\'}\n {\'bacon\'}\n >>> keys ^ {\'sausage\', \'juice\'}\n {\'juice\', \'sausage\', \'bacon\', \'spam\'}\n', 'typesmethods': '\nMethods\n*******\n\nMethods are functions that are called using the attribute notation.\nThere are two flavors: built-in methods (such as append() on\nlists) and class instance methods. Built-in methods are described\nwith the types that support them.\n\nIf you access a method (a function defined in a class namespace)\nthrough an instance, you get a special object: a *bound method* (also\ncalled *instance method*) object. When called, it will add the\nself argument to the argument list. Bound methods have two\nspecial read-only attributes: m.__self__ is the object on which\nthe method operates, and m.__func__ is the function implementing\nthe method. Calling m(arg-1, arg-2, ..., arg-n) is completely\nequivalent to calling m.__func__(m.__self__, arg-1, arg-2, ...,\narg-n).\n\nLike function objects, bound method objects support getting arbitrary\nattributes. However, since method attributes are actually stored on\nthe underlying function object (meth.__func__), setting method\nattributes on bound methods is disallowed. Attempting to set an\nattribute on a method results in an AttributeError being raised.\nIn order to set a method attribute, you need to explicitly set it on\nthe underlying function object:\n\n >>> class C:\n ... def method(self):\n ... pass\n ...\n >>> c = C()\n >>> c.method.whoami = \'my name is method\' # can\'t set on the method\n Traceback (most recent call last):\n File "", line 1, in \n AttributeError: \'method\' object has no attribute \'whoami\'\n >>> c.method.__func__.whoami = \'my name is method\'\n >>> c.method.whoami\n \'my name is method\'\n\nSee *The standard type hierarchy* for more information.\n', 'typesmodules': "\nModules\n*******\n\nThe only special operation on a module is attribute access:\nm.name, where *m* is a module and *name* accesses a name defined\nin *m*'s symbol table. Module attributes can be assigned to. (Note\nthat the import statement is not, strictly speaking, an operation\non a module object; import foo does not require a module object\nnamed *foo* to exist, rather it requires an (external) *definition*\nfor a module named *foo* somewhere.)\n\nA special attribute of every module is __dict__. This is the\ndictionary containing the module's symbol table. Modifying this\ndictionary will actually change the module's symbol table, but direct\nassignment to the __dict__ attribute is not possible (you can\nwrite m.__dict__['a'] = 1, which defines m.a to be 1, but\nyou can't write m.__dict__ = {}). Modifying __dict__ directly\nis not recommended.\n\nModules built into the interpreter are written like this: . If loaded from a file, they are written as\n.\n", 'typesseq': '\nSequence Types --- list, tuple, range\n*************************************************\n\nThere are three basic sequence types: lists, tuples, and range\nobjects. Additional sequence types tailored for processing of *binary\ndata* and *text strings* are described in dedicated sections.\n\n\nCommon Sequence Operations\n==========================\n\nThe operations in the following table are supported by most sequence\ntypes, both mutable and immutable. The collections.abc.Sequence\nABC is provided to make it easier to correctly implement these\noperations on custom sequence types.\n\nThis table lists the sequence operations sorted in ascending priority\n(operations in the same box have the same priority). In the table,\n*s* and *t* are sequences of the same type, *n*, *i*, *j* and *k* are\nintegers and *x* is an arbitrary object that meets any type and value\nrestrictions imposed by *s*.\n\nThe in and not in operations have the same priorities as the\ncomparison operations. The + (concatenation) and *\n(repetition) operations have the same priority as the corresponding\nnumeric operations.\n\n+----------------------------+----------------------------------+------------+\n| Operation | Result | Notes |\n+============================+==================================+============+\n| x in s | True if an item of *s* is | (1) |\n| | equal to *x*, else False | |\n+----------------------------+----------------------------------+------------+\n| x not in s | False if an item of *s* is | (1) |\n| | equal to *x*, else True | |\n+----------------------------+----------------------------------+------------+\n| s + t | the concatenation of *s* and *t* | (6)(7) |\n+----------------------------+----------------------------------+------------+\n| s * n or n * s | *n* shallow copies of *s* | (2)(7) |\n| | concatenated | |\n+----------------------------+----------------------------------+------------+\n| s[i] | *i*th item of *s*, origin 0 | (3) |\n+----------------------------+----------------------------------+------------+\n| s[i:j] | slice of *s* from *i* to *j* | (3)(4) |\n+----------------------------+----------------------------------+------------+\n| s[i:j:k] | slice of *s* from *i* to *j* | (3)(5) |\n| | with step *k* | |\n+----------------------------+----------------------------------+------------+\n| len(s) | length of *s* | |\n+----------------------------+----------------------------------+------------+\n| min(s) | smallest item of *s* | |\n+----------------------------+----------------------------------+------------+\n| max(s) | largest item of *s* | |\n+----------------------------+----------------------------------+------------+\n| s.index(x[, i[, j]]) | index of the first occurrence of | (8) |\n| | *x* in *s* (at or after index | |\n| | *i* and before index *j*) | |\n+----------------------------+----------------------------------+------------+\n| s.count(x) | total number of occurrences of | |\n| | *x* in *s* | |\n+----------------------------+----------------------------------+------------+\n\nSequences of the same type also support comparisons. In particular,\ntuples and lists are compared lexicographically by comparing\ncorresponding elements. This means that to compare equal, every\nelement must compare equal and the two sequences must be of the same\ntype and have the same length. (For full details see *Comparisons* in\nthe language reference.)\n\nNotes:\n\n1. While the in and not in operations are used only for simple\n containment testing in the general case, some specialised sequences\n (such as str, bytes and bytearray) also use them for\n subsequence testing:\n\n >>> "gg" in "eggs"\n True\n\n2. Values of *n* less than 0 are treated as 0 (which yields an\n empty sequence of the same type as *s*). Note also that the copies\n are shallow; nested structures are not copied. This often haunts\n new Python programmers; consider:\n\n >>> lists = [[]] * 3\n >>> lists\n [[], [], []]\n >>> lists[0].append(3)\n >>> lists\n [[3], [3], [3]]\n\n What has happened is that [[]] is a one-element list containing\n an empty list, so all three elements of [[]] * 3 are (pointers\n to) this single empty list. Modifying any of the elements of\n lists modifies this single list. You can create a list of\n different lists this way:\n\n >>> lists = [[] for i in range(3)]\n >>> lists[0].append(3)\n >>> lists[1].append(5)\n >>> lists[2].append(7)\n >>> lists\n [[3], [5], [7]]\n\n3. If *i* or *j* is negative, the index is relative to the end of the\n string: len(s) + i or len(s) + j is substituted. But note\n that -0 is still 0.\n\n4. The slice of *s* from *i* to *j* is defined as the sequence of\n items with index *k* such that i <= k < j. If *i* or *j* is\n greater than len(s), use len(s). If *i* is omitted or\n None, use 0. If *j* is omitted or None, use\n len(s). If *i* is greater than or equal to *j*, the slice is\n empty.\n\n5. The slice of *s* from *i* to *j* with step *k* is defined as the\n sequence of items with index x = i + n*k such that 0 <= n <\n (j-i)/k. In other words, the indices are i, i+k,\n i+2*k, i+3*k and so on, stopping when *j* is reached (but\n never including *j*). If *i* or *j* is greater than len(s),\n use len(s). If *i* or *j* are omitted or None, they become\n "end" values (which end depends on the sign of *k*). Note, *k*\n cannot be zero. If *k* is None, it is treated like 1.\n\n6. Concatenating immutable sequences always results in a new object.\n This means that building up a sequence by repeated concatenation\n will have a quadratic runtime cost in the total sequence length.\n To get a linear runtime cost, you must switch to one of the\n alternatives below:\n\n * if concatenating str objects, you can build a list and use\n str.join() at the end or else write to a io.StringIO\n instance and retrieve its value when complete\n\n * if concatenating bytes objects, you can similarly use\n bytes.join() or io.BytesIO, or you can do in-place\n concatenation with a bytearray object. bytearray objects\n are mutable and have an efficient overallocation mechanism\n\n * if concatenating tuple objects, extend a list instead\n\n * for other types, investigate the relevant class documentation\n\n7. Some sequence types (such as range) only support item sequences\n that follow specific patterns, and hence don\'t support sequence\n concatenation or repetition.\n\n8. index raises ValueError when *x* is not found in *s*. When\n supported, the additional arguments to the index method allow\n efficient searching of subsections of the sequence. Passing the\n extra arguments is roughly equivalent to using s[i:j].index(x),\n only without copying any data and with the returned index being\n relative to the start of the sequence rather than the start of the\n slice.\n\n\nImmutable Sequence Types\n========================\n\nThe only operation that immutable sequence types generally implement\nthat is not also implemented by mutable sequence types is support for\nthe hash() built-in.\n\nThis support allows immutable sequences, such as tuple instances,\nto be used as dict keys and stored in set and frozenset\ninstances.\n\nAttempting to hash an immutable sequence that contains unhashable\nvalues will result in TypeError.\n\n\nMutable Sequence Types\n======================\n\nThe operations in the following table are defined on mutable sequence\ntypes. The collections.abc.MutableSequence ABC is provided to make\nit easier to correctly implement these operations on custom sequence\ntypes.\n\nIn the table *s* is an instance of a mutable sequence type, *t* is any\niterable object and *x* is an arbitrary object that meets any type and\nvalue restrictions imposed by *s* (for example, bytearray only\naccepts integers that meet the value restriction 0 <= x <= 255).\n\n+--------------------------------+----------------------------------+-----------------------+\n| Operation | Result | Notes |\n+================================+==================================+=======================+\n| s[i] = x | item *i* of *s* is replaced by | |\n| | *x* | |\n+--------------------------------+----------------------------------+-----------------------+\n| s[i:j] = t | slice of *s* from *i* to *j* is | |\n| | replaced by the contents of the | |\n| | iterable *t* | |\n+--------------------------------+----------------------------------+-----------------------+\n| del s[i:j] | same as s[i:j] = [] | |\n+--------------------------------+----------------------------------+-----------------------+\n| s[i:j:k] = t | the elements of s[i:j:k] are | (1) |\n| | replaced by those of *t* | |\n+--------------------------------+----------------------------------+-----------------------+\n| del s[i:j:k] | removes the elements of | |\n| | s[i:j:k] from the list | |\n+--------------------------------+----------------------------------+-----------------------+\n| s.append(x) | appends *x* to the end of the | |\n| | sequence (same as | |\n| | s[len(s):len(s)] = [x]) | |\n+--------------------------------+----------------------------------+-----------------------+\n| s.clear() | removes all items from s | (5) |\n| | (same as del s[:]) | |\n+--------------------------------+----------------------------------+-----------------------+\n| s.copy() | creates a shallow copy of s | (5) |\n| | (same as s[:]) | |\n+--------------------------------+----------------------------------+-----------------------+\n| s.extend(t) | extends *s* with the contents of | |\n| | *t* (same as s[len(s):len(s)] | |\n| | = t) | |\n+--------------------------------+----------------------------------+-----------------------+\n| s.insert(i, x) | inserts *x* into *s* at the | |\n| | index given by *i* (same as | |\n| | s[i:i] = [x]) | |\n+--------------------------------+----------------------------------+-----------------------+\n| s.pop([i]) | retrieves the item at *i* and | (2) |\n| | also removes it from *s* | |\n+--------------------------------+----------------------------------+-----------------------+\n| s.remove(x) | remove the first item from *s* | (3) |\n| | where s[i] == x | |\n+--------------------------------+----------------------------------+-----------------------+\n| s.reverse() | reverses the items of *s* in | (4) |\n| | place | |\n+--------------------------------+----------------------------------+-----------------------+\n\nNotes:\n\n1. *t* must have the same length as the slice it is replacing.\n\n2. The optional argument *i* defaults to -1, so that by default\n the last item is removed and returned.\n\n3. remove raises ValueError when *x* is not found in *s*.\n\n4. The reverse() method modifies the sequence in place for economy\n of space when reversing a large sequence. To remind users that it\n operates by side effect, it does not return the reversed sequence.\n\n5. clear() and copy() are included for consistency with the\n interfaces of mutable containers that don\'t support slicing\n operations (such as dict and set)\n\n New in version 3.3: clear() and copy() methods.\n\n\nLists\n=====\n\nLists are mutable sequences, typically used to store collections of\nhomogeneous items (where the precise degree of similarity will vary by\napplication).\n\nclass class list([iterable])\n\n Lists may be constructed in several ways:\n\n * Using a pair of square brackets to denote the empty list: []\n\n * Using square brackets, separating items with commas: [a],\n [a, b, c]\n\n * Using a list comprehension: [x for x in iterable]\n\n * Using the type constructor: list() or list(iterable)\n\n The constructor builds a list whose items are the same and in the\n same order as *iterable*\'s items. *iterable* may be either a\n sequence, a container that supports iteration, or an iterator\n object. If *iterable* is already a list, a copy is made and\n returned, similar to iterable[:]. For example, list(\'abc\')\n returns [\'a\', \'b\', \'c\'] and list( (1, 2, 3) ) returns [1,\n 2, 3]. If no argument is given, the constructor creates a new\n empty list, [].\n\n Many other operations also produce lists, including the\n sorted() built-in.\n\n Lists implement all of the *common* and *mutable* sequence\n operations. Lists also provide the following additional method:\n\n sort(*, key=None, reverse=None)\n\n This method sorts the list in place, using only <\n comparisons between items. Exceptions are not suppressed - if\n any comparison operations fail, the entire sort operation will\n fail (and the list will likely be left in a partially modified\n state).\n\n *key* specifies a function of one argument that is used to\n extract a comparison key from each list element (for example,\n key=str.lower). The key corresponding to each item in the\n list is calculated once and then used for the entire sorting\n process. The default value of None means that list items are\n sorted directly without calculating a separate key value.\n\n The functools.cmp_to_key() utility is available to convert a\n 2.x style *cmp* function to a *key* function.\n\n *reverse* is a boolean value. If set to True, then the list\n elements are sorted as if each comparison were reversed.\n\n This method modifies the sequence in place for economy of space\n when sorting a large sequence. To remind users that it operates\n by side effect, it does not return the sorted sequence (use\n sorted() to explicitly request a new sorted list instance).\n\n The sort() method is guaranteed to be stable. A sort is\n stable if it guarantees not to change the relative order of\n elements that compare equal --- this is helpful for sorting in\n multiple passes (for example, sort by department, then by salary\n grade).\n\n **CPython implementation detail:** While a list is being sorted,\n the effect of attempting to mutate, or even inspect, the list is\n undefined. The C implementation of Python makes the list appear\n empty for the duration, and raises ValueError if it can\n detect that the list has been mutated during a sort.\n\n\nTuples\n======\n\nTuples are immutable sequences, typically used to store collections of\nheterogeneous data (such as the 2-tuples produced by the\nenumerate() built-in). Tuples are also used for cases where an\nimmutable sequence of homogeneous data is needed (such as allowing\nstorage in a set or dict instance).\n\nclass class tuple([iterable])\n\n Tuples may be constructed in a number of ways:\n\n * Using a pair of parentheses to denote the empty tuple: ()\n\n * Using a trailing comma for a singleton tuple: a, or (a,)\n\n * Separating items with commas: a, b, c or (a, b, c)\n\n * Using the tuple() built-in: tuple() or\n tuple(iterable)\n\n The constructor builds a tuple whose items are the same and in the\n same order as *iterable*\'s items. *iterable* may be either a\n sequence, a container that supports iteration, or an iterator\n object. If *iterable* is already a tuple, it is returned\n unchanged. For example, tuple(\'abc\') returns (\'a\', \'b\',\n \'c\') and tuple( [1, 2, 3] ) returns (1, 2, 3). If no\n argument is given, the constructor creates a new empty tuple,\n ().\n\n Note that it is actually the comma which makes a tuple, not the\n parentheses. The parentheses are optional, except in the empty\n tuple case, or when they are needed to avoid syntactic ambiguity.\n For example, f(a, b, c) is a function call with three\n arguments, while f((a, b, c)) is a function call with a 3-tuple\n as the sole argument.\n\n Tuples implement all of the *common* sequence operations.\n\nFor heterogeneous collections of data where access by name is clearer\nthan access by index, collections.namedtuple() may be a more\nappropriate choice than a simple tuple object.\n\n\nRanges\n======\n\nThe range type represents an immutable sequence of numbers and is\ncommonly used for looping a specific number of times in for loops.\n\nclass class range(stop)\nclass class range(start, stop[, step])\n\n The arguments to the range constructor must be integers (either\n built-in int or any object that implements the __index__\n special method). If the *step* argument is omitted, it defaults to\n 1. If the *start* argument is omitted, it defaults to 0. If\n *step* is zero, ValueError is raised.\n\n For a positive *step*, the contents of a range r are determined\n by the formula r[i] = start + step*i where i >= 0 and\n r[i] < stop.\n\n For a negative *step*, the contents of the range are still\n determined by the formula r[i] = start + step*i, but the\n constraints are i >= 0 and r[i] > stop.\n\n A range object will be empty if r[0] does not meet the value\n constraint. Ranges do support negative indices, but these are\n interpreted as indexing from the end of the sequence determined by\n the positive indices.\n\n Ranges containing absolute values larger than sys.maxsize are\n permitted but some features (such as len()) may raise\n OverflowError.\n\n Range examples:\n\n >>> list(range(10))\n [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n >>> list(range(1, 11))\n [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n >>> list(range(0, 30, 5))\n [0, 5, 10, 15, 20, 25]\n >>> list(range(0, 10, 3))\n [0, 3, 6, 9]\n >>> list(range(0, -10, -1))\n [0, -1, -2, -3, -4, -5, -6, -7, -8, -9]\n >>> list(range(0))\n []\n >>> list(range(1, 0))\n []\n\n Ranges implement all of the *common* sequence operations except\n concatenation and repetition (due to the fact that range objects\n can only represent sequences that follow a strict pattern and\n repetition and concatenation will usually violate that pattern).\n\nThe advantage of the range type over a regular list or\ntuple is that a range object will always take the same (small)\namount of memory, no matter the size of the range it represents (as it\nonly stores the start, stop and step values, calculating\nindividual items and subranges as needed).\n\nRange objects implement the collections.Sequence ABC, and provide\nfeatures such as containment tests, element index lookup, slicing and\nsupport for negative indices (see *Sequence Types --- list, tuple,\nrange*):\n\n>>> r = range(0, 20, 2)\n>>> r\nrange(0, 20, 2)\n>>> 11 in r\nFalse\n>>> 10 in r\nTrue\n>>> r.index(10)\n5\n>>> r[5]\n10\n>>> r[:5]\nrange(0, 10, 2)\n>>> r[-1]\n18\n\nTesting range objects for equality with == and != compares\nthem as sequences. That is, two range objects are considered equal if\nthey represent the same sequence of values. (Note that two range\nobjects that compare equal might have different start, stop\nand step attributes, for example range(0) == range(2, 1, 3) or\nrange(0, 3, 2) == range(0, 4, 2).)\n\nChanged in version 3.2: Implement the Sequence ABC. Support slicing\nand negative indices. Test int objects for membership in constant\ntime instead of iterating through all items.\n\nChanged in version 3.3: Define \'==\' and \'!=\' to compare range objects\nbased on the sequence of values they define (instead of comparing\nbased on object identity).\n\nNew in version 3.3: The start, stop and step attributes.\n', 'typesseq-mutable': "\nMutable Sequence Types\n**********************\n\nThe operations in the following table are defined on mutable sequence\ntypes. The collections.abc.MutableSequence ABC is provided to make\nit easier to correctly implement these operations on custom sequence\ntypes.\n\nIn the table *s* is an instance of a mutable sequence type, *t* is any\niterable object and *x* is an arbitrary object that meets any type and\nvalue restrictions imposed by *s* (for example, bytearray only\naccepts integers that meet the value restriction 0 <= x <= 255).\n\n+--------------------------------+----------------------------------+-----------------------+\n| Operation | Result | Notes |\n+================================+==================================+=======================+\n| s[i] = x | item *i* of *s* is replaced by | |\n| | *x* | |\n+--------------------------------+----------------------------------+-----------------------+\n| s[i:j] = t | slice of *s* from *i* to *j* is | |\n| | replaced by the contents of the | |\n| | iterable *t* | |\n+--------------------------------+----------------------------------+-----------------------+\n| del s[i:j] | same as s[i:j] = [] | |\n+--------------------------------+----------------------------------+-----------------------+\n| s[i:j:k] = t | the elements of s[i:j:k] are | (1) |\n| | replaced by those of *t* | |\n+--------------------------------+----------------------------------+-----------------------+\n| del s[i:j:k] | removes the elements of | |\n| | s[i:j:k] from the list | |\n+--------------------------------+----------------------------------+-----------------------+\n| s.append(x) | appends *x* to the end of the | |\n| | sequence (same as | |\n| | s[len(s):len(s)] = [x]) | |\n+--------------------------------+----------------------------------+-----------------------+\n| s.clear() | removes all items from s | (5) |\n| | (same as del s[:]) | |\n+--------------------------------+----------------------------------+-----------------------+\n| s.copy() | creates a shallow copy of s | (5) |\n| | (same as s[:]) | |\n+--------------------------------+----------------------------------+-----------------------+\n| s.extend(t) | extends *s* with the contents of | |\n| | *t* (same as s[len(s):len(s)] | |\n| | = t) | |\n+--------------------------------+----------------------------------+-----------------------+\n| s.insert(i, x) | inserts *x* into *s* at the | |\n| | index given by *i* (same as | |\n| | s[i:i] = [x]) | |\n+--------------------------------+----------------------------------+-----------------------+\n| s.pop([i]) | retrieves the item at *i* and | (2) |\n| | also removes it from *s* | |\n+--------------------------------+----------------------------------+-----------------------+\n| s.remove(x) | remove the first item from *s* | (3) |\n| | where s[i] == x | |\n+--------------------------------+----------------------------------+-----------------------+\n| s.reverse() | reverses the items of *s* in | (4) |\n| | place | |\n+--------------------------------+----------------------------------+-----------------------+\n\nNotes:\n\n1. *t* must have the same length as the slice it is replacing.\n\n2. The optional argument *i* defaults to -1, so that by default\n the last item is removed and returned.\n\n3. remove raises ValueError when *x* is not found in *s*.\n\n4. The reverse() method modifies the sequence in place for economy\n of space when reversing a large sequence. To remind users that it\n operates by side effect, it does not return the reversed sequence.\n\n5. clear() and copy() are included for consistency with the\n interfaces of mutable containers that don't support slicing\n operations (such as dict and set)\n\n New in version 3.3: clear() and copy() methods.\n", 'unary': '\nUnary arithmetic and bitwise operations\n***************************************\n\nAll unary arithmetic and bitwise operations have the same priority:\n\n u_expr ::= power | "-" u_expr | "+" u_expr | "~" u_expr\n\nThe unary - (minus) operator yields the negation of its numeric\nargument.\n\nThe unary + (plus) operator yields its numeric argument unchanged.\n\nThe unary ~ (invert) operator yields the bitwise inversion of its\ninteger argument. The bitwise inversion of x is defined as\n-(x+1). It only applies to integral numbers.\n\nIn all three cases, if the argument does not have the proper type, a\nTypeError exception is raised.\n', 'while': '\nThe while statement\n***********************\n\nThe while statement is used for repeated execution as long as an\nexpression is true:\n\n while_stmt ::= "while" expression ":" suite\n ["else" ":" suite]\n\nThis repeatedly tests the expression and, if it is true, executes the\nfirst suite; if the expression is false (which may be the first time\nit is tested) the suite of the else clause, if present, is\nexecuted and the loop terminates.\n\nA break statement executed in the first suite terminates the loop\nwithout executing the else clause\'s suite. A continue\nstatement executed in the first suite skips the rest of the suite and\ngoes back to testing the expression.\n', 'with': '\nThe with statement\n**********************\n\nThe with statement is used to wrap the execution of a block with\nmethods defined by a context manager (see section *With Statement\nContext Managers*). This allows common\ntry...except...finally usage patterns to be encapsulated\nfor convenient reuse.\n\n with_stmt ::= "with" with_item ("," with_item)* ":" suite\n with_item ::= expression ["as" target]\n\nThe execution of the with statement with one "item" proceeds as\nfollows:\n\n1. The context expression (the expression given in the with_item)\n is evaluated to obtain a context manager.\n\n2. The context manager\'s __exit__() is loaded for later use.\n\n3. The context manager\'s __enter__() method is invoked.\n\n4. If a target was included in the with statement, the return\n value from __enter__() is assigned to it.\n\n Note: The with statement guarantees that if the __enter__()\n method returns without an error, then __exit__() will always\n be called. Thus, if an error occurs during the assignment to the\n target list, it will be treated the same as an error occurring\n within the suite would be. See step 6 below.\n\n5. The suite is executed.\n\n6. The context manager\'s __exit__() method is invoked. If an\n exception caused the suite to be exited, its type, value, and\n traceback are passed as arguments to __exit__(). Otherwise,\n three None arguments are supplied.\n\n If the suite was exited due to an exception, and the return value\n from the __exit__() method was false, the exception is\n reraised. If the return value was true, the exception is\n suppressed, and execution continues with the statement following\n the with statement.\n\n If the suite was exited for any reason other than an exception, the\n return value from __exit__() is ignored, and execution proceeds\n at the normal location for the kind of exit that was taken.\n\nWith more than one item, the context managers are processed as if\nmultiple with statements were nested:\n\n with A() as a, B() as b:\n suite\n\nis equivalent to\n\n with A() as a:\n with B() as b:\n suite\n\nChanged in version 3.1: Support for multiple context expressions.\n\nSee also:\n\n **PEP 0343** - The "with" statement\n The specification, background, and examples for the Python\n with statement.\n', 'yield': '\nThe yield statement\n***********************\n\n yield_stmt ::= yield_expression\n\nThe yield statement is only used when defining a generator\nfunction, and is only used in the body of the generator function.\nUsing a yield statement in a function definition is sufficient to\ncause that definition to create a generator function instead of a\nnormal function.\n\nWhen a generator function is called, it returns an iterator known as a\ngenerator iterator, or more commonly, a generator. The body of the\ngenerator function is executed by calling the next() function on\nthe generator repeatedly until it raises an exception.\n\nWhen a yield statement is executed, the state of the generator is\nfrozen and the value of expression_list is returned to\nnext()\'s caller. By "frozen" we mean that all local state is\nretained, including the current bindings of local variables, the\ninstruction pointer, and the internal evaluation stack: enough\ninformation is saved so that the next time next() is invoked, the\nfunction can proceed exactly as if the yield statement were just\nanother external call.\n\nThe yield statement is allowed in the try clause of a try\n... finally construct. If the generator is not resumed before it\nis finalized (by reaching a zero reference count or by being garbage\ncollected), the generator-iterator\'s close() method will be\ncalled, allowing any pending finally clauses to execute.\n\nWhen yield from  is used, it treats the supplied expression\nas a subiterator, producing values from it until the underlying\niterator is exhausted.\n\n Changed in version 3.3: Added yield from  to delegate\n control flow to a subiterator\n\nFor full details of yield semantics, refer to the *Yield\nexpressions* section.\n\nSee also:\n\n **PEP 0255** - Simple Generators\n The proposal for adding generators and the yield statement\n to Python.\n\n **PEP 0342** - Coroutines via Enhanced Generators\n The proposal to enhance the API and syntax of generators, making\n them usable as simple coroutines.\n\n **PEP 0380** - Syntax for Delegating to a Subgenerator\n The proposal to introduce the yield_from syntax, making\n delegation to sub-generators easy.\n'}