Lines Matching full:detail
4 'assignment': u'\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\n object must be an iterable with the same number of items as there\n are targets in the target list, and the items are assigned, from\n left to right, to the corresponding targets.\n\nAssignment of an object to a single target is recursively defined as\nfollows.\n\n* If the target is an identifier (name):\n\n * If the name does not occur in a "global" 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\n square brackets: The object must be an iterable with the same number\n of 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 always\n set as an instance attribute, creating it if necessary. Thus, the\n two occurrences of "a.x" do not necessarily refer to the same\n attribute: if the RHS expression refers to a class attribute, the\n 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\n reference is evaluated. It should yield a mutable sequence object\n (such as a list). The assigned object should be a sequence object\n of the same type. Next, the lower and upper bound expressions are\n evaluated, insofar they are present; defaults are zero and the\n sequence\'s length. The bounds should evaluate to (small) integers.\n If either bound is negative, the sequence\'s length is added to it.\n The resulting bounds are clipped to lie between zero and the\n sequence\'s length, inclusive. Finally, the sequence object is asked\n to replace the slice with the items of the assigned sequence. The\n length of the slice may be different from the length of the assigned\n sequence, thus changing the length of the target sequence, if the\n object 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 the\naugmented version, "x" is only evaluated once. Also, when possible,\nthe actual operation is performed *in-place*, meaning that rather than\ncreating a new object and assigning that to the target, the old object\nis 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',
20 'calls': u'\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 ["," keyword_arguments] ["," "**" expression]\n | "**" expression\n positional_arguments ::= expression ("," expression)*\n keyword_arguments ::= keyword_item ("," keyword_item)*\n keyword_item ::= identifier "=" expression\n\nA trailing comma may be present after the positional and keyword\narguments but does not affect the semantics.\n\nThe primary must evaluate to a callable object (user-defined\nfunctions, built-in functions, methods of built-in objects, class\nobjects, methods of class instances, and 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 "TypeError"\nexception is raised. Otherwise, the list of filled slots is used as\nthe argument list for the call.\n\n**CPython implementation detail
23 nd that the same "pre-computed" value is\nused for each call. This is especially important to understand when a\ndefault parameter is a mutable object, such as a list or a dictionary:\nif the function modifies the object (e.g. by appending an item to a\nlist), the default value is in effect modified. This is generally not\nwhat was intended. A way around this is to use "None" as the\ndefault, and explicitly test for it in the body of the function, e.g.:\n\n def whats_on_the_telly(penguin=None):\n if penguin is None:\n penguin = []\n penguin.append("property of the zoo")\n return penguin\n\nFunction call semantics are described in more detail in section Calls.\nA function call always assigns values to all parameters mentioned in\nthe parameter list, either from position arguments, from keyword\narguments, or from default values. If the form ""*identifier"" is\npresent, it is initialized to a tuple receiving any excess positional\nparameters, defaulting to the empty tuple. If the form\n""**identifier"" is present, it is initialized to a new dictionary\nreceiving any excess keyword arguments, defaulting to a new empty\ndictionary.\n\nIt is also possible to create anonymous functions (functions not bound\nto a name), for immediate use in expressions. This uses lambda\nexpressions, described in section Lambdas. Note that the lambda\nexpression is merely a shorthand for a simplified function definition;\na function defined in a ""def"" statement can be passed around or\nassigned to another name just like a function defined by a lambda\nexpression. The ""def"" form is actually more powerful since it\nallows the execution of multiple statements.\n\n**Programmer\'s note:** Functions are first-class objects. A ""def""\nform executed inside a function definition defines a local function\nthat can be returned or passed around. Free variables used in the\nnested function can access the local variables of the function\ncontaining the def. See section Naming and binding for details.\n\n\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". Both\nclass 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\n there is a "finally" clause which happens to raise another\n exception. That new exception causes the old one to be lost.\n\n[2] Currently, control "flows off the end" except in the case of\n an 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\n function body is transformed into the function\'s "__doc__"\n attribute and 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',
35 detail:** Users should not touch\n"__builtins__"; it is strictly an implementation detail. Users\nwanting to override values in the builtins namespace should "import"\nthe "__builtin__" (no \'s\') module and modify its attributes\nappropriately.\n\nThe namespace for a module is automatically created the first time a\nmodule is imported. The main module for a script is always called\n"__main__".\n\nThe "global" statement has the same scope as a name binding operation\nin the same block. If the nearest enclosing scope for a free variable\ncontains a global statement, the free variable is treated as a global.\n\nA class definition is an executable statement that may use and define\nnames. These references follow the normal rules for name resolution.\nThe namespace of the class definition becomes the attribute dictionary\nof the class. Names defined at the class scope are not visible in\nmethods.\n\n\nInteraction with dynamic features\n---------------------------------\n\nThere are several cases where Python statements are illegal when used\nin conjunction with nested scopes that contain free variables.\n\nIf a variable is referenced in an enclosing scope, it is illegal to\ndelete the name. An error will be reported at compile time.\n\nIf the wild card form of import --- "import *" --- is used in a\nfunction and the function contains or is a nested block with free\nvariables, the compiler will raise a "SyntaxError".\n\nIf "exec" is used in a function and the function contains or is a\nnested block with free variables, the compiler will raise a\n"SyntaxError" unless the exec explicitly specifies the local namespace\nfor the "exec". (In other words, "exec obj" would be illegal, but\n"exec obj in ns" would be legal.)\n\nThe "eval()", "execfile()", and "input()" functions and the "exec"\nstatement do not have access to the full environment for resolving\nnames. Names may be resolved in the local and global namespaces of\nthe caller. Free variables are not resolved in the nearest enclosing\nnamespace, but in the global namespace. [1] The "exec" statement and\nthe "eval()" and "execfile()" functions have optional arguments to\noverride the global and local namespace. If only one namespace is\nspecified, it is used for both.\n\n\nExceptions\n==========\n\nExceptions are a means of breaking out of the normal flow of control\nof a code block in order to handle errors or other exceptional\nconditions. An exception is *raised* at the point where the error is\ndetected; it may be *handled* by the surrounding code block or by any\ncode block that directly or indirectly invoked the code block where\nthe error occurred.\n\nThe Python interpreter raises an exception when it detects a run-time\nerror (such as division by zero). A Python program can also\nexplicitly raise an exception with the "raise" statement. Exception\nhandlers are specified with the "try" ... "except" statement. The\n"finally" clause of such a statement can be used to specify cleanup\ncode which does not handle the exception, but is executed whether an\nexception occurred or not in the preceding code.\n\nPython uses the "termination" model of error handling: an exception\nhandler can find out what happened and continue execution at an outer\nlevel, but it cannot repair the cause of the error and retry the\nfailing operation (except by re-entering the offending piece of code\nfrom the top).\n\nWhen an exception is not handled at all, the interpreter terminates\nexecution of the program, or returns to its interactive main loop. In\neither case, it prints a stack backtrace, except when the exception is\n"SystemExit".\n\nExceptions are identified by class instances. The "except" clause is\nselected depending on the class of the instance: it must reference the\nclass of the instance or a base class thereof. The instance can be\nreceived by the handler and can carry additional information about the\nexceptional condition.\n\nExceptions can also be identified by strings, in which case the\n"except" clause is selected by object identity. An arbitrary value\ncan be raised along with the identifying string which can be passed to\nthe handler.\n\nNote: Messages to exceptions are not part of the Python API. Their\n contents may change from one version of Python to the next without\n warning and should not be relied on by code which will run under\n multiple versions of the interpreter.\n\nSee also the description of the "try" statement in section The try\nstatement and "raise" statement in section The raise statement.\n\n-[ Footnotes ]-\n\n[1] This limitation occurs because the code that is executed by\n these operations is not available at the time the module is\n compiled.\n',
40 detail in section Calls.\nA function call always assigns values to all parameters mentioned in\nthe parameter list, either from position arguments, from keyword\narguments, or from default values. If the form ""*identifier"" is\npresent, it is initialized to a tuple receiving any excess positional\nparameters, defaulting to the empty tuple. If the form\n""**identifier"" is present, it is initialized to a new dictionary\nreceiving any excess keyword arguments, defaulting to a new empty\ndictionary.\n\nIt is also possible to create anonymous functions (functions not bound\nto a name), for immediate use in expressions. This uses lambda\nexpressions, described in section Lambdas. Note that the lambda\nexpression is merely a shorthand for a simplified function definition;\na function defined in a ""def"" statement can be passed around or\nassigned to another name just like a function defined by a lambda\nexpression. The ""def"" form is actually more powerful since it\nallows the execution of multiple statements.\n\n**Programmer\'s note:** Functions are first-class objects. A ""def""\nform executed inside a function definition defines a local function\nthat can be returned or passed around. Free variables used in the\nnested function can access the local variables of the function\ncontaining the def. See section Naming and binding for details.\n',
41 'global': u'\nThe "global" statement\n**********************\n\n global_stmt ::= "global" identifier ("," identifier)*\n\nThe "global" statement is a declaration which holds for the entire\ncurrent code block. It means that the listed identifiers are to be\ninterpreted as globals. It would be impossible to assign to a global\nvariable without "global", although free variables may refer to\nglobals without being declared global.\n\nNames listed in a "global" statement must not be used in the same code\nblock textually preceding that "global" statement.\n\nNames listed in a "global" statement must not be defined as formal\nparameters or in a "for" loop control target, "class" definition,\nfunction definition, or "import" statement.\n\n**CPython implementation detail:** The current implementation does not\nenforce the latter two restrictions, but programs should not abuse\nthis freedom, as future implementations may enforce them or silently\nchange the meaning of the program.\n\n**Programmer\'s note:** the "global" is a directive to the parser. It\napplies only to code parsed at the same time as the "global"\nstatement. In particular, a "global" statement contained in an "exec"\nstatement does not affect the code block *containing* the "exec"\nstatement, and code contained in an "exec" statement is unaffected by\n"global" statements in the code containing the "exec" statement. The\nsame applies to the "eval()", "execfile()" and "compile()" functions.\n',
51 detail:** Users should not touch\n"__builtins__"; it is strictly an implementation detail. Users\nwanting to override values in the builtins namespace should "import"\nthe "__builtin__" (no \'s\') module and modify its attributes\nappropriately.\n\nThe namespace for a module is automatically created the first time a\nmodule is imported. The main module for a script is always called\n"__main__".\n\nThe "global" statement has the same scope as a name binding operation\nin the same block. If the nearest enclosing scope for a free variable\ncontains a global statement, the free variable is treated as a global.\n\nA class definition is an executable statement that may use and define\nnames. These references follow the normal rules for name resolution.\nThe namespace of the class definition becomes the attribute dictionary\nof the class. Names defined at the class scope are not visible in\nmethods.\n\n\nInteraction with dynamic features\n=================================\n\nThere are several cases where Python statements are illegal when used\nin conjunction with nested scopes that contain free variables.\n\nIf a variable is referenced in an enclosing scope, it is illegal to\ndelete the name. An error will be reported at compile time.\n\nIf the wild card form of import --- "import *" --- is used in a\nfunction and the function contains or is a nested block with free\nvariables, the compiler will raise a "SyntaxError".\n\nIf "exec" is used in a function and the function contains or is a\nnested block with free variables, the compiler will raise a\n"SyntaxError" unless the exec explicitly specifies the local namespace\nfor the "exec". (In other words, "exec obj" would be illegal, but\n"exec obj in ns" would be legal.)\n\nThe "eval()", "execfile()", and "input()" functions and the "exec"\nstatement do not have access to the full environment for resolving\nnames. Names may be resolved in the local and global namespaces of\nthe caller. Free variables are not resolved in the nearest enclosing\nnamespace, but in the global namespace. [1] The "exec" statement and\nthe "eval()" and "execfile()" functions have optional arguments to\noverride the global and local namespace. If only one namespace is\nspecified, it is used for both.\n',
54 'objects': u'\nObjects, values and types\n*************************\n\n*Objects* are Python\'s abstraction for data. All data in a Python\nprogram is represented by objects or by relations between objects. (In\na sense, and in conformance to Von Neumann\'s model of a "stored\nprogram computer," code is also represented by objects.)\n\nEvery object has an identity, a type and a value. An object\'s\n*identity* never changes once it has been created; you may think of it\nas the object\'s address in memory. The \'"is"\' operator compares the\nidentity of two objects; the "id()" function returns an integer\nrepresenting its identity (currently implemented as its address). An\nobject\'s *type* is also unchangeable. [1] An object\'s type determines\nthe operations that the object supports (e.g., "does it have a\nlength?") and also defines the possible values for objects of that\ntype. The "type()" function returns an object\'s type (which is an\nobject itself). The *value* of some objects can change. Objects\nwhose value can change are said to be *mutable*; objects whose value\nis unchangeable once they are created are called *immutable*. (The\nvalue of an immutable container object that contains a reference to a\nmutable object can change when the latter\'s value is changed; however\nthe container is still considered immutable, because the collection of\nobjects it contains cannot be changed. So, immutability is not\nstrictly the same as having an unchangeable value, it is more subtle.)\nAn object\'s mutability is determined by its type; for instance,\nnumbers, strings and tuples are immutable, while dictionaries and\nlists are mutable.\n\nObjects are never explicitly destroyed; however, when they become\nunreachable they may be garbage-collected. An implementation is\nallowed to postpone garbage collection or omit it altogether --- it is\na matter of implementation quality how garbage collection is\nimplemented, as long as no objects are collected that are still\nreachable.\n\n**CPython implementation detail
66 'string-methods': u'\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 "UnicodeError".\n Other possible values are "\'ignore\'", "\'replace\'",\n "\'xmlcharrefreplace\'", "\'backslashreplace\'" and any other name\n registered via "codecs.register_error()", see section Codec Base\n Classes. For a list of possible encodings, see section Standard\n Encodings.\n\n 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 suffixes\n to look for. With optional *start*, test beginning at that\n position. With optional *end*, stop comparing at that 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 result\n until the current column is equal to the next tab position. (The\n tab character itself is not copied.) If the character is a newline\n ("\\n") or return ("\\r"), it is copied and the current column is\n reset to zero. Any other character is copied unchanged and the\n 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 within the slice "s[start:end]". Optional arguments *start*\n and *end* are interpreted as in slice notation. Return "-1" if\n *sub* is not found.\n\n Note: The "find()" method should be used only if you need to know\n the position of *sub*. To check if *sub* is a substring or not,\n use 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 String\n 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* is\n 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 splitting\n from the right, "rsplit()" behaves like "split()" which is\n described in detail
71 get and\n set such attributes. *Note that the current implementation only\n supports function attributes on user-defined functions. Function\n attributes on built-in functions may be supported in the\n future.*\n\n Additional information about a function\'s definition can be\n retrieved from its code object; see the description of internal\n types below.\n\n User-defined methods\n A user-defined method object combines a class, a class instance\n (or "None") and any callable object (normally a user-defined\n function).\n\n Special read-only attributes: "im_self" is the class instance\n object, "im_func" is the function object; "im_class" is the\n class of "im_self" for bound methods or the class that asked for\n the method for unbound methods; "__doc__" is the method\'s\n documentation (same as "im_func.__doc__"); "__name__" is the\n method name (same as "im_func.__name__"); "__module__" is the\n name of the module the method was defined in, or "None" if\n unavailable.\n\n Changed in version 2.2: "im_self" used to refer to the class\n that defined the method.\n\n Changed in version 2.6: For Python 3 forward-compatibility,\n "im_func" is also available as "__func__", and "im_self" as\n "__self__".\n\n Methods also support accessing (but not setting) the arbitrary\n function attributes on the underlying function object.\n\n User-defined method objects may be created when getting an\n attribute of a class (perhaps via an instance of that class), if\n that attribute is a user-defined function object, an unbound\n user-defined method object, or a class method object. When the\n attribute is a user-defined method object, a new method object\n is only created if the class from which it is being retrieved is\n the same as, or a derived class of, the class stored in the\n original method object; otherwise, the original method object is\n used as it is.\n\n When a user-defined method object is created by retrieving a\n user-defined function object from a class, its "im_self"\n attribute is "None" and the method object is said to be unbound.\n When one is created by retrieving a user-defined function object\n from a class via one of its instances, its "im_self" attribute\n is the instance, and the method object is said to be bound. In\n either case, the new method\'s "im_class" attribute is the class\n from which the retrieval takes place, and its "im_func"\n attribute is the original function object.\n\n When a user-defined method object is created by retrieving\n another method object from a class or instance, the behaviour is\n the same as for a function object, except that the "im_func"\n attribute of the new instance is not the original method object\n but its "im_func" attribute.\n\n When a user-defined method object is created by retrieving a\n class method object from a class or instance, its "im_self"\n attribute is the class itself, and its "im_func" attribute is\n the function object underlying the class method.\n\n When an unbound user-defined method object is called, the\n underlying function ("im_func") is called, with the restriction\n that the first argument must be an instance of the proper class\n ("im_class") or of a derived class thereof.\n\n When a bound user-defined method object is called, the\n underlying function ("im_func") is called, inserting the class\n instance ("im_self") in front of the argument list. For\n instance, when "C" is a class which contains a definition for a\n function "f()", and "x" is an instance of "C", calling "x.f(1)"\n is equivalent to calling "C.f(x, 1)".\n\n When a user-defined method object is derived from a class method\n object, the "class instance" stored in "im_self" will actually\n be the class itself, so that calling either "x.f(1)" or "C.f(1)"\n is equivalent to calling "f(C,1)" where "f" is the underlying\n function.\n\n Note that the transformation from function object to (unbound or\n bound) method object happens each time the attribute is\n retrieved from the class or instance. In some cases, a fruitful\n optimization is to assign the attribute to a local variable and\n call that local variable. Also notice that this transformation\n only happens for user-defined functions; other callable objects\n (and all non-callable objects) are retrieved without\n transformation. It is also important to note that user-defined\n functions which are attributes of a class instance are not\n converted to bound methods; this *only* happens when the\n function is an attribute of the class.\n\n Generator functions\n A function or method which uses the "yield" statement (see\n section The yield statement) is called a *generator function*.\n Such a function, when called, always returns an iterator object\n which can be used to execute the body of the function: calling\n the iterator\'s "next()" method will cause the function to\n execute until it provides a value using the "yield" statement.\n When the function executes a "return" statement or falls off the\n end, a "StopIteration" exception is raised and the iterator will\n have reached the end of the set of values to be returned.\n\n Built-in functions\n A built-in function object is a wrapper around a C function.\n Examples of built-in functions are "len()" and "math.sin()"\n ("math" is a standard built-in module). The number and type of\n the arguments are determined by the C function. Special read-\n only attributes: "__doc__" is the function\'s documentation\n string, or "None" if unavailable; "__name__" is the function\'s\n name; "__self__" is set to "None" (but see the next item);\n "__module__" is the name of the module the function was defined\n in or "None" if unavailable.\n\n Built-in methods\n This is really a different disguise of a built-in function, this\n time containing an object passed to the C function as an\n implicit extra argument. An example of a built-in method is\n "alist.append()", assuming *alist* is a list object. In this\n case, the special read-only attribute "__self__" is set to the\n object denoted by *alist*.\n\n Class Types\n Class types, or "new-style classes," are callable. These\n objects normally act as factories for new instances of\n themselves, but variations are possible for class types that\n override "__new__()". The arguments of the call are passed to\n "__new__()" and, in the typical case, to "__init__()" to\n initialize the new instance.\n\n Classic Classes\n Class objects are described below. When a class object is\n called, a new class instance (also described below) is created\n and returned. This implies a call to the class\'s "__init__()"\n method if it has one. Any arguments are passed on to the\n "__init__()" method. If there is no "__init__()" method, the\n class must be called without arguments.\n\n Class instances\n Class instances are described below. Class instances are\n callable only when the class has a "__call__()" method;\n "x(arguments)" is a shorthand for "x.__call__(arguments)".\n\nModules\n Modules are imported by the "import" statement (see section The\n import statement). A module object has a namespace implemented by a\n dictionary object (this is the dictionary referenced by the\n func_globals attribute of functions defined in the module).\n Attribute references are translated to lookups in this dictionary,\n e.g., "m.x" is equivalent to "m.__dict__["x"]". A module object\n does not contain the code object used to initialize the module\n (since it isn\'t needed once the initialization is done).\n\n Attribute assignment updates the module\'s namespace dictionary,\n e.g., "m.x = 1" is equivalent to "m.__dict__["x"] = 1".\n\n Special read-only attribute: "__dict__" is the module\'s namespace\n as a dictionary object.\n\n **CPython implementation detail
73 'typesmapping': u'\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" module.)\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 index\nthe same dictionary entry. (Note however, that since computers store\nfloating-point numbers as approximations it is usually unwise to use\nthem as dictionary keys.)\n\nDictionaries can be created by placing a comma-separated list of "key:\nvalue" pairs within braces, for example: "{\'jack\': 4098, \'sjoerd\':\n4127}" or "{4098: \'jack\', 4127: \'sjoerd\'}", or by the "dict"\nconstructor.\n\nclass dict(**kwarg)\nclass dict(mapping, **kwarg)\nclass 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 *iterable*\n object. Each item in the iterable must itself be an iterable 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 If a subclass of dict defines a method "__missing__()" and *key*\n is not present, the "d[key]" operation calls that method with\n the key *key* as argument. The "d[key]" operation then returns\n or raises whatever is returned or raised by the\n "__missing__(key)" call. No other operations or methods invoke\n "__missing__()". If "__missing__()" is not defined, "KeyError"\n is raised. "__missing__()" must be a method; it cannot be an\n instance variable:\n\n >>> class Counter(dict):\n ... def __missing__(self, key):\n ... return 0\n >>> c = Counter()\n >>> c[\'red\']\n 0\n >>> c[\'red\'] += 1\n >>> c[\'red\']\n 1\n\n The example above shows part of the implementation of\n "collections.Counter". A different "__missing__" method is used\n by "collections.defaultdict".\n\n New in version 2.5: Recognition of __missing__ methods of dict\n subclasses.\n\n d[key] = value\n\n Set "d[key]" to *value*.\n\n del d[key]\n\n Remove "d[key]" from *d*. Raises a "KeyError" if *key* is not\n in the map.\n\n key in d\n\n Return "True" if *d* has a key *key*, else "False".\n\n New in version 2.2.\n\n key not in d\n\n Equivalent to "not key in d".\n\n New in version 2.2.\n\n iter(d)\n\n Return an iterator over the keys of the dictionary. This is a\n shortcut for "iterkeys()".\n\n clear()\n\n Remove all items from the dictionary.\n\n copy()\n\n Return a shallow copy of the dictionary.\n\n fromkeys(seq[, value])\n\n Create a new dictionary with keys from *seq* and values set to\n *value*.\n\n "fromkeys()" is a class method that returns a new dictionary.\n *value* defaults to "None".\n\n New in version 2.3.\n\n get(key[, default])\n\n Return the value for *key* if *key* is in the dictionary, else\n *default*. If *default* is not given, it defaults to "None", so\n that this method never raises a "KeyError".\n\n has_key(key)\n\n Test for the presence of *key* in the dictionary. "has_key()"\n is deprecated in favor of "key in d".\n\n items()\n\n Return a copy of the dictionary\'s list of "(key, value)" pairs.\n\n **CPython implementation detail
76 'typesseq': u'\nSequence Types --- "str", "unicode", "list", "tuple", "bytearray", "buffer", "xrange"\n*************************************************************************************\n\nThere are seven sequence types: strings, Unicode strings, lists,\ntuples, bytearrays, buffers, and xrange objects.\n\nFor other containers see the built in "dict" and "set" classes, and\nthe "collections" module.\n\nString literals are written in single or double quotes: "\'xyzzy\'",\n""frobozz"". See String literals for more about string literals.\nUnicode strings are much like strings, but are specified in the syntax\nusing a preceding "\'u\'" character: "u\'abc\'", "u"def"". In addition to\nthe functionality described here, there are also string-specific\nmethods described in the String Methods section. Lists are constructed\nwith square brackets, separating items with commas: "[a, b, c]".\nTuples are constructed by the comma operator (not within square\nbrackets), with or without enclosing parentheses, but an empty tuple\nmust have the enclosing parentheses, such as "a, b, c" or "()". A\nsingle item tuple must have a trailing comma, such as "(d,)".\n\nBytearray objects are created with the built-in function\n"bytearray()".\n\nBuffer objects are not directly supported by Python syntax, but can be\ncreated by calling the built-in function "buffer()". They don\'t\nsupport concatenation or repetition.\n\nObjects of type xrange are similar to buffers in that there is no\nspecific syntax to create them, but they are created using the\n"xrange()" function. They don\'t support slicing, concatenation or\nrepetition, and using "in", "not in", "min()" or "max()" on them is\ninefficient.\n\nMost sequence types support the following operations. The "in" and\n"not in" operations have the same priorities as the comparison\noperations. The "+" and "*" operations have the same priority as the\ncorresponding numeric operations. [3] Additional methods are provided\nfor Mutable Sequence Types.\n\nThis table lists the sequence operations sorted in ascending priority.\nIn the table, *s* and *t* are sequences of the same type; *n*, *i* and\n*j* are integers:\n\n+--------------------+----------------------------------+------------+\n| Operation | Result | Notes |\n+====================+==================================+============+\n| "x in s" | "True" if an item of *s* is | (1) |\n| | equal to *x*, else "False" | |\n+--------------------+----------------------------------+------------+\n| "x not in s" | "False" if an item of *s* is | (1) |\n| | equal to *x*, else "True" | |\n+--------------------+----------------------------------+------------+\n| "s + t" | the concatenation of *s* and *t* | (6) |\n+--------------------+----------------------------------+------------+\n| "s * n, n * s" | equivalent to adding *s* to | (2) |\n| | itself *n* times | |\n+--------------------+----------------------------------+------------+\n| "s[i]" | *i*th item of *s*, origin 0 | (3) |\n+--------------------+----------------------------------+------------+\n| "s[i:j]" | slice of *s* from *i* to *j* | (3)(4) |\n+--------------------+----------------------------------+------------+\n| "s[i:j:k]" | slice of *s* from *i* to *j* | (3)(5) |\n| | with step *k* | |\n+--------------------+----------------------------------+------------+\n| "len(s)" | length of *s* | |\n+--------------------+----------------------------------+------------+\n| "min(s)" | smallest item of *s* | |\n+--------------------+----------------------------------+------------+\n| "max(s)" | largest item of *s* | |\n+--------------------+----------------------------------+------------+\n| "s.index(x)" | index of the first occurrence of | |\n| | *x* in *s* | |\n+--------------------+----------------------------------+------------+\n| "s.count(x)" | total number of occurrences of | |\n| | *x* in *s* | |\n+--------------------+----------------------------------+------------+\n\nSequence types also support comparisons. In particular, tuples and\nlists are compared lexicographically by comparing corresponding\nelements. This means that to compare equal, every element must compare\nequal and the two sequences must be of the same type and have the same\nlength. (For full details see Comparisons in the language reference.)\n\nNotes:\n\n1. When *s* is a string or Unicode string object the "in" and "not\n in" operations act like a substring test. In Python versions\n before 2.3, *x* had to be a string of length 1. In Python 2.3 and\n beyond, *x* may be a string of any length.\n\n2. Values of *n* less than "0" are treated as "0" (which yields an\n empty sequence of the same type as *s*). Note that items in the\n sequence *s* are not copied; they are referenced multiple times.\n This often haunts new Python programmers; consider:\n\n >>> lists = [[]] * 3\n >>> lists\n [[], [], []]\n >>> lists[0].append(3)\n >>> lists\n [[3], [3], [3]]\n\n What has happened is that "[[]]" is a one-element list containing\n an empty list, so all three elements of "[[]] * 3" are references\n to this single empty list. Modifying any of the elements of\n "lists" modifies this single list. You can create a list of\n different lists this way:\n\n >>> lists = [[] for i in range(3)]\n >>> lists[0].append(3)\n >>> lists[1].append(5)\n >>> lists[2].append(7)\n >>> lists\n [[3], [5], [7]]\n\n Further explanation is available in the FAQ entry How do I create a\n multidimensional list?.\n\n3. If *i* or *j* is negative, the index is relative to the end of\n the string: "len(s) + i" or "len(s) + j" is substituted. But note\n that "-0" is still "0".\n\n4. The slice of *s* from *i* to *j* is defined as the sequence of\n items with index *k* such that "i <= k < j". If *i* or *j* is\n greater than "len(s)", use "len(s)". If *i* is omitted or "None",\n use "0". If *j* is omitted or "None", use "len(s)". If *i* is\n greater than or equal to *j*, the slice is empty.\n\n5. The slice of *s* from *i* to *j* with step *k* is defined as the\n sequence of items with index "x = i + n*k" such that "0 <= n <\n (j-i)/k". In other words, the indices are "i", "i+k", "i+2*k",\n "i+3*k" and so on, stopping when *j* is reached (but never\n including *j*). If *i* or *j* is greater than "len(s)", use\n "len(s)". If *i* or *j* are omitted or "None", they become "end"\n values (which end depends on the sign of *k*). Note, *k* cannot be\n zero. If *k* is "None", it is treated like "1".\n\n6. **CPython implementation detaildetaildetail:** While a list is being\n sorted, the effect of attempting to mutate, or even inspect, the\n list is undefined. The C implementation of Python 2.3 and newer\n makes the list appear empty for the duration, and raises\n "ValueError" if it can detect that the list has been mutated\n during a sort.\n\n11. The value *n* is an integer, or an object implementing\n "__index__()". Zero and negative values of *n* clear the\n sequence. Items in the sequence are not copied; they are\n referenced multiple times, as explained for "s * n" under Sequence\n Types --- str, unicode, list, tuple, bytearray, buffer, xrange.\n',
77 'typesseq-mutable': u'\nMutable Sequence Types\n**********************\n\nList and "bytearray" objects support additional operations that allow\nin-place modification of the object. Other mutable sequence types\n(when added to the language) should also support these operations.\nStrings and tuples are immutable sequence types: such objects cannot\nbe modified once created. The following operations are defined on\nmutable 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)] = [x]" | (2) |\n+--------------------------------+----------------------------------+-----------------------+\n| "s.extend(t)" or "s += t" | for the most part the same as | (3) |\n| | "s[len(s):len(s)] = t" | |\n+--------------------------------+----------------------------------+-----------------------+\n| "s *= n" | updates *s* with its contents | (11) |\n| | repeated *n* times | |\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\n multiple parameters and implicitly joined them into a tuple; this\n no longer works in Python 2.0. Use of this misfeature has been\n deprecated since Python 1.4.\n\n3. *t* can be any iterable object.\n\n4. Raises "ValueError" when *x* is not found in *s*. When a\n negative index is passed as the second or third parameter to the\n "index()" method, the list length is added, as for slice indices.\n If it is still negative, it is truncated to zero, as for slice\n indices.\n\n Changed in version 2.3: Previously, "index()" didn\'t have arguments\n 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 indices.\n If it is still negative, it is truncated to zero, as for slice\n indices.\n\n Changed in version 2.3: Previously, all negative indices were\n truncated to zero.\n\n6. The "pop()" method\'s optional argument *i* defaults to "-1", so\n that by default 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 to\n 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\n be 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\n sorted, the effect of attempting to mutate, or even inspect, the\n list is undefined. The C implementation of Python 2.3 and newer\n makes the list appear empty for the duration, and raises\n "ValueError" if it can detect that the list has been mutated\n during a sort.\n\n11. The value *n* is an integer, or an object implementing\n "__index__()". Zero and negative values of *n* clear the\n sequence. Items in the sequence are not copied; they are\n referenced multiple times, as explained for "s * n" under Sequence\n Types --- str, unicode, list, tuple, bytearray, buffer, xrange.\n',
