[Python-checkins] cpython: Update pydoc topic index for Python 3.4.0a2.

[larry.hastings]

http://hg.python.org/cpython/rev/b94c73c0eaca
changeset:   85642:b94c73c0eaca
parent:      85584:98f82b124c7d
user:        Larry Hastings <larry at hastings.org>
date:        Sat Sep 07 23:41:12 2013 +1200
summary:
  Update pydoc topic index for Python 3.4.0a2.

files:
  Lib/pydoc_data/topics.py |  4 ++--
  1 files changed, 2 insertions(+), 2 deletions(-)


diff --git a/Lib/pydoc_data/topics.py b/Lib/pydoc_data/topics.py
--- a/Lib/pydoc_data/topics.py
+++ b/Lib/pydoc_data/topics.py
@@ -1,5 +1,5 @@
 # -*- coding: utf-8 -*-
-# Autogenerated by Sphinx on Sat Aug  3 12:46:08 2013
+# Autogenerated by Sphinx on Sat Sep  7 23:40:23 2013
 topics = {'assert': '\nThe ``assert`` statement\n************************\n\nAssert statements are a convenient way to insert debugging assertions\ninto a program:\n\n   assert_stmt ::= "assert" expression ["," expression]\n\nThe simple form, ``assert expression``, is equivalent to\n\n   if __debug__:\n      if not expression: raise AssertionError\n\nThe extended form, ``assert expression1, expression2``, is equivalent\nto\n\n   if __debug__:\n      if not expression1: raise AssertionError(expression2)\n\nThese equivalences assume that ``__debug__`` and ``AssertionError``\nrefer to the built-in variables with those names.  In the current\nimplementation, the built-in variable ``__debug__`` is ``True`` under\nnormal circumstances, ``False`` when optimization is requested\n(command line option -O).  The current code generator emits no code\nfor an assert statement when optimization is requested at compile\ntime.  Note that it is unnecessary to include the source code for the\nexpression that failed in the error message; it will be displayed as\npart of the stack trace.\n\nAssignments to ``__debug__`` are illegal.  The value for the built-in\nvariable is determined when the interpreter starts.\n',
  'assignment': '\nAssignment statements\n*********************\n\nAssignment statements are used to (re)bind names to values and to\nmodify attributes or items of mutable objects:\n\n   assignment_stmt ::= (target_list "=")+ (expression_list | yield_expression)\n   target_list     ::= target ("," target)* [","]\n   target          ::= identifier\n              | "(" target_list ")"\n              | "[" target_list "]"\n              | attributeref\n              | subscription\n              | slicing\n              | "*" target\n\n(See section *Primaries* for the syntax definitions for the last three\nsymbols.)\n\nAn assignment statement evaluates the expression list (remember that\nthis can be a single expression or a comma-separated list, the latter\nyielding a tuple) and assigns the single resulting object to each of\nthe target lists, from left to right.\n\nAssignment is defined recursively depending on the form of the target\n(list). When a target is part of a mutable object (an attribute\nreference, subscription or slicing), the mutable object must\nultimately perform the assignment and decide about its validity, and\nmay raise an exception if the assignment is unacceptable.  The rules\nobserved by various types and the exceptions raised are given with the\ndefinition of the object types (see section *The standard type\nhierarchy*).\n\nAssignment of an object to a target list, optionally enclosed in\nparentheses or square brackets, is recursively defined as follows.\n\n* If the target list is a single target: The object is assigned to\n  that target.\n\n* If the target list is a comma-separated list of targets: The object\n  must be an iterable with the same number of items as there are\n  targets in the target list, and the items are assigned, from left to\n  right, to the corresponding targets.\n\n  * If the target list contains one target prefixed with an asterisk,\n    called a "starred" target: The object must be a sequence with at\n    least as many items as there are targets in the target list, minus\n    one.  The first items of the sequence are assigned, from left to\n    right, to the targets before the starred target.  The final items\n    of the sequence are assigned to the targets after the starred\n    target.  A list of the remaining items in the sequence is then\n    assigned to the starred target (the list can be empty).\n\n  * Else: The object must be a sequence with the same number of items\n    as there are targets in the target list, and the items are\n    assigned, from left to right, to the corresponding targets.\n\nAssignment of an object to a single target is recursively defined as\nfollows.\n\n* If the target is an identifier (name):\n\n  * If the name does not occur in a ``global`` or ``nonlocal``\n    statement in the current code block: the name is bound to the\n    object in the current local namespace.\n\n  * Otherwise: the name is bound to the object in the global namespace\n    or the outer namespace determined by ``nonlocal``, respectively.\n\n  The name is rebound if it was already bound.  This may cause the\n  reference count for the object previously bound to the name to reach\n  zero, causing the object to be deallocated and its destructor (if it\n  has one) to be called.\n\n* If the target is a target list enclosed in parentheses or in square\n  brackets: The object must be an iterable with the same number of\n  items as there are targets in the target list, and its items are\n  assigned, from left to right, to the corresponding targets.\n\n* If the target is an attribute reference: The primary expression in\n  the reference is evaluated.  It should yield an object with\n  assignable attributes; if this is not the case, ``TypeError`` is\n  raised.  That object is then asked to assign the assigned object to\n  the given attribute; if it cannot perform the assignment, it raises\n  an exception (usually but not necessarily ``AttributeError``).\n\n  Note: If the object is a class instance and the attribute reference\n  occurs on both sides of the assignment operator, the RHS expression,\n  ``a.x`` can access either an instance attribute or (if no instance\n  attribute exists) a class attribute.  The LHS target ``a.x`` is\n  always set as an instance attribute, creating it if necessary.\n  Thus, the two occurrences of ``a.x`` do not necessarily refer to the\n  same attribute: if the RHS expression refers to a class attribute,\n  the LHS creates a new instance attribute as the target of the\n  assignment:\n\n     class Cls:\n         x = 3             # class variable\n     inst = Cls()\n     inst.x = inst.x + 1   # writes inst.x as 4 leaving Cls.x as 3\n\n  This description does not necessarily apply to descriptor\n  attributes, such as properties created with ``property()``.\n\n* If the target is a subscription: The primary expression in the\n  reference is evaluated.  It should yield either a mutable sequence\n  object (such as a list) or a mapping object (such as a dictionary).\n  Next, the subscript expression is evaluated.\n\n  If the primary is a mutable sequence object (such as a list), the\n  subscript must yield an integer.  If it is negative, the sequence\'s\n  length is added to it.  The resulting value must be a nonnegative\n  integer less than the sequence\'s length, and the sequence is asked\n  to assign the assigned object to its item with that index.  If the\n  index is out of range, ``IndexError`` is raised (assignment to a\n  subscripted sequence cannot add new items to a list).\n\n  If the primary is a mapping object (such as a dictionary), the\n  subscript must have a type compatible with the mapping\'s key type,\n  and the mapping is then asked to create a key/datum pair which maps\n  the subscript to the assigned object.  This can either replace an\n  existing key/value pair with the same key value, or insert a new\n  key/value pair (if no key with the same value existed).\n\n  For user-defined objects, the ``__setitem__()`` method is called\n  with appropriate arguments.\n\n* If the target is a slicing: The primary expression in the reference\n  is evaluated.  It should yield a mutable sequence object (such as a\n  list).  The assigned object should be a sequence object of the same\n  type.  Next, the lower and upper bound expressions are evaluated,\n  insofar they are present; defaults are zero and the sequence\'s\n  length.  The bounds should evaluate to integers. If either bound is\n  negative, the sequence\'s length is added to it.  The resulting\n  bounds are clipped to lie between zero and the sequence\'s length,\n  inclusive.  Finally, the sequence object is asked to replace the\n  slice with the items of the assigned sequence.  The length of the\n  slice may be different from the length of the assigned sequence,\n  thus changing the length of the target sequence, if the object\n  allows it.\n\n**CPython implementation detail:** In the current implementation, the\nsyntax for targets is taken to be the same as for expressions, and\ninvalid syntax is rejected during the code generation phase, causing\nless detailed error messages.\n\nWARNING: Although the definition of assignment implies that overlaps\nbetween the left-hand side and the right-hand side are \'safe\' (for\nexample ``a, b = b, a`` swaps two variables), overlaps *within* the\ncollection of assigned-to variables are not safe!  For instance, the\nfollowing program prints ``[0, 2]``:\n\n   x = [0, 1]\n   i = 0\n   i, x[i] = 1, 2\n   print(x)\n\nSee also:\n\n   **PEP 3132** - Extended Iterable Unpacking\n      The specification for the ``*target`` feature.\n\n\nAugmented assignment statements\n===============================\n\nAugmented assignment is the combination, in a single statement, of a\nbinary operation and an assignment statement:\n\n   augmented_assignment_stmt ::= augtarget augop (expression_list | yield_expression)\n   augtarget                 ::= identifier | attributeref | subscription | slicing\n   augop                     ::= "+=" | "-=" | "*=" | "/=" | "//=" | "%=" | "**="\n             | ">>=" | "<<=" | "&=" | "^=" | "|="\n\n(See section *Primaries* for the syntax definitions for the last three\nsymbols.)\n\nAn augmented assignment evaluates the target (which, unlike normal\nassignment statements, cannot be an unpacking) and the expression\nlist, performs the binary operation specific to the type of assignment\non the two operands, and assigns the result to the original target.\nThe target is only evaluated once.\n\nAn augmented assignment expression like ``x += 1`` can be rewritten as\n``x = x + 1`` to achieve a similar, but not exactly equal effect. In\nthe augmented version, ``x`` is only evaluated once. Also, when\npossible, the actual operation is performed *in-place*, meaning that\nrather than creating a new object and assigning that to the target,\nthe old object is modified instead.\n\nWith the exception of assigning to tuples and multiple targets in a\nsingle statement, the assignment done by augmented assignment\nstatements is handled the same way as normal assignments. Similarly,\nwith the exception of the possible *in-place* behavior, the binary\noperation performed by augmented assignment is the same as the normal\nbinary operations.\n\nFor targets which are attribute references, the same *caveat about\nclass and instance attributes* applies as for regular assignments.\n',
  'atom-identifiers': '\nIdentifiers (Names)\n*******************\n\nAn identifier occurring as an atom is a name.  See section\n*Identifiers and keywords* for lexical definition and section *Naming\nand binding* for documentation of naming and binding.\n\nWhen the name is bound to an object, evaluation of the atom yields\nthat object. When a name is not bound, an attempt to evaluate it\nraises a ``NameError`` exception.\n\n**Private name mangling:** When an identifier that textually occurs in\na class definition begins with two or more underscore characters and\ndoes not end in two or more underscores, it is considered a *private\nname* of that class. Private names are transformed to a longer form\nbefore code is generated for them.  The transformation inserts the\nclass name, with leading underscores removed and a single underscore\ninserted, in front of the name.  For example, the identifier\n``__spam`` occurring in a class named ``Ham`` will be transformed to\n``_Ham__spam``.  This transformation is independent of the syntactical\ncontext in which the identifier is used.  If the transformed name is\nextremely long (longer than 255 characters), implementation defined\ntruncation may happen. If the class name consists only of underscores,\nno transformation is done.\n',
@@ -66,7 +66,7 @@
  'subscriptions': '\nSubscriptions\n*************\n\nA subscription selects an item of a sequence (string, tuple or list)\nor mapping (dictionary) object:\n\n   subscription ::= primary "[" expression_list "]"\n\nThe primary must evaluate to an object that supports subscription,\ne.g. a list or dictionary.  User-defined objects can support\nsubscription by defining a ``__getitem__()`` method.\n\nFor built-in objects, there are two types of objects that support\nsubscription:\n\nIf the primary is a mapping, the expression list must evaluate to an\nobject whose value is one of the keys of the mapping, and the\nsubscription selects the value in the mapping that corresponds to that\nkey.  (The expression list is a tuple except if it has exactly one\nitem.)\n\nIf the primary is a sequence, the expression (list) must evaluate to\nan integer or a slice (as discussed in the following section).\n\nThe formal syntax makes no special provision for negative indices in\nsequences; however, built-in sequences all provide a ``__getitem__()``\nmethod that interprets negative indices by adding the length of the\nsequence to the index (so that ``x[-1]`` selects the last item of\n``x``).  The resulting value must be a nonnegative integer less than\nthe number of items in the sequence, and the subscription selects the\nitem whose index is that value (counting from zero). Since the support\nfor negative indices and slicing occurs in the object\'s\n``__getitem__()`` method, subclasses overriding this method will need\nto explicitly add that support.\n\nA string\'s items are characters.  A character is not a separate data\ntype but a string of exactly one character.\n',
  'truth': "\nTruth Value Testing\n*******************\n\nAny object can be tested for truth value, for use in an ``if`` or\n``while`` condition or as operand of the Boolean operations below. The\nfollowing values are considered false:\n\n* ``None``\n\n* ``False``\n\n* zero of any numeric type, for example, ``0``, ``0.0``, ``0j``.\n\n* any empty sequence, for example, ``''``, ``()``, ``[]``.\n\n* any empty mapping, for example, ``{}``.\n\n* instances of user-defined classes, if the class defines a\n  ``__bool__()`` or ``__len__()`` method, when that method returns the\n  integer zero or ``bool`` value ``False``. [1]\n\nAll other values are considered true --- so objects of many types are\nalways true.\n\nOperations and built-in functions that have a Boolean result always\nreturn ``0`` or ``False`` for false and ``1`` or ``True`` for true,\nunless otherwise stated. (Important exception: the Boolean operations\n``or`` and ``and`` always return one of their operands.)\n",
  'try': '\nThe ``try`` statement\n*********************\n\nThe ``try`` statement specifies exception handlers and/or cleanup code\nfor a group of statements:\n\n   try_stmt  ::= try1_stmt | try2_stmt\n   try1_stmt ::= "try" ":" suite\n                 ("except" [expression ["as" target]] ":" suite)+\n                 ["else" ":" suite]\n                 ["finally" ":" suite]\n   try2_stmt ::= "try" ":" suite\n                 "finally" ":" suite\n\nThe ``except`` clause(s) specify one or more exception handlers. When\nno exception occurs in the ``try`` clause, no exception handler is\nexecuted. When an exception occurs in the ``try`` suite, a search for\nan exception handler is started.  This search inspects the except\nclauses in turn until one is found that matches the exception.  An\nexpression-less except clause, if present, must be last; it matches\nany exception.  For an except clause with an expression, that\nexpression is evaluated, and the clause matches the exception if the\nresulting object is "compatible" with the exception.  An object is\ncompatible with an exception if it is the class or a base class of the\nexception object or a tuple containing an item compatible with the\nexception.\n\nIf no except clause matches the exception, the search for an exception\nhandler continues in the surrounding code and on the invocation stack.\n[1]\n\nIf the evaluation of an expression in the header of an except clause\nraises an exception, the original search for a handler is canceled and\na search starts for the new exception in the surrounding code and on\nthe call stack (it is treated as if the entire ``try`` statement\nraised the exception).\n\nWhen a matching except clause is found, the exception is assigned to\nthe target specified after the ``as`` keyword in that except clause,\nif present, and the except clause\'s suite is executed.  All except\nclauses must have an executable block.  When the end of this block is\nreached, execution continues normally after the entire try statement.\n(This means that if two nested handlers exist for the same exception,\nand the exception occurs in the try clause of the inner handler, the\nouter handler will not handle the exception.)\n\nWhen an exception has been assigned using ``as target``, it is cleared\nat the end of the except clause.  This is as if\n\n   except E as N:\n       foo\n\nwas translated to\n\n   except E as N:\n       try:\n           foo\n       finally:\n           del N\n\nThis means the exception must be assigned to a different name to be\nable to refer to it after the except clause.  Exceptions are cleared\nbecause with the traceback attached to them, they form a reference\ncycle with the stack frame, keeping all locals in that frame alive\nuntil the next garbage collection occurs.\n\nBefore an except clause\'s suite is executed, details about the\nexception are stored in the ``sys`` module and can be access via\n``sys.exc_info()``. ``sys.exc_info()`` returns a 3-tuple consisting of\nthe exception class, the exception instance and a traceback object\n(see section *The standard type hierarchy*) identifying the point in\nthe program where the exception occurred.  ``sys.exc_info()`` values\nare restored to their previous values (before the call) when returning\nfrom a function that handled an exception.\n\nThe optional ``else`` clause is executed if and when control flows off\nthe end of the ``try`` clause. [2] Exceptions in the ``else`` clause\nare not handled by the preceding ``except`` clauses.\n\nIf ``finally`` is present, it specifies a \'cleanup\' handler.  The\n``try`` clause is executed, including any ``except`` and ``else``\nclauses.  If an exception occurs in any of the clauses and is not\nhandled, the exception is temporarily saved. The ``finally`` clause is\nexecuted.  If there is a saved exception it is re-raised at the end of\nthe ``finally`` clause.  If the ``finally`` clause raises another\nexception, the saved exception is set as the context of the new\nexception. If the ``finally`` clause executes a ``return`` or\n``break`` statement, the saved exception is discarded:\n\n   def f():\n       try:\n           1/0\n       finally:\n           return 42\n\n   >>> f()\n   42\n\nThe exception information is not available to the program during\nexecution of the ``finally`` clause.\n\nWhen a ``return``, ``break`` or ``continue`` statement is executed in\nthe ``try`` suite of a ``try``...``finally`` statement, the\n``finally`` clause is also executed \'on the way out.\' A ``continue``\nstatement is illegal in the ``finally`` clause. (The reason is a\nproblem with the current implementation --- this restriction may be\nlifted in the future).\n\nAdditional information on exceptions can be found in section\n*Exceptions*, and information on using the ``raise`` statement to\ngenerate exceptions may be found in section *The raise statement*.\n',
- 'types': '\nThe standard type hierarchy\n***************************\n\nBelow is a list of the types that are built into Python.  Extension\nmodules (written in C, Java, or other languages, depending on the\nimplementation) can define additional types.  Future versions of\nPython may add types to the type hierarchy (e.g., rational numbers,\nefficiently stored arrays of integers, etc.), although such additions\nwill often be provided via the standard library instead.\n\nSome of the type descriptions below contain a paragraph listing\n\'special attributes.\'  These are attributes that provide access to the\nimplementation and are not intended for general use.  Their definition\nmay change in the future.\n\nNone\n   This type has a single value.  There is a single object with this\n   value. This object is accessed through the built-in name ``None``.\n   It is used to signify the absence of a value in many situations,\n   e.g., it is returned from functions that don\'t explicitly return\n   anything. Its truth value is false.\n\nNotImplemented\n   This type has a single value.  There is a single object with this\n   value. This object is accessed through the built-in name\n   ``NotImplemented``. Numeric methods and rich comparison methods may\n   return this value if they do not implement the operation for the\n   operands provided.  (The interpreter will then try the reflected\n   operation, or some other fallback, depending on the operator.)  Its\n   truth value is true.\n\nEllipsis\n   This type has a single value.  There is a single object with this\n   value. This object is accessed through the literal ``...`` or the\n   built-in name ``Ellipsis``.  Its truth value is true.\n\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 two types of integers:\n\n      Integers (``int``)\n\n         These represent numbers in an unlimited range, subject to\n         available (virtual) memory only.  For the purpose of shift\n         and mask operations, a binary representation is assumed, and\n         negative numbers are represented in a variant of 2\'s\n         complement which gives the illusion of an infinite string of\n         sign bits extending to the left.\n\n      Booleans (``bool``)\n         These represent the truth values False and True.  The two\n         objects representing the values False and True are the only\n         Boolean objects. The Boolean type is a subtype of the integer\n         type, and Boolean values behave like the values 0 and 1,\n         respectively, in almost all contexts, the exception being\n         that when converted to a string, the strings ``"False"`` or\n         ``"True"`` are returned, respectively.\n\n      The rules for integer representation are intended to give the\n      most meaningful interpretation of shift and mask operations\n      involving negative integers.\n\n   ``numbers.Real`` (``float``)\n      These represent machine-level double precision floating point\n      numbers. You are at the mercy of the underlying machine\n      architecture (and C or Java implementation) for the accepted\n      range and handling of overflow. Python does not support single-\n      precision floating point numbers; the savings in processor and\n      memory usage that are usually the reason for using these is\n      dwarfed by the overhead of using objects in Python, so there is\n      no reason to complicate the language with two kinds of floating\n      point numbers.\n\n   ``numbers.Complex`` (``complex``)\n      These represent complex numbers as a pair of machine-level\n      double precision floating point numbers.  The same caveats apply\n      as for floating point numbers. The real and imaginary parts of a\n      complex number ``z`` can be retrieved through the read-only\n      attributes ``z.real`` and ``z.imag``.\n\nSequences\n   These represent finite ordered sets indexed by non-negative\n   numbers. The built-in function ``len()`` returns the number of\n   items of a sequence. When the length of a sequence is *n*, the\n   index set contains the numbers 0, 1, ..., *n*-1.  Item *i* of\n   sequence *a* is selected by ``a[i]``.\n\n   Sequences also support slicing: ``a[i:j]`` selects all items with\n   index *k* such that *i* ``<=`` *k* ``<`` *j*.  When used as an\n   expression, a slice is a sequence of the same type.  This implies\n   that the index set is renumbered so that it starts at 0.\n\n   Some sequences also support "extended slicing" with a third "step"\n   parameter: ``a[i:j:k]`` selects all items of *a* with index *x*\n   where ``x = i + n*k``, *n* ``>=`` ``0`` and *i* ``<=`` *x* ``<``\n   *j*.\n\n   Sequences are distinguished according to their mutability:\n\n   Immutable sequences\n      An object of an immutable sequence type cannot change once it is\n      created.  (If the object contains references to other objects,\n      these other objects may be mutable and may be changed; however,\n      the collection of objects directly referenced by an immutable\n      object cannot change.)\n\n      The following types are immutable sequences:\n\n      Strings\n         A string is a sequence of values that represent Unicode\n         codepoints. All the codepoints in range ``U+0000 - U+10FFFF``\n         can be represented in a string.  Python doesn\'t have a\n         ``chr`` type, and every character in the string is\n         represented as a string object with length ``1``.  The built-\n         in function ``ord()`` converts a character to its codepoint\n         (as an integer); ``chr()`` converts an integer in range ``0 -\n         10FFFF`` to the corresponding character. ``str.encode()`` can\n         be used to convert a ``str`` to ``bytes`` using the given\n         encoding, and ``bytes.decode()`` can be used to achieve the\n         opposite.\n\n      Tuples\n         The items of a tuple are arbitrary Python objects. Tuples of\n         two or more items are formed by comma-separated lists of\n         expressions.  A tuple of one item (a \'singleton\') can be\n         formed by affixing a comma to an expression (an expression by\n         itself does not create a tuple, since parentheses must be\n         usable for grouping of expressions).  An empty tuple can be\n         formed by an empty pair of parentheses.\n\n      Bytes\n         A bytes object is an immutable array.  The items are 8-bit\n         bytes, represented by integers in the range 0 <= x < 256.\n         Bytes literals (like ``b\'abc\'``) and the built-in function\n         ``bytes()`` can be used to construct bytes objects.  Also,\n         bytes objects can be decoded to strings via the ``decode()``\n         method.\n\n   Mutable sequences\n      Mutable sequences can be changed after they are created.  The\n      subscription and slicing notations can be used as the target of\n      assignment and ``del`` (delete) statements.\n\n      There are currently two intrinsic mutable sequence types:\n\n      Lists\n         The items of a list are arbitrary Python objects.  Lists are\n         formed by placing a comma-separated list of expressions in\n         square brackets. (Note that there are no special cases needed\n         to form lists of length 0 or 1.)\n\n      Byte Arrays\n         A bytearray object is a mutable array. They are created by\n         the built-in ``bytearray()`` constructor.  Aside from being\n         mutable (and hence unhashable), byte arrays otherwise provide\n         the same interface and functionality as immutable bytes\n         objects.\n\n      The extension module ``array`` provides an additional example of\n      a mutable sequence type, as does the ``collections`` module.\n\nSet types\n   These represent unordered, finite sets of unique, immutable\n   objects. As such, they cannot be indexed by any subscript. However,\n   they can be iterated over, and the built-in function ``len()``\n   returns the number of items in a set. Common uses for sets are fast\n   membership testing, removing duplicates from a sequence, and\n   computing mathematical operations such as intersection, union,\n   difference, and symmetric difference.\n\n   For set elements, the same immutability rules apply as for\n   dictionary keys. Note that numeric types obey the normal rules for\n   numeric comparison: if two numbers compare equal (e.g., ``1`` and\n   ``1.0``), only one of them can be contained in a set.\n\n   There are currently two intrinsic set types:\n\n   Sets\n      These represent a mutable set. They are created by the built-in\n      ``set()`` constructor and can be modified afterwards by several\n      methods, such as ``add()``.\n\n   Frozen sets\n      These represent an immutable set.  They are created by the\n      built-in ``frozenset()`` constructor.  As a frozenset is\n      immutable and *hashable*, it can be used again as an element of\n      another set, or as a dictionary key.\n\nMappings\n   These represent finite sets of objects indexed by arbitrary index\n   sets. The subscript notation ``a[k]`` selects the item indexed by\n   ``k`` from the mapping ``a``; this can be used in expressions and\n   as the target of assignments or ``del`` statements. The built-in\n   function ``len()`` returns the number of items in a mapping.\n\n   There is currently a single intrinsic mapping type:\n\n   Dictionaries\n      These represent finite sets of objects indexed by nearly\n      arbitrary values.  The only types of values not acceptable as\n      keys are values containing lists or dictionaries or other\n      mutable types that are compared by value rather than by object\n      identity, the reason being that the efficient implementation of\n      dictionaries requires a key\'s hash value to remain constant.\n      Numeric types used for keys obey the normal rules for numeric\n      comparison: if two numbers compare equal (e.g., ``1`` and\n      ``1.0``) then they can be used interchangeably to index the same\n      dictionary entry.\n\n      Dictionaries are mutable; they can be created by the ``{...}``\n      notation (see section *Dictionary displays*).\n\n      The extension modules ``dbm.ndbm`` and ``dbm.gnu`` provide\n      additional examples of mapping types, as does the\n      ``collections`` module.\n\nCallable types\n   These are the types to which the function call operation (see\n   section *Calls*) can be applied:\n\n   User-defined functions\n      A user-defined function object is created by a function\n      definition (see section *Function definitions*).  It should be\n      called with an argument list containing the same number of items\n      as the function\'s formal parameter list.\n\n      Special attributes:\n\n      +---------------------------+---------------------------------+-------------+\n      | Attribute                 | Meaning                         |             |\n      +===========================+=================================+=============+\n      | ``__doc__``               | The function\'s documentation    | Writable    |\n      |                           | string, or ``None`` if          |             |\n      |                           | unavailable                     |             |\n      +---------------------------+---------------------------------+-------------+\n      | ``__name__``              | The function\'s name             | Writable    |\n      +---------------------------+---------------------------------+-------------+\n      | ``__qualname__``          | The function\'s *qualified name* | Writable    |\n      |                           | New in version 3.3.             |             |\n      +---------------------------+---------------------------------+-------------+\n      | ``__module__``            | The name of the module the      | Writable    |\n      |                           | function was defined in, or     |             |\n      |                           | ``None`` if unavailable.        |             |\n      +---------------------------+---------------------------------+-------------+\n      | ``__defaults__``          | A tuple containing default      | Writable    |\n      |                           | argument values for those       |             |\n      |                           | arguments that have defaults,   |             |\n      |                           | or ``None`` if no arguments     |             |\n      |                           | have a default value            |             |\n      +---------------------------+---------------------------------+-------------+\n      | ``__code__``              | The code object representing    | Writable    |\n      |                           | the compiled function body.     |             |\n      +---------------------------+---------------------------------+-------------+\n      | ``__globals__``           | A reference to the dictionary   | Read-only   |\n      |                           | that holds the function\'s       |             |\n      |                           | global variables --- the global |             |\n      |                           | namespace of the module in      |             |\n      |                           | which the function was defined. |             |\n      +---------------------------+---------------------------------+-------------+\n      | ``__dict__``              | The namespace supporting        | Writable    |\n      |                           | arbitrary function attributes.  |             |\n      +---------------------------+---------------------------------+-------------+\n      | ``__closure__``           | ``None`` or a tuple of cells    | Read-only   |\n      |                           | that contain bindings for the   |             |\n      |                           | function\'s free variables.      |             |\n      +---------------------------+---------------------------------+-------------+\n      | ``__annotations__``       | A dict containing annotations   | Writable    |\n      |                           | of parameters.  The keys of the |             |\n      |                           | dict are the parameter names,   |             |\n      |                           | or ``\'return\'`` for the return  |             |\n      |                           | annotation, if provided.        |             |\n      +---------------------------+---------------------------------+-------------+\n      | ``__kwdefaults__``        | A dict containing defaults for  | Writable    |\n      |                           | keyword-only parameters.        |             |\n      +---------------------------+---------------------------------+-------------+\n\n      Most of the attributes labelled "Writable" check the type of the\n      assigned value.\n\n      Function objects also support getting and setting arbitrary\n      attributes, which can be used, for example, to attach metadata\n      to functions.  Regular attribute dot-notation is used to get and\n      set such attributes. *Note that the current implementation only\n      supports function attributes on user-defined functions. Function\n      attributes on built-in functions may be supported in the\n      future.*\n\n      Additional information about a function\'s definition can be\n      retrieved from its code object; see the description of internal\n      types below.\n\n   Instance methods\n      An instance method object combines a class, a class instance and\n      any callable object (normally a user-defined function).\n\n      Special read-only attributes: ``__self__`` is the class instance\n      object, ``__func__`` is the function object; ``__doc__`` is the\n      method\'s documentation (same as ``__func__.__doc__``);\n      ``__name__`` is the method name (same as ``__func__.__name__``);\n      ``__module__`` is the name of the module the method was defined\n      in, or ``None`` if unavailable.\n\n      Methods also support accessing (but not setting) the arbitrary\n      function attributes on the underlying function object.\n\n      User-defined method objects may be created when getting an\n      attribute of a class (perhaps via an instance of that class), if\n      that attribute is a user-defined function object or a class\n      method object.\n\n      When an instance method object is created by retrieving a user-\n      defined function object from a class via one of its instances,\n      its ``__self__`` attribute is the instance, and the method\n      object is said to be bound.  The new method\'s ``__func__``\n      attribute is the original function object.\n\n      When a user-defined method object is created by retrieving\n      another method object from a class or instance, the behaviour is\n      the same as for a function object, except that the ``__func__``\n      attribute of the new instance is not the original method object\n      but its ``__func__`` attribute.\n\n      When an instance method object is created by retrieving a class\n      method object from a class or instance, its ``__self__``\n      attribute is the class itself, and its ``__func__`` attribute is\n      the function object underlying the class method.\n\n      When an instance method object is called, the underlying\n      function (``__func__``) is called, inserting the class instance\n      (``__self__``) in front of the argument list.  For instance,\n      when ``C`` is a class which contains a definition for a function\n      ``f()``, and ``x`` is an instance of ``C``, calling ``x.f(1)``\n      is equivalent to calling ``C.f(x, 1)``.\n\n      When an instance method object is derived from a class method\n      object, the "class instance" stored in ``__self__`` will\n      actually be the class itself, so that calling either ``x.f(1)``\n      or ``C.f(1)`` is equivalent to calling ``f(C,1)`` where ``f`` is\n      the underlying function.\n\n      Note that the transformation from function object to instance\n      method object happens each time the attribute is retrieved from\n      the instance.  In some cases, a fruitful optimization is to\n      assign the attribute to a local variable and call that local\n      variable. Also notice that this transformation only happens for\n      user-defined functions; other callable objects (and all non-\n      callable objects) are retrieved without transformation.  It is\n      also important to note that user-defined functions which are\n      attributes of a class instance are not converted to bound\n      methods; this *only* happens when the function is an attribute\n      of the class.\n\n   Generator functions\n      A function or method which uses the ``yield`` statement (see\n      section *The yield statement*) is called a *generator function*.\n      Such a function, when called, always returns an iterator object\n      which can be used to execute the body of the function:  calling\n      the iterator\'s ``iterator__next__()`` method will cause the\n      function to execute until it provides a value using the\n      ``yield`` statement.  When the function executes a ``return``\n      statement or falls off the end, a ``StopIteration`` exception is\n      raised and the iterator will have reached the end of the set of\n      values to be returned.\n\n   Built-in functions\n      A built-in function object is a wrapper around a C function.\n      Examples of built-in functions are ``len()`` and ``math.sin()``\n      (``math`` is a standard built-in module). The number and type of\n      the arguments are determined by the C function. Special read-\n      only attributes: ``__doc__`` is the function\'s documentation\n      string, or ``None`` if unavailable; ``__name__`` is the\n      function\'s name; ``__self__`` is set to ``None`` (but see the\n      next item); ``__module__`` is the name of the module the\n      function was defined in or ``None`` if unavailable.\n\n   Built-in methods\n      This is really a different disguise of a built-in function, this\n      time containing an object passed to the C function as an\n      implicit extra argument.  An example of a built-in method is\n      ``alist.append()``, assuming *alist* is a list object. In this\n      case, the special read-only attribute ``__self__`` is set to the\n      object denoted by *alist*.\n\n   Classes\n      Classes are callable.  These objects normally act as factories\n      for new instances of themselves, but variations are possible for\n      class types that override ``__new__()``.  The arguments of the\n      call are passed to ``__new__()`` and, in the typical case, to\n      ``__init__()`` to initialize the new instance.\n\n   Class Instances\n      Instances of arbitrary classes can be made callable by defining\n      a ``__call__()`` method in their class.\n\nModules\n   Modules are a basic organizational unit of Python code, and are\n   created by the *import system* as invoked either by the ``import``\n   statement (see ``import``), or by calling functions such as\n   ``importlib.import_module()`` and built-in ``__import__()``.  A\n   module object has a namespace implemented by a dictionary object\n   (this is the dictionary referenced by the ``__globals__`` attribute\n   of functions defined in the module).  Attribute references are\n   translated to lookups in this dictionary, e.g., ``m.x`` is\n   equivalent to ``m.__dict__["x"]``. A module object does not contain\n   the code object used to initialize the module (since it isn\'t\n   needed once the initialization is done).\n\n   Attribute assignment updates the module\'s namespace dictionary,\n   e.g., ``m.x = 1`` is equivalent to ``m.__dict__["x"] = 1``.\n\n   Special read-only attribute: ``__dict__`` is the module\'s namespace\n   as a dictionary object.\n\n   **CPython implementation detail:** Because of the way CPython\n   clears module dictionaries, the module dictionary will be cleared\n   when the module falls out of scope even if the dictionary still has\n   live references.  To avoid this, copy the dictionary or keep the\n   module around while using its dictionary directly.\n\n   Predefined (writable) attributes: ``__name__`` is the module\'s\n   name; ``__doc__`` is the module\'s documentation string, or ``None``\n   if unavailable; ``__file__`` is the pathname of the file from which\n   the module was loaded, if it was loaded from a file. The\n   ``__file__`` attribute may be missing for certain types of modules,\n   such as C modules that are statically linked into the interpreter;\n   for extension modules loaded dynamically from a shared library, it\n   is the pathname of the shared library file.\n\nCustom classes\n   Custom class types are typically created by class definitions (see\n   section *Class definitions*).  A class has a namespace implemented\n   by a dictionary object. Class attribute references are translated\n   to lookups in this dictionary, e.g., ``C.x`` is translated to\n   ``C.__dict__["x"]`` (although there are a number of hooks which\n   allow for other means of locating attributes). When the attribute\n   name is not found there, the attribute search continues in the base\n   classes. This search of the base classes uses the C3 method\n   resolution order which behaves correctly even in the presence of\n   \'diamond\' inheritance structures where there are multiple\n   inheritance paths leading back to a common ancestor. Additional\n   details on the C3 MRO used by Python can be found in the\n   documentation accompanying the 2.3 release at\n   http://www.python.org/download/releases/2.3/mro/.\n\n   When a class attribute reference (for class ``C``, say) would yield\n   a class method object, it is transformed into an instance method\n   object whose ``__self__`` attributes is ``C``.  When it would yield\n   a static method object, it is transformed into the object wrapped\n   by the static method object. See section *Implementing Descriptors*\n   for another way in which attributes retrieved from a class may\n   differ from those actually contained in its ``__dict__``.\n\n   Class attribute assignments update the class\'s dictionary, never\n   the dictionary of a base class.\n\n   A class object can be called (see above) to yield a class instance\n   (see below).\n\n   Special attributes: ``__name__`` is the class name; ``__module__``\n   is the module name in which the class was defined; ``__dict__`` is\n   the dictionary containing the class\'s namespace; ``__bases__`` is a\n   tuple (possibly empty or a singleton) containing the base classes,\n   in the order of their occurrence in the base class list;\n   ``__doc__`` is the class\'s documentation string, or None if\n   undefined.\n\nClass instances\n   A class instance is created by calling a class object (see above).\n   A class instance has a namespace implemented as a dictionary which\n   is the first place in which attribute references are searched.\n   When an attribute is not found there, and the instance\'s class has\n   an attribute by that name, the search continues with the class\n   attributes.  If a class attribute is found that is a user-defined\n   function object, it is transformed into an instance method object\n   whose ``__self__`` attribute is the instance.  Static method and\n   class method objects are also transformed; see above under\n   "Classes".  See section *Implementing Descriptors* for another way\n   in which attributes of a class retrieved via its instances may\n   differ from the objects actually stored in the class\'s\n   ``__dict__``.  If no class attribute is found, and the object\'s\n   class has a ``__getattr__()`` method, that is called to satisfy the\n   lookup.\n\n   Attribute assignments and deletions update the instance\'s\n   dictionary, never a class\'s dictionary.  If the class has a\n   ``__setattr__()`` or ``__delattr__()`` method, this is called\n   instead of updating the instance dictionary directly.\n\n   Class instances can pretend to be numbers, sequences, or mappings\n   if they have methods with certain special names.  See section\n   *Special method names*.\n\n   Special attributes: ``__dict__`` is the attribute dictionary;\n   ``__class__`` is the instance\'s class.\n\nI/O objects (also known as file objects)\n   A *file object* represents an open file.  Various shortcuts are\n   available to create file objects: the ``open()`` built-in function,\n   and also ``os.popen()``, ``os.fdopen()``, and the ``makefile()``\n   method of socket objects (and perhaps by other functions or methods\n   provided by extension modules).\n\n   The objects ``sys.stdin``, ``sys.stdout`` and ``sys.stderr`` are\n   initialized to file objects corresponding to the interpreter\'s\n   standard input, output and error streams; they are all open in text\n   mode and therefore follow the interface defined by the\n   ``io.TextIOBase`` abstract class.\n\nInternal types\n   A few types used internally by the interpreter are exposed to the\n   user. Their definitions may change with future versions of the\n   interpreter, but they are mentioned here for completeness.\n\n   Code objects\n      Code objects represent *byte-compiled* executable Python code,\n      or *bytecode*. The difference between a code object and a\n      function object is that the function object contains an explicit\n      reference to the function\'s globals (the module in which it was\n      defined), while a code object contains no context; also the\n      default argument values are stored in the function object, not\n      in the code object (because they represent values calculated at\n      run-time).  Unlike function objects, code objects are immutable\n      and contain no references (directly or indirectly) to mutable\n      objects.\n\n      Special read-only attributes: ``co_name`` gives the function\n      name; ``co_argcount`` is the number of positional arguments\n      (including arguments with default values); ``co_nlocals`` is the\n      number of local variables used by the function (including\n      arguments); ``co_varnames`` is a tuple containing the names of\n      the local variables (starting with the argument names);\n      ``co_cellvars`` is a tuple containing the names of local\n      variables that are referenced by nested functions;\n      ``co_freevars`` is a tuple containing the names of free\n      variables; ``co_code`` is a string representing the sequence of\n      bytecode instructions; ``co_consts`` is a tuple containing the\n      literals used by the bytecode; ``co_names`` is a tuple\n      containing the names used by the bytecode; ``co_filename`` is\n      the filename from which the code was compiled;\n      ``co_firstlineno`` is the first line number of the function;\n      ``co_lnotab`` is a string encoding the mapping from bytecode\n      offsets to line numbers (for details see the source code of the\n      interpreter); ``co_stacksize`` is the required stack size\n      (including local variables); ``co_flags`` is an integer encoding\n      a number of flags for the interpreter.\n\n      The following flag bits are defined for ``co_flags``: bit\n      ``0x04`` is set if the function uses the ``*arguments`` syntax\n      to accept an arbitrary number of positional arguments; bit\n      ``0x08`` is set if the function uses the ``**keywords`` syntax\n      to accept arbitrary keyword arguments; bit ``0x20`` is set if\n      the function is a generator.\n\n      Future feature declarations (``from __future__ import\n      division``) also use bits in ``co_flags`` to indicate whether a\n      code object was compiled with a particular feature enabled: bit\n      ``0x2000`` is set if the function was compiled with future\n      division enabled; bits ``0x10`` and ``0x1000`` were used in\n      earlier versions of Python.\n\n      Other bits in ``co_flags`` are reserved for internal use.\n\n      If a code object represents a function, the first item in\n      ``co_consts`` is the documentation string of the function, or\n      ``None`` if undefined.\n\n   Frame objects\n      Frame objects represent execution frames.  They may occur in\n      traceback objects (see below).\n\n      Special read-only attributes: ``f_back`` is to the previous\n      stack frame (towards the caller), or ``None`` if this is the\n      bottom stack frame; ``f_code`` is the code object being executed\n      in this frame; ``f_locals`` is the dictionary used to look up\n      local variables; ``f_globals`` is used for global variables;\n      ``f_builtins`` is used for built-in (intrinsic) names;\n      ``f_lasti`` gives the precise instruction (this is an index into\n      the bytecode string of the code object).\n\n      Special writable attributes: ``f_trace``, if not ``None``, is a\n      function called at the start of each source code line (this is\n      used by the debugger); ``f_lineno`` is the current line number\n      of the frame --- writing to this from within a trace function\n      jumps to the given line (only for the bottom-most frame).  A\n      debugger can implement a Jump command (aka Set Next Statement)\n      by writing to f_lineno.\n\n   Traceback objects\n      Traceback objects represent a stack trace of an exception.  A\n      traceback object is created when an exception occurs.  When the\n      search for an exception handler unwinds the execution stack, at\n      each unwound level a traceback object is inserted in front of\n      the current traceback.  When an exception handler is entered,\n      the stack trace is made available to the program. (See section\n      *The try statement*.) It is accessible as the third item of the\n      tuple returned by ``sys.exc_info()``. When the program contains\n      no suitable handler, the stack trace is written (nicely\n      formatted) to the standard error stream; if the interpreter is\n      interactive, it is also made available to the user as\n      ``sys.last_traceback``.\n\n      Special read-only attributes: ``tb_next`` is the next level in\n      the stack trace (towards the frame where the exception\n      occurred), or ``None`` if there is no next level; ``tb_frame``\n      points to the execution frame of the current level;\n      ``tb_lineno`` gives the line number where the exception\n      occurred; ``tb_lasti`` indicates the precise instruction.  The\n      line number and last instruction in the traceback may differ\n      from the line number of its frame object if the exception\n      occurred in a ``try`` statement with no matching except clause\n      or with a finally clause.\n\n   Slice objects\n      Slice objects are used to represent slices for ``__getitem__()``\n      methods.  They are also created by the built-in ``slice()``\n      function.\n\n      Special read-only attributes: ``start`` is the lower bound;\n      ``stop`` is the upper bound; ``step`` is the step value; each is\n      ``None`` if omitted. These attributes can have any type.\n\n      Slice objects support one method:\n\n      slice.indices(self, length)\n\n         This method takes a single integer argument *length* and\n         computes information about the slice that the slice object\n         would describe if applied to a sequence of *length* items.\n         It returns a tuple of three integers; respectively these are\n         the *start* and *stop* indices and the *step* or stride\n         length of the slice. Missing or out-of-bounds indices are\n         handled in a manner consistent with regular slices.\n\n   Static method objects\n      Static method objects provide a way of defeating the\n      transformation of function objects to method objects described\n      above. A static method object is a wrapper around any other\n      object, usually a user-defined method object. When a static\n      method object is retrieved from a class or a class instance, the\n      object actually returned is the wrapped object, which is not\n      subject to any further transformation. Static method objects are\n      not themselves callable, although the objects they wrap usually\n      are. Static method objects are created by the built-in\n      ``staticmethod()`` constructor.\n\n   Class method objects\n      A class method object, like a static method object, is a wrapper\n      around another object that alters the way in which that object\n      is retrieved from classes and class instances. The behaviour of\n      class method objects upon such retrieval is described above,\n      under "User-defined methods". Class method objects are created\n      by the built-in ``classmethod()`` constructor.\n',
+ 'types': '\nThe standard type hierarchy\n***************************\n\nBelow is a list of the types that are built into Python.  Extension\nmodules (written in C, Java, or other languages, depending on the\nimplementation) can define additional types.  Future versions of\nPython may add types to the type hierarchy (e.g., rational numbers,\nefficiently stored arrays of integers, etc.), although such additions\nwill often be provided via the standard library instead.\n\nSome of the type descriptions below contain a paragraph listing\n\'special attributes.\'  These are attributes that provide access to the\nimplementation and are not intended for general use.  Their definition\nmay change in the future.\n\nNone\n   This type has a single value.  There is a single object with this\n   value. This object is accessed through the built-in name ``None``.\n   It is used to signify the absence of a value in many situations,\n   e.g., it is returned from functions that don\'t explicitly return\n   anything. Its truth value is false.\n\nNotImplemented\n   This type has a single value.  There is a single object with this\n   value. This object is accessed through the built-in name\n   ``NotImplemented``. Numeric methods and rich comparison methods may\n   return this value if they do not implement the operation for the\n   operands provided.  (The interpreter will then try the reflected\n   operation, or some other fallback, depending on the operator.)  Its\n   truth value is true.\n\nEllipsis\n   This type has a single value.  There is a single object with this\n   value. This object is accessed through the literal ``...`` or the\n   built-in name ``Ellipsis``.  Its truth value is true.\n\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 two types of integers:\n\n      Integers (``int``)\n\n         These represent numbers in an unlimited range, subject to\n         available (virtual) memory only.  For the purpose of shift\n         and mask operations, a binary representation is assumed, and\n         negative numbers are represented in a variant of 2\'s\n         complement which gives the illusion of an infinite string of\n         sign bits extending to the left.\n\n      Booleans (``bool``)\n         These represent the truth values False and True.  The two\n         objects representing the values False and True are the only\n         Boolean objects. The Boolean type is a subtype of the integer\n         type, and Boolean values behave like the values 0 and 1,\n         respectively, in almost all contexts, the exception being\n         that when converted to a string, the strings ``"False"`` or\n         ``"True"`` are returned, respectively.\n\n      The rules for integer representation are intended to give the\n      most meaningful interpretation of shift and mask operations\n      involving negative integers.\n\n   ``numbers.Real`` (``float``)\n      These represent machine-level double precision floating point\n      numbers. You are at the mercy of the underlying machine\n      architecture (and C or Java implementation) for the accepted\n      range and handling of overflow. Python does not support single-\n      precision floating point numbers; the savings in processor and\n      memory usage that are usually the reason for using these is\n      dwarfed by the overhead of using objects in Python, so there is\n      no reason to complicate the language with two kinds of floating\n      point numbers.\n\n   ``numbers.Complex`` (``complex``)\n      These represent complex numbers as a pair of machine-level\n      double precision floating point numbers.  The same caveats apply\n      as for floating point numbers. The real and imaginary parts of a\n      complex number ``z`` can be retrieved through the read-only\n      attributes ``z.real`` and ``z.imag``.\n\nSequences\n   These represent finite ordered sets indexed by non-negative\n   numbers. The built-in function ``len()`` returns the number of\n   items of a sequence. When the length of a sequence is *n*, the\n   index set contains the numbers 0, 1, ..., *n*-1.  Item *i* of\n   sequence *a* is selected by ``a[i]``.\n\n   Sequences also support slicing: ``a[i:j]`` selects all items with\n   index *k* such that *i* ``<=`` *k* ``<`` *j*.  When used as an\n   expression, a slice is a sequence of the same type.  This implies\n   that the index set is renumbered so that it starts at 0.\n\n   Some sequences also support "extended slicing" with a third "step"\n   parameter: ``a[i:j:k]`` selects all items of *a* with index *x*\n   where ``x = i + n*k``, *n* ``>=`` ``0`` and *i* ``<=`` *x* ``<``\n   *j*.\n\n   Sequences are distinguished according to their mutability:\n\n   Immutable sequences\n      An object of an immutable sequence type cannot change once it is\n      created.  (If the object contains references to other objects,\n      these other objects may be mutable and may be changed; however,\n      the collection of objects directly referenced by an immutable\n      object cannot change.)\n\n      The following types are immutable sequences:\n\n      Strings\n         A string is a sequence of values that represent Unicode\n         codepoints. All the codepoints in range ``U+0000 - U+10FFFF``\n         can be represented in a string.  Python doesn\'t have a\n         ``chr`` type, and every character in the string is\n         represented as a string object with length ``1``.  The built-\n         in function ``ord()`` converts a character to its codepoint\n         (as an integer); ``chr()`` converts an integer in range ``0 -\n         10FFFF`` to the corresponding character. ``str.encode()`` can\n         be used to convert a ``str`` to ``bytes`` using the given\n         encoding, and ``bytes.decode()`` can be used to achieve the\n         opposite.\n\n      Tuples\n         The items of a tuple are arbitrary Python objects. Tuples of\n         two or more items are formed by comma-separated lists of\n         expressions.  A tuple of one item (a \'singleton\') can be\n         formed by affixing a comma to an expression (an expression by\n         itself does not create a tuple, since parentheses must be\n         usable for grouping of expressions).  An empty tuple can be\n         formed by an empty pair of parentheses.\n\n      Bytes\n         A bytes object is an immutable array.  The items are 8-bit\n         bytes, represented by integers in the range 0 <= x < 256.\n         Bytes literals (like ``b\'abc\'``) and the built-in function\n         ``bytes()`` can be used to construct bytes objects.  Also,\n         bytes objects can be decoded to strings via the ``decode()``\n         method.\n\n   Mutable sequences\n      Mutable sequences can be changed after they are created.  The\n      subscription and slicing notations can be used as the target of\n      assignment and ``del`` (delete) statements.\n\n      There are currently two intrinsic mutable sequence types:\n\n      Lists\n         The items of a list are arbitrary Python objects.  Lists are\n         formed by placing a comma-separated list of expressions in\n         square brackets. (Note that there are no special cases needed\n         to form lists of length 0 or 1.)\n\n      Byte Arrays\n         A bytearray object is a mutable array. They are created by\n         the built-in ``bytearray()`` constructor.  Aside from being\n         mutable (and hence unhashable), byte arrays otherwise provide\n         the same interface and functionality as immutable bytes\n         objects.\n\n      The extension module ``array`` provides an additional example of\n      a mutable sequence type, as does the ``collections`` module.\n\nSet types\n   These represent unordered, finite sets of unique, immutable\n   objects. As such, they cannot be indexed by any subscript. However,\n   they can be iterated over, and the built-in function ``len()``\n   returns the number of items in a set. Common uses for sets are fast\n   membership testing, removing duplicates from a sequence, and\n   computing mathematical operations such as intersection, union,\n   difference, and symmetric difference.\n\n   For set elements, the same immutability rules apply as for\n   dictionary keys. Note that numeric types obey the normal rules for\n   numeric comparison: if two numbers compare equal (e.g., ``1`` and\n   ``1.0``), only one of them can be contained in a set.\n\n   There are currently two intrinsic set types:\n\n   Sets\n      These represent a mutable set. They are created by the built-in\n      ``set()`` constructor and can be modified afterwards by several\n      methods, such as ``add()``.\n\n   Frozen sets\n      These represent an immutable set.  They are created by the\n      built-in ``frozenset()`` constructor.  As a frozenset is\n      immutable and *hashable*, it can be used again as an element of\n      another set, or as a dictionary key.\n\nMappings\n   These represent finite sets of objects indexed by arbitrary index\n   sets. The subscript notation ``a[k]`` selects the item indexed by\n   ``k`` from the mapping ``a``; this can be used in expressions and\n   as the target of assignments or ``del`` statements. The built-in\n   function ``len()`` returns the number of items in a mapping.\n\n   There is currently a single intrinsic mapping type:\n\n   Dictionaries\n      These represent finite sets of objects indexed by nearly\n      arbitrary values.  The only types of values not acceptable as\n      keys are values containing lists or dictionaries or other\n      mutable types that are compared by value rather than by object\n      identity, the reason being that the efficient implementation of\n      dictionaries requires a key\'s hash value to remain constant.\n      Numeric types used for keys obey the normal rules for numeric\n      comparison: if two numbers compare equal (e.g., ``1`` and\n      ``1.0``) then they can be used interchangeably to index the same\n      dictionary entry.\n\n      Dictionaries are mutable; they can be created by the ``{...}``\n      notation (see section *Dictionary displays*).\n\n      The extension modules ``dbm.ndbm`` and ``dbm.gnu`` provide\n      additional examples of mapping types, as does the\n      ``collections`` module.\n\nCallable types\n   These are the types to which the function call operation (see\n   section *Calls*) can be applied:\n\n   User-defined functions\n      A user-defined function object is created by a function\n      definition (see section *Function definitions*).  It should be\n      called with an argument list containing the same number of items\n      as the function\'s formal parameter list.\n\n      Special attributes:\n\n      +---------------------------+---------------------------------+-------------+\n      | Attribute                 | Meaning                         |             |\n      +===========================+=================================+=============+\n      | ``__doc__``               | The function\'s documentation    | Writable    |\n      |                           | string, or ``None`` if          |             |\n      |                           | unavailable                     |             |\n      +---------------------------+---------------------------------+-------------+\n      | ``__name__``              | The function\'s name             | Writable    |\n      +---------------------------+---------------------------------+-------------+\n      | ``__qualname__``          | The function\'s *qualified name* | Writable    |\n      |                           | New in version 3.3.             |             |\n      +---------------------------+---------------------------------+-------------+\n      | ``__module__``            | The name of the module the      | Writable    |\n      |                           | function was defined in, or     |             |\n      |                           | ``None`` if unavailable.        |             |\n      +---------------------------+---------------------------------+-------------+\n      | ``__defaults__``          | A tuple containing default      | Writable    |\n      |                           | argument values for those       |             |\n      |                           | arguments that have defaults,   |             |\n      |                           | or ``None`` if no arguments     |             |\n      |                           | have a default value            |             |\n      +---------------------------+---------------------------------+-------------+\n      | ``__code__``              | The code object representing    | Writable    |\n      |                           | the compiled function body.     |             |\n      +---------------------------+---------------------------------+-------------+\n      | ``__globals__``           | A reference to the dictionary   | Read-only   |\n      |                           | that holds the function\'s       |             |\n      |                           | global variables --- the global |             |\n      |                           | namespace of the module in      |             |\n      |                           | which the function was defined. |             |\n      +---------------------------+---------------------------------+-------------+\n      | ``__dict__``              | The namespace supporting        | Writable    |\n      |                           | arbitrary function attributes.  |             |\n      +---------------------------+---------------------------------+-------------+\n      | ``__closure__``           | ``None`` or a tuple of cells    | Read-only   |\n      |                           | that contain bindings for the   |             |\n      |                           | function\'s free variables.      |             |\n      +---------------------------+---------------------------------+-------------+\n      | ``__annotations__``       | A dict containing annotations   | Writable    |\n      |                           | of parameters.  The keys of the |             |\n      |                           | dict are the parameter names,   |             |\n      |                           | or ``\'return\'`` for the return  |             |\n      |                           | annotation, if provided.        |             |\n      +---------------------------+---------------------------------+-------------+\n      | ``__kwdefaults__``        | A dict containing defaults for  | Writable    |\n      |                           | keyword-only parameters.        |             |\n      +---------------------------+---------------------------------+-------------+\n\n      Most of the attributes labelled "Writable" check the type of the\n      assigned value.\n\n      Function objects also support getting and setting arbitrary\n      attributes, which can be used, for example, to attach metadata\n      to functions.  Regular attribute dot-notation is used to get and\n      set such attributes. *Note that the current implementation only\n      supports function attributes on user-defined functions. Function\n      attributes on built-in functions may be supported in the\n      future.*\n\n      Additional information about a function\'s definition can be\n      retrieved from its code object; see the description of internal\n      types below.\n\n   Instance methods\n      An instance method object combines a class, a class instance and\n      any callable object (normally a user-defined function).\n\n      Special read-only attributes: ``__self__`` is the class instance\n      object, ``__func__`` is the function object; ``__doc__`` is the\n      method\'s documentation (same as ``__func__.__doc__``);\n      ``__name__`` is the method name (same as ``__func__.__name__``);\n      ``__module__`` is the name of the module the method was defined\n      in, or ``None`` if unavailable.\n\n      Methods also support accessing (but not setting) the arbitrary\n      function attributes on the underlying function object.\n\n      User-defined method objects may be created when getting an\n      attribute of a class (perhaps via an instance of that class), if\n      that attribute is a user-defined function object or a class\n      method object.\n\n      When an instance method object is created by retrieving a user-\n      defined function object from a class via one of its instances,\n      its ``__self__`` attribute is the instance, and the method\n      object is said to be bound.  The new method\'s ``__func__``\n      attribute is the original function object.\n\n      When a user-defined method object is created by retrieving\n      another method object from a class or instance, the behaviour is\n      the same as for a function object, except that the ``__func__``\n      attribute of the new instance is not the original method object\n      but its ``__func__`` attribute.\n\n      When an instance method object is created by retrieving a class\n      method object from a class or instance, its ``__self__``\n      attribute is the class itself, and its ``__func__`` attribute is\n      the function object underlying the class method.\n\n      When an instance method object is called, the underlying\n      function (``__func__``) is called, inserting the class instance\n      (``__self__``) in front of the argument list.  For instance,\n      when ``C`` is a class which contains a definition for a function\n      ``f()``, and ``x`` is an instance of ``C``, calling ``x.f(1)``\n      is equivalent to calling ``C.f(x, 1)``.\n\n      When an instance method object is derived from a class method\n      object, the "class instance" stored in ``__self__`` will\n      actually be the class itself, so that calling either ``x.f(1)``\n      or ``C.f(1)`` is equivalent to calling ``f(C,1)`` where ``f`` is\n      the underlying function.\n\n      Note that the transformation from function object to instance\n      method object happens each time the attribute is retrieved from\n      the instance.  In some cases, a fruitful optimization is to\n      assign the attribute to a local variable and call that local\n      variable. Also notice that this transformation only happens for\n      user-defined functions; other callable objects (and all non-\n      callable objects) are retrieved without transformation.  It is\n      also important to note that user-defined functions which are\n      attributes of a class instance are not converted to bound\n      methods; this *only* happens when the function is an attribute\n      of the class.\n\n   Generator functions\n      A function or method which uses the ``yield`` statement (see\n      section *The yield statement*) is called a *generator function*.\n      Such a function, when called, always returns an iterator object\n      which can be used to execute the body of the function:  calling\n      the iterator\'s ``iterator__next__()`` method will cause the\n      function to execute until it provides a value using the\n      ``yield`` statement.  When the function executes a ``return``\n      statement or falls off the end, a ``StopIteration`` exception is\n      raised and the iterator will have reached the end of the set of\n      values to be returned.\n\n   Built-in functions\n      A built-in function object is a wrapper around a C function.\n      Examples of built-in functions are ``len()`` and ``math.sin()``\n      (``math`` is a standard built-in module). The number and type of\n      the arguments are determined by the C function. Special read-\n      only attributes: ``__doc__`` is the function\'s documentation\n      string, or ``None`` if unavailable; ``__name__`` is the\n      function\'s name; ``__self__`` is set to ``None`` (but see the\n      next item); ``__module__`` is the name of the module the\n      function was defined in or ``None`` if unavailable.\n\n   Built-in methods\n      This is really a different disguise of a built-in function, this\n      time containing an object passed to the C function as an\n      implicit extra argument.  An example of a built-in method is\n      ``alist.append()``, assuming *alist* is a list object. In this\n      case, the special read-only attribute ``__self__`` is set to the\n      object denoted by *alist*.\n\n   Classes\n      Classes are callable.  These objects normally act as factories\n      for new instances of themselves, but variations are possible for\n      class types that override ``__new__()``.  The arguments of the\n      call are passed to ``__new__()`` and, in the typical case, to\n      ``__init__()`` to initialize the new instance.\n\n   Class Instances\n      Instances of arbitrary classes can be made callable by defining\n      a ``__call__()`` method in their class.\n\nModules\n   Modules are a basic organizational unit of Python code, and are\n   created by the *import system* as invoked either by the ``import``\n   statement (see ``import``), or by calling functions such as\n   ``importlib.import_module()`` and built-in ``__import__()``.  A\n   module object has a namespace implemented by a dictionary object\n   (this is the dictionary referenced by the ``__globals__`` attribute\n   of functions defined in the module).  Attribute references are\n   translated to lookups in this dictionary, e.g., ``m.x`` is\n   equivalent to ``m.__dict__["x"]``. A module object does not contain\n   the code object used to initialize the module (since it isn\'t\n   needed once the initialization is done).\n\n   Attribute assignment updates the module\'s namespace dictionary,\n   e.g., ``m.x = 1`` is equivalent to ``m.__dict__["x"] = 1``.\n\n   Special read-only attribute: ``__dict__`` is the module\'s namespace\n   as a dictionary object.\n\n   **CPython implementation detail:** Because of the way CPython\n   clears module dictionaries, the module dictionary will be cleared\n   when the module falls out of scope even if the dictionary still has\n   live references.  To avoid this, copy the dictionary or keep the\n   module around while using its dictionary directly.\n\n   Predefined (writable) attributes: ``__name__`` is the module\'s\n   name; ``__doc__`` is the module\'s documentation string, or ``None``\n   if unavailable; ``__file__`` is the pathname of the file from which\n   the module was loaded, if it was loaded from a file. The\n   ``__file__`` attribute may be missing for certain types of modules,\n   such as C modules that are statically linked into the interpreter;\n   for extension modules loaded dynamically from a shared library, it\n   is the pathname of the shared library file.\n\nCustom classes\n   Custom class types are typically created by class definitions (see\n   section *Class definitions*).  A class has a namespace implemented\n   by a dictionary object. Class attribute references are translated\n   to lookups in this dictionary, e.g., ``C.x`` is translated to\n   ``C.__dict__["x"]`` (although there are a number of hooks which\n   allow for other means of locating attributes). When the attribute\n   name is not found there, the attribute search continues in the base\n   classes. This search of the base classes uses the C3 method\n   resolution order which behaves correctly even in the presence of\n   \'diamond\' inheritance structures where there are multiple\n   inheritance paths leading back to a common ancestor. Additional\n   details on the C3 MRO used by Python can be found in the\n   documentation accompanying the 2.3 release at\n   http://www.python.org/download/releases/2.3/mro/.\n\n   When a class attribute reference (for class ``C``, say) would yield\n   a class method object, it is transformed into an instance method\n   object whose ``__self__`` attributes is ``C``.  When it would yield\n   a static method object, it is transformed into the object wrapped\n   by the static method object. See section *Implementing Descriptors*\n   for another way in which attributes retrieved from a class may\n   differ from those actually contained in its ``__dict__``.\n\n   Class attribute assignments update the class\'s dictionary, never\n   the dictionary of a base class.\n\n   A class object can be called (see above) to yield a class instance\n   (see below).\n\n   Special attributes: ``__name__`` is the class name; ``__module__``\n   is the module name in which the class was defined; ``__dict__`` is\n   the dictionary containing the class\'s namespace; ``__bases__`` is a\n   tuple (possibly empty or a singleton) containing the base classes,\n   in the order of their occurrence in the base class list;\n   ``__doc__`` is the class\'s documentation string, or None if\n   undefined.\n\nClass instances\n   A class instance is created by calling a class object (see above).\n   A class instance has a namespace implemented as a dictionary which\n   is the first place in which attribute references are searched.\n   When an attribute is not found there, and the instance\'s class has\n   an attribute by that name, the search continues with the class\n   attributes.  If a class attribute is found that is a user-defined\n   function object, it is transformed into an instance method object\n   whose ``__self__`` attribute is the instance.  Static method and\n   class method objects are also transformed; see above under\n   "Classes".  See section *Implementing Descriptors* for another way\n   in which attributes of a class retrieved via its instances may\n   differ from the objects actually stored in the class\'s\n   ``__dict__``.  If no class attribute is found, and the object\'s\n   class has a ``__getattr__()`` method, that is called to satisfy the\n   lookup.\n\n   Attribute assignments and deletions update the instance\'s\n   dictionary, never a class\'s dictionary.  If the class has a\n   ``__setattr__()`` or ``__delattr__()`` method, this is called\n   instead of updating the instance dictionary directly.\n\n   Class instances can pretend to be numbers, sequences, or mappings\n   if they have methods with certain special names.  See section\n   *Special method names*.\n\n   Special attributes: ``__dict__`` is the attribute dictionary;\n   ``__class__`` is the instance\'s class.\n\nI/O objects (also known as file objects)\n   A *file object* represents an open file.  Various shortcuts are\n   available to create file objects: the ``open()`` built-in function,\n   and also ``os.popen()``, ``os.fdopen()``, and the ``makefile()``\n   method of socket objects (and perhaps by other functions or methods\n   provided by extension modules).\n\n   The objects ``sys.stdin``, ``sys.stdout`` and ``sys.stderr`` are\n   initialized to file objects corresponding to the interpreter\'s\n   standard input, output and error streams; they are all open in text\n   mode and therefore follow the interface defined by the\n   ``io.TextIOBase`` abstract class.\n\nInternal types\n   A few types used internally by the interpreter are exposed to the\n   user. Their definitions may change with future versions of the\n   interpreter, but they are mentioned here for completeness.\n\n   Code objects\n      Code objects represent *byte-compiled* executable Python code,\n      or *bytecode*. The difference between a code object and a\n      function object is that the function object contains an explicit\n      reference to the function\'s globals (the module in which it was\n      defined), while a code object contains no context; also the\n      default argument values are stored in the function object, not\n      in the code object (because they represent values calculated at\n      run-time).  Unlike function objects, code objects are immutable\n      and contain no references (directly or indirectly) to mutable\n      objects.\n\n      Special read-only attributes: ``co_name`` gives the function\n      name; ``co_argcount`` is the number of positional arguments\n      (including arguments with default values); ``co_nlocals`` is the\n      number of local variables used by the function (including\n      arguments); ``co_varnames`` is a tuple containing the names of\n      the local variables (starting with the argument names);\n      ``co_cellvars`` is a tuple containing the names of local\n      variables that are referenced by nested functions;\n      ``co_freevars`` is a tuple containing the names of free\n      variables; ``co_code`` is a string representing the sequence of\n      bytecode instructions; ``co_consts`` is a tuple containing the\n      literals used by the bytecode; ``co_names`` is a tuple\n      containing the names used by the bytecode; ``co_filename`` is\n      the filename from which the code was compiled;\n      ``co_firstlineno`` is the first line number of the function;\n      ``co_lnotab`` is a string encoding the mapping from bytecode\n      offsets to line numbers (for details see the source code of the\n      interpreter); ``co_stacksize`` is the required stack size\n      (including local variables); ``co_flags`` is an integer encoding\n      a number of flags for the interpreter.\n\n      The following flag bits are defined for ``co_flags``: bit\n      ``0x04`` is set if the function uses the ``*arguments`` syntax\n      to accept an arbitrary number of positional arguments; bit\n      ``0x08`` is set if the function uses the ``**keywords`` syntax\n      to accept arbitrary keyword arguments; bit ``0x20`` is set if\n      the function is a generator.\n\n      Future feature declarations (``from __future__ import\n      division``) also use bits in ``co_flags`` to indicate whether a\n      code object was compiled with a particular feature enabled: bit\n      ``0x2000`` is set if the function was compiled with future\n      division enabled; bits ``0x10`` and ``0x1000`` were used in\n      earlier versions of Python.\n\n      Other bits in ``co_flags`` are reserved for internal use.\n\n      If a code object represents a function, the first item in\n      ``co_consts`` is the documentation string of the function, or\n      ``None`` if undefined.\n\n   Frame objects\n      Frame objects represent execution frames.  They may occur in\n      traceback objects (see below).\n\n      Special read-only attributes: ``f_back`` is to the previous\n      stack frame (towards the caller), or ``None`` if this is the\n      bottom stack frame; ``f_code`` is the code object being executed\n      in this frame; ``f_locals`` is the dictionary used to look up\n      local variables; ``f_globals`` is used for global variables;\n      ``f_builtins`` is used for built-in (intrinsic) names;\n      ``f_lasti`` gives the precise instruction (this is an index into\n      the bytecode string of the code object).\n\n      Special writable attributes: ``f_trace``, if not ``None``, is a\n      function called at the start of each source code line (this is\n      used by the debugger); ``f_lineno`` is the current line number\n      of the frame --- writing to this from within a trace function\n      jumps to the given line (only for the bottom-most frame).  A\n      debugger can implement a Jump command (aka Set Next Statement)\n      by writing to f_lineno.\n\n      Frame objects support one method:\n\n      frame.clear()\n\n         This method clears all references to local variables held by\n         the frame.  Also, if the frame belonged to a generator, the\n         generator is finalized.  This helps break reference cycles\n         involving frame objects (for example when catching an\n         exception and storing its traceback for later use).\n\n         ``RuntimeError`` is raised if the frame is currently\n         executing.\n\n         New in version 3.4.\n\n   Traceback objects\n      Traceback objects represent a stack trace of an exception.  A\n      traceback object is created when an exception occurs.  When the\n      search for an exception handler unwinds the execution stack, at\n      each unwound level a traceback object is inserted in front of\n      the current traceback.  When an exception handler is entered,\n      the stack trace is made available to the program. (See section\n      *The try statement*.) It is accessible as the third item of the\n      tuple returned by ``sys.exc_info()``. When the program contains\n      no suitable handler, the stack trace is written (nicely\n      formatted) to the standard error stream; if the interpreter is\n      interactive, it is also made available to the user as\n      ``sys.last_traceback``.\n\n      Special read-only attributes: ``tb_next`` is the next level in\n      the stack trace (towards the frame where the exception\n      occurred), or ``None`` if there is no next level; ``tb_frame``\n      points to the execution frame of the current level;\n      ``tb_lineno`` gives the line number where the exception\n      occurred; ``tb_lasti`` indicates the precise instruction.  The\n      line number and last instruction in the traceback may differ\n      from the line number of its frame object if the exception\n      occurred in a ``try`` statement with no matching except clause\n      or with a finally clause.\n\n   Slice objects\n      Slice objects are used to represent slices for ``__getitem__()``\n      methods.  They are also created by the built-in ``slice()``\n      function.\n\n      Special read-only attributes: ``start`` is the lower bound;\n      ``stop`` is the upper bound; ``step`` is the step value; each is\n      ``None`` if omitted. These attributes can have any type.\n\n      Slice objects support one method:\n\n      slice.indices(self, length)\n\n         This method takes a single integer argument *length* and\n         computes information about the slice that the slice object\n         would describe if applied to a sequence of *length* items.\n         It returns a tuple of three integers; respectively these are\n         the *start* and *stop* indices and the *step* or stride\n         length of the slice. Missing or out-of-bounds indices are\n         handled in a manner consistent with regular slices.\n\n   Static method objects\n      Static method objects provide a way of defeating the\n      transformation of function objects to method objects described\n      above. A static method object is a wrapper around any other\n      object, usually a user-defined method object. When a static\n      method object is retrieved from a class or a class instance, the\n      object actually returned is the wrapped object, which is not\n      subject to any further transformation. Static method objects are\n      not themselves callable, although the objects they wrap usually\n      are. Static method objects are created by the built-in\n      ``staticmethod()`` constructor.\n\n   Class method objects\n      A class method object, like a static method object, is a wrapper\n      around another object that alters the way in which that object\n      is retrieved from classes and class instances. The behaviour of\n      class method objects upon such retrieval is described above,\n      under "User-defined methods". Class method objects are created\n      by the built-in ``classmethod()`` constructor.\n',
  'typesfunctions': '\nFunctions\n*********\n\nFunction objects are created by function definitions.  The only\noperation on a function object is to call it: ``func(argument-list)``.\n\nThere are really two flavors of function objects: built-in functions\nand user-defined functions.  Both support the same operation (to call\nthe function), but the implementation is different, hence the\ndifferent object types.\n\nSee *Function definitions* for more information.\n',
  'typesmapping': '\nMapping Types --- ``dict``\n**************************\n\nA *mapping* object maps *hashable* values to arbitrary objects.\nMappings are mutable objects.  There is currently only one standard\nmapping type, the *dictionary*.  (For other containers see the built-\nin ``list``, ``set``, and ``tuple`` classes, and the ``collections``\nmodule.)\n\nA dictionary\'s keys are *almost* arbitrary values.  Values that are\nnot *hashable*, that is, values containing lists, dictionaries or\nother mutable types (that are compared by value rather than by object\nidentity) may not be used as keys.  Numeric types used for keys obey\nthe normal rules for numeric comparison: if two numbers compare equal\n(such as ``1`` and ``1.0``) then they can be used interchangeably to\nindex the same dictionary entry.  (Note however, that since computers\nstore floating-point numbers as approximations it is usually unwise to\nuse them as dictionary keys.)\n\nDictionaries can be created by placing a comma-separated list of\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   These are the operations that dictionaries support (and therefore,\n   custom mapping types should support too):\n\n   len(d)\n\n      Return the number of items in the dictionary *d*.\n\n   d[key]\n\n      Return the item of *d* with key *key*.  Raises a ``KeyError`` if\n      *key* is not in the map.\n\n      If a subclass of dict defines a method ``__missing__()``, if the\n      key *key* is not present, the ``d[key]`` operation calls that\n      method with the key *key* as argument.  The ``d[key]`` operation\n      then returns or raises whatever is returned or raised by the\n      ``__missing__(key)`` call if the key is not present. No other\n      operations or methods invoke ``__missing__()``. If\n      ``__missing__()`` is not defined, ``KeyError`` is raised.\n      ``__missing__()`` must be a method; it cannot be an instance\n      variable:\n\n         >>> class Counter(dict):\n         ...     def __missing__(self, key):\n         ...         return 0\n         >>> c = Counter()\n         >>> c[\'red\']\n         0\n         >>> c[\'red\'] += 1\n         >>> c[\'red\']\n         1\n\n      See ``collections.Counter`` for a complete implementation\n      including other methods helpful for accumulating and managing\n      tallies.\n\n   d[key] = value\n\n      Set ``d[key]`` to *value*.\n\n   del d[key]\n\n      Remove ``d[key]`` from *d*.  Raises a ``KeyError`` if *key* is\n      not in the map.\n\n   key in d\n\n      Return ``True`` if *d* has a key *key*, else ``False``.\n\n   key not in d\n\n      Equivalent to ``not key in d``.\n\n   iter(d)\n\n      Return an iterator over the keys of the dictionary.  This is a\n      shortcut for ``iter(d.keys())``.\n\n   clear()\n\n      Remove all items from the dictionary.\n\n   copy()\n\n      Return a shallow copy of the dictionary.\n\n   classmethod fromkeys(seq[, value])\n\n      Create a new dictionary with keys from *seq* and values set to\n      *value*.\n\n      ``fromkeys()`` is a class method that returns a new dictionary.\n      *value* defaults to ``None``.\n\n   get(key[, default])\n\n      Return the value for *key* if *key* is in the dictionary, else\n      *default*. If *default* is not given, it defaults to ``None``,\n      so that this method never raises a ``KeyError``.\n\n   items()\n\n      Return a new view of the dictionary\'s items (``(key, value)``\n      pairs). See the *documentation of view objects*.\n\n   keys()\n\n      Return a new view of the dictionary\'s keys.  See the\n      *documentation of view objects*.\n\n   pop(key[, default])\n\n      If *key* is in the dictionary, remove it and return its value,\n      else return *default*.  If *default* is not given and *key* is\n      not in the dictionary, a ``KeyError`` is raised.\n\n   popitem()\n\n      Remove and return an arbitrary ``(key, value)`` pair from the\n      dictionary.\n\n      ``popitem()`` is useful to destructively iterate over a\n      dictionary, as often used in set algorithms.  If the dictionary\n      is empty, calling ``popitem()`` raises a ``KeyError``.\n\n   setdefault(key[, default])\n\n      If *key* is in the dictionary, return its value.  If not, insert\n      *key* with a value of *default* and return *default*.  *default*\n      defaults to ``None``.\n\n   update([other])\n\n      Update the dictionary with the key/value pairs from *other*,\n      overwriting existing keys.  Return ``None``.\n\n      ``update()`` accepts either another dictionary object or an\n      iterable of key/value pairs (as tuples or other iterables of\n      length two).  If keyword arguments are specified, the dictionary\n      is then updated with those key/value pairs: ``d.update(red=1,\n      blue=2)``.\n\n   values()\n\n      Return a new view of the dictionary\'s values.  See the\n      *documentation of view objects*.\n\nSee also:\n\n   ``types.MappingProxyType`` can be used to create a read-only view\n   of a ``dict``.\n\n\nDictionary view objects\n=======================\n\nThe objects returned by ``dict.keys()``, ``dict.values()`` and\n``dict.items()`` are *view objects*.  They provide a dynamic view on\nthe dictionary\'s entries, which means that when the dictionary\nchanges, the view reflects these changes.\n\nDictionary views can be iterated over to yield their respective data,\nand support membership tests:\n\nlen(dictview)\n\n   Return the number of entries in the dictionary.\n\niter(dictview)\n\n   Return an iterator over the keys, values or items (represented as\n   tuples of ``(key, value)``) in the dictionary.\n\n   Keys and values are iterated over in an arbitrary order which is\n   non-random, varies across Python implementations, and depends on\n   the dictionary\'s history of insertions and deletions. If keys,\n   values and items views are iterated over with no intervening\n   modifications to the dictionary, the order of items will directly\n   correspond.  This allows the creation of ``(value, key)`` pairs\n   using ``zip()``: ``pairs = zip(d.values(), d.keys())``.  Another\n   way to create the same list is ``pairs = [(v, k) for (k, v) in\n   d.items()]``.\n\n   Iterating views while adding or deleting entries in the dictionary\n   may raise a ``RuntimeError`` or fail to iterate over all entries.\n\nx in dictview\n\n   Return ``True`` if *x* is in the underlying dictionary\'s keys,\n   values or items (in the latter case, *x* should be a ``(key,\n   value)`` tuple).\n\nKeys views are set-like since their entries are unique and hashable.\nIf all values are hashable, so that ``(key, value)`` pairs are unique\nand hashable, then the items view is also set-like.  (Values views are\nnot treated as set-like since the entries are generally not unique.)\nFor set-like views, all of the operations defined for the abstract\nbase class ``collections.abc.Set`` are available (for example, ``==``,\n``<``, or ``^``).\n\nAn example of dictionary view usage:\n\n   >>> dishes = {\'eggs\': 2, \'sausage\': 1, \'bacon\': 1, \'spam\': 500}\n   >>> keys = dishes.keys()\n   >>> values = dishes.values()\n\n   >>> # iteration\n   >>> n = 0\n   >>> for val in values:\n   ...     n += val\n   >>> print(n)\n   504\n\n   >>> # keys and values are iterated over in the same order\n   >>> list(keys)\n   [\'eggs\', \'bacon\', \'sausage\', \'spam\']\n   >>> list(values)\n   [2, 1, 1, 500]\n\n   >>> # view objects are dynamic and reflect dict changes\n   >>> del dishes[\'eggs\']\n   >>> del dishes[\'sausage\']\n   >>> list(keys)\n   [\'spam\', \'bacon\']\n\n   >>> # set operations\n   >>> keys & {\'eggs\', \'bacon\', \'salad\'}\n   {\'bacon\'}\n   >>> keys ^ {\'sausage\', \'juice\'}\n   {\'juice\', \'sausage\', \'bacon\', \'spam\'}\n',
  'typesmethods': '\nMethods\n*******\n\nMethods are functions that are called using the attribute notation.\nThere are two flavors: built-in methods (such as ``append()`` on\nlists) and class instance methods.  Built-in methods are described\nwith the types that support them.\n\nIf you access a method (a function defined in a class namespace)\nthrough an instance, you get a special object: a *bound method* (also\ncalled *instance method*) object. When called, it will add the\n``self`` argument to the argument list.  Bound methods have two\nspecial read-only attributes: ``m.__self__`` is the object on which\nthe method operates, and ``m.__func__`` is the function implementing\nthe method.  Calling ``m(arg-1, arg-2, ..., arg-n)`` is completely\nequivalent to calling ``m.__func__(m.__self__, arg-1, arg-2, ...,\narg-n)``.\n\nLike function objects, bound method objects support getting arbitrary\nattributes.  However, since method attributes are actually stored on\nthe underlying function object (``meth.__func__``), setting method\nattributes on bound methods is disallowed.  Attempting to set an\nattribute on a method results in an ``AttributeError`` being raised.\nIn order to set a method attribute, you need to explicitly set it on\nthe underlying function object:\n\n   >>> class C:\n   ...     def method(self):\n   ...         pass\n   ...\n   >>> c = C()\n   >>> c.method.whoami = \'my name is method\'  # can\'t set on the method\n   Traceback (most recent call last):\n     File "<stdin>", line 1, in <module>\n   AttributeError: \'method\' object has no attribute \'whoami\'\n   >>> c.method.__func__.whoami = \'my name is method\'\n   >>> c.method.whoami\n   \'my name is method\'\n\nSee *The standard type hierarchy* for more information.\n',

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Repository URL: http://hg.python.org/cpython