Lines Matching refs:dictionaries
22 'comparisons': u'\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',
34 'exec': u'\nThe ``exec`` statement\n**********************\n\n exec_stmt ::= "exec" or_expr ["in" expression ["," expression]]\n\nThis statement supports dynamic execution of Python code. The first\nexpression should evaluate to either a string, an open file object, or\na code object. If it is a string, the string is parsed as a suite of\nPython statements which is then executed (unless a syntax error\noccurs). [1] If it is an open file, the file is parsed until EOF and\nexecuted. If it is a code object, it is simply executed. In all\ncases, the code that\'s executed is expected to be valid as file input\n(see section *File input*). Be aware that the ``return`` and\n``yield`` statements may not be used outside of function definitions\neven within the context of code passed to the ``exec`` statement.\n\nIn all cases, if the optional parts are omitted, the code is executed\nin the current scope. If only the first expression after ``in`` is\nspecified, it should be a dictionary, which will be used for both the\nglobal and the local variables. If two expressions are given, they\nare used for the global and local variables, respectively. If\nprovided, *locals* can be any mapping object.\n\nChanged in version 2.4: Formerly, *locals* was required to be a\ndictionary.\n\nAs a side effect, an implementation may insert additional keys into\nthe dictionaries
47 'in': u'\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
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:** CPython currently uses a reference-\ncounting scheme with (optional) delayed detection of cyclically linked\ngarbage, which collects most objects as soon as they become\nunreachable, but is not guaranteed to collect garbage containing\ncircular references. See the documentation of the ``gc`` module for\ninformation on controlling the collection of cyclic garbage. Other\nimplementations act differently and CPython may change. Do not depend\non immediate finalization of objects when they become unreachable (ex:\nalways close files).\n\nNote that the use of the implementation\'s tracing or debugging\nfacilities may keep objects alive that would normally be collectable.\nAlso note that catching an exception with a \'``try``...``except``\'\nstatement may keep objects alive.\n\nSome objects contain references to "external" resources such as open\nfiles or windows. It is understood that these resources are freed\nwhen the object is garbage-collected, but since garbage collection is\nnot guaranteed to happen, such objects also provide an explicit way to\nrelease the external resource, usually a ``close()`` method. Programs\nare strongly recommended to explicitly close such objects. The\n\'``try``...``finally``\' statement provides a convenient way to do\nthis.\n\nSome objects contain references to other objects; these are called\n*containers*. Examples of containers are tuples, lists and\ndictionaries. The references are part of a container\'s value. In\nmost cases, when we talk about the value of a container, we imply the\nvalues, not the identities of the contained objects; however, when we\ntalk about the mutability of a container, only the identities of the\nimmediately contained objects are implied. So, if an immutable\ncontainer (like a tuple) contains a reference to a mutable object, its\nvalue changes if that mutable object is changed.\n\nTypes affect almost all aspects of object behavior. Even the\nimportance of object identity is affected in some sense: for immutable\ntypes, operations that compute new values may actually return a\nreference to any existing object with the same type and value, while\nfor mutable objects this is not allowed. E.g., after ``a = 1; b =\n1``, ``a`` and ``b`` may or may not refer to the same object with the\nvalue one, depending on the implementation, but after ``c = []; d =\n[]``, ``c`` and ``d`` are guaranteed to refer to two different,\nunique, newly created empty lists. (Note that ``c = d = []`` assigns\nthe same object to both ``c`` and ``d``.)\n',
55 'operator-summary': u'\nSummary\n*******\n\nThe following table summarizes the operator precedences in Python,\nfrom lowest precedence (least binding) to highest precedence (most\nbinding). Operators in the same box have the same precedence. Unless\nthe syntax is explicitly given, operators are binary. Operators in\nthe same box group left to right (except for comparisons, including\ntests, which all have the same precedence and chain from left to right\n--- see section *Comparisons* --- and exponentiation, which groups\nfrom right to left).\n\n+-------------------------------------------------+---------------------------------------+\n| Operator | Description |\n+=================================================+=======================================+\n| ``lambda`` | Lambda expression |\n+-------------------------------------------------+---------------------------------------+\n| ``if`` -- ``else`` | Conditional expression |\n+-------------------------------------------------+---------------------------------------+\n| ``or`` | Boolean OR |\n+-------------------------------------------------+---------------------------------------+\n| ``and`` | Boolean AND |\n+-------------------------------------------------+---------------------------------------+\n| ``not`` *x* | Boolean NOT |\n+-------------------------------------------------+---------------------------------------+\n| ``in``, ``not`` ``in``, ``is``, ``is not``, | Comparisons, including membership |\n| ``<``, ``<=``, ``>``, ``>=``, ``<>``, ``!=``, | tests and identity tests, |\n| ``==`` | |\n+-------------------------------------------------+---------------------------------------+\n| ``|`` | Bitwise OR |\n+-------------------------------------------------+---------------------------------------+\n| ``^`` | Bitwise XOR |\n+-------------------------------------------------+---------------------------------------+\n| ``&`` | Bitwise AND |\n+-------------------------------------------------+---------------------------------------+\n| ``<<``, ``>>`` | Shifts |\n+-------------------------------------------------+---------------------------------------+\n| ``+``, ``-`` | Addition and subtraction |\n+-------------------------------------------------+---------------------------------------+\n| ``*``, ``/``, ``//``, ``%`` | Multiplication, division, remainder |\n| | [8] |\n+-------------------------------------------------+---------------------------------------+\n| ``+x``, ``-x``, ``~x`` | Positive, negative, bitwise NOT |\n+-------------------------------------------------+---------------------------------------+\n| ``**`` | Exponentiation [9] |\n+-------------------------------------------------+---------------------------------------+\n| ``x[index]``, ``x[index:index]``, | Subscription, slicing, call, |\n| ``x(arguments...)``, ``x.attribute`` | attribute reference |\n+-------------------------------------------------+---------------------------------------+\n| ``(expressions...)``, ``[expressions...]``, | Binding or tuple display, list |\n| ``{key:datum...}``, ```expressions...``` | display, dictionary display, string |\n| | conversion |\n+-------------------------------------------------+---------------------------------------+\n\n-[ Footnotes ]-\n\n[1] In Python 2.3 and later releases, a list comprehension "leaks" the\n control variables of each ``for`` it contains into the containing\n scope. However, this behavior is deprecated, and relying on it\n will not work in Python 3.0\n\n[2] While ``abs(x%y) < abs(y)`` is true mathematically, for floats it\n may not be true numerically due to roundoff. For example, and\n assuming a platform on which a Python float is an IEEE 754 double-\n precision number, in order that ``-1e-100 % 1e100`` have the same\n sign as ``1e100``, the computed result is ``-1e-100 + 1e100``,\n which is numerically exactly equal to ``1e100``. The function\n ``math.fmod()`` returns a result whose sign matches the sign of\n the first argument instead, and so returns ``-1e-100`` in this\n case. Which approach is more appropriate depends on the\n application.\n\n[3] If x is very close to an exact integer multiple of y, it\'s\n possible for ``floor(x/y)`` to be one larger than ``(x-x%y)/y``\n due to rounding. In such cases, Python returns the latter result,\n in order to preserve that ``divmod(x,y)[0] * y + x % y`` be very\n close to ``x``.\n\n[4] While comparisons between unicode strings make sense at the byte\n level, they may be counter-intuitive to users. For example, the\n strings ``u"\\u00C7"`` and ``u"\\u0043\\u0327"`` compare differently,\n even though they both represent the same unicode character (LATIN\n CAPITAL LETTER C WITH CEDILLA). To compare strings in a human\n recognizable way, compare using ``unicodedata.normalize()``.\n\n[5] The implementation computes this efficiently, without constructing\n lists or sorting.\n\n[6] Earlier versions of Python used lexicographic comparison of the\n sorted (key, value) lists, but this was very expensive for the\n common case of comparing for equality. An even earlier version of\n Python compared dictionaries by identity only, but this caused\n surprises because people expected to be able to test a dictionary\n for emptiness by comparing it to ``{}``.\n\n[7] Due to automatic garbage-collection, free lists, and the dynamic\n nature of descriptors, you may notice seemingly unusual behaviour\n in certain uses of the ``is`` operator, like those involving\n comparisons between instance methods, or constants. Check their\n documentation for more info.\n\n[8] The ``%`` operator is also used for string formatting; the same\n precedence applies.\n\n[9] The power operator ``**`` binds less tightly than an arithmetic or\n bitwise unary operator on its right, that is, ``2**-1`` is\n ``0.5``.\n',
62 'sequence-types': u"\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",
66 dictionariesocumenting what one version of one particular\nimplementation does is undesirable. Instead, here are some informal\nguidelines regarding coercion. In Python 3.0, coercion will not be\nsupported.\n\n* If the left operand of a % operator is a string or Unicode object,\n no coercion takes place and the string formatting operation is\n invoked instead.\n\n* It is no longer recommended to define a coercion operation. Mixed-\n mode operations on types that don\'t define coercion pass the\n original arguments to the operation.\n\n* New-style classes (those derived from ``object``) never invoke the\n ``__coerce__()`` method in response to a binary operator; the only\n time ``__coerce__()`` is invoked is when the built-in function\n ``coerce()`` is called.\n\n* For most intents and purposes, an operator that returns\n ``NotImplemented`` is treated the same as one that is not\n implemented at all.\n\n* Below, ``__op__()`` and ``__rop__()`` are used to signify the\n generic method names corresponding to an operator; ``__iop__()`` is\n used for the corresponding in-place operator. For example, for the\n operator \'``+``\', ``__add__()`` and ``__radd__()`` are used for the\n left and right variant of the binary operator, and ``__iadd__()``\n for the in-place variant.\n\n* For objects *x* and *y*, first ``x.__op__(y)`` is tried. If this is\n not implemented or returns ``NotImplemented``, ``y.__rop__(x)`` is\n tried. If this is also not implemented or returns\n ``NotImplemented``, a ``TypeError`` exception is raised. But see\n the following exception:\n\n* Exception to the previous item: if the left operand is an instance\n of a built-in type or a new-style class, and the right operand is an\n instance of a proper subclass of that type or class and overrides\n the base\'s ``__rop__()`` method, the right operand\'s ``__rop__()``\n method is tried *before* the left operand\'s ``__op__()`` method.\n\n This is done so that a subclass can completely override binary\n operators. Otherwise, the left operand\'s ``__op__()`` method would\n always accept the right operand: when an instance of a given class\n is expected, an instance of a subclass of that class is always\n acceptable.\n\n* When either operand type defines a coercion, this coercion is called\n before that type\'s ``__op__()`` or ``__rop__()`` method is called,\n but no sooner. If the coercion returns an object of a different\n type for the operand whose coercion is invoked, part of the process\n is redone using the new object.\n\n* When an in-place operator (like \'``+=``\') is used, if the left\n operand implements ``__iop__()``, it is invoked without any\n coercion. When the operation falls back to ``__op__()`` and/or\n ``__rop__()``, the normal coercion rules apply.\n\n* In ``x + y``, if *x* is a sequence that implements sequence\n concatenation, sequence concatenation is invoked.\n\n* In ``x * y``, if one operator is a sequence that implements sequence\n repetition, and the other is an integer (``int`` or ``long``),\n sequence repetition is invoked.\n\n* Rich comparisons (implemented by methods ``__eq__()`` and so on)\n never use coercion. Three-way comparison (implemented by\n ``__cmp__()``) does use coercion under the same conditions as other\n binary operations use it.\n\n* In the current implementation, the built-in numeric types ``int``,\n ``long``, ``float``, and ``complex`` do not use coercion. All these\n types implement a ``__coerce__()`` method, for use by the built-in\n ``coerce()`` function.\n\n Changed in version 2.7.\n\n\nWith Statement Context Managers\n===============================\n\nNew in version 2.5.\n\nA *context manager* is an object that defines the runtime context to\nbe established when executing a ``with`` statement. The context\nmanager handles the entry into, and the exit from, the desired runtime\ncontext for the execution of the block of code. Context managers are\nnormally invoked using the ``with`` statement (described in section\n*The with statement*), but can also be used by directly invoking their\nmethods.\n\nTypical uses of context managers include saving and restoring various\nkinds of global state, locking and unlocking resources, closing opened\nfiles, etc.\n\nFor more information on context managers, see *Context Manager Types*.\n\nobject.__enter__(self)\n\n Enter the runtime context related to this object. The ``with``\n statement will bind this method\'s return value to the target(s)\n specified in the ``as`` clause of the statement, if any.\n\nobject.__exit__(self, exc_type, exc_value, traceback)\n\n Exit the runtime context related to this object. The parameters\n describe the exception that caused the context to be exited. If the\n context was exited without an exception, all three arguments will\n be ``None``.\n\n If an exception is supplied, and the method wishes to suppress the\n exception (i.e., prevent it from being propagated), it should\n return a true value. Otherwise, the exception will be processed\n normally upon exit from this method.\n\n Note that ``__exit__()`` methods should not reraise the passed-in\n exception; this is the caller\'s responsibility.\n\nSee also:\n\n **PEP 0343** - The "with" statement\n The specification, background, and examples for the Python\n ``with`` statement.\n\n\nSpecial method lookup for old-style classes\n===========================================\n\nFor old-style classes, special methods are always looked up in exactly\nthe same way as any other method or attribute. This is the case\nregardless of whether the method is being looked up explicitly as in\n``x.__getitem__(i)`` or implicitly as in ``x[i]``.\n\nThis behaviour means that special methods may exhibit different\nbehaviour for different instances of a single old-style class if the\nappropriate special attributes are set differently:\n\n >>> class C:\n ... pass\n ...\n >>> c1 = C()\n >>> c2 = C()\n >>> c1.__len__ = lambda: 5\n >>> c2.__len__ = lambda: 9\n >>> len(c1)\n 5\n >>> len(c2)\n 9\n\n\nSpecial method lookup for new-style classes\n===========================================\n\nFor new-style classes, implicit invocations of special methods are\nonly guaranteed to work correctly if defined on an object\'s type, not\nin the object\'s instance dictionary. That behaviour is the reason why\nthe following code raises an exception (unlike the equivalent example\nwith old-style classes):\n\n >>> class C(object):\n ... pass\n ...\n >>> c = C()\n >>> c.__len__ = lambda: 5\n >>> len(c)\n Traceback (most recent call last):\n File "<stdin>", line 1, in <module>\n TypeError: object of type \'C\' has no len()\n\nThe rationale behind this behaviour lies with a number of special\nmethods such as ``__hash__()`` and ``__repr__()`` that are implemented\nby all objects, including type objects. If the implicit lookup of\nthese methods used the conventional lookup process, they would fail\nwhen invoked on the type object itself:\n\n >>> 1 .__hash__() == hash(1)\n True\n >>> int.__hash__() == hash(int)\n Traceback (most recent call last):\n File "<stdin>", line 1, in <module>\n TypeError: descriptor \'__hash__\' of \'int\' object needs an argument\n\nIncorrectly attempting to invoke an unbound method of a class in this\nway is sometimes referred to as \'metaclass confusion\', and is avoided\nby bypassing the instance when looking up special methods:\n\n >>> type(1).__hash__(1) == hash(1)\n True\n >>> type(int).__hash__(int) == hash(int)\n True\n\nIn addition to bypassing any instance attributes in the interest of\ncorrectness, implicit special method lookup generally also bypasses\nthe ``__getattribute__()`` method even of the object\'s metaclass:\n\n >>> class Meta(type):\n ... def __getattribute__(*args):\n ... print "Metaclass getattribute invoked"\n ... return type.__getattribute__(*args)\n ...\n >>> class C(object):\n ... __metaclass__ = Meta\n ... def __len__(self):\n ... return 10\n ... def __getattribute__(*args):\n ... print "Class getattribute invoked"\n ... return object.__getattribute__(*args)\n ...\n >>> c = C()\n >>> c.__len__() # Explicit lookup via instance\n Class getattribute invoked\n 10\n >>> type(c).__len__(c) # Explicit lookup via type\n Metaclass getattribute invoked\n 10\n >>> len(c) # Implicit lookup\n 10\n\nBypassing the ``__getattribute__()`` machinery in this fashion\nprovides significant scope for speed optimisations within the\ninterpreter, at the cost of some flexibility in the handling of\nspecial methods (the special method *must* be set on the class object\nitself in order to be consistently invoked by the interpreter).\n\n-[ Footnotes ]-\n\n[1] It *is* possible in some cases to change an object\'s type, under\n certain controlled conditions. It generally isn\'t a good idea\n though, since it can lead to some very strange behaviour if it is\n handled incorrectly.\n\n[2] For operands of the same type, it is assumed that if the non-\n reflected method (such as ``__add__()``) fails the operation is\n not supported, which is why the reflected method is not called.\n',
67 'string-conversions': u'\nString conversions\n******************\n\nA string conversion is an expression list enclosed in reverse (a.k.a.\nbackward) quotes:\n\n string_conversion ::= "\'" expression_list "\'"\n\nA string conversion evaluates the contained expression list and\nconverts the resulting object into a string according to rules\nspecific to its type.\n\nIf the object is a string, a number, ``None``, or a tuple, list or\ndictionary containing only objects whose type is one of these, the\nresulting string is a valid Python expression which can be passed to\nthe built-in function ``eval()`` to yield an expression with the same\nvalue (or an approximation, if floating point numbers are involved).\n\n(In particular, converting a string adds quotes around it and converts\n"funny" characters to escape sequences that are safe to print.)\n\nRecursive objects (for example, lists or dictionaries that contain a\nreference to themselves, directly or indirectly) use ``...`` to\nindicate a recursive reference, and the result cannot be passed to\n``eval()`` to get an equal value (``SyntaxError`` will be raised\ninstead).\n\nThe built-in function ``repr()`` performs exactly the same conversion\nin its argument as enclosing it in parentheses and reverse quotes\ndoes. The built-in function ``str()`` performs a similar but more\nuser-friendly conversion.\n',
73 Dictionaries\n These represent finite sets of objects indexed by nearly\n arbitrary values. The only types of values not acceptable as\n keys are values containing lists or dictionaries or other\n mutable types that are compared by value rather than by object\n identity, the reason being that the efficient implementation of\n dictionaries requires a key\'s hash value to remain constant.\n Numeric types used for keys obey the normal rules for numeric\n comparison: if two numbers compare equal (e.g., ``1`` and\n ``1.0``) then they can be used interchangeably to index the same\n dictionary entry.\n\n Dictionariesdictionaries
75 '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``\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([arg])\n\n Return a new dictionary initialized from an optional positional\n argument or from a set of keyword arguments. If no arguments are\n given, return a new empty dictionary. If the positional argument\n *arg* is a mapping object, return a dictionary mapping the same\n keys to the same values as does the mapping object. Otherwise the\n positional argument must be a sequence, a container that supports\n iteration, or an iterator object. The elements of the argument\n must each also be of one of those kinds, and each must in turn\n contain exactly two objects. The first is used as a key in the new\n dictionary, and the second as the key\'s value. If a given key is\n seen more than once, the last value associated with it is retained\n in the new dictionary.\n\n If keyword arguments are given, the keywords themselves with their\n associated values are added as items to the dictionary. If a key is\n specified both in the positional argument and as a keyword\n argument, the value associated with the keyword is retained in the\n dictionary. For example, these all return a dictionary equal to\n ``{"one": 1, "two": 2}``:\n\n * ``dict(one=1, two=2)``\n\n * ``dict({\'one\': 1, \'two\': 2})``\n\n * ``dict(zip((\'one\', \'two\'), (1, 2)))``\n\n * ``dict([[\'two\', 2], [\'one\', 1]])``\n\n The first example only works for keys that are valid Python\n identifiers; the others work with any valid keys.\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 then returns or raises\n whatever is returned or raised by the ``__missing__(key)`` call\n if the key is not present. No other operations or methods invoke\n ``__missing__()``. If ``__missing__()`` is not defined,\n ``KeyError`` is raised. ``__missing__()`` must be a method; it\n cannot be an instance variable. For an example, see\n ``collections.defaultdict``.\n\n d[key] = value\n\n Set ``d[key]`` to *value*.\n\n del d[key]\n\n Remove ``d[key]`` from *d*. Raises a ``KeyError`` if *key* is\n not in the map.\n\n key in d\n\n Return ``True`` if *d* has a key *key*, else ``False``.\n\n 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``,\n so 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)``\n pairs.\n\n **CPython implementation detail:** Keys and values are listed in\n an arbitrary order which is non-random, varies across Python\n implementations, and depends on the dictionary\'s history of\n insertions and deletions.\n\n If ``items()``, ``keys()``, ``values()``, ``iteritems()``,\n ``iterkeys()``, and ``itervalues()`` are called with no\n intervening modifications to the dictionary, the lists will\n directly correspond. This allows the creation of ``(value,\n key)`` pairs using ``zip()``: ``pairs = zip(d.values(),\n d.keys())``. The same relationship holds for the ``iterkeys()``\n and ``itervalues()`` methods: ``pairs = zip(d.itervalues(),\n d.iterkeys())`` provides the same value for ``pairs``. Another\n way to create the same list is ``pairs = [(v, k) for (k, v) in\n d.iteritems()]``.\n\n iteritems()\n\n Return an iterator over the dictionary\'s ``(key, value)`` pairs.\n See the note for ``dict.items()``.\n\n Using ``iteritems()`` while adding or deleting entries in the\n dictionary may raise a ``RuntimeError`` or fail to iterate over\n all entries.\n\n New in version 2.2.\n\n iterkeys()\n\n Return an iterator over the dictionary\'s keys. See the note for\n ``dict.items()``.\n\n Using ``iterkeys()`` while adding or deleting entries in the\n dictionary may raise a ``RuntimeError`` or fail to iterate over\n all entries.\n\n New in version 2.2.\n\n itervalues()\n\n Return an iterator over the dictionary\'s values. See the note\n for ``dict.items()``.\n\n Using ``itervalues()`` while adding or deleting entries in the\n dictionary may raise a ``RuntimeError`` or fail to iterate over\n all entries.\n\n New in version 2.2.\n\n keys()\n\n Return a copy of the dictionary\'s list of keys. See the note\n for ``dict.items()``.\n\n pop(key[, default])\n\n If *key* is in the dictionary, remove it and return its value,\n else return *default*. If *default* is not given and *key* is\n not in the dictionary, a ``KeyError`` is raised.\n\n New in version 2.3.\n\n popitem()\n\n Remove and return an arbitrary ``(key, value)`` pair from the\n dictionary.\n\n ``popitem()`` is useful to destructively iterate over a\n dictionary, as often used in set algorithms. If the dictionary\n is empty, calling ``popitem()`` raises a ``KeyError``.\n\n setdefault(key[, default])\n\n If *key* is in the dictionary, return its value. If not, insert\n *key* with a value of *default* and return *default*. *default*\n defaults to ``None``.\n\n update([other])\n\n Update the dictionary with the key/value pairs from *other*,\n overwriting existing keys. Return ``None``.\n\n ``update()`` accepts either another dictionary object or an\n iterable of key/value pairs (as tuples or other iterables of\n length two). If keyword arguments are specified, the dictionary\n is then updated with those key/value pairs: ``d.update(red=1,\n blue=2)``.\n\n 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',
