Lines Matching refs:dictionaries
20 'comparisons': '\nComparisons\n***********\n\nUnlike C, all comparison operations in Python have the same priority,\nwhich is lower than that of any arithmetic, shifting or bitwise\noperation. Also unlike C, expressions like ``a < b < c`` have the\ninterpretation that is conventional in mathematics:\n\n comparison ::= or_expr ( comp_operator or_expr )*\n comp_operator ::= "<" | ">" | "==" | ">=" | "<=" | "<>" | "!="\n | "is" ["not"] | ["not"] "in"\n\nComparisons yield boolean values: ``True`` or ``False``.\n\nComparisons can be chained arbitrarily, e.g., ``x < y <= z`` is\nequivalent to ``x < y and y <= z``, except that ``y`` is evaluated\nonly once (but in both cases ``z`` is not evaluated at all when ``x <\ny`` is found to be false).\n\nFormally, if *a*, *b*, *c*, ..., *y*, *z* are expressions and *op1*,\n*op2*, ..., *opN* are comparison operators, then ``a op1 b op2 c ... y\nopN z`` is equivalent to ``a op1 b and b op2 c and ... y opN z``,\nexcept that each expression is evaluated at most once.\n\nNote that ``a op1 b op2 c`` doesn\'t imply any kind of comparison\nbetween *a* and *c*, so that, e.g., ``x < y > z`` is perfectly legal\n(though perhaps not pretty).\n\nThe forms ``<>`` and ``!=`` are equivalent; for consistency with C,\n``!=`` is preferred; where ``!=`` is mentioned below ``<>`` is also\naccepted. The ``<>`` spelling is considered obsolescent.\n\nThe operators ``<``, ``>``, ``==``, ``>=``, ``<=``, and ``!=`` compare\nthe values of two objects. The objects need not have the same type.\nIf both are numbers, they are converted to a common type. Otherwise,\nobjects of different types *always* compare unequal, and are ordered\nconsistently but arbitrarily. You can control comparison behavior of\nobjects of non-built-in types by defining a ``__cmp__`` method or rich\ncomparison methods like ``__gt__``, described in section *Special\nmethod names*.\n\n(This unusual definition of comparison was used to simplify the\ndefinition of operations like sorting and the ``in`` and ``not in``\noperators. In the future, the comparison rules for objects of\ndifferent types are likely to change.)\n\nComparison of objects of the same type depends on the type:\n\n* Numbers are compared arithmetically.\n\n* Strings are compared lexicographically using the numeric equivalents\n (the result of the built-in function ``ord()``) of their characters.\n Unicode and 8-bit strings are fully interoperable in this behavior.\n [4]\n\n* Tuples and lists are compared lexicographically using comparison of\n corresponding elements. This means that to compare equal, each\n element must compare equal and the two sequences must be of the same\n type and have the same length.\n\n If not equal, the sequences are ordered the same as their first\n differing elements. For example, ``cmp([1,2,x], [1,2,y])`` returns\n the same as ``cmp(x,y)``. If the corresponding element does not\n exist, the shorter sequence is ordered first (for example, ``[1,2] <\n [1,2,3]``).\n\n* Mappings (dictionaries) compare equal if and only if their sorted\n (key, value) lists compare equal. [5] Outcomes other than equality\n are resolved consistently, but are not otherwise defined. [6]\n\n* Most other objects of built-in types compare unequal unless they are\n the same object; the choice whether one object is considered smaller\n or larger than another one is made arbitrarily but consistently\n within one execution of a program.\n\nThe operators ``in`` and ``not in`` test for collection membership.\n``x in s`` evaluates to true if *x* is a member of the collection *s*,\nand false otherwise. ``x not in s`` returns the negation of ``x in\ns``. The collection membership test has traditionally been bound to\nsequences; an object is a member of a collection if the collection is\na sequence and contains an element equal to that object. However, it\nmake sense for many other object types to support membership tests\nwithout being a sequence. In particular, dictionaries (for keys) and\nsets support membership testing.\n\nFor the list and tuple types, ``x in y`` is true if and only if there\nexists an index *i* such that ``x == y[i]`` is true.\n\nFor the Unicode and string types, ``x in y`` is true if and only if\n*x* is a substring of *y*. An equivalent test is ``y.find(x) != -1``.\nNote, *x* and *y* need not be the same type; consequently, ``u\'ab\' in\n\'abc\'`` will return ``True``. Empty strings are always considered to\nbe a substring of any other string, so ``"" in "abc"`` will return\n``True``.\n\nChanged in version 2.3: Previously, *x* was required to be a string of\nlength ``1``.\n\nFor user-defined classes which define the ``__contains__()`` method,\n``x in y`` is true if and only if ``y.__contains__(x)`` is true.\n\nFor user-defined classes which do not define ``__contains__()`` but do\ndefine ``__iter__()``, ``x in y`` is true if some value ``z`` with ``x\n== z`` is produced while iterating over ``y``. If an exception is\nraised during the iteration, it is as if ``in`` raised that exception.\n\nLastly, the old-style iteration protocol is tried: if a class defines\n``__getitem__()``, ``x in y`` is true if and only if there is a non-\nnegative integer index *i* such that ``x == y[i]``, and all lower\ninteger indices do not raise ``IndexError`` exception. (If any other\nexception is raised, it is as if ``in`` raised that exception).\n\nThe operator ``not in`` is defined to have the inverse true value of\n``in``.\n\nThe operators ``is`` and ``is not`` test for object identity: ``x is\ny`` is true if and only if *x* and *y* are the same object. ``x is\nnot y`` yields the inverse truth value. [7]\n',
44 'in': '\nComparisons\n***********\n\nUnlike C, all comparison operations in Python have the same priority,\nwhich is lower than that of any arithmetic, shifting or bitwise\noperation. Also unlike C, expressions like ``a < b < c`` have the\ninterpretation that is conventional in mathematics:\n\n comparison ::= or_expr ( comp_operator or_expr )*\n comp_operator ::= "<" | ">" | "==" | ">=" | "<=" | "<>" | "!="\n | "is" ["not"] | ["not"] "in"\n\nComparisons yield boolean values: ``True`` or ``False``.\n\nComparisons can be chained arbitrarily, e.g., ``x < y <= z`` is\nequivalent to ``x < y and y <= z``, except that ``y`` is evaluated\nonly once (but in both cases ``z`` is not evaluated at all when ``x <\ny`` is found to be false).\n\nFormally, if *a*, *b*, *c*, ..., *y*, *z* are expressions and *op1*,\n*op2*, ..., *opN* are comparison operators, then ``a op1 b op2 c ... y\nopN z`` is equivalent to ``a op1 b and b op2 c and ... y opN z``,\nexcept that each expression is evaluated at most once.\n\nNote that ``a op1 b op2 c`` doesn\'t imply any kind of comparison\nbetween *a* and *c*, so that, e.g., ``x < y > z`` is perfectly legal\n(though perhaps not pretty).\n\nThe forms ``<>`` and ``!=`` are equivalent; for consistency with C,\n``!=`` is preferred; where ``!=`` is mentioned below ``<>`` is also\naccepted. The ``<>`` spelling is considered obsolescent.\n\nThe operators ``<``, ``>``, ``==``, ``>=``, ``<=``, and ``!=`` compare\nthe values of two objects. The objects need not have the same type.\nIf both are numbers, they are converted to a common type. Otherwise,\nobjects of different types *always* compare unequal, and are ordered\nconsistently but arbitrarily. You can control comparison behavior of\nobjects of non-built-in types by defining a ``__cmp__`` method or rich\ncomparison methods like ``__gt__``, described in section *Special\nmethod names*.\n\n(This unusual definition of comparison was used to simplify the\ndefinition of operations like sorting and the ``in`` and ``not in``\noperators. In the future, the comparison rules for objects of\ndifferent types are likely to change.)\n\nComparison of objects of the same type depends on the type:\n\n* Numbers are compared arithmetically.\n\n* Strings are compared lexicographically using the numeric equivalents\n (the result of the built-in function ``ord()``) of their characters.\n Unicode and 8-bit strings are fully interoperable in this behavior.\n [4]\n\n* Tuples and lists are compared lexicographically using comparison of\n corresponding elements. This means that to compare equal, each\n element must compare equal and the two sequences must be of the same\n type and have the same length.\n\n If not equal, the sequences are ordered the same as their first\n differing elements. For example, ``cmp([1,2,x], [1,2,y])`` returns\n the same as ``cmp(x,y)``. If the corresponding element does not\n exist, the shorter sequence is ordered first (for example, ``[1,2] <\n [1,2,3]``).\n\n* Mappings (dictionaries) compare equal if and only if their sorted\n (key, value) lists compare equal. [5] Outcomes other than equality\n are resolved consistently, but are not otherwise defined. [6]\n\n* Most other objects of built-in types compare unequal unless they are\n the same object; the choice whether one object is considered smaller\n or larger than another one is made arbitrarily but consistently\n within one execution of a program.\n\nThe operators ``in`` and ``not in`` test for collection membership.\n``x in s`` evaluates to true if *x* is a member of the collection *s*,\nand false otherwise. ``x not in s`` returns the negation of ``x in\ns``. The collection membership test has traditionally been bound to\nsequences; an object is a member of a collection if the collection is\na sequence and contains an element equal to that object. However, it\nmake sense for many other object types to support membership tests\nwithout being a sequence. In particular, dictionaries (for keys) and\nsets support membership testing.\n\nFor the list and tuple types, ``x in y`` is true if and only if there\nexists an index *i* such that ``x == y[i]`` is true.\n\nFor the Unicode and string types, ``x in y`` is true if and only if\n*x* is a substring of *y*. An equivalent test is ``y.find(x) != -1``.\nNote, *x* and *y* need not be the same type; consequently, ``u\'ab\' in\n\'abc\'`` will return ``True``. Empty strings are always considered to\nbe a substring of any other string, so ``"" in "abc"`` will return\n``True``.\n\nChanged in version 2.3: Previously, *x* was required to be a string of\nlength ``1``.\n\nFor user-defined classes which define the ``__contains__()`` method,\n``x in y`` is true if and only if ``y.__contains__(x)`` is true.\n\nFor user-defined classes which do not define ``__contains__()`` but do\ndefine ``__iter__()``, ``x in y`` is true if some value ``z`` with ``x\n== z`` is produced while iterating over ``y``. If an exception is\nraised during the iteration, it is as if ``in`` raised that exception.\n\nLastly, the old-style iteration protocol is tried: if a class defines\n``__getitem__()``, ``x in y`` is true if and only if there is a non-\nnegative integer index *i* such that ``x == y[i]``, and all lower\ninteger indices do not raise ``IndexError`` exception. (If any other\nexception is raised, it is as if ``in`` raised that exception).\n\nThe operator ``not in`` is defined to have the inverse true value of\n``in``.\n\nThe operators ``is`` and ``is not`` test for object identity: ``x is\ny`` is true if and only if *x* and *y* are the same object. ``x is\nnot y`` yields the inverse truth value. [7]\n',
51 'objects': '\nObjects, values and types\n*************************\n\n*Objects* are Python\'s abstraction for data. All data in a Python\nprogram is represented by objects or by relations between objects. (In\na sense, and in conformance to Von Neumann\'s model of a "stored\nprogram computer," code is also represented by objects.)\n\nEvery object has an identity, a type and a value. An object\'s\n*identity* never changes once it has been created; you may think of it\nas the object\'s address in memory. The \'``is``\' operator compares the\nidentity of two objects; the ``id()`` function returns an integer\nrepresenting its identity (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',
52 'operator-summary': '\nOperator precedence\n*******************\n\nThe following table summarizes the operator precedences in Python,\nfrom lowest precedence (least binding) to highest precedence (most\nbinding). Operators in the same box have the same precedence. Unless\nthe syntax is explicitly given, operators are binary. Operators in\nthe same box group left to right (except for comparisons, including\ntests, which all have the same precedence and chain from left to right\n--- see section *Comparisons* --- and exponentiation, which groups\nfrom right to left).\n\n+-------------------------------------------------+---------------------------------------+\n| Operator | Description |\n+=================================================+=======================================+\n| ``lambda`` | Lambda expression |\n+-------------------------------------------------+---------------------------------------+\n| ``if`` -- ``else`` | Conditional expression |\n+-------------------------------------------------+---------------------------------------+\n| ``or`` | Boolean OR |\n+-------------------------------------------------+---------------------------------------+\n| ``and`` | Boolean AND |\n+-------------------------------------------------+---------------------------------------+\n| ``not`` ``x`` | Boolean NOT |\n+-------------------------------------------------+---------------------------------------+\n| ``in``, ``not in``, ``is``, ``is not``, ``<``, | Comparisons, including membership |\n| ``<=``, ``>``, ``>=``, ``<>``, ``!=``, ``==`` | tests and identity tests |\n+-------------------------------------------------+---------------------------------------+\n| ``|`` | Bitwise OR |\n+-------------------------------------------------+---------------------------------------+\n| ``^`` | Bitwise XOR |\n+-------------------------------------------------+---------------------------------------+\n| ``&`` | Bitwise AND |\n+-------------------------------------------------+---------------------------------------+\n| ``<<``, ``>>`` | Shifts |\n+-------------------------------------------------+---------------------------------------+\n| ``+``, ``-`` | Addition and subtraction |\n+-------------------------------------------------+---------------------------------------+\n| ``*``, ``/``, ``//``, ``%`` | Multiplication, division, remainder |\n| | [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: value...}``, ```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.\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',
57 'sequence-types': "\nEmulating container types\n*************************\n\nThe following methods can be defined to implement container objects.\nContainers usually are sequences (such as lists or tuples) or mappings\n(like dictionaries), but can represent other containers as well. The\nfirst set of methods is used either to emulate a sequence or to\nemulate a mapping; the difference is that for a sequence, the\nallowable keys should be the integers *k* for which ``0 <= k < N``\nwhere *N* is the length of the sequence, or slice objects, which\ndefine a range of items. (For backwards compatibility, the method\n``__getslice__()`` (see below) can also be defined to handle simple,\nbut not extended slices.) It is also recommended that mappings provide\nthe methods ``keys()``, ``values()``, ``items()``, ``has_key()``,\n``get()``, ``clear()``, ``setdefault()``, ``iterkeys()``,\n``itervalues()``, ``iteritems()``, ``pop()``, ``popitem()``,\n``copy()``, and ``update()`` behaving similar to those for Python's\nstandard dictionary objects. The ``UserDict`` module provides a\n``DictMixin`` class to help create those methods from a base set of\n``__getitem__()``, ``__setitem__()``, ``__delitem__()``, and\n``keys()``. Mutable sequences should provide methods ``append()``,\n``count()``, ``index()``, ``extend()``, ``insert()``, ``pop()``,\n``remove()``, ``reverse()`` and ``sort()``, like Python standard list\nobjects. Finally, sequence types should implement addition (meaning\nconcatenation) and multiplication (meaning repetition) by defining the\nmethods ``__add__()``, ``__radd__()``, ``__iadd__()``, ``__mul__()``,\n``__rmul__()`` and ``__imul__()`` described below; they should not\ndefine ``__coerce__()`` or other numerical operators. It is\nrecommended that both mappings and sequences implement the\n``__contains__()`` method to allow efficient use of the ``in``\noperator; for mappings, ``in`` should be equivalent of ``has_key()``;\nfor sequences, it should search through the values. It is further\nrecommended that both mappings and sequences implement the\n``__iter__()`` method to allow efficient iteration through the\ncontainer; for mappings, ``__iter__()`` should be the same as\n``iterkeys()``; for sequences, it should iterate through the values.\n\nobject.__len__(self)\n\n Called to implement the built-in function ``len()``. Should return\n the length of the object, an integer ``>=`` 0. Also, an object\n that doesn't define a ``__nonzero__()`` method and whose\n ``__len__()`` method returns zero is considered to be false in a\n Boolean context.\n\nobject.__getitem__(self, key)\n\n Called to implement evaluation of ``self[key]``. For sequence\n types, the accepted keys should be integers and slice objects.\n Note that the special interpretation of negative indexes (if the\n class wishes to emulate a sequence type) is up to the\n ``__getitem__()`` method. If *key* is of an inappropriate type,\n ``TypeError`` may be raised; if of a value outside the set of\n indexes for the sequence (after any special interpretation of\n negative values), ``IndexError`` should be raised. For mapping\n types, if *key* is missing (not in the container), ``KeyError``\n should be raised.\n\n Note: ``for`` loops expect that an ``IndexError`` will be raised for\n illegal indexes to allow proper detection of the end of the\n sequence.\n\nobject.__setitem__(self, key, value)\n\n Called to implement assignment to ``self[key]``. Same note as for\n ``__getitem__()``. This should only be implemented for mappings if\n the objects support changes to the values for keys, or if new keys\n can be added, or for sequences if elements can be replaced. The\n same exceptions should be raised for improper *key* values as for\n the ``__getitem__()`` method.\n\nobject.__delitem__(self, key)\n\n Called to implement deletion of ``self[key]``. Same note as for\n ``__getitem__()``. This should only be implemented for mappings if\n the objects support removal of keys, or for sequences if elements\n can be removed from the sequence. The same exceptions should be\n raised for improper *key* values as for the ``__getitem__()``\n method.\n\nobject.__iter__(self)\n\n This method is called when an iterator is required for a container.\n This method should return a new iterator object that can iterate\n over all the objects in the container. For mappings, it should\n iterate over the keys of the container, and should also be made\n available as the method ``iterkeys()``.\n\n Iterator objects also need to implement this method; they are\n required to return themselves. For more information on iterator\n objects, see *Iterator Types*.\n\nobject.__reversed__(self)\n\n Called (if present) by the ``reversed()`` built-in to implement\n reverse iteration. It should return a new iterator object that\n iterates over all the objects in the container in reverse order.\n\n If the ``__reversed__()`` method is not provided, the\n ``reversed()`` built-in will fall back to using the sequence\n protocol (``__len__()`` and ``__getitem__()``). Objects that\n support the sequence protocol should only provide\n ``__reversed__()`` if they can provide an implementation that is\n more efficient than the one provided by ``reversed()``.\n\n New in version 2.6.\n\nThe membership test operators (``in`` and ``not in``) are normally\nimplemented as an iteration through a sequence. However, container\nobjects can supply the following special method with a more efficient\nimplementation, which also does not require the object be a sequence.\n\nobject.__contains__(self, item)\n\n Called to implement membership test operators. Should return true\n if *item* is in *self*, false otherwise. For mapping objects, this\n should consider the keys of the mapping rather than the values or\n the key-item pairs.\n\n For objects that don't define ``__contains__()``, the membership\n test first tries iteration via ``__iter__()``, then the old\n sequence iteration protocol via ``__getitem__()``, see *this\n section in the language reference*.\n",
61 dictionaries
67 'types': '\nThe standard type hierarchy\n***************************\n\nBelow is a list of the types that are built into Python. Extension\nmodules (written in C, Java, or other languages, depending on the\nimplementation) can define additional types. Future versions of\nPython may add types to the type hierarchy (e.g., rational numbers,\nefficiently stored arrays of integers, etc.).\n\nSome of the type descriptions below contain a paragraph listing\n\'special attributes.\' These are attributes that provide access to the\nimplementation and are not intended for general use. Their definition\nmay change in the future.\n\nNone\n This type has a single value. There is a single object with this\n value. This object is accessed through the built-in name ``None``.\n It is used to signify the absence of a value in many situations,\n e.g., it is returned from functions that don\'t explicitly return\n anything. Its truth value is false.\n\nNotImplemented\n This type has a single value. There is a single object with this\n value. This object is accessed through the built-in name\n ``NotImplemented``. Numeric methods and rich comparison methods may\n return this value if they do not implement the operation for the\n operands provided. (The interpreter will then try the reflected\n operation, or some other fallback, depending on the operator.) Its\n truth value is true.\n\nEllipsis\n This type has a single value. There is a single object with this\n value. This object is accessed through the built-in name\n ``Ellipsis``. It is used to indicate the presence of the ``...``\n syntax in a slice. Its truth value is true.\n\n``numbers.Number``\n These are created by numeric literals and returned as results by\n arithmetic operators and arithmetic built-in functions. Numeric\n objects are immutable; once created their value never changes.\n Python numbers are of course strongly related to mathematical\n numbers, but subject to the limitations of numerical representation\n in computers.\n\n Python distinguishes between integers, floating point numbers, and\n complex numbers:\n\n ``numbers.Integral``\n These represent elements from the mathematical set of integers\n (positive and negative).\n\n There are three types of integers:\n\n Plain integers\n These represent numbers in the range -2147483648 through\n 2147483647. (The range may be larger on machines with a\n larger natural word size, but not smaller.) When the result\n of an operation would fall outside this range, the result is\n normally returned as a long integer (in some cases, the\n exception ``OverflowError`` is raised instead). For the\n purpose of shift and mask operations, integers are assumed to\n have a binary, 2\'s complement notation using 32 or more bits,\n and hiding no bits from the user (i.e., all 4294967296\n different bit patterns correspond to different values).\n\n Long integers\n These represent numbers in an unlimited range, subject to\n available (virtual) memory only. For the purpose of shift\n and mask operations, a binary representation is assumed, and\n negative numbers are represented in a variant of 2\'s\n complement which gives the illusion of an infinite string of\n sign bits extending to the left.\n\n Booleans\n These represent the truth values False and True. The two\n objects representing the values False and True are the only\n Boolean objects. The Boolean type is a subtype of plain\n integers, and Boolean values behave like the values 0 and 1,\n respectively, in almost all contexts, the exception being\n that when converted to a string, the strings ``"False"`` or\n ``"True"`` are returned, respectively.\n\n The rules for integer representation are intended to give the\n most meaningful interpretation of shift and mask operations\n involving negative integers and the least surprises when\n switching between the plain and long integer domains. Any\n operation, if it yields a result in the plain integer domain,\n will yield the same result in the long integer domain or when\n using mixed operands. The switch between domains is transparent\n to the programmer.\n\n ``numbers.Real`` (``float``)\n These represent machine-level double precision floating point\n numbers. You are at the mercy of the underlying machine\n architecture (and C or Java implementation) for the accepted\n range and handling of overflow. Python does not support single-\n precision floating point numbers; the savings in processor and\n memory usage that are usually the reason for using these is\n dwarfed by the overhead of using objects in Python, so there is\n no reason to complicate the language with two kinds of floating\n point numbers.\n\n ``numbers.Complex``\n These represent complex numbers as a pair of machine-level\n double precision floating point numbers. The same caveats apply\n as for floating point numbers. The real and imaginary parts of a\n complex number ``z`` can be retrieved through the read-only\n attributes ``z.real`` and ``z.imag``.\n\nSequences\n These represent finite ordered sets indexed by non-negative\n numbers. The built-in function ``len()`` returns the number of\n items of a sequence. When the length of a sequence is *n*, the\n index set contains the numbers 0, 1, ..., *n*-1. Item *i* of\n sequence *a* is selected by ``a[i]``.\n\n Sequences also support slicing: ``a[i:j]`` selects all items with\n index *k* such that *i* ``<=`` *k* ``<`` *j*. When used as an\n expression, a slice is a sequence of the same type. This implies\n that the index set is renumbered so that it starts at 0.\n\n Some sequences also support "extended slicing" with a third "step"\n parameter: ``a[i:j:k]`` selects all items of *a* with index *x*\n where ``x = i + n*k``, *n* ``>=`` ``0`` and *i* ``<=`` *x* ``<``\n *j*.\n\n Sequences are distinguished according to their mutability:\n\n Immutable sequences\n An object of an immutable sequence type cannot change once it is\n created. (If the object contains references to other objects,\n these other objects may be mutable and may be changed; however,\n the collection of objects directly referenced by an immutable\n object cannot change.)\n\n The following types are immutable sequences:\n\n Strings\n The items of a string are characters. There is no separate\n character type; a character is represented by a string of one\n item. Characters represent (at least) 8-bit bytes. The\n built-in functions ``chr()`` and ``ord()`` convert between\n characters and nonnegative integers representing the byte\n values. Bytes with the values 0-127 usually represent the\n corresponding ASCII values, but the interpretation of values\n is up to the program. The string data type is also used to\n represent arrays of bytes, e.g., to hold data read from a\n file.\n\n (On systems whose native character set is not ASCII, strings\n may use EBCDIC in their internal representation, provided the\n functions ``chr()`` and ``ord()`` implement a mapping between\n ASCII and EBCDIC, and string comparison preserves the ASCII\n order. Or perhaps someone can propose a better rule?)\n\n Unicode\n The items of a Unicode object are Unicode code units. A\n Unicode code unit is represented by a Unicode object of one\n item and can hold either a 16-bit or 32-bit value\n representing a Unicode ordinal (the maximum value for the\n ordinal is given in ``sys.maxunicode``, and depends on how\n Python is configured at compile time). Surrogate pairs may\n be present in the Unicode object, and will be reported as two\n separate items. The built-in functions ``unichr()`` and\n ``ord()`` convert between code units and nonnegative integers\n representing the Unicode ordinals as defined in the Unicode\n Standard 3.0. Conversion from and to other encodings are\n possible through the Unicode method ``encode()`` and the\n built-in function ``unicode()``.\n\n Tuples\n The items of a tuple are arbitrary Python objects. Tuples of\n two or more items are formed by comma-separated lists of\n expressions. A tuple of one item (a \'singleton\') can be\n formed by affixing a comma to an expression (an expression by\n itself does not create a tuple, since parentheses must be\n usable for grouping of expressions). An empty tuple can be\n formed by an empty pair of parentheses.\n\n Mutable sequences\n Mutable sequences can be changed after they are created. The\n subscription and slicing notations can be used as the target of\n assignment and ``del`` (delete) statements.\n\n There are currently two intrinsic mutable sequence types:\n\n Lists\n The items of a list are arbitrary Python objects. Lists are\n formed by placing a comma-separated list of expressions in\n square brackets. (Note that there are no special cases needed\n to form lists of length 0 or 1.)\n\n Byte Arrays\n A bytearray object is a mutable array. They are created by\n the built-in ``bytearray()`` constructor. Aside from being\n mutable (and hence unhashable), byte arrays otherwise provide\n the same interface and functionality as immutable bytes\n objects.\n\n The extension module ``array`` provides an additional example of\n a mutable sequence type.\n\nSet types\n These represent unordered, finite sets of unique, immutable\n objects. As such, they cannot be indexed by any subscript. However,\n they can be iterated over, and the built-in function ``len()``\n returns the number of items in a set. Common uses for sets are fast\n membership testing, removing duplicates from a sequence, and\n computing mathematical operations such as intersection, union,\n difference, and symmetric difference.\n\n For set elements, the same immutability rules apply as for\n dictionary keys. Note that numeric types obey the normal rules for\n numeric comparison: if two numbers compare equal (e.g., ``1`` and\n ``1.0``), only one of them can be contained in a set.\n\n There are currently two intrinsic set types:\n\n Sets\n These represent a mutable set. They are created by the built-in\n ``set()`` constructor and can be modified afterwards by several\n methods, such as ``add()``.\n\n Frozen sets\n These represent an immutable set. They are created by the\n built-in ``frozenset()`` constructor. As a frozenset is\n immutable and *hashable*, it can be used again as an element of\n another set, or as a dictionary key.\n\nMappings\n These represent finite sets of objects indexed by arbitrary index\n sets. The subscript notation ``a[k]`` selects the item indexed by\n ``k`` from the mapping ``a``; this can be used in expressions and\n as the target of assignments or ``del`` statements. The built-in\n function ``len()`` returns the number of items in a mapping.\n\n There is currently a single intrinsic mapping type:\n\n Dictionaries\n These represent finite sets of objects indexed by nearly\n arbitrary values. The only types of values not acceptable as\n keys are values containing lists or dictionaries or other\n mutable types that are compared by value rather than by object\n identity, the reason being that the efficient implementation of\n dictionaries requires a key\'s hash value to remain constant.\n Numeric types used for keys obey the normal rules for numeric\n comparison: if two numbers compare equal (e.g., ``1`` and\n ``1.0``) then they can be used interchangeably to index the same\n dictionary entry.\n\n Dictionariesdictionaries
69 'typesmapping': '\nMapping Types --- ``dict``\n**************************\n\nA *mapping* object maps *hashable* values to arbitrary objects.\nMappings are mutable objects. There is currently only one standard\nmapping type, the *dictionary*. (For other containers see the built\nin ``list``, ``set``, and ``tuple`` classes, and the ``collections``\nmodule.)\n\nA dictionary\'s keys are *almost* arbitrary values. Values that are\nnot *hashable*, that is, values containing lists, dictionaries or\nother mutable types (that are compared by value rather than by object\nidentity) may not be used as keys. Numeric types used for keys obey\nthe normal rules for numeric comparison: if two numbers compare equal\n(such as ``1`` and ``1.0``) then they can be used interchangeably to\nindex the same dictionary entry. (Note however, that since computers\nstore floating-point numbers as approximations it is usually unwise to\nuse them as dictionary keys.)\n\nDictionaries can be created by placing a comma-separated list of\n``key: value`` pairs within braces, for example: ``{\'jack\': 4098,\n\'sjoerd\': 4127}`` or ``{4098: \'jack\', 4127: \'sjoerd\'}``, or by the\n``dict`` constructor.\n\nclass class dict(**kwarg)\nclass class dict(mapping, **kwarg)\nclass class dict(iterable, **kwarg)\n\n Return a new dictionary initialized from an optional positional\n argument and a possibly empty set of keyword arguments.\n\n If no positional argument is given, an empty dictionary is created.\n If a positional argument is given and it is a mapping object, a\n dictionary is created with the same key-value pairs as the mapping\n object. Otherwise, the positional argument must be an *iterator*\n object. Each item in the iterable must itself be an iterator with\n exactly two objects. The first object of each item becomes a key\n in the new dictionary, and the second object the corresponding\n value. If a key occurs more than once, the last value for that key\n becomes the corresponding value in the new dictionary.\n\n If keyword arguments are given, the keyword arguments and their\n values are added to the dictionary created from the positional\n argument. If a key being added is already present, the value from\n the keyword argument replaces the value from the positional\n argument.\n\n To illustrate, the following examples all return a dictionary equal\n to ``{"one": 1, "two": 2, "three": 3}``:\n\n >>> a = dict(one=1, two=2, three=3)\n >>> b = {\'one\': 1, \'two\': 2, \'three\': 3}\n >>> c = dict(zip([\'one\', \'two\', \'three\'], [1, 2, 3]))\n >>> d = dict([(\'two\', 2), (\'one\', 1), (\'three\', 3)])\n >>> e = dict({\'three\': 3, \'one\': 1, \'two\': 2})\n >>> a == b == c == d == e\n True\n\n Providing keyword arguments as in the first example only works for\n keys that are valid Python identifiers. Otherwise, any valid keys\n can be used.\n\n New in version 2.2.\n\n Changed in version 2.3: Support for building a dictionary from\n keyword arguments added.\n\n These are the operations that dictionaries
