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3 '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\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 is recursively defined as\nfollows.\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\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`` statement in the\n current code block: the name is bound to the object in the current\n local namespace.\n\n * Otherwise: the name is bound to the object in the current global\n namespace.\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 a plain integer. If it is negative, the\n sequence\'s length is added to it. The resulting value must be a\n nonnegative integer less than the sequence\'s length, and the\n sequence is asked to assign the assigned object to its item with\n that index. If the index is out of range, ``IndexError`` is raised\n (assignment to a subscripted sequence cannot add new items to a\n 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* 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 (small) integers. If either\n bound is negative, the sequence\'s length is added to it. The\n resulting bounds are clipped to lie between zero and the sequence\'s\n length, inclusive. Finally, the sequence object is asked to replace\n the slice with the items of the assigned sequence. The length of\n the 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\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\n``x = 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',
6 then\n``type(a).__dict__[\'x\']``, and continuing through the base classes of\n``type(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. Note that descriptors are only invoked for new\nstyle objects or classes (ones that subclass ``object()`` or\n``type()``).\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 a new-style object instance, ``a.x`` is transformed\n into the call: ``type(a).__dict__[\'x\'].__get__(a, type(a))``.\n\nClass Binding\n If binding to a new-style class, ``A.x`` is transformed into the\n call: ``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 both old and new-style classes have a\ndictionary for attribute storage. This wastes space for objects\nhaving very few instance variables. The space consumption can become\nacute when creating large numbers of instances.\n\nThe default can be overridden by defining *__slots__* in a new-style\nclass definition. The *__slots__* declaration takes a sequence of\ninstance variables and reserves just enough space in each instance to\nhold a value for each variable. Space is saved because *__dict__* is\nnot created for each instance.\n\n__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 new-style class, *__slots__* reserves space for the declared\n variables and prevents the automatic creation of *__dict__* and\n *__weakref__* for each instance.\n\n New in version 2.2.\n\nNotes on using *__slots__*\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 Changed in version 2.3: Previously, adding ``\'__dict__\'`` to the\n *__slots__* declaration would not enable the assignment of new\n attributes not specifically listed in the sequence of instance\n variable names.\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 Changed in version 2.3: Previously, adding ``\'__weakref__\'`` to the\n *__slots__* declaration would not enable support for weak\n references.\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 ``long``, ``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 Changed in version 2.6: Previously, *__class__* assignment raised an\n error if either new or old class had *__slots__*.\n',
7 '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, e.g., a module, list, or an instance. This\nobject is then asked to produce the attribute whose name is the\nidentifier. If this attribute is not available, the exception\n``AttributeError`` is raised. Otherwise, the type and value of the\nobject produced is determined by the object. Multiple evaluations of\nthe same attribute reference may yield different objects.\n',
9 '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 (plain or long) and the other must be a sequence.\nIn the former case, the numbers are converted to a common type and\nthen multiplied together. In the latter case, sequence repetition is\nperformed; a negative 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. Plain or long integer division yields an\ninteger of the same type; the result is that of mathematical division\nwith the \'floor\' function applied to the result. Division by zero\nraises 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\n``ZeroDivisionError`` exception. The arguments may be floating point\nnumbers, e.g., ``3.14%0.7`` equals ``0.34`` (since ``3.14`` equals\n``4*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 [2].\n\nThe integer division and modulo operators are connected by the\nfollowing identity: ``x == (x/y)*y + (x%y)``. Integer division and\nmodulo are also connected with the built-in function ``divmod()``:\n``divmod(x, y) == (x/y, x%y)``. These identities don\'t hold for\nfloating point numbers; there similar identities hold approximately\nwhere ``x/y`` is replaced by ``floor(x/y)`` or ``floor(x/y) - 1`` [3].\n\nIn addition to performing the modulo operation on numbers, the ``%``\noperator is also overloaded by string and unicode objects to perform\nstring formatting (also known as interpolation). The syntax for string\nformatting is described in the Python Library Reference, section\n*String Formatting Operations*.\n\nDeprecated since version 2.3: The floor division operator, the modulo\noperator, and the ``divmod()`` function are no longer defined for\ncomplex numbers. Instead, convert to a floating point number using\nthe ``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',
18 '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 [","]\n | expression genexpr_for] ")"\n argument_list ::= positional_arguments ["," keyword_arguments]\n ["," "*" expression] ["," keyword_arguments]\n ["," "**" expression]\n | keyword_arguments ["," "*" expression]\n ["," "**" expression]\n | "*" expression ["," "*" expression] ["," "**" 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 certain class instances\nthemselves are callable; extensions may define additional callable\nobject types). All argument expressions are evaluated before the call\nis attempted. Please refer to section *Function definitions* for the\nsyntax 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\n``TypeError`` 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,\n``expression`` 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 "<stdin>", 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,\n``expression`` 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. Formal parameters using the syntax ``(sublist)`` cannot be\nused as keyword argument names; the outermost sublist corresponds to a\nsingle unnamed argument slot, and the argument value is assigned to\nthe sublist using the usual tuple assignment rules after all other\nparameter processing is done.\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',
19 'class': '\nClass definitions\n*****************\n\nA class definition defines a class object (see section *The standard\ntype hierarchy*):\n\n classdef ::= "class" classname [inheritance] ":" suite\n inheritance ::= "(" [expression_list] ")"\n classname ::= identifier\n\nA class definition is an executable statement. It first evaluates the\ninheritance list, if present. Each item in the inheritance list\nshould evaluate to a class object or class type which allows\nsubclassing. The class\'s suite is then executed in a new execution\nframe (see section *Naming and binding*), using a newly created local\nnamespace and the original global namespace. (Usually, the suite\ncontains only function definitions.) When the class\'s suite finishes\nexecution, its execution frame is discarded but its local namespace is\nsaved. [4] A class object is then created using the inheritance list\nfor the base classes and the saved local namespace for the attribute\ndictionary. The class name is bound to this class object in the\noriginal local namespace.\n\n**Programmer\'s note:** Variables defined in the class definition are\nclass variables; they are shared by all instances. To create instance\nvariables, they can be set in a method with ``self.name = value``.\nBoth class and instance variables are accessible through the notation\n"``self.name``", and an instance variable hides a class variable with\nthe same name when accessed in this way. Class variables can be used\nas defaults for instance variables, but using mutable values there can\nlead to unexpected results. For *new-style class*es, descriptors can\nbe used to create instance variables with different implementation\ndetails.\n\nClass definitions, like function definitions, may be wrapped by one or\nmore *decorator* expressions. The evaluation rules for the decorator\nexpressions are the same as for functions. The result must be a class\nobject, which is then bound to the class name.\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',
20 '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 forms ``<>`` and ``!=`` are equivalent; for consistency with C,\n``!=`` is preferred; where ``!=`` is mentioned below ``<>`` is also\naccepted. The ``<>`` spelling is considered obsolescent.\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,\nobjects of different types *always* compare unequal, and are ordered\nconsistently but arbitrarily. You can control comparison behavior of\nobjects of non-built-in types by defining a ``__cmp__`` method or rich\ncomparison methods like ``__gt__``, described in section *Special\nmethod names*.\n\n(This unusual definition of comparison was used to simplify the\ndefinition of operations like sorting and the ``in`` and ``not in``\noperators. In the future, the comparison rules for objects of\ndifferent types are likely to change.)\n\nComparison of objects of the same type depends on the type:\n\n* Numbers are compared arithmetically.\n\n* Strings are compared lexicographically using the numeric equivalents\n (the result of the built-in function ``ord()``) of their characters.\n Unicode and 8-bit strings are fully interoperable in this behavior.\n [4]\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, ``cmp([1,2,x], [1,2,y])`` returns\n the same as ``cmp(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 their sorted\n (key, value) lists compare equal. [5] Outcomes other than equality\n are resolved consistently, but are not otherwise defined. [6]\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\nThe operators ``in`` and ``not in`` test for collection membership.\n``x in s`` evaluates to true if *x* is a member of the collection *s*,\nand false otherwise. ``x not in s`` returns the negation of ``x in\ns``. The collection membership test has traditionally been bound to\nsequences; an object is a member of a collection if the collection is\na sequence and contains an element equal to that object. However, it\nmake sense for many other object types to support membership tests\nwithout being a sequence. In particular, dictionaries (for keys) and\nsets support membership testing.\n\nFor the list and tuple types, ``x in y`` is true if and only if there\nexists an index *i* such that ``x == y[i]`` is true.\n\nFor the Unicode and string types, ``x in y`` is true if and only if\n*x* is a substring of *y*. An equivalent test is ``y.find(x) != -1``.\nNote, *x* and *y* need not be the same type; consequently, ``u\'ab\' in\n\'abc\'`` will return ``True``. Empty strings are always considered to\nbe a substring of any other string, so ``"" in "abc"`` will return\n``True``.\n\nChanged in version 2.3: Previously, *x* was required to be a string of\nlength ``1``.\n\nFor user-defined classes which define the ``__contains__()`` method,\n``x 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\n``in``.\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. [7]\n',
21 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\n``expression_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, and then the suite is executed. When the items are\nexhausted (which is immediately when the sequence is empty), the suite\nin the ``else`` clause, if present, is executed, and the loop\nterminates.\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\nThe target list is not deleted when the loop is finished, but if the\nsequence is empty, it will not have been assigned to at all by the\nloop. Hint: the built-in function ``range()`` returns a sequence of\nintegers suitable to emulate the effect of Pascal\'s ``for i := a to b\ndo``; e.g., ``range(3)`` returns the list ``[0, 1, 2]``.\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 internal\n counter is used to keep track of which item is used next, and this\n is incremented on each iteration. When this counter has reached the\n length of the sequence the loop terminates. This means that if the\n suite deletes the current (or a previous) item from the sequence,\n the next item will be skipped (since it gets the index of the\n 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\nChanged in version 2.5: In previous versions of Python,\n``try``...``except``...``finally`` did not work. ``try``...``except``\nhad to be nested in ``try``...``finally``.\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 in that except clause, if present, and the except\nclause\'s suite is executed. All except clauses must have an\nexecutable block. When the end of this block is reached, execution\ncontinues normally after the entire try statement. (This means that\nif two nested handlers exist for the same exception, and the exception\noccurs in the try clause of the inner handler, the outer handler will\nnot handle the exception.)\n\nBefore an except clause\'s suite is executed, details about the\nexception are assigned to three variables in the ``sys`` module:\n``sys.exc_type`` receives the object identifying the exception;\n``sys.exc_value`` receives the exception\'s parameter;\n``sys.exc_traceback`` receives a traceback object (see section *The\nstandard type hierarchy*) identifying the point in the program where\nthe exception occurred. These details are also available through the\n``sys.exc_info()`` function, which returns a tuple ``(exc_type,\nexc_value, exc_traceback)``. Use of the corresponding variables is\ndeprecated in favor of this function, since their use is unsafe in a\nthreaded program. As of Python 1.5, the variables are restored to\ntheir previous values (before the call) when returning from a function\nthat 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\n``try`` 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\nof the ``finally`` clause. If the ``finally`` clause raises another\nexception or executes a ``return`` or ``break`` statement, the saved\nexception 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\n``finally`` 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\nNew in version 2.5.\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\n``try``...``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, thenthen executed in a new execution\nframe (see section *Naming and binding*), using a newly created local\nnamespace and the original global namespace. (Usually, the suite\ncontains only function definitions.) When the class\'s suite finishes\nexecution, its execution frame is discarded but its local namespace is\nsaved. [4] A class object is then created using the inheritance list\nfor the base classes and the saved local namespace for the attribute\ndictionary. The class name is bound to this class object in the\noriginal local namespace.\n\n**Programmer\'s note:** Variables defined in the class definition are\nclass variables; they are shared by all instances. To create instance\nvariables, they can be set in a method with ``self.name = value``.\nBoth class and instance variables are accessible through the notation\n"``self.name``", and an instance variable hides a class variable with\nthe same name when accessed in this way. Class variables can be used\nas defaults for instance variables, but using mutable values there can\nlead to unexpected results. For *new-style class*es, descriptors can\nbe used to create instance variables with different implementation\ndetails.\n\nClass definitions, like function definitions, may be wrapped by one or\nmore *decorator* expressions. The evaluation rules for the decorator\nexpressions are the same as for functions. The result must be a class\nobject, which is then bound to the class name.\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',
25 '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_traceback`` 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.exc_traceback`` or ``sys.last_traceback``. Circular\n references which are garbage are detected when the option cycle\n detector is enabled (it\'s on by default), but can only be cleaned\n up if there are no Python-level ``__del__()`` methods involved.\n Refer to the documentation for the ``gc`` module for more\n information about how ``__del__()`` methods are handled by the\n cycle detector, particularly the description of the ``garbage``\n value.\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\n See also the *-R* command-line option.\n\nobject.__repr__(self)\n\n Called by the ``repr()`` built-in function and by string\n conversions (reverse quotes) to compute the "official" string\n representation of an object. If at all possible, this should look\n like a valid Python expression that could be used to recreate an\n object with the same value (given an appropriate environment). If\n this is not possible, a string of the form ``<...some useful\n description...>`` should be returned. The return value must be a\n string object. If a class defines ``__repr__()`` but not\n ``__str__()``, thenthen derived from the hash of\n that object.\n\n Changed in version 2.6: ``__hash__`` may now be set to ``None`` to\n explicitly flag instances of a class as unhashable.\n\nobject.__nonzero__(self)\n\n Called to implement truth value testing and the built-in operation\n ``bool()``; should return ``False`` or ``True``, or their integer\n equivalents ``0`` or ``1``. When this method is not defined,\n ``__len__()`` is called, if it is defined, and the object is\n considered true if its result is nonzero. If a class defines\n neither ``__len__()`` nor ``__nonzero__()``, all its instances are\n considered true.\n\nobject.__unicode__(self)\n\n Called to implement ``unicode()`` built-in; should return a Unicode\n object. When this method is not defined, string conversion is\n attempted, and the result of string conversion is converted to\n Unicode using the system default encoding.\n',
26 'debugger': '\n``pdb`` --- 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\n``Pdb``. 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 > <string>(0)?()\n (Pdb) continue\n > <string>(1)?()\n (Pdb) continue\n NameError: \'spam\'\n > <string>(1)?()\n (Pdb)\n\n``pdb.py`` can also be invoked as a script to debug other scripts.\nFor example:\n\n python -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 2.4: Restarting post-mortem behavior added.\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 ``c`` 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 "<stdin>", 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[, locals]])\n\n Execute the *statement* (given as a string) under debugger control.\n The debugger prompt appears before any code is executed; you can\n set breakpoints and type ``continue``, or you can step through the\n statement using ``step`` or ``next`` (all these commands are\n explained below). The optional *globals* and *locals* arguments\n specify the environment in which the code is executed; by default\n the dictionary of the module ``__main__`` is used. (See the\n explanation of the ``exec`` statement or the ``eval()`` built-in\n function.)\n\npdb.runeval(expression[, globals[, locals]])\n\n Evaluate the *expression* (given as a string) under debugger\n control. When ``runeval()`` returns, it returns the value of the\n expression. Otherwise this function is similar to ``run()``.\n\npdb.runcall(function[, argument, ...])\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])\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)\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 Example call to enable tracing with *skip*:\n\n import pdb; pdb.Pdb(skip=[\'django.*\']).set_trace()\n\n New in version 2.7: The *skip* argument.\n\n run(statement[, globals[, locals]])\n runeval(expression[, globals[, locals]])\n runcall(function[, argument, ...])\n set_trace()\n\n See the documentation for the functions explained above.\n',
30 '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',
35 '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\n``expression_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, and then the suite is executed. When the items are\nexhausted (which is immediately when the sequence is empty), the suite\nin the ``else`` clause, if present, is executed, and the loop\nterminates.\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\nThe target list is not deleted when the loop is finished, but if the\nsequence is empty, it will not have been assigned to at all by the\nloop. Hint: the built-in function ``range()`` returns a sequence of\nintegers suitable to emulate the effect of Pascal\'s ``for i := a to b\ndo``; e.g., ``range(3)`` returns the list ``[0, 1, 2]``.\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 internal\n counter is used to keep track of which item is used next, and this\n is incremented on each iteration. When this counter has reached the\n length of the sequence the loop terminates. This means that if the\n suite deletes the current (or a previous) item from the sequence,\n the next item will be skipped (since it gets the index of the\n 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',
36 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 is only valid for integers, and only for binary,\noctal, or hexadecimal output. If present, it specifies that the\noutput will be prefixed by ``\'0b\'``, ``\'0o\'``, or ``\'0x\'``,\nrespectively.\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 2.7: 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\'``. |\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
41 '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',
43 '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\nImport statements are executed in two steps: (1) find a module, and\ninitialize it if necessary; (2) define a name or names in the local\nnamespace (of the scope where the ``import`` statement occurs). The\nstatement comes in two forms differing on whether it uses the ``from``\nkeyword. The first form (without ``from``) repeats these steps for\neach identifier in the list. The form with ``from`` performs step (1)\nonce, and then performs step (2) repeatedly.\n\nTo understand how step (1) occurs, one must first understand how\nPython handles hierarchical naming of modules. To help organize\nmodules and provide a hierarchy in naming, Python has a concept of\npackages. A package can contain other packages and modules while\nmodules cannot contain other modules or packages. From a file system\nperspective, packages are directories and modules are files. The\noriginal specification for packages is still available to read,\nalthough minor details have changed since the writing of that\ndocument.\n\nOnce the name of the module is known (unless otherwise specified, the\nterm "module" will refer to both packages and modules), searching for\nthe module or package can begin. The first place checked is\n``sys.modules``, the cache of all modules that have been imported\npreviously. If the module is found there then it is used in step (2)\nof import.\n\nIf the module is not found in the cache, thenthen a second argument to\n``find_module()`` is given as the value of the ``__path__`` attribute\nfrom the parent package (everything up to the last dot in the name of\nthe module being imported). If a finder can find the module it returns\na *loader* (discussed later) or returns ``None``.\n\nIf none of the finders on ``sys.meta_path`` are able to find the\nmodule then some implicitly defined finders are queried.\nImplementations of Python vary in what implicit meta path finders are\ndefined. The one they all do define, though, is one that handles\n``sys.path_hooks``, ``sys.path_importer_cache``, and ``sys.path``.\n\nThe implicit finder searches for the requested module in the "paths"\nspecified in one of two places ("paths" do not have to be file system\npaths). If the module being imported is supposed to be contained\nwithin a package then the second argument passed to ``find_module()``,\n``__path__`` on the parent package, is used as the source of paths. If\nthe module is not contained in a package then ``sys.path`` is used as\nthe source of paths.\n\nOnce the source of paths is chosen it is iterated over to find a\nfinder that can handle that path. The dict at\n``sys.path_importer_cache`` caches finders for paths and is checked\nfor a finder. If the path does not have a finder cached then\n``sys.path_hooks`` is searched by calling each object in the list with\na single argument of the path, returning a finder or raises\n``ImportError``. If a finder is returned then it is cached in\n``sys.path_importer_cache`` and then used for that path entry. If no\nfinder can be found but the path exists then a value of ``None`` is\nstored in ``sys.path_importer_cache`` to signify that an implicit,\nfile-based finder that handles modules stored as individual files\nshould be used for that path. If the path does not exist then a finder\nwhich always returns ``None`` is placed in the cache for the path.\n\nIf no finder can find the module then ``ImportError`` is raised.\nOtherwise some finder returned a loader whose ``load_module()`` method\nis called with the name of the module to load (see **PEP 302** for the\noriginal definition of loaders). A loader has several responsibilities\nto perform on a module it loads. First, if the module already exists\nin ``sys.modules`` (a possibility if the loader is called outside of\nthe import machinery) then it is to use that module for initialization\nand not a new module. But if the module does not exist in\n``sys.modules`` then it is to be added to that dict before\ninitialization begins. If an error occurs during loading of the module\nand it was added to ``sys.modules`` it is to be removed from the dict.\nIf an error occurs but the module was already in ``sys.modules`` it is\nleft in the dict.\n\nThe loader must set several attributes on the module. ``__name__`` is\nto be set to the name of the module. ``__file__`` is to be the "path"\nto the file unless the module is built-in (and thus listed in\n``sys.builtin_module_names``) in which case the attribute is not set.\nIf what is being imported is a package then ``__path__`` is to be set\nto a list of paths to be searched when looking for modules and\npackages contained within the package being imported. ``__package__``\nis optional but should be set to the name of package that contains the\nmodule or package (the empty string is used for module not contained\nin a package). ``__loader__`` is also optional but should be set to\nthe loader object that is loading the module.\n\nIf an error occurs during loading then the loader raises\n``ImportError`` if some other exception is not already being\npropagated. Otherwise the loader returns the module that was loaded\nand initialized.\n\nWhen step (1) finishes without raising an exception, step (2) can\nbegin.\n\nThe first form of ``import`` statement binds the module name in the\nlocal namespace to the module object, and then goes on to import the\nnext identifier, if any. If the module name is followed by ``as``,\nthe name following ``as`` is used as the local name for the module.\n\nThe ``from`` form does not bind the module name: it goes through the\nlist of identifiers, looks each one of them up in the module found in\nstep (1), and binds the name in the local namespace to the object thus\nfound. As with the first form of ``import``, an alternate local name\ncan be supplied by specifying "``as`` localname". If a name is not\nfound, ``ImportError`` is raised. If the list of identifiers is\nreplaced by a star (``\'*\'``), all public names defined in the module\nare bound in the local namespace of the ``import`` statement..\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. If the\nwild card form of import --- ``import *`` --- is used in a function\nand the function contains or is a nested block with free variables,\nthe compiler will 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\n``from . 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\n``pkg.subpkg2.mod``. The specification for relative imports is\ncontained within **PEP 328**.\n\n``importlib.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 2.6 are ``unicode_literals``,\n``print_function``, ``absolute_import``, ``division``, ``generators``,\n``nested_scopes`` and ``with_statement``. ``generators``,\n``with_statement``, ``nested_scopes`` are redundant in Python version\n2.6 and above because they are always enabled.\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 an ``exec`` statement or calls to the built-in\nfunctions ``compile()`` and ``execfile()`` that occur in a module\n``M`` containing a future statement will, by default, use the new\nsyntax or semantics associated with the future statement. This can,\nstarting with Python 2.2 be controlled by optional arguments to\n``compile()`` --- see the documentation of that function for 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',
44 '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 forms ``<>`` and ``!=`` are equivalent; for consistency with C,\n``!=`` is preferred; where ``!=`` is mentioned below ``<>`` is also\naccepted. The ``<>`` spelling is considered obsolescent.\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,\nobjects of different types *always* compare unequal, and are ordered\nconsistently but arbitrarily. You can control comparison behavior of\nobjects of non-built-in types by defining a ``__cmp__`` method or rich\ncomparison methods like ``__gt__``, described in section *Special\nmethod names*.\n\n(This unusual definition of comparison was used to simplify the\ndefinition of operations like sorting and the ``in`` and ``not in``\noperators. In the future, the comparison rules for objects of\ndifferent types are likely to change.)\n\nComparison of objects of the same type depends on the type:\n\n* Numbers are compared arithmetically.\n\n* Strings are compared lexicographically using the numeric equivalents\n (the result of the built-in function ``ord()``) of their characters.\n Unicode and 8-bit strings are fully interoperable in this behavior.\n [4]\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, ``cmp([1,2,x], [1,2,y])`` returns\n the same as ``cmp(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 their sorted\n (key, value) lists compare equal. [5] Outcomes other than equality\n are resolved consistently, but are not otherwise defined. [6]\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\nThe operators ``in`` and ``not in`` test for collection membership.\n``x in s`` evaluates to true if *x* is a member of the collection *s*,\nand false otherwise. ``x not in s`` returns the negation of ``x in\ns``. The collection membership test has traditionally been bound to\nsequences; an object is a member of a collection if the collection is\na sequence and contains an element equal to that object. However, it\nmake sense for many other object types to support membership tests\nwithout being a sequence. In particular, dictionaries (for keys) and\nsets support membership testing.\n\nFor the list and tuple types, ``x in y`` is true if and only if there\nexists an index *i* such that ``x == y[i]`` is true.\n\nFor the Unicode and string types, ``x in y`` is true if and only if\n*x* is a substring of *y*. An equivalent test is ``y.find(x) != -1``.\nNote, *x* and *y* need not be the same type; consequently, ``u\'ab\' in\n\'abc\'`` will return ``True``. Empty strings are always considered to\nbe a substring of any other string, so ``"" in "abc"`` will return\n``True``.\n\nChanged in version 2.3: Previously, *x* was required to be a string of\nlength ``1``.\n\nFor user-defined classes which define the ``__contains__()`` method,\n``x 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\n``in``.\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. [7]\n',
57 '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. (For backwards compatibility, the method\n``__getslice__()`` (see below) can also be defined to handle simple,\nbut not extended slices.) It is also recommended that mappings provide\nthe methods ``keys()``, ``values()``, ``items()``, ``has_key()``,\n``get()``, ``clear()``, ``setdefault()``, ``iterkeys()``,\n``itervalues()``, ``iteritems()``, ``pop()``, ``popitem()``,\n``copy()``, and ``update()`` behaving similar to those for Python's\nstandard dictionary objects. The ``UserDict`` module provides a\n``DictMixin`` class to help create those methods from a base set of\n``__getitem__()``, ``__setitem__()``, ``__delitem__()``, and\n``keys()``. Mutable sequences should provide methods ``append()``,\n``count()``, ``index()``, ``extend()``, ``insert()``, ``pop()``,\n``remove()``, ``reverse()`` and ``sort()``, like Python standard list\nobjects. Finally, sequence types should implement addition (meaning\nconcatenation) and multiplication (meaning repetition) by defining the\nmethods ``__add__()``, ``__radd__()``, ``__iadd__()``, ``__mul__()``,\n``__rmul__()`` and ``__imul__()`` described below; they should not\ndefine ``__coerce__()`` or other numerical operators. It is\nrecommended that both mappings and sequences implement the\n``__contains__()`` method to allow efficient use of the ``in``\noperator; for mappings, ``in`` should be equivalent of ``has_key()``;\nfor sequences, 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\n``iterkeys()``; 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 ``__nonzero__()`` method and whose\n ``__len__()`` method returns zero is considered to be false in a\n Boolean context.\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 ``iterkeys()``.\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\n New in version 2.6.\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",
60 '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\nobject.__methods__\n\n Deprecated since version 2.2: Use the built-in function ``dir()``\n to get a list of an object\'s attributes. This attribute is no\n longer available.\n\nobject.__members__\n\n Deprecated since version 2.2: Use the built-in function ``dir()``\n to get a list of an object\'s attributes. This attribute is no\n longer available.\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\nThe following attributes are only supported by *new-style class*es.\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 new-style class keeps a list of weak references to its\n immediate subclasses. This method returns a list of all those\n references still alive. Example:\n\n >>> int.__subclasses__()\n [<type \'bool\'>]\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\n[6] The advantage of leaving the newline on is that returning an empty\n string is then an unambiguous EOF indication. It is also possible\n (in cases where it might matter, for example, if you want to make\n an exact copy of a file while scanning its lines) to tell whether\n the last line of a file ended in a newline or not (yes this\n happens!).\n',
61 '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 ``x.__getitem__(i)`` for\nold-style classes and ``type(x).__getitem__(x, i)`` for new-style\nclasses. Except where mentioned, attempts to execute an operation\nraise an exception when no appropriate method is defined (typically\n``AttributeError`` 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\n``NodeList`` 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*, thenthen 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_traceback`` 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.exc_traceback`` or ``sys.last_traceback``. Circular\n references which are garbage are detected when the option cycle\n detector is enabled (it\'s on by default), but can only be cleaned\n up if there are no Python-level ``__del__()`` methods involved.\n Refer to the documentation for the ``gc`` module for more\n information about how ``__del__()`` methods are handled by the\n cycle detector, particularly the description of the ``garbage``\n value.\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\n See also the *-R* command-line option.\n\nobject.__repr__(self)\n\n Called by the ``repr()`` built-in function and by string\n conversions (reverse quotes) to compute the "official" string\n representation of an object. If at all possible, this should look\n like a valid Python expression that could be used to recreate an\n object with the same value (given an appropriate environment). If\n this is not possible, a string of the form ``<...some useful\n description...>`` should be returned. The return value must be a\n string object. If a class defines ``__repr__()`` but not\n ``__str__()``, then ``__repr__()`` is also used when an "informal"\n string representation of instances of that class is 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 the ``str()`` built-in function and by the ``print``\n statement to compute the "informal" string representation of an\n object. This differs from ``__repr__()`` in that it does not have\n to be a valid Python expression: a more convenient or concise\n representation may be used instead. The return value must be a\n 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 New in version 2.1.\n\n These are the so-called "rich comparison" methods, and are called\n for comparison operators in preference to ``__cmp__()`` below. The\n correspondence between operator symbols and method names is as\n follows: ``x<y`` calls ``x.__lt__(y)``, ``x<=y`` calls\n ``x.__le__(y)``, ``x==y`` calls ``x.__eq__(y)``, ``x!=y`` and\n ``x<>y`` call ``x.__ne__(y)``, ``x>y`` calls ``x.__gt__(y)``, and\n ``x>=y`` calls ``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.__cmp__(self, other)\n\n Called by comparison operations if rich comparison (see above) is\n not defined. Should return a negative integer if ``self < other``,\n zero if ``self == other``, a positive integer if ``self > other``.\n If no ``__cmp__()``, ``__eq__()`` or ``__ne__()`` operation is\n defined, class instances are compared by object identity\n ("address"). See also the description of ``__hash__()`` for some\n important notes on creating *hashable* objects which support custom\n comparison operations and are usable as dictionary keys. (Note: the\n restriction that exceptions are not propagated by ``__cmp__()`` has\n been removed since Python 1.5.)\n\nobject.__rcmp__(self, other)\n\n Changed in version 2.1: No longer supported.\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 If a class does not define a ``__cmp__()`` or ``__eq__()`` method\n it should not define a ``__hash__()`` operation either; if it\n defines ``__cmp__()`` or ``__eq__()`` but not ``__hash__()``, its\n instances will not be usable in hashed collections. If a class\n defines mutable objects and implements a ``__cmp__()`` or\n ``__eq__()`` method, it should not implement ``__hash__()``, since\n hashable collection implementations require that a object\'s hash\n value is immutable (if the object\'s hash value changes, it will be\n in the wrong hash bucket).\n\n User-defined classes have ``__cmp__()`` and ``__hash__()`` methods\n by default; with them, all objects compare unequal (except with\n themselves) and ``x.__hash__()`` returns ``id(x)``.\n\n Classes which inherit a ``__hash__()`` method from a parent class\n but change the meaning of ``__cmp__()`` or ``__eq__()`` such that\n the hash value returned is no longer appropriate (e.g. by switching\n to a value-based concept of equality instead of the default\n identity based equality) can explicitly flag themselves as being\n unhashable by setting ``__hash__ = None`` in the class definition.\n Doing so means that not only will instances of the class raise an\n appropriate ``TypeError`` when a program attempts to retrieve their\n hash value, but they will also be correctly identified as\n unhashable when checking ``isinstance(obj, collections.Hashable)``\n (unlike classes which define their own ``__hash__()`` to explicitly\n raise ``TypeError``).\n\n Changed in version 2.5: ``__hash__()`` may now also return a long\n integer object; the 32-bit integer is then derived from the hash of\n that object.\n\n Changed in version 2.6: ``__hash__`` may now be set to ``None`` to\n explicitly flag instances of a class as unhashable.\n\nobject.__nonzero__(self)\n\n Called to implement truth value testing and the built-in operation\n ``bool()``; should return ``False`` or ``True``, or their integer\n equivalents ``0`` or ``1``. When this method is not defined,\n ``__len__()`` is called, if it is defined, and the object is\n considered true if its result is nonzero. If a class defines\n neither ``__len__()`` nor ``__nonzero__()``, all its instances are\n considered true.\n\nobject.__unicode__(self)\n\n Called to implement ``unicode()`` built-in; should return a Unicode\n object. When this method is not defined, string conversion is\n attempted, and the result of string conversion is converted to\n Unicode using the system default encoding.\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 in new-style classes.\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 not simply execute ``self.name = value`` --- this would\n cause a recursive call to itself. Instead, it should insert the\n value in the dictionary of instance attributes, e.g.,\n ``self.__dict__[name] = value``. For new-style classes, rather\n than accessing the instance dictionary, it should call the base\n 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\n\nMore attribute access for new-style classes\n-------------------------------------------\n\nThe following methods only apply to new-style classes.\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 for new-style\n classes*.\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\n``type(a).__dict__[\'x\']``, and continuing through the base classes of\n``type(a)`` excluding metaclasses.\n\nHowever, if the looked-up value is an object defining one of the\ndescriptor methods, thenthen 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 both old and new-style classes have a\ndictionary for attribute storage. This wastes space for objects\nhaving very few instance variables. The space consumption can become\nacute when creating large numbers of instances.\n\nThe default can be overridden by defining *__slots__* in a new-style\nclass definition. The *__slots__* declaration takes a sequence of\ninstance variables and reserves just enough space in each instance to\nhold a value for each variable. Space is saved because *__dict__* is\nnot created for each instance.\n\n__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 new-style class, *__slots__* reserves space for the declared\n variables and prevents the automatic creation of *__dict__* and\n *__weakref__* for each instance.\n\n New in version 2.2.\n\nNotes on using *__slots__*\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 Changed in version 2.3: Previously, adding ``\'__dict__\'`` to the\n *__slots__* declaration would not enable the assignment of new\n attributes not specifically listed in the sequence of instance\n variable names.\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 Changed in version 2.3: Previously, adding ``\'__weakref__\'`` to the\n *__slots__* declaration would not enable support for weak\n references.\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 ``long``, ``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 Changed in version 2.6: Previously, *__class__* assignment raised an\n error if either new or old class had *__slots__*.\n\n\nCustomizing class creation\n==========================\n\nBy default, new-style classes are constructed using ``type()``. A\nclass definition is read into a separate namespace and the value of\nclass name is bound to the result of ``type(name, bases, dict)``.\n\nWhen the class definition is read, if *__metaclass__* is defined then\nthe callable assigned to it will be called instead of ``type()``. This\nallows classes or functions to be written which monitor or alter the\nclass creation process:\n\n* Modifying the class dictionary prior to the class being created.\n\n* Returning an instance of another class -- essentially performing the\n role of a factory function.\n\nThese steps will have to be performed in the metaclass\'s ``__new__()``\nmethod -- ``type.__new__()`` can then be called from this method to\ncreate a class with different properties. This example adds a new\nelement to the class dictionary before creating the class:\n\n class metacls(type):\n def __new__(mcs, name, bases, dict):\n dict[\'foo\'] = \'metacls was here\'\n return type.__new__(mcs, name, bases, dict)\n\nYou can of course also override other class methods (or add new\nmethods); for example defining a custom ``__call__()`` method in the\nmetaclass allows custom behavior when the class is called, e.g. not\nalways creating a new instance.\n\n__metaclass__\n\n This variable can be any callable accepting arguments for ``name``,\n ``bases``, and ``dict``. Upon class creation, the callable is used\n instead of the built-in ``type()``.\n\n New in version 2.2.\n\nThe appropriate metaclass is determined by the following precedence\nrules:\n\n* If ``dict[\'__metaclass__\']`` exists, it is used.\n\n* Otherwise, if there is at least one base class, its metaclass is\n used (this looks for a *__class__* attribute first and if not found,\n uses its type).\n\n* Otherwise, if a global variable named __metaclass__ exists, it is\n used.\n\n* Otherwise, the old-style, classic metaclass (types.ClassType) is\n used.\n\nThe potential uses for metaclasses are boundless. Some ideas that have\nbeen explored including logging, interface checking, automatic\ndelegation, automatic property creation, proxies, frameworks, and\nautomatic resource locking/synchronization.\n\n\nCustomizing instance and subclass checks\n========================================\n\nNew in version 2.6.\n\nThe following methods are used to override the default behavior of the\n``isinstance()`` 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. (For backwards compatibility, the method\n``__getslice__()`` (see below) can also be defined to handle simple,\nbut not extended slices.) It is also recommended that mappings provide\nthe methods ``keys()``, ``values()``, ``items()``, ``has_key()``,\n``get()``, ``clear()``, ``setdefault()``, ``iterkeys()``,\n``itervalues()``, ``iteritems()``, ``pop()``, ``popitem()``,\n``copy()``, and ``update()`` behaving similar to those for Python\'s\nstandard dictionary objects. The ``UserDict`` module provides a\n``DictMixin`` class to help create those methods from a base set of\n``__getitem__()``, ``__setitem__()``, ``__delitem__()``, and\n``keys()``. Mutable sequences should provide methods ``append()``,\n``count()``, ``index()``, ``extend()``, ``insert()``, ``pop()``,\n``remove()``, ``reverse()`` and ``sort()``, like Python standard list\nobjects. Finally, sequence types should implement addition (meaning\nconcatenation) and multiplication (meaning repetition) by defining the\nmethods ``__add__()``, ``__radd__()``, ``__iadd__()``, ``__mul__()``,\n``__rmul__()`` and ``__imul__()`` described below; they should not\ndefine ``__coerce__()`` or other numerical operators. It is\nrecommended that both mappings and sequences implement the\n``__contains__()`` method to allow efficient use of the ``in``\noperator; for mappings, ``in`` should be equivalent of ``has_key()``;\nfor sequences, 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\n``iterkeys()``; 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 ``__nonzero__()`` method and whose\n ``__len__()`` method returns zero is considered to be false in a\n Boolean context.\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 ``iterkeys()``.\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\n New in version 2.6.\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
62 'string-methods': '\nString Methods\n**************\n\nBelow are listed the string methods which both 8-bit strings and\nUnicode objects support. Some of them are also available on\n``bytearray`` objects.\n\nIn addition, Python\'s strings support the sequence type methods\ndescribed in the *Sequence Types --- str, unicode, list, tuple,\nbytearray, buffer, xrange* section. To output formatted strings use\ntemplate strings or the ``%`` operator described in the *String\nFormatting Operations* section. Also, see the ``re`` module for string\nfunctions based on regular expressions.\n\nstr.capitalize()\n\n Return a copy of the string with its first character capitalized\n and the rest lowercased.\n\n For 8-bit strings, this method is locale-dependent.\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\n Changed in version 2.4: Support for the *fillchar* argument.\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.decode([encoding[, errors]])\n\n Decodes the string using the codec registered for *encoding*.\n *encoding* defaults to the default string encoding. *errors* may\n be given to set a different error handling scheme. The default is\n ``\'strict\'``, meaning that encoding errors raise ``UnicodeError``.\n Other possible values are ``\'ignore\'``, ``\'replace\'`` and any other\n name registered via ``codecs.register_error()``, see section *Codec\n Base Classes*.\n\n New in version 2.2.\n\n Changed in version 2.3: Support for other error handling schemes\n added.\n\n Changed in version 2.7: Support for keyword arguments added.\n\nstr.encode([encoding[, errors]])\n\n Return an encoded version of the string. Default encoding is the\n current default string encoding. *errors* may be given to set a\n different error handling scheme. The default for *errors* is\n ``\'strict\'``, meaning that encoding errors raise a\n ``UnicodeError``. Other possible values are ``\'ignore\'``,\n ``\'replace\'``, ``\'xmlcharrefreplace\'``, ``\'backslashreplace\'`` and\n any other name registered via ``codecs.register_error()``, see\n section *Codec Base Classes*. For a list of possible encodings, see\n section *Standard Encodings*.\n\n New in version 2.0.\n\n Changed in version 2.3: Support for ``\'xmlcharrefreplace\'`` and\n ``\'backslashreplace\'`` and other error handling schemes added.\n\n Changed in version 2.7: 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\n Changed in version 2.5: Accept tuples as *suffix*.\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\n This method of string formatting is the new standard in Python 3,\n and should be preferred to the ``%`` formatting described in\n *String Formatting Operations* in new code.\n\n New in version 2.6.\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.\n\n For 8-bit strings, this method is locale-dependent.\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.\n\n For 8-bit strings, this method is locale-dependent.\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.\n\n For 8-bit strings, this method is locale-dependent.\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\n For 8-bit strings, this method is locale-dependent.\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.\n\n For 8-bit strings, this method is locale-dependent.\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\n For 8-bit strings, this method is locale-dependent.\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\n For 8-bit strings, this method is locale-dependent.\n\nstr.join(iterable)\n\n Return a string which is the concatenation of the strings in the\n *iterable* *iterable*. The separator between elements is the\n 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\n Changed in version 2.4: Support for the *fillchar* argument.\n\nstr.lower()\n\n Return a copy of the string with all the cased characters [4]\n converted to lowercase.\n\n For 8-bit strings, this method is locale-dependent.\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\n Changed in version 2.2.2: Support for the *chars* argument.\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\n New in version 2.5.\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\n Changed in version 2.4: Support for the *fillchar* argument.\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\n New in version 2.5.\n\nstr.rsplit([sep[, maxsplit]])\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\n New in version 2.4.\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\n Changed in version 2.2.2: Support for the *chars* argument.\n\nstr.split([sep[, maxsplit]])\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
63 'strings': '\nString literals\n***************\n\nString literals are described by the following lexical definitions:\n\n stringliteral ::= [stringprefix](shortstring | longstring)\n stringprefix ::= "r" | "u" | "ur" | "R" | "U" | "UR" | "Ur" | "uR"\n | "b" | "B" | "br" | "Br" | "bR" | "BR"\n shortstring ::= "\'" shortstringitem* "\'" | \'"\' shortstringitem* \'"\'\n longstring ::= "\'\'\'" longstringitem* "\'\'\'"\n | \'"""\' longstringitem* \'"""\'\n shortstringitem ::= shortstringchar | escapeseq\n longstringitem ::= longstringchar | escapeseq\n shortstringchar ::= <any source character except "\\" or newline or the quote>\n longstringchar ::= <any source character except "\\">\n escapeseq ::= "\\" <any ASCII character>\n\nOne syntactic restriction not indicated by these productions is that\nwhitespace is not allowed between the ``stringprefix`` and the rest of\nthe string literal. The source character set is defined by the\nencoding declaration; it is ASCII if no encoding declaration is given\nin the source file; see section *Encoding declarations*.\n\nIn plain English: String literals can be enclosed in matching single\nquotes (``\'``) or double quotes (``"``). They can also be enclosed in\nmatching groups of three single or double quotes (these are generally\nreferred to as *triple-quoted strings*). The backslash (``\\``)\ncharacter is used to escape characters that otherwise have a special\nmeaning, such as newline, backslash itself, or the quote character.\nString literals may optionally be prefixed with a letter ``\'r\'`` or\n``\'R\'``; such strings are called *raw strings* and use different rules\nfor interpreting backslash escape sequences. A prefix of ``\'u\'`` or\n``\'U\'`` makes the string a Unicode string. Unicode strings use the\nUnicode character set as defined by the Unicode Consortium and ISO\n10646. Some additional escape sequences, described below, are\navailable in Unicode strings. A prefix of ``\'b\'`` or ``\'B\'`` is\nignored in Python 2; it indicates that the literal should become a\nbytes literal in Python 3 (e.g. when code is automatically converted\nwith 2to3). A ``\'u\'`` or ``\'b\'`` prefix may be followed by an ``\'r\'``\nprefix.\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`` | 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| ``\\N{name}`` | Character named *name* in the | |\n| | Unicode database (Unicode only) | |\n+-------------------+-----------------------------------+---------+\n| ``\\r`` | ASCII Carriage Return (CR) | |\n+-------------------+-----------------------------------+---------+\n| ``\\t`` | ASCII Horizontal Tab (TAB) | |\n+-------------------+-----------------------------------+---------+\n| ``\\uxxxx`` | Character with 16-bit hex value | (1) |\n| | *xxxx* (Unicode only) | |\n+-------------------+-----------------------------------+---------+\n| ``\\Uxxxxxxxx`` | Character with 32-bit hex value | (2) |\n| | *xxxxxxxx* (Unicode only) | |\n+-------------------+-----------------------------------+---------+\n| ``\\v`` | ASCII Vertical Tab (VT) | |\n+-------------------+-----------------------------------+---------+\n| ``\\ooo`` | Character with octal value *ooo* | (3,5) |\n+-------------------+-----------------------------------+---------+\n| ``\\xhh`` | Character with hex value *hh* | (4,5) |\n+-------------------+-----------------------------------+---------+\n\nNotes:\n\n1. Individual code units which form parts of a surrogate pair can be\n encoded using this escape sequence.\n\n2. Any Unicode character can be encoded this way, but characters\n outside the Basic Multilingual Plane (BMP) will be encoded using a\n surrogate pair if Python is compiled to use 16-bit code units (the\n default). Individual code units which form parts of a surrogate\n pair can be encoded using this escape sequence.\n\n3. As in Standard C, up to three octal digits are accepted.\n\n4. Unlike in Standard C, exactly two hex digits are required.\n\n5. In a string literal, hexadecimal and octal escapes denote the byte\n with the given value; it is not necessary that the byte encodes a\n character in the source character set. In a Unicode literal, these\n escapes denote a Unicode character with the given value.\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 marked as "(Unicode only)"\nin the table above fall into the category of unrecognized escapes for\nnon-Unicode string literals.\n\nWhen an ``\'r\'`` or ``\'R\'`` prefix is present, a character following a\nbackslash is included in the string without change, and *all\nbackslashes are left in the string*. For example, the string literal\n``r"\\n"`` consists of two characters: a backslash and a lowercase\n``\'n\'``. String quotes can be escaped with a backslash, but the\nbackslash remains in the string; for example, ``r"\\""`` is a valid\nstring literal consisting of two characters: a backslash and a double\nquote; ``r"\\"`` is not a valid string literal (even a raw string\ncannot end in an odd number of backslashes). Specifically, *a raw\nstring cannot end in a single backslash* (since the backslash would\nescape 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\nWhen an ``\'r\'`` or ``\'R\'`` prefix is used in conjunction with a\n``\'u\'`` or ``\'U\'`` prefix, then the ``\\uXXXX`` and ``\\UXXXXXXXX``\nescape sequences are processed while *all other backslashes are left\nin the string*. For example, the string literal ``ur"\\u0062\\n"``\nconsists of three Unicode characters: \'LATIN SMALL LETTER B\', \'REVERSE\nSOLIDUS\', and \'LATIN SMALL LETTER N\'. Backslashes can be escaped with\na preceding backslash; however, both remain in the string. As a\nresult, ``\\uXXXX`` escape sequences are only recognized when there are\nan odd number of backslashes.\n',
67 '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.).\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 built-in name\n ``Ellipsis``. It is used to indicate the presence of the ``...``\n syntax in a slice. Its truth value is true.\n\n``numbers.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 three types of integers:\n\n Plain integers\n These represent numbers in the range -2147483648 through\n 2147483647. (The range may be larger on machines with a\n larger natural word size, but not smaller.) When the result\n of an operation would fall outside this range, the result is\n normally returned as a long integer (in some cases, the\n exception ``OverflowError`` is raised instead). For the\n purpose of shift and mask operations, integers are assumed to\n have a binary, 2\'s complement notation using 32 or more bits,\n and hiding no bits from the user (i.e., all 4294967296\n different bit patterns correspond to different values).\n\n Long integers\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\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 plain\n integers, 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 and the least surprises when\n switching between the plain and long integer domains. Any\n operation, if it yields a result in the plain integer domain,\n will yield the same result in the long integer domain or when\n using mixed operands. The switch between domains is transparent\n to the programmer.\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``\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 The items of a string are characters. There is no separate\n character type; a character is represented by a string of one\n item. Characters represent (at least) 8-bit bytes. The\n built-in functions ``chr()`` and ``ord()`` convert between\n characters and nonnegative integers representing the byte\n values. Bytes with the values 0-127 usually represent the\n corresponding ASCII values, but the interpretation of values\n is up to the program. The string data type is also used to\n represent arrays of bytes, e.g., to hold data read from a\n file.\n\n (On systems whose native character set is not ASCII, strings\n may use EBCDIC in their internal representation, provided the\n functions ``chr()`` and ``ord()`` implement a mapping between\n ASCII and EBCDIC, and string comparison preserves the ASCII\n order. Or perhaps someone can propose a better rule?)\n\n Unicode\n The items of a Unicode object are Unicode code units. A\n Unicode code unit is represented by a Unicode object of one\n item and can hold either a 16-bit or 32-bit value\n representing a Unicode ordinal (the maximum value for the\n ordinal is given in ``sys.maxunicode``, and depends on how\n Python is configured at compile time). Surrogate pairs may\n be present in the Unicode object, and will be reported as two\n separate items. The built-in functions ``unichr()`` and\n ``ord()`` convert between code units and nonnegative integers\n representing the Unicode ordinals as defined in the Unicode\n Standard 3.0. Conversion from and to other encodings are\n possible through the Unicode method ``encode()`` and the\n built-in function ``unicode()``.\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 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.\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
69 '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\n``key: value`` pairs within braces, for example: ``{\'jack\': 4098,\n\'sjoerd\': 4127}`` or ``{4098: \'jack\', 4127: \'sjoerd\'}``, or by the\n``dict`` 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 New in version 2.2.\n\n Changed in version 2.3: Support for building a dictionary from\n keyword arguments added.\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 New in version 2.5: If a subclass of dict defines a method\n ``__missing__()``, if the key *key* is not present, the\n ``d[key]`` operation calls that method with the key *key* as\n argument. The ``d[key]`` operation thenthen updated with those key/value pairs: ``d.update(red=1,\n blue=2)``.\n\n Changed in version 2.4: Allowed the argument to be an iterable\n of key/value pairs and allowed keyword arguments.\n\n values()\n\n Return a copy of the dictionary\'s list of values. See the note\n for ``dict.items()``.\n\n viewitems()\n\n Return a new view of the dictionary\'s items (``(key, value)``\n pairs). See below for documentation of view objects.\n\n New in version 2.7.\n\n viewkeys()\n\n Return a new view of the dictionary\'s keys. See below for\n documentation of view objects.\n\n New in version 2.7.\n\n viewvalues()\n\n Return a new view of the dictionary\'s values. See below for\n documentation of view objects.\n\n New in version 2.7.\n\n\nDictionary view objects\n=======================\n\nThe objects returned by ``dict.viewkeys()``, ``dict.viewvalues()`` and\n``dict.viewitems()`` are *view objects*. They provide a dynamic view\non the 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 and\nhashable, then the items view is also set-like. (Values views are not\ntreated as set-like since the entries are generally not unique.) Then\nthese set operations are available ("other" refers either to another\nview or a set):\n\ndictview & other\n\n Return the intersection of the dictview and the other object as a\n new set.\n\ndictview | other\n\n Return the union of the dictview and the other object as a new set.\n\ndictview - other\n\n Return the difference between the dictview and the other object\n (all elements in *dictview* that aren\'t in *other*) as a new set.\n\ndictview ^ other\n\n Return the symmetric difference (all elements either in *dictview*\n or *other*, but not in both) of the dictview and the other object\n as a new set.\n\nAn example of dictionary view usage:\n\n >>> dishes = {\'eggs\': 2, \'sausage\': 1, \'bacon\': 1, \'spam\': 500}\n >>> keys = dishes.viewkeys()\n >>> values = dishes.viewvalues()\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',
72 ring 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\n New in version 2.5.\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\n Changed in version 2.4: Support for the *fillchar* argument.\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\n New in version 2.5.\n\nstr.rsplit([sep[, maxsplit]])\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\n New in version 2.4.\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\n Changed in version 2.2.2: Support for the *chars* argument.\n\nstr.split([sep[, maxsplit]])\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``, thenthenthen the list\n elements are sorted as if each comparison were reversed.\n\n In general, the *key* and *reverse* conversion processes are much\n faster than specifying an equivalent *cmp* function. This is\n because *cmp* is called multiple times for each list element while\n *key* and *reverse* touch each element only once. Use\n ``functools.cmp_to_key()`` to convert an old-style *cmp* function\n to a *key* function.\n\n Changed in version 2.3: Support for ``None`` as an equivalent to\n omitting *cmp* was added.\n\n Changed in version 2.4: Support for *key* and *reverse* was added.\n\n9. Starting with Python 2.3, the ``sort()`` method is guaranteed to be\n stable. A sort is stable if it guarantees not to change the\n relative order of elements that compare equal --- this is helpful\n for sorting in multiple passes (for example, sort by department,\n then by salary grade).\n\n10. **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 2.3 and newer makes the\n list appear empty for the duration, and raises ``ValueError`` if\n it can detect that the list has been mutated during a sort.\n',
73 'typesseq-mutable': "\nMutable Sequence Types\n**********************\n\nList and ``bytearray`` objects support additional operations that\nallow in-place modification of the object. Other mutable sequence\ntypes (when added to the language) should also support these\noperations. Strings and tuples are immutable sequence types: such\nobjects cannot be modified once created. The following operations are\ndefined on mutable sequence types (where *x* is an arbitrary object):\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)`` | same as ``s[len(s):len(s)] = | (2) |\n| | [x]`` | |\n+--------------------------------+----------------------------------+-----------------------+\n| ``s.extend(x)`` | same as ``s[len(s):len(s)] = x`` | (3) |\n+--------------------------------+----------------------------------+-----------------------+\n| ``s.count(x)`` | return number of *i*'s for which | |\n| | ``s[i] == x`` | |\n+--------------------------------+----------------------------------+-----------------------+\n| ``s.index(x[, i[, j]])`` | return smallest *k* such that | (4) |\n| | ``s[k] == x`` and ``i <= k < j`` | |\n+--------------------------------+----------------------------------+-----------------------+\n| ``s.insert(i, x)`` | same as ``s[i:i] = [x]`` | (5) |\n+--------------------------------+----------------------------------+-----------------------+\n| ``s.pop([i])`` | same as ``x = s[i]; del s[i]; | (6) |\n| | return x`` | |\n+--------------------------------+----------------------------------+-----------------------+\n| ``s.remove(x)`` | same as ``del s[s.index(x)]`` | (4) |\n+--------------------------------+----------------------------------+-----------------------+\n| ``s.reverse()`` | reverses the items of *s* in | (7) |\n| | place | |\n+--------------------------------+----------------------------------+-----------------------+\n| ``s.sort([cmp[, key[, | sort the items of *s* in place | (7)(8)(9)(10) |\n| reverse]]])`` | | |\n+--------------------------------+----------------------------------+-----------------------+\n\nNotes:\n\n1. *t* must have the same length as the slice it is replacing.\n\n2. The C implementation of Python has historically accepted multiple\n parameters and implicitly joined them into a tuple; this no longer\n works in Python 2.0. Use of this misfeature has been deprecated\n since Python 1.4.\n\n3. *x* can be any iterable object.\n\n4. Raises ``ValueError`` when *x* is not found in *s*. When a negative\n index is passed as the second or third parameter to the ``index()``\n method, the list length is added, as for slice indices. If it is\n still negative, it is truncated to zero, as for slice indices.\n\n Changed in version 2.3: Previously, ``index()`` didn't have\n arguments for specifying start and stop positions.\n\n5. When a negative index is passed as the first parameter to the\n ``insert()`` method, the list length is added, as for slice\n indices. If it is still negative, it is truncated to zero, as for\n slice indices.\n\n Changed in version 2.3: Previously, all negative indices were\n truncated to zero.\n\n6. The ``pop()`` method is only supported by the list and array types.\n The optional argument *i* defaults to ``-1``, so that by default\n the last item is removed and returned.\n\n7. The ``sort()`` and ``reverse()`` methods modify the list in place\n for economy of space when sorting or reversing a large list. To\n remind you that they operate by side effect, they don't return the\n sorted or reversed list.\n\n8. The ``sort()`` method takes optional arguments for controlling the\n comparisons.\n\n *cmp* specifies a custom comparison function of two arguments (list\n items) which should return a negative, zero or positive number\n depending on whether the first argument is considered smaller than,\n equal to, or larger than the second argument: ``cmp=lambda x,y:\n cmp(x.lower(), y.lower())``. The default value is ``None``.\n\n *key* specifies a function of one argument that is used to extract\n a comparison key from each list element: ``key=str.lower``. The\n default value is ``None``.\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 In general, the *key* and *reverse* conversion processes are much\n faster than specifying an equivalent *cmp* function. This is\n because *cmp* is called multiple times for each list element while\n *key* and *reverse* touch each element only once. Use\n ``functools.cmp_to_key()`` to convert an old-style *cmp* function\n to a *key* function.\n\n Changed in version 2.3: Support for ``None`` as an equivalent to\n omitting *cmp* was added.\n\n Changed in version 2.4: Support for *key* and *reverse* was added.\n\n9. Starting with Python 2.3, the ``sort()`` method is guaranteed to be\n stable. A sort is stable if it guarantees not to change the\n relative order of elements that compare equal --- this is helpful\n for sorting in multiple passes (for example, sort by department,\n then by salary grade).\n\n10. **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 2.3 and newer makes the\n list appear empty for the duration, and raises ``ValueError`` if\n it can detect that the list has been mutated during a sort.\n",
76 'with': '\nThe ``with`` statement\n**********************\n\nNew in version 2.5.\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\n``try``...``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\nNote: In Python 2.5, the ``with`` statement is only allowed when the\n ``with_statement`` feature has been enabled. It is always enabled\n in Python 2.6.\n\nChanged in version 2.7: 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',
