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✍️ Editing: topics.pyc

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The "assert" statement
**********************

Assert statements are a convenient way to insert debugging assertions
into a program:

assert_stmt ::= "assert" expression ["," expression]

The simple form, "assert expression", is equivalent to

if __debug__:
       if not expression: raise AssertionError

The extended form, "assert expression1, expression2", is equivalent to

if __debug__:
       if not expression1: raise AssertionError(expression2)

These equivalences assume that "__debug__" and "AssertionError" refer
to the built-in variables with those names.  In the current
implementation, the built-in variable "__debug__" is "True" under
normal circumstances, "False" when optimization is requested (command
line option -O).  The current code generator emits no code for an
assert statement when optimization is requested at compile time.  Note
that it is unnecessary to include the source code for the expression
that failed in the error message; it will be displayed as part of the
stack trace.

Assignments to "__debug__" are illegal.  The value for the built-in
variable is determined when the interpreter starts.
t���asserts��
Assignment statements
*********************

Assignment statements are used to (re)bind names to values and to
modify attributes or items of mutable objects:

(See section Primaries for the syntax definitions for the last three
symbols.)

An assignment statement evaluates the expression list (remember that
this can be a single expression or a comma-separated list, the latter
yielding a tuple) and assigns the single resulting object to each of
the target lists, from left to right.

Assignment is defined recursively depending on the form of the target
(list). When a target is part of a mutable object (an attribute
reference, subscription or slicing), the mutable object must
ultimately perform the assignment and decide about its validity, and
may raise an exception if the assignment is unacceptable.  The rules
observed by various types and the exceptions raised are given with the
definition of the object types (see section The standard type
hierarchy).

Assignment of an object to a target list is recursively defined as
follows.

* If the target list is a single target: The object is assigned to
  that target.

* If the target list is a comma-separated list of targets: The
  object must be an iterable with the same number of items as there
  are targets in the target list, and the items are assigned, from
  left to right, to the corresponding targets.

Assignment of an object to a single target is recursively defined as
follows.

* If the target is an identifier (name):

* If the name does not occur in a "global" statement in the
    current code block: the name is bound to the object in the current
    local namespace.

* Otherwise: the name is bound to the object in the current global
    namespace.

The name is rebound if it was already bound.  This may cause the
  reference count for the object previously bound to the name to reach
  zero, causing the object to be deallocated and its destructor (if it
  has one) to be called.

* If the target is a target list enclosed in parentheses or in
  square brackets: The object must be an iterable with the same number
  of items as there are targets in the target list, and its items are
  assigned, from left to right, to the corresponding targets.

* If the target is an attribute reference: The primary expression in
  the reference is evaluated.  It should yield an object with
  assignable attributes; if this is not the case, "TypeError" is
  raised.  That object is then asked to assign the assigned object to
  the given attribute; if it cannot perform the assignment, it raises
  an exception (usually but not necessarily "AttributeError").

Note: If the object is a class instance and the attribute reference
  occurs on both sides of the assignment operator, the RHS expression,
  "a.x" can access either an instance attribute or (if no instance
  attribute exists) a class attribute.  The LHS target "a.x" is always
  set as an instance attribute, creating it if necessary.  Thus, the
  two occurrences of "a.x" do not necessarily refer to the same
  attribute: if the RHS expression refers to a class attribute, the
  LHS creates a new instance attribute as the target of the
  assignment:

class Cls:
         x = 3             # class variable
     inst = Cls()
     inst.x = inst.x + 1   # writes inst.x as 4 leaving Cls.x as 3

This description does not necessarily apply to descriptor
  attributes, such as properties created with "property()".

* If the target is a subscription: The primary expression in the
  reference is evaluated.  It should yield either a mutable sequence
  object (such as a list) or a mapping object (such as a dictionary).
  Next, the subscript expression is evaluated.

If the primary is a mutable sequence object (such as a list), the
  subscript must yield a plain integer.  If it is negative, the
  sequence's length is added to it. The resulting value must be a
  nonnegative integer less than the sequence's length, and the
  sequence is asked to assign the assigned object to its item with
  that index.  If the index is out of range, "IndexError" is raised
  (assignment to a subscripted sequence cannot add new items to a
  list).

If the primary is a mapping object (such as a dictionary), the
  subscript must have a type compatible with the mapping's key type,
  and the mapping is then asked to create a key/datum pair which maps
  the subscript to the assigned object.  This can either replace an
  existing key/value pair with the same key value, or insert a new
  key/value pair (if no key with the same value existed).

* If the target is a slicing: The primary expression in the
  reference is evaluated.  It should yield a mutable sequence object
  (such as a list).  The assigned object should be a sequence object
  of the same type.  Next, the lower and upper bound expressions are
  evaluated, insofar they are present; defaults are zero and the
  sequence's length.  The bounds should evaluate to (small) integers.
  If either bound is negative, the sequence's length is added to it.
  The resulting bounds are clipped to lie between zero and the
  sequence's length, inclusive.  Finally, the sequence object is asked
  to replace the slice with the items of the assigned sequence.  The
  length of the slice may be different from the length of the assigned
  sequence, thus changing the length of the target sequence, if the
  object allows it.

**CPython implementation detail:** In the current implementation, the
syntax for targets is taken to be the same as for expressions, and
invalid syntax is rejected during the code generation phase, causing
less detailed error messages.

WARNING: Although the definition of assignment implies that overlaps
between the left-hand side and the right-hand side are 'safe' (for
example "a, b = b, a" swaps two variables), overlaps *within* the
collection of assigned-to variables are not safe!  For instance, the
following program prints "[0, 2]":

x = [0, 1]
   i = 0
   i, x[i] = 1, 2
   print x

Augmented assignment statements
===============================

Augmented assignment is the combination, in a single statement, of a
binary operation and an assignment statement:

augmented_assignment_stmt ::= augtarget augop (expression_list | yield_expression)
   augtarget                 ::= identifier | attributeref | subscription | slicing
   augop                     ::= "+=" | "-=" | "*=" | "/=" | "//=" | "%=" | "**="
             | ">>=" | "<<=" | "&=" | "^=" | "|="

(See section Primaries for the syntax definitions for the last three
symbols.)

An augmented assignment evaluates the target (which, unlike normal
assignment statements, cannot be an unpacking) and the expression
list, performs the binary operation specific to the type of assignment
on the two operands, and assigns the result to the original target.
The target is only evaluated once.

An augmented assignment expression like "x += 1" can be rewritten as
"x = x + 1" to achieve a similar, but not exactly equal effect. In the
augmented version, "x" is only evaluated once. Also, when possible,
the actual operation is performed *in-place*, meaning that rather than
creating a new object and assigning that to the target, the old object
is modified instead.

With the exception of assigning to tuples and multiple targets in a
single statement, the assignment done by augmented assignment
statements is handled the same way as normal assignments. Similarly,
with the exception of the possible *in-place* behavior, the binary
operation performed by augmented assignment is the same as the normal
binary operations.

For targets which are attribute references, the same caveat about
class and instance attributes applies as for regular assignments.
t
���assignments���
Identifiers (Names)
*******************

An identifier occurring as an atom is a name.  See section Identifiers
and keywords for lexical definition and section Naming and binding for
documentation of naming and binding.

When the name is bound to an object, evaluation of the atom yields
that object. When a name is not bound, an attempt to evaluate it
raises a "NameError" exception.

**Private name mangling:** When an identifier that textually occurs in
a class definition begins with two or more underscore characters and
does not end in two or more underscores, it is considered a *private
name* of that class. Private names are transformed to a longer form
before code is generated for them.  The transformation inserts the
class name, with leading underscores removed and a single underscore
inserted, in front of the name.  For example, the identifier "__spam"
occurring in a class named "Ham" will be transformed to "_Ham__spam".
This transformation is independent of the syntactical context in which
the identifier is used.  If the transformed name is extremely long
(longer than 255 characters), implementation defined truncation may
happen. If the class name consists only of underscores, no
transformation is done.
s���atom-identifierss��
Literals
********

Python supports string literals and various numeric literals:

literal ::= stringliteral | integer | longinteger
               | floatnumber | imagnumber

Evaluation of a literal yields an object of the given type (string,
integer, long integer, floating point number, complex number) with the
given value.  The value may be approximated in the case of floating
point and imaginary (complex) literals.  See section Literals for
details.

All literals correspond to immutable data types, and hence the
object's identity is less important than its value.  Multiple
evaluations of literals with the same value (either the same
occurrence in the program text or a different occurrence) may obtain
the same object or a different object with the same value.
s
���atom-literalssU*��
Customizing attribute access
****************************

The following methods can be defined to customize the meaning of
attribute access (use of, assignment to, or deletion of "x.name") for
class instances.

object.__getattr__(self, name)

Called when an attribute lookup has not found the attribute in the
   usual places (i.e. it is not an instance attribute nor is it found
   in the class tree for "self").  "name" is the attribute name. This
   method should return the (computed) attribute value or raise an
   "AttributeError" exception.

Note that if the attribute is found through the normal mechanism,
   "__getattr__()" is not called.  (This is an intentional asymmetry
   between "__getattr__()" and "__setattr__()".) This is done both for
   efficiency reasons and because otherwise "__getattr__()" would have
   no way to access other attributes of the instance.  Note that at
   least for instance variables, you can fake total control by not
   inserting any values in the instance attribute dictionary (but
   instead inserting them in another object).  See the
   "__getattribute__()" method below for a way to actually get total
   control in new-style classes.

object.__setattr__(self, name, value)

Called when an attribute assignment is attempted.  This is called
   instead of the normal mechanism (i.e. store the value in the
   instance dictionary).  *name* is the attribute name, *value* is the
   value to be assigned to it.

If "__setattr__()" wants to assign to an instance attribute, it
   should not simply execute "self.name = value" --- this would cause
   a recursive call to itself.  Instead, it should insert the value in
   the dictionary of instance attributes, e.g., "self.__dict__[name] =
   value".  For new-style classes, rather than accessing the instance
   dictionary, it should call the base class method with the same
   name, for example, "object.__setattr__(self, name, value)".

object.__delattr__(self, name)

Like "__setattr__()" but for attribute deletion instead of
   assignment.  This should only be implemented if "del obj.name" is
   meaningful for the object.

More attribute access for new-style classes
===========================================

The following methods only apply to new-style classes.

object.__getattribute__(self, name)

Called unconditionally to implement attribute accesses for
   instances of the class. If the class also defines "__getattr__()",
   the latter will not be called unless "__getattribute__()" either
   calls it explicitly or raises an "AttributeError". This method
   should return the (computed) attribute value or raise an
   "AttributeError" exception. In order to avoid infinite recursion in
   this method, its implementation should always call the base class
   method with the same name to access any attributes it needs, for
   example, "object.__getattribute__(self, name)".

Note: This method may still be bypassed when looking up special
     methods as the result of implicit invocation via language syntax
     or built-in functions. See Special method lookup for new-style
     classes.

Implementing Descriptors
========================

The following methods only apply when an instance of the class
containing the method (a so-called *descriptor* class) appears in an
*owner* class (the descriptor must be in either the owner's class
dictionary or in the class dictionary for one of its parents).  In the
examples below, "the attribute" refers to the attribute whose name is
the key of the property in the owner class' "__dict__".

object.__get__(self, instance, owner)

Called to get the attribute of the owner class (class attribute
   access) or of an instance of that class (instance attribute
   access). *owner* is always the owner class, while *instance* is the
   instance that the attribute was accessed through, or "None" when
   the attribute is accessed through the *owner*.  This method should
   return the (computed) attribute value or raise an "AttributeError"
   exception.

object.__set__(self, instance, value)

Called to set the attribute on an instance *instance* of the owner
   class to a new value, *value*.

object.__delete__(self, instance)

Called to delete the attribute on an instance *instance* of the
   owner class.

Invoking Descriptors
====================

In general, a descriptor is an object attribute with "binding
behavior", one whose attribute access has been overridden by methods
in the descriptor protocol:  "__get__()", "__set__()", and
"__delete__()". If any of those methods are defined for an object, it
is said to be a descriptor.

The default behavior for attribute access is to get, set, or delete
the attribute from an object's dictionary. For instance, "a.x" has a
lookup chain starting with "a.__dict__['x']", then
"type(a).__dict__['x']", and continuing through the base classes of
"type(a)" excluding metaclasses.

However, if the looked-up value is an object defining one of the
descriptor methods, then Python may override the default behavior and
invoke the descriptor method instead.  Where this occurs in the
precedence chain depends on which descriptor methods were defined and
how they were called.  Note that descriptors are only invoked for new
style objects or classes (ones that subclass "object()" or "type()").

The starting point for descriptor invocation is a binding, "a.x". How
the arguments are assembled depends on "a":

Direct Call
   The simplest and least common call is when user code directly
   invokes a descriptor method:    "x.__get__(a)".

Instance Binding
   If binding to a new-style object instance, "a.x" is transformed
   into the call: "type(a).__dict__['x'].__get__(a, type(a))".

Class Binding
   If binding to a new-style class, "A.x" is transformed into the
   call: "A.__dict__['x'].__get__(None, A)".

Super Binding
   If "a" is an instance of "super", then the binding "super(B,
   obj).m()" searches "obj.__class__.__mro__" for the base class "A"
   immediately preceding "B" and then invokes the descriptor with the
   call: "A.__dict__['m'].__get__(obj, obj.__class__)".

For instance bindings, the precedence of descriptor invocation depends
on the which descriptor methods are defined.  A descriptor can define
any combination of "__get__()", "__set__()" and "__delete__()".  If it
does not define "__get__()", then accessing the attribute will return
the descriptor object itself unless there is a value in the object's
instance dictionary.  If the descriptor defines "__set__()" and/or
"__delete__()", it is a data descriptor; if it defines neither, it is
a non-data descriptor.  Normally, data descriptors define both
"__get__()" and "__set__()", while non-data descriptors have just the
"__get__()" method.  Data descriptors with "__set__()" and "__get__()"
defined always override a redefinition in an instance dictionary.  In
contrast, non-data descriptors can be overridden by instances.

Python methods (including "staticmethod()" and "classmethod()") are
implemented as non-data descriptors.  Accordingly, instances can
redefine and override methods.  This allows individual instances to
acquire behaviors that differ from other instances of the same class.

The "property()" function is implemented as a data descriptor.
Accordingly, instances cannot override the behavior of a property.

__slots__
=========

By default, instances of both old and new-style classes have a
dictionary for attribute storage.  This wastes space for objects
having very few instance variables.  The space consumption can become
acute when creating large numbers of instances.

The default can be overridden by defining *__slots__* in a new-style
class definition.  The *__slots__* declaration takes a sequence of
instance variables and reserves just enough space in each instance to
hold a value for each variable.  Space is saved because *__dict__* is
not created for each instance.

__slots__

This class variable can be assigned a string, iterable, or sequence
   of strings with variable names used by instances.  If defined in a
   new-style class, *__slots__* reserves space for the declared
   variables and prevents the automatic creation of *__dict__* and
   *__weakref__* for each instance.

New in version 2.2.

Notes on using *__slots__*

* When inheriting from a class without *__slots__*, the *__dict__*
  attribute of that class will always be accessible, so a *__slots__*
  definition in the subclass is meaningless.

* Without a *__dict__* variable, instances cannot be assigned new
  variables not listed in the *__slots__* definition.  Attempts to
  assign to an unlisted variable name raises "AttributeError". If
  dynamic assignment of new variables is desired, then add
  "'__dict__'" to the sequence of strings in the *__slots__*
  declaration.

Changed in version 2.3: Previously, adding "'__dict__'" to the
  *__slots__* declaration would not enable the assignment of new
  attributes not specifically listed in the sequence of instance
  variable names.

* Without a *__weakref__* variable for each instance, classes
  defining *__slots__* do not support weak references to its
  instances. If weak reference support is needed, then add
  "'__weakref__'" to the sequence of strings in the *__slots__*
  declaration.

Changed in version 2.3: Previously, adding "'__weakref__'" to the
  *__slots__* declaration would not enable support for weak
  references.

* *__slots__* are implemented at the class level by creating
  descriptors (Implementing Descriptors) for each variable name.  As a
  result, class attributes cannot be used to set default values for
  instance variables defined by *__slots__*; otherwise, the class
  attribute would overwrite the descriptor assignment.

* The action of a *__slots__* declaration is limited to the class
  where it is defined.  As a result, subclasses will have a *__dict__*
  unless they also define *__slots__* (which must only contain names
  of any *additional* slots).

* If a class defines a slot also defined in a base class, the
  instance variable defined by the base class slot is inaccessible
  (except by retrieving its descriptor directly from the base class).
  This renders the meaning of the program undefined.  In the future, a
  check may be added to prevent this.

* Nonempty *__slots__* does not work for classes derived from
  "variable-length" built-in types such as "long", "str" and "tuple".

* Any non-string iterable may be assigned to *__slots__*. Mappings
  may also be used; however, in the future, special meaning may be
  assigned to the values corresponding to each key.

* *__class__* assignment works only if both classes have the same
  *__slots__*.

Changed in version 2.6: Previously, *__class__* assignment raised an
  error if either new or old class had *__slots__*.
s���attribute-accesss_��
Attribute references
********************

An attribute reference is a primary followed by a period and a name:

attributeref ::= primary "." identifier

The primary must evaluate to an object of a type that supports
attribute references, e.g., a module, list, or an instance.  This
object is then asked to produce the attribute whose name is the
identifier.  If this attribute is not available, the exception
"AttributeError" is raised. Otherwise, the type and value of the
object produced is determined by the object.  Multiple evaluations of
the same attribute reference may yield different objects.
s���attribute-referencess���
Augmented assignment statements
*******************************

Augmented assignment is the combination, in a single statement, of a
binary operation and an assignment statement:

augmented_assignment_stmt ::= augtarget augop (expression_list | yield_expression)
   augtarget                 ::= identifier | attributeref | subscription | slicing
   augop                     ::= "+=" | "-=" | "*=" | "/=" | "//=" | "%=" | "**="
             | ">>=" | "<<=" | "&=" | "^=" | "|="

(See section Primaries for the syntax definitions for the last three
symbols.)

An augmented assignment evaluates the target (which, unlike normal
assignment statements, cannot be an unpacking) and the expression
list, performs the binary operation specific to the type of assignment
on the two operands, and assigns the result to the original target.
The target is only evaluated once.

An augmented assignment expression like "x += 1" can be rewritten as
"x = x + 1" to achieve a similar, but not exactly equal effect. In the
augmented version, "x" is only evaluated once. Also, when possible,
the actual operation is performed *in-place*, meaning that rather than
creating a new object and assigning that to the target, the old object
is modified instead.

With the exception of assigning to tuples and multiple targets in a
single statement, the assignment done by augmented assignment
statements is handled the same way as normal assignments. Similarly,
with the exception of the possible *in-place* behavior, the binary
operation performed by augmented assignment is the same as the normal
binary operations.

For targets which are attribute references, the same caveat about
class and instance attributes applies as for regular assignments.
t	���augassignsn��
Binary arithmetic operations
****************************

The binary arithmetic operations have the conventional priority
levels.  Note that some of these operations also apply to certain non-
numeric types.  Apart from the power operator, there are only two
levels, one for multiplicative operators and one for additive
operators:

The "*" (multiplication) operator yields the product of its arguments.
The arguments must either both be numbers, or one argument must be an
integer (plain or long) and the other must be a sequence. In the
former case, the numbers are converted to a common type and then
multiplied together.  In the latter case, sequence repetition is
performed; a negative repetition factor yields an empty sequence.

The "/" (division) and "//" (floor division) operators yield the
quotient of their arguments.  The numeric arguments are first
converted to a common type. Plain or long integer division yields an
integer of the same type; the result is that of mathematical division
with the 'floor' function applied to the result. Division by zero
raises the "ZeroDivisionError" exception.

The "%" (modulo) operator yields the remainder from the division of
the first argument by the second.  The numeric arguments are first
converted to a common type.  A zero right argument raises the
"ZeroDivisionError" exception.  The arguments may be floating point
numbers, e.g., "3.14%0.7" equals "0.34" (since "3.14" equals "4*0.7 +
0.34".)  The modulo operator always yields a result with the same sign
as its second operand (or zero); the absolute value of the result is
strictly smaller than the absolute value of the second operand [2].

The integer division and modulo operators are connected by the
following identity: "x == (x/y)*y + (x%y)".  Integer division and
modulo are also connected with the built-in function "divmod()":
"divmod(x, y) == (x/y, x%y)".  These identities don't hold for
floating point numbers; there similar identities hold approximately
where "x/y" is replaced by "floor(x/y)" or "floor(x/y) - 1" [3].

In addition to performing the modulo operation on numbers, the "%"
operator is also overloaded by string and unicode objects to perform
string formatting (also known as interpolation). The syntax for string
formatting is described in the Python Library Reference, section
String Formatting Operations.

Deprecated since version 2.3: The floor division operator, the modulo
operator, and the "divmod()" function are no longer defined for
complex numbers.  Instead, convert to a floating point number using
the "abs()" function if appropriate.

The "+" (addition) operator yields the sum of its arguments. The
arguments must either both be numbers or both sequences of the same
type.  In the former case, the numbers are converted to a common type
and then added together.  In the latter case, the sequences are
concatenated.

The "-" (subtraction) operator yields the difference of its arguments.
The numeric arguments are first converted to a common type.
t���binarys���
Binary bitwise operations
*************************

Each of the three bitwise operations has a different priority level:

and_expr ::= shift_expr | and_expr "&" shift_expr
   xor_expr ::= and_expr | xor_expr "^" and_expr
   or_expr  ::= xor_expr | or_expr "|" xor_expr

The "&" operator yields the bitwise AND of its arguments, which must
be plain or long integers.  The arguments are converted to a common
type.

The "^" operator yields the bitwise XOR (exclusive OR) of its
arguments, which must be plain or long integers.  The arguments are
converted to a common type.

The "|" operator yields the bitwise (inclusive) OR of its arguments,
which must be plain or long integers.  The arguments are converted to
a common type.
t���bitwises~��
Code Objects
************

Code objects are used by the implementation to represent "pseudo-
compiled" executable Python code such as a function body. They differ
from function objects because they don't contain a reference to their
global execution environment.  Code objects are returned by the built-
in "compile()" function and can be extracted from function objects
through their "func_code" attribute. See also the "code" module.

A code object can be executed or evaluated by passing it (instead of a
source string) to the "exec" statement or the built-in "eval()"
function.

See The standard type hierarchy for more information.
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The Ellipsis Object
*******************

This object is used by extended slice notation (see Slicings).  It
supports no special operations.  There is exactly one ellipsis object,
named "Ellipsis" (a built-in name).

It is written as "Ellipsis".  When in a subscript, it can also be
written as "...", for example "seq[...]".
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File Objects
************

File objects are implemented using C's "stdio" package and can be
created with the built-in "open()" function.  File objects are also
returned by some other built-in functions and methods, such as
"os.popen()" and "os.fdopen()" and the "makefile()" method of socket
objects. Temporary files can be created using the "tempfile" module,
and high-level file operations such as copying, moving, and deleting
files and directories can be achieved with the "shutil" module.

When a file operation fails for an I/O-related reason, the exception
"IOError" is raised.  This includes situations where the operation is
not defined for some reason, like "seek()" on a tty device or writing
a file opened for reading.

Files have the following methods:

file.close()

Close the file.  A closed file cannot be read or written any more.
   Any operation which requires that the file be open will raise a
   "ValueError" after the file has been closed.  Calling "close()"
   more than once is allowed.

As of Python 2.5, you can avoid having to call this method
   explicitly if you use the "with" statement.  For example, the
   following code will automatically close *f* when the "with" block
   is exited:

from __future__ import with_statement # This isn't required in Python 2.6

with open("hello.txt") as f:
          for line in f:
              print line,

In older versions of Python, you would have needed to do this to
   get the same effect:

f = open("hello.txt")
      try:
          for line in f:
              print line,
      finally:
          f.close()

Note: Not all "file-like" types in Python support use as a
     context manager for the "with" statement.  If your code is
     intended to work with any file-like object, you can use the
     function "contextlib.closing()" instead of using the object
     directly.

file.flush()

Flush the internal buffer, like "stdio"'s "fflush()".  This may be
   a no-op on some file-like objects.

Note: "flush()" does not necessarily write the file's data to
     disk. Use "flush()" followed by "os.fsync()" to ensure this
     behavior.

file.fileno()

Return the integer "file descriptor" that is used by the underlying
   implementation to request I/O operations from the operating system.
   This can be useful for other, lower level interfaces that use file
   descriptors, such as the "fcntl" module or "os.read()" and friends.

Note: File-like objects which do not have a real file descriptor
     should *not* provide this method!

file.isatty()

Return "True" if the file is connected to a tty(-like) device, else
   "False".

Note: If a file-like object is not associated with a real file,
     this method should *not* be implemented.

file.next()

A file object is its own iterator, for example "iter(f)" returns
   *f* (unless *f* is closed).  When a file is used as an iterator,
   typically in a "for" loop (for example, "for line in f: print
   line.strip()"), the "next()" method is called repeatedly.  This
   method returns the next input line, or raises "StopIteration" when
   EOF is hit when the file is open for reading (behavior is undefined
   when the file is open for writing).  In order to make a "for" loop
   the most efficient way of looping over the lines of a file (a very
   common operation), the "next()" method uses a hidden read-ahead
   buffer.  As a consequence of using a read-ahead buffer, combining
   "next()" with other file methods (like "readline()") does not work
   right.  However, using "seek()" to reposition the file to an
   absolute position will flush the read-ahead buffer.

New in version 2.3.

file.read([size])

Read at most *size* bytes from the file (less if the read hits EOF
   before obtaining *size* bytes).  If the *size* argument is negative
   or omitted, read all data until EOF is reached.  The bytes are
   returned as a string object.  An empty string is returned when EOF
   is encountered immediately.  (For certain files, like ttys, it
   makes sense to continue reading after an EOF is hit.)  Note that
   this method may call the underlying C function "fread()" more than
   once in an effort to acquire as close to *size* bytes as possible.
   Also note that when in non-blocking mode, less data than was
   requested may be returned, even if no *size* parameter was given.

Note: This function is simply a wrapper for the underlying
     "fread()" C function, and will behave the same in corner cases,
     such as whether the EOF value is cached.

file.readline([size])

Read one entire line from the file.  A trailing newline character
   is kept in the string (but may be absent when a file ends with an
   incomplete line). [6] If the *size* argument is present and non-
   negative, it is a maximum byte count (including the trailing
   newline) and an incomplete line may be returned. When *size* is not
   0, an empty string is returned *only* when EOF is encountered
   immediately.

Note: Unlike "stdio"'s "fgets()", the returned string contains
     null characters ("'\0'") if they occurred in the input.

file.readlines([sizehint])

Read until EOF using "readline()" and return a list containing the
   lines thus read.  If the optional *sizehint* argument is present,
   instead of reading up to EOF, whole lines totalling approximately
   *sizehint* bytes (possibly after rounding up to an internal buffer
   size) are read.  Objects implementing a file-like interface may
   choose to ignore *sizehint* if it cannot be implemented, or cannot
   be implemented efficiently.

file.xreadlines()

This method returns the same thing as "iter(f)".

New in version 2.1.

Deprecated since version 2.3: Use "for line in file" instead.

file.seek(offset[, whence])

Set the file's current position, like "stdio"'s "fseek()". The
   *whence* argument is optional and defaults to  "os.SEEK_SET" or "0"
   (absolute file positioning); other values are "os.SEEK_CUR" or "1"
   (seek relative to the current position) and "os.SEEK_END" or "2"
   (seek relative to the file's end).  There is no return value.

For example, "f.seek(2, os.SEEK_CUR)" advances the position by two
   and "f.seek(-3, os.SEEK_END)" sets the position to the third to
   last.

Note that if the file is opened for appending (mode "'a'" or
   "'a+'"), any "seek()" operations will be undone at the next write.
   If the file is only opened for writing in append mode (mode "'a'"),
   this method is essentially a no-op, but it remains useful for files
   opened in append mode with reading enabled (mode "'a+'").  If the
   file is opened in text mode (without "'b'"), only offsets returned
   by "tell()" are legal.  Use of other offsets causes undefined
   behavior.

Note that not all file objects are seekable.

Changed in version 2.6: Passing float values as offset has been
   deprecated.

file.tell()

Return the file's current position, like "stdio"'s "ftell()".

Note: On Windows, "tell()" can return illegal values (after an
     "fgets()") when reading files with Unix-style line-endings. Use
     binary mode ("'rb'") to circumvent this problem.

file.truncate([size])

Truncate the file's size.  If the optional *size* argument is
   present, the file is truncated to (at most) that size.  The size
   defaults to the current position. The current file position is not
   changed.  Note that if a specified size exceeds the file's current
   size, the result is platform-dependent:  possibilities include that
   the file may remain unchanged, increase to the specified size as if
   zero-filled, or increase to the specified size with undefined new
   content. Availability:  Windows, many Unix variants.

file.write(str)

Write a string to the file.  There is no return value.  Due to
   buffering, the string may not actually show up in the file until
   the "flush()" or "close()" method is called.

file.writelines(sequence)

Write a sequence of strings to the file.  The sequence can be any
   iterable object producing strings, typically a list of strings.
   There is no return value. (The name is intended to match
   "readlines()"; "writelines()" does not add line separators.)

Files support the iterator protocol.  Each iteration returns the same
result as "readline()", and iteration ends when the "readline()"
method returns an empty string.

File objects also offer a number of other interesting attributes.
These are not required for file-like objects, but should be
implemented if they make sense for the particular object.

file.closed

bool indicating the current state of the file object.  This is a
   read-only attribute; the "close()" method changes the value. It may
   not be available on all file-like objects.

file.encoding

The encoding that this file uses. When Unicode strings are written
   to a file, they will be converted to byte strings using this
   encoding. In addition, when the file is connected to a terminal,
   the attribute gives the encoding that the terminal is likely to use
   (that  information might be incorrect if the user has misconfigured
   the  terminal). The attribute is read-only and may not be present
   on all file-like objects. It may also be "None", in which case the
   file uses the system default encoding for converting Unicode
   strings.

New in version 2.3.

file.errors

The Unicode error handler used along with the encoding.

New in version 2.6.

file.mode

The I/O mode for the file.  If the file was created using the
   "open()" built-in function, this will be the value of the *mode*
   parameter.  This is a read-only attribute and may not be present on
   all file-like objects.

file.name

If the file object was created using "open()", the name of the
   file. Otherwise, some string that indicates the source of the file
   object, of the form "<...>".  This is a read-only attribute and may
   not be present on all file-like objects.

file.newlines

If Python was built with *universal newlines* enabled (the default)
   this read-only attribute exists, and for files opened in universal
   newline read mode it keeps track of the types of newlines
   encountered while reading the file. The values it can take are
   "'\r'", "'\n'", "'\r\n'", "None" (unknown, no newlines read yet) or
   a tuple containing all the newline types seen, to indicate that
   multiple newline conventions were encountered. For files not opened
   in universal newlines read mode the value of this attribute will be
   "None".

file.softspace

Boolean that indicates whether a space character needs to be
   printed before another value when using the "print" statement.
   Classes that are trying to simulate a file object should also have
   a writable "softspace" attribute, which should be initialized to
   zero.  This will be automatic for most classes implemented in
   Python (care may be needed for objects that override attribute
   access); types implemented in C will have to provide a writable
   "softspace" attribute.

Note: This attribute is not used to control the "print"
     statement, but to allow the implementation of "print" to keep
     track of its internal state.
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The Null Object
***************

This object is returned by functions that don't explicitly return a
value.  It supports no special operations.  There is exactly one null
object, named "None" (a built-in name).

It is written as "None".
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Type Objects
************

Type objects represent the various object types.  An object's type is
accessed by the built-in function "type()".  There are no special
operations on types.  The standard module "types" defines names for
all standard built-in types.

Types are written like this: "<type 'int'>".
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Boolean operations
******************

or_test  ::= and_test | or_test "or" and_test
   and_test ::= not_test | and_test "and" not_test
   not_test ::= comparison | "not" not_test

In the context of Boolean operations, and also when expressions are
used by control flow statements, the following values are interpreted
as false: "False", "None", numeric zero of all types, and empty
strings and containers (including strings, tuples, lists,
dictionaries, sets and frozensets).  All other values are interpreted
as true.  (See the "__nonzero__()" special method for a way to change
this.)

The operator "not" yields "True" if its argument is false, "False"
otherwise.

The expression "x and y" first evaluates *x*; if *x* is false, its
value is returned; otherwise, *y* is evaluated and the resulting value
is returned.

The expression "x or y" first evaluates *x*; if *x* is true, its value
is returned; otherwise, *y* is evaluated and the resulting value is
returned.

(Note that neither "and" nor "or" restrict the value and type they
return to "False" and "True", but rather return the last evaluated
argument. This is sometimes useful, e.g., if "s" is a string that
should be replaced by a default value if it is empty, the expression
"s or 'foo'" yields the desired value.  Because "not" has to invent a
value anyway, it does not bother to return a value of the same type as
its argument, so e.g., "not 'foo'" yields "False", not "''".)
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The "break" statement
*********************

break_stmt ::= "break"

"break" may only occur syntactically nested in a "for" or "while"
loop, but not nested in a function or class definition within that
loop.

It terminates the nearest enclosing loop, skipping the optional "else"
clause if the loop has one.

If a "for" loop is terminated by "break", the loop control target
keeps its current value.

When "break" passes control out of a "try" statement with a "finally"
clause, that "finally" clause is executed before really leaving the
loop.
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Emulating callable objects
**************************

object.__call__(self[, args...])

Called when the instance is "called" as a function; if this method
   is defined, "x(arg1, arg2, ...)" is a shorthand for
   "x.__call__(arg1, arg2, ...)".
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Calls
*****

A call calls a callable object (e.g., a *function*) with a possibly
empty series of *arguments*:

call                 ::= primary "(" [argument_list [","]
            | expression genexpr_for] ")"
   argument_list        ::= positional_arguments ["," keyword_arguments]
                       ["," "*" expression] ["," keyword_arguments]
                       ["," "**" expression]
                     | keyword_arguments ["," "*" expression]
                       ["," "**" expression]
                     | "*" expression ["," keyword_arguments] ["," "**" expression]
                     | "**" expression
   positional_arguments ::= expression ("," expression)*
   keyword_arguments    ::= keyword_item ("," keyword_item)*
   keyword_item         ::= identifier "=" expression

A trailing comma may be present after the positional and keyword
arguments but does not affect the semantics.

The primary must evaluate to a callable object (user-defined
functions, built-in functions, methods of built-in objects, class
objects, methods of class instances, and certain class instances
themselves are callable; extensions may define additional callable
object types).  All argument expressions are evaluated before the call
is attempted.  Please refer to section Function definitions for the
syntax of formal *parameter* lists.

If keyword arguments are present, they are first converted to
positional arguments, as follows.  First, a list of unfilled slots is
created for the formal parameters.  If there are N positional
arguments, they are placed in the first N slots.  Next, for each
keyword argument, the identifier is used to determine the
corresponding slot (if the identifier is the same as the first formal
parameter name, the first slot is used, and so on).  If the slot is
already filled, a "TypeError" exception is raised. Otherwise, the
value of the argument is placed in the slot, filling it (even if the
expression is "None", it fills the slot).  When all arguments have
been processed, the slots that are still unfilled are filled with the
corresponding default value from the function definition.  (Default
values are calculated, once, when the function is defined; thus, a
mutable object such as a list or dictionary used as default value will
be shared by all calls that don't specify an argument value for the
corresponding slot; this should usually be avoided.)  If there are any
unfilled slots for which no default value is specified, a "TypeError"
exception is raised.  Otherwise, the list of filled slots is used as
the argument list for the call.

**CPython implementation detail:** An implementation may provide
built-in functions whose positional parameters do not have names, even
if they are 'named' for the purpose of documentation, and which
therefore cannot be supplied by keyword.  In CPython, this is the case
for functions implemented in C that use "PyArg_ParseTuple()" to parse
their arguments.

If there are more positional arguments than there are formal parameter
slots, a "TypeError" exception is raised, unless a formal parameter
using the syntax "*identifier" is present; in this case, that formal
parameter receives a tuple containing the excess positional arguments
(or an empty tuple if there were no excess positional arguments).

If any keyword argument does not correspond to a formal parameter
name, a "TypeError" exception is raised, unless a formal parameter
using the syntax "**identifier" is present; in this case, that formal
parameter receives a dictionary containing the excess keyword
arguments (using the keywords as keys and the argument values as
corresponding values), or a (new) empty dictionary if there were no
excess keyword arguments.

If the syntax "*expression" appears in the function call, "expression"
must evaluate to an iterable.  Elements from this iterable are treated
as if they were additional positional arguments; if there are
positional arguments *x1*, ..., *xN*, and "expression" evaluates to a
sequence *y1*, ..., *yM*, this is equivalent to a call with M+N
positional arguments *x1*, ..., *xN*, *y1*, ..., *yM*.

A consequence of this is that although the "*expression" syntax may
appear *after* some keyword arguments, it is processed *before* the
keyword arguments (and the "**expression" argument, if any -- see
below).  So:

>>> def f(a, b):
   ...     print a, b
   ...
   >>> f(b=1, *(2,))
   2 1
   >>> f(a=1, *(2,))
   Traceback (most recent call last):
     File "<stdin>", line 1, in <module>
   TypeError: f() got multiple values for keyword argument 'a'
   >>> f(1, *(2,))
   1 2

It is unusual for both keyword arguments and the "*expression" syntax
to be used in the same call, so in practice this confusion does not
arise.

If the syntax "**expression" appears in the function call,
"expression" must evaluate to a mapping, the contents of which are
treated as additional keyword arguments.  In the case of a keyword
appearing in both "expression" and as an explicit keyword argument, a
"TypeError" exception is raised.

Formal parameters using the syntax "*identifier" or "**identifier"
cannot be used as positional argument slots or as keyword argument
names.  Formal parameters using the syntax "(sublist)" cannot be used
as keyword argument names; the outermost sublist corresponds to a
single unnamed argument slot, and the argument value is assigned to
the sublist using the usual tuple assignment rules after all other
parameter processing is done.

A call always returns some value, possibly "None", unless it raises an
exception.  How this value is computed depends on the type of the
callable object.

If it is---

a user-defined function:
   The code block for the function is executed, passing it the
   argument list.  The first thing the code block will do is bind the
   formal parameters to the arguments; this is described in section
   Function definitions.  When the code block executes a "return"
   statement, this specifies the return value of the function call.

a built-in function or method:
   The result is up to the interpreter; see Built-in Functions for the
   descriptions of built-in functions and methods.

a class object:
   A new instance of that class is returned.

a class instance method:
   The corresponding user-defined function is called, with an argument
   list that is one longer than the argument list of the call: the
   instance becomes the first argument.

a class instance:
   The class must define a "__call__()" method; the effect is then the
   same as if that method was called.
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Class definitions
*****************

A class definition defines a class object (see section The standard
type hierarchy):

classdef    ::= "class" classname [inheritance] ":" suite
   inheritance ::= "(" [expression_list] ")"
   classname   ::= identifier

A class definition is an executable statement.  It first evaluates the
inheritance list, if present.  Each item in the inheritance list
should evaluate to a class object or class type which allows
subclassing.  The class's suite is then executed in a new execution
frame (see section Naming and binding), using a newly created local
namespace and the original global namespace. (Usually, the suite
contains only function definitions.)  When the class's suite finishes
execution, its execution frame is discarded but its local namespace is
saved. [4] A class object is then created using the inheritance list
for the base classes and the saved local namespace for the attribute
dictionary.  The class name is bound to this class object in the
original local namespace.

**Programmer's note:** Variables defined in the class definition are
class variables; they are shared by all instances.  To create instance
variables, they can be set in a method with "self.name = value".  Both
class and instance variables are accessible through the notation
""self.name"", and an instance variable hides a class variable with
the same name when accessed in this way. Class variables can be used
as defaults for instance variables, but using mutable values there can
lead to unexpected results.  For *new-style class*es, descriptors can
be used to create instance variables with different implementation
details.

Class definitions, like function definitions, may be wrapped by one or
more *decorator* expressions.  The evaluation rules for the decorator
expressions are the same as for functions.  The result must be a class
object, which is then bound to the class name.

-[ Footnotes ]-

[1] The exception is propagated to the invocation stack unless
    there is a "finally" clause which happens to raise another
    exception. That new exception causes the old one to be lost.

[2] Currently, control "flows off the end" except in the case of
    an exception or the execution of a "return", "continue", or
    "break" statement.

[3] A string literal appearing as the first statement in the
    function body is transformed into the function's "__doc__"
    attribute and therefore the function's *docstring*.

[4] A string literal appearing as the first statement in the class
    body is transformed into the namespace's "__doc__" item and
    therefore the class's *docstring*.
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Comparisons
***********

Unlike C, all comparison operations in Python have the same priority,
which is lower than that of any arithmetic, shifting or bitwise
operation.  Also unlike C, expressions like "a < b < c" have the
interpretation that is conventional in mathematics:

comparison    ::= or_expr ( comp_operator or_expr )*
   comp_operator ::= "<" | ">" | "==" | ">=" | "<=" | "<>" | "!="
                     | "is" ["not"] | ["not"] "in"

Comparisons yield boolean values: "True" or "False".

Comparisons can be chained arbitrarily, e.g., "x < y <= z" is
equivalent to "x < y and y <= z", except that "y" is evaluated only
once (but in both cases "z" is not evaluated at all when "x < y" is
found to be false).

Formally, if *a*, *b*, *c*, ..., *y*, *z* are expressions and *op1*,
*op2*, ..., *opN* are comparison operators, then "a op1 b op2 c ... y
opN z" is equivalent to "a op1 b and b op2 c and ... y opN z", except
that each expression is evaluated at most once.

Note that "a op1 b op2 c" doesn't imply any kind of comparison between
*a* and *c*, so that, e.g., "x < y > z" is perfectly legal (though
perhaps not pretty).

The forms "<>" and "!=" are equivalent; for consistency with C, "!="
is preferred; where "!=" is mentioned below "<>" is also accepted.
The "<>" spelling is considered obsolescent.

Value comparisons
=================

The operators "<", ">", "==", ">=", "<=", and "!=" compare the values
of two objects.  The objects do not need to have the same type.

Chapter Objects, values and types states that objects have a value (in
addition to type and identity).  The value of an object is a rather
abstract notion in Python: For example, there is no canonical access
method for an object's value.  Also, there is no requirement that the
value of an object should be constructed in a particular way, e.g.
comprised of all its data attributes. Comparison operators implement a
particular notion of what the value of an object is.  One can think of
them as defining the value of an object indirectly, by means of their
comparison implementation.

Types can customize their comparison behavior by implementing a
"__cmp__()" method or *rich comparison methods* like "__lt__()",
described in Basic customization.

The default behavior for equality comparison ("==" and "!=") is based
on the identity of the objects.  Hence, equality comparison of
instances with the same identity results in equality, and equality
comparison of instances with different identities results in
inequality.  A motivation for this default behavior is the desire that
all objects should be reflexive (i.e. "x is y" implies "x == y").

The default order comparison ("<", ">", "<=", and ">=") gives a
consistent but arbitrary order.

(This unusual definition of comparison was used to simplify the
definition of operations like sorting and the "in" and "not in"
operators. In the future, the comparison rules for objects of
different types are likely to change.)

The behavior of the default equality comparison, that instances with
different identities are always unequal, may be in contrast to what
types will need that have a sensible definition of object value and
value-based equality.  Such types will need to customize their
comparison behavior, and in fact, a number of built-in types have done
that.

The following list describes the comparison behavior of the most
important built-in types.

* Numbers of built-in numeric types (Numeric Types --- int, float,
  long, complex) and of the standard library types
  "fractions.Fraction" and "decimal.Decimal" can be compared within
  and across their types, with the restriction that complex numbers do
  not support order comparison.  Within the limits of the types
  involved, they compare mathematically (algorithmically) correct
  without loss of precision.

* Strings (instances of "str" or "unicode") compare
  lexicographically using the numeric equivalents (the result of the
  built-in function "ord()") of their characters. [4] When comparing
  an 8-bit string and a Unicode string, the 8-bit string is converted
  to Unicode.  If the conversion fails, the strings are considered
  unequal.

* Instances of "tuple" or "list" can be compared only within each of
  their types.  Equality comparison across these types results in
  unequality, and ordering comparison across these types gives an
  arbitrary order.

These sequences compare lexicographically using comparison of
  corresponding elements, whereby reflexivity of the elements is
  enforced.

In enforcing reflexivity of elements, the comparison of collections
  assumes that for a collection element "x", "x == x" is always true.
  Based on that assumption, element identity is compared first, and
  element comparison is performed only for distinct elements.  This
  approach yields the same result as a strict element comparison
  would, if the compared elements are reflexive.  For non-reflexive
  elements, the result is different than for strict element
  comparison.

Lexicographical comparison between built-in collections works as
  follows:

* For two collections to compare equal, they must be of the same
    type, have the same length, and each pair of corresponding
    elements must compare equal (for example, "[1,2] == (1,2)" is
    false because the type is not the same).

* Collections are ordered the same as their first unequal elements
    (for example, "cmp([1,2,x], [1,2,y])" returns the same as
    "cmp(x,y)").  If a corresponding element does not exist, the
    shorter collection is ordered first (for example, "[1,2] <
    [1,2,3]" is true).

* Mappings (instances of "dict") compare equal if and only if they
  have equal *(key, value)* pairs. Equality comparison of the keys and
  values enforces reflexivity.

Outcomes other than equality are resolved consistently, but are not
  otherwise defined. [5]

* Most other objects of built-in types compare unequal unless they
  are the same object; the choice whether one object is considered
  smaller or larger than another one is made arbitrarily but
  consistently within one execution of a program.

User-defined classes that customize their comparison behavior should
follow some consistency rules, if possible:

* Equality comparison should be reflexive. In other words, identical
  objects should compare equal:

"x is y" implies "x == y"

* Comparison should be symmetric. In other words, the following
  expressions should have the same result:

"x == y" and "y == x"

"x != y" and "y != x"

"x < y" and "y > x"

"x <= y" and "y >= x"

* Comparison should be transitive. The following (non-exhaustive)
  examples illustrate that:

"x > y and y > z" implies "x > z"

"x < y and y <= z" implies "x < z"

* Inverse comparison should result in the boolean negation. In other
  words, the following expressions should have the same result:

"x == y" and "not x != y"

"x < y" and "not x >= y" (for total ordering)

"x > y" and "not x <= y" (for total ordering)

The last two expressions apply to totally ordered collections (e.g.
  to sequences, but not to sets or mappings). See also the
  "total_ordering()" decorator.

* The "hash()" result should be consistent with equality. Objects
  that are equal should either have the same hash value, or be marked
  as unhashable.

Python does not enforce these consistency rules.

Membership test operations
==========================

The operators "in" and "not in" test for membership.  "x in s"
evaluates to "True" if *x* is a member of *s*, and "False" otherwise.
"x not in s" returns the negation of "x in s".  All built-in sequences
and set types support this as well as dictionary, for which "in" tests
whether the dictionary has a given key. For container types such as
list, tuple, set, frozenset, dict, or collections.deque, the
expression "x in y" is equivalent to "any(x is e or x == e for e in
y)".

For the string and bytes types, "x in y" is "True" if and only if *x*
is a substring of *y*.  An equivalent test is "y.find(x) != -1".
Empty strings are always considered to be a substring of any other
string, so """ in "abc"" will return "True".

For user-defined classes which define the "__contains__()" method, "x
in y" returns "True" if "y.__contains__(x)" returns a true value, and
"False" otherwise.

For user-defined classes which do not define "__contains__()" but do
define "__iter__()", "x in y" is "True" if some value "z" with "x ==
z" is produced while iterating over "y".  If an exception is raised
during the iteration, it is as if "in" raised that exception.

Lastly, the old-style iteration protocol is tried: if a class defines
"__getitem__()", "x in y" is "True" if and only if there is a non-
negative integer index *i* such that "x == y[i]", and all lower
integer indices do not raise "IndexError" exception. (If any other
exception is raised, it is as if "in" raised that exception).

The operator "not in" is defined to have the inverse true value of
"in".

Identity comparisons
====================

The operators "is" and "is not" test for object identity: "x is y" is
true if and only if *x* and *y* are the same object.  "x is not y"
yields the inverse truth value. [6]
t���comparisonsspP��
Compound statements
*******************

Compound statements contain (groups of) other statements; they affect
or control the execution of those other statements in some way.  In
general, compound statements span multiple lines, although in simple
incarnations a whole compound statement may be contained in one line.

The "if", "while" and "for" statements implement traditional control
flow constructs.  "try" specifies exception handlers and/or cleanup
code for a group of statements.  Function and class definitions are
also syntactically compound statements.

Compound statements consist of one or more 'clauses.'  A clause
consists of a header and a 'suite.'  The clause headers of a
particular compound statement are all at the same indentation level.
Each clause header begins with a uniquely identifying keyword and ends
with a colon.  A suite is a group of statements controlled by a
clause.  A suite can be one or more semicolon-separated simple
statements on the same line as the header, following the header's
colon, or it can be one or more indented statements on subsequent
lines.  Only the latter form of suite can contain nested compound
statements; the following is illegal, mostly because it wouldn't be
clear to which "if" clause a following "else" clause would belong:

if test1: if test2: print x

Also note that the semicolon binds tighter than the colon in this
context, so that in the following example, either all or none of the
"print" statements are executed:

if x < y < z: print x; print y; print z

Summarizing:

Note that statements always end in a "NEWLINE" possibly followed by a
"DEDENT". Also note that optional continuation clauses always begin
with a keyword that cannot start a statement, thus there are no
ambiguities (the 'dangling "else"' problem is solved in Python by
requiring nested "if" statements to be indented).

The formatting of the grammar rules in the following sections places
each clause on a separate line for clarity.

The "if" statement
==================

The "if" statement is used for conditional execution:

if_stmt ::= "if" expression ":" suite
               ( "elif" expression ":" suite )*
               ["else" ":" suite]

It selects exactly one of the suites by evaluating the expressions one
by one until one is found to be true (see section Boolean operations
for the definition of true and false); then that suite is executed
(and no other part of the "if" statement is executed or evaluated).
If all expressions are false, the suite of the "else" clause, if
present, is executed.

The "while" statement
=====================

The "while" statement is used for repeated execution as long as an
expression is true:

while_stmt ::= "while" expression ":" suite
                  ["else" ":" suite]

This repeatedly tests the expression and, if it is true, executes the
first suite; if the expression is false (which may be the first time
it is tested) the suite of the "else" clause, if present, is executed
and the loop terminates.

A "break" statement executed in the first suite terminates the loop
without executing the "else" clause's suite.  A "continue" statement
executed in the first suite skips the rest of the suite and goes back
to testing the expression.

The "for" statement
===================

The "for" statement is used to iterate over the elements of a sequence
(such as a string, tuple or list) or other iterable object:

for_stmt ::= "for" target_list "in" expression_list ":" suite
                ["else" ":" suite]

The expression list is evaluated once; it should yield an iterable
object.  An iterator is created for the result of the
"expression_list".  The suite is then executed once for each item
provided by the iterator, in the order of ascending indices.  Each
item in turn is assigned to the target list using the standard rules
for assignments, and then the suite is executed.  When the items are
exhausted (which is immediately when the sequence is empty), the suite
in the "else" clause, if present, is executed, and the loop
terminates.

A "break" statement executed in the first suite terminates the loop
without executing the "else" clause's suite.  A "continue" statement
executed in the first suite skips the rest of the suite and continues
with the next item, or with the "else" clause if there was no next
item.

The suite may assign to the variable(s) in the target list; this does
not affect the next item assigned to it.

The target list is not deleted when the loop is finished, but if the
sequence is empty, it will not have been assigned to at all by the
loop.  Hint: the built-in function "range()" returns a sequence of
integers suitable to emulate the effect of Pascal's "for i := a to b
do"; e.g., "range(3)" returns the list "[0, 1, 2]".

Note: There is a subtlety when the sequence is being modified by the
  loop (this can only occur for mutable sequences, i.e. lists). An
  internal counter is used to keep track of which item is used next,
  and this is incremented on each iteration.  When this counter has
  reached the length of the sequence the loop terminates.  This means
  that if the suite deletes the current (or a previous) item from the
  sequence, the next item will be skipped (since it gets the index of
  the current item which has already been treated).  Likewise, if the
  suite inserts an item in the sequence before the current item, the
  current item will be treated again the next time through the loop.
  This can lead to nasty bugs that can be avoided by making a
  temporary copy using a slice of the whole sequence, e.g.,

for x in a[:]:
         if x < 0: a.remove(x)

The "try" statement
===================

The "try" statement specifies exception handlers and/or cleanup code
for a group of statements:

try_stmt  ::= try1_stmt | try2_stmt
   try1_stmt ::= "try" ":" suite
                 ("except" [expression [("as" | ",") identifier]] ":" suite)+
                 ["else" ":" suite]
                 ["finally" ":" suite]
   try2_stmt ::= "try" ":" suite
                 "finally" ":" suite

Changed in version 2.5: In previous versions of Python,
"try"..."except"..."finally" did not work. "try"..."except" had to be
nested in "try"..."finally".

The "except" clause(s) specify one or more exception handlers. When no
exception occurs in the "try" clause, no exception handler is
executed. When an exception occurs in the "try" suite, a search for an
exception handler is started.  This search inspects the except clauses
in turn until one is found that matches the exception.  An expression-
less except clause, if present, must be last; it matches any
exception.  For an except clause with an expression, that expression
is evaluated, and the clause matches the exception if the resulting
object is "compatible" with the exception.  An object is compatible
with an exception if it is the class or a base class of the exception
object, or a tuple containing an item compatible with the exception.

If no except clause matches the exception, the search for an exception
handler continues in the surrounding code and on the invocation stack.
[1]

If the evaluation of an expression in the header of an except clause
raises an exception, the original search for a handler is canceled and
a search starts for the new exception in the surrounding code and on
the call stack (it is treated as if the entire "try" statement raised
the exception).

When a matching except clause is found, the exception is assigned to
the target specified in that except clause, if present, and the except
clause's suite is executed.  All except clauses must have an
executable block.  When the end of this block is reached, execution
continues normally after the entire try statement.  (This means that
if two nested handlers exist for the same exception, and the exception
occurs in the try clause of the inner handler, the outer handler will
not handle the exception.)

Before an except clause's suite is executed, details about the
exception are assigned to three variables in the "sys" module:
"sys.exc_type" receives the object identifying the exception;
"sys.exc_value" receives the exception's parameter;
"sys.exc_traceback" receives a traceback object (see section The
standard type hierarchy) identifying the point in the program where
the exception occurred. These details are also available through the
"sys.exc_info()" function, which returns a tuple "(exc_type,
exc_value, exc_traceback)".  Use of the corresponding variables is
deprecated in favor of this function, since their use is unsafe in a
threaded program.  As of Python 1.5, the variables are restored to
their previous values (before the call) when returning from a function
that handled an exception.

The optional "else" clause is executed if and when control flows off
the end of the "try" clause. [2] Exceptions in the "else" clause are
not handled by the preceding "except" clauses.

If "finally" is present, it specifies a 'cleanup' handler.  The "try"
clause is executed, including any "except" and "else" clauses.  If an
exception occurs in any of the clauses and is not handled, the
exception is temporarily saved. The "finally" clause is executed.  If
there is a saved exception, it is re-raised at the end of the
"finally" clause. If the "finally" clause raises another exception or
executes a "return" or "break" statement, the saved exception is
discarded:

>>> def f():
   ...     try:
   ...         1/0
   ...     finally:
   ...         return 42
   ...
   >>> f()
   42

The exception information is not available to the program during
execution of the "finally" clause.

When a "return", "break" or "continue" statement is executed in the
"try" suite of a "try"..."finally" statement, the "finally" clause is
also executed 'on the way out.' A "continue" statement is illegal in
the "finally" clause. (The reason is a problem with the current
implementation --- this restriction may be lifted in the future).

The return value of a function is determined by the last "return"
statement executed.  Since the "finally" clause always executes, a
"return" statement executed in the "finally" clause will always be the
last one executed:

>>> def foo():
   ...     try:
   ...         return 'try'
   ...     finally:
   ...         return 'finally'
   ...
   >>> foo()
   'finally'

Additional information on exceptions can be found in section
Exceptions, and information on using the "raise" statement to generate
exceptions may be found in section The raise statement.

The "with" statement
====================

New in version 2.5.

The "with" statement is used to wrap the execution of a block with
methods defined by a context manager (see section With Statement
Context Managers). This allows common "try"..."except"..."finally"
usage patterns to be encapsulated for convenient reuse.

with_stmt ::= "with" with_item ("," with_item)* ":" suite
   with_item ::= expression ["as" target]

The execution of the "with" statement with one "item" proceeds as
follows:

1. The context expression (the expression given in the "with_item")
   is evaluated to obtain a context manager.

2. The context manager's "__exit__()" is loaded for later use.

3. The context manager's "__enter__()" method is invoked.

4. If a target was included in the "with" statement, the return
   value from "__enter__()" is assigned to it.

Note: The "with" statement guarantees that if the "__enter__()"
     method returns without an error, then "__exit__()" will always be
     called. Thus, if an error occurs during the assignment to the
     target list, it will be treated the same as an error occurring
     within the suite would be. See step 6 below.

5. The suite is executed.

6. The context manager's "__exit__()" method is invoked. If an
   exception caused the suite to be exited, its type, value, and
   traceback are passed as arguments to "__exit__()". Otherwise, three
   "None" arguments are supplied.

If the suite was exited due to an exception, and the return value
   from the "__exit__()" method was false, the exception is reraised.
   If the return value was true, the exception is suppressed, and
   execution continues with the statement following the "with"
   statement.

If the suite was exited for any reason other than an exception, the
   return value from "__exit__()" is ignored, and execution proceeds
   at the normal location for the kind of exit that was taken.

With more than one item, the context managers are processed as if
multiple "with" statements were nested:

with A() as a, B() as b:
       suite

is equivalent to

with A() as a:
       with B() as b:
           suite

Note: In Python 2.5, the "with" statement is only allowed when the
  "with_statement" feature has been enabled.  It is always enabled in
  Python 2.6.

Changed in version 2.7: Support for multiple context expressions.

See also:

**PEP 343** - The "with" statement
     The specification, background, and examples for the Python "with"
     statement.

Function definitions
====================

A function definition defines a user-defined function object (see
section The standard type hierarchy):

decorated      ::= decorators (classdef | funcdef)
   decorators     ::= decorator+
   decorator      ::= "@" dotted_name ["(" [argument_list [","]] ")"] NEWLINE
   funcdef        ::= "def" funcname "(" [parameter_list] ")" ":" suite
   dotted_name    ::= identifier ("." identifier)*
   parameter_list ::= (defparameter ",")*
                      (  "*" identifier ["," "**" identifier]
                      | "**" identifier
                      | defparameter [","] )
   defparameter   ::= parameter ["=" expression]
   sublist        ::= parameter ("," parameter)* [","]
   parameter      ::= identifier | "(" sublist ")"
   funcname       ::= identifier

A function definition is an executable statement.  Its execution binds
the function name in the current local namespace to a function object
(a wrapper around the executable code for the function).  This
function object contains a reference to the current global namespace
as the global namespace to be used when the function is called.

The function definition does not execute the function body; this gets
executed only when the function is called. [3]

A function definition may be wrapped by one or more *decorator*
expressions. Decorator expressions are evaluated when the function is
defined, in the scope that contains the function definition.  The
result must be a callable, which is invoked with the function object
as the only argument. The returned value is bound to the function name
instead of the function object.  Multiple decorators are applied in
nested fashion. For example, the following code:

@f1(arg)
   @f2
   def func(): pass

is equivalent to:

def func(): pass
   func = f1(arg)(f2(func))

When one or more top-level *parameters* have the form *parameter* "="
*expression*, the function is said to have "default parameter values."
For a parameter with a default value, the corresponding *argument* may
be omitted from a call, in which case the parameter's default value is
substituted.  If a parameter has a default value, all following
parameters must also have a default value --- this is a syntactic
restriction that is not expressed by the grammar.

**Default parameter values are evaluated when the function definition
is executed.**  This means that the expression is evaluated once, when
the function is defined, and that the same "pre-computed" value is
used for each call.  This is especially important to understand when a
default parameter is a mutable object, such as a list or a dictionary:
if the function modifies the object (e.g. by appending an item to a
list), the default value is in effect modified. This is generally not
what was intended.  A way around this  is to use "None" as the
default, and explicitly test for it in the body of the function, e.g.:

def whats_on_the_telly(penguin=None):
       if penguin is None:
           penguin = []
       penguin.append("property of the zoo")
       return penguin

Function call semantics are described in more detail in section Calls.
A function call always assigns values to all parameters mentioned in
the parameter list, either from position arguments, from keyword
arguments, or from default values.  If the form ""*identifier"" is
present, it is initialized to a tuple receiving any excess positional
parameters, defaulting to the empty tuple.  If the form
""**identifier"" is present, it is initialized to a new dictionary
receiving any excess keyword arguments, defaulting to a new empty
dictionary.

It is also possible to create anonymous functions (functions not bound
to a name), for immediate use in expressions.  This uses lambda
expressions, described in section Lambdas.  Note that the lambda
expression is merely a shorthand for a simplified function definition;
a function defined in a ""def"" statement can be passed around or
assigned to another name just like a function defined by a lambda
expression.  The ""def"" form is actually more powerful since it
allows the execution of multiple statements.

**Programmer's note:** Functions are first-class objects.  A ""def""
form executed inside a function definition defines a local function
that can be returned or passed around.  Free variables used in the
nested function can access the local variables of the function
containing the def.  See section Naming and binding for details.

Class definitions
=================

A class definition defines a class object (see section The standard
type hierarchy):

classdef    ::= "class" classname [inheritance] ":" suite
   inheritance ::= "(" [expression_list] ")"
   classname   ::= identifier

A class definition is an executable statement.  It first evaluates the
inheritance list, if present.  Each item in the inheritance list
should evaluate to a class object or class type which allows
subclassing.  The class's suite is then executed in a new execution
frame (see section Naming and binding), using a newly created local
namespace and the original global namespace. (Usually, the suite
contains only function definitions.)  When the class's suite finishes
execution, its execution frame is discarded but its local namespace is
saved. [4] A class object is then created using the inheritance list
for the base classes and the saved local namespace for the attribute
dictionary.  The class name is bound to this class object in the
original local namespace.

**Programmer's note:** Variables defined in the class definition are
class variables; they are shared by all instances.  To create instance
variables, they can be set in a method with "self.name = value".  Both
class and instance variables are accessible through the notation
""self.name"", and an instance variable hides a class variable with
the same name when accessed in this way. Class variables can be used
as defaults for instance variables, but using mutable values there can
lead to unexpected results.  For *new-style class*es, descriptors can
be used to create instance variables with different implementation
details.

Class definitions, like function definitions, may be wrapped by one or
more *decorator* expressions.  The evaluation rules for the decorator
expressions are the same as for functions.  The result must be a class
object, which is then bound to the class name.

-[ Footnotes ]-

[1] The exception is propagated to the invocation stack unless
    there is a "finally" clause which happens to raise another
    exception. That new exception causes the old one to be lost.

[2] Currently, control "flows off the end" except in the case of
    an exception or the execution of a "return", "continue", or
    "break" statement.

[3] A string literal appearing as the first statement in the
    function body is transformed into the function's "__doc__"
    attribute and therefore the function's *docstring*.

[4] A string literal appearing as the first statement in the class
    body is transformed into the namespace's "__doc__" item and
    therefore the class's *docstring*.
t���compounds���
With Statement Context Managers
*******************************

New in version 2.5.

A *context manager* is an object that defines the runtime context to
be established when executing a "with" statement. The context manager
handles the entry into, and the exit from, the desired runtime context
for the execution of the block of code.  Context managers are normally
invoked using the "with" statement (described in section The with
statement), but can also be used by directly invoking their methods.

Typical uses of context managers include saving and restoring various
kinds of global state, locking and unlocking resources, closing opened
files, etc.

For more information on context managers, see Context Manager Types.

object.__enter__(self)

Enter the runtime context related to this object. The "with"
   statement will bind this method's return value to the target(s)
   specified in the "as" clause of the statement, if any.

object.__exit__(self, exc_type, exc_value, traceback)

Exit the runtime context related to this object. The parameters
   describe the exception that caused the context to be exited. If the
   context was exited without an exception, all three arguments will
   be "None".

If an exception is supplied, and the method wishes to suppress the
   exception (i.e., prevent it from being propagated), it should
   return a true value. Otherwise, the exception will be processed
   normally upon exit from this method.

Note that "__exit__()" methods should not reraise the passed-in
   exception; this is the caller's responsibility.

See also:

**PEP 343** - The "with" statement
     The specification, background, and examples for the Python "with"
     statement.
s���context-managerss���
The "continue" statement
************************

continue_stmt ::= "continue"

"continue" may only occur syntactically nested in a "for" or "while"
loop, but not nested in a function or class definition or "finally"
clause within that loop.  It continues with the next cycle of the
nearest enclosing loop.

When "continue" passes control out of a "try" statement with a
"finally" clause, that "finally" clause is executed before really
starting the next loop cycle.
t���continuesB��
Arithmetic conversions
**********************

When a description of an arithmetic operator below uses the phrase
"the numeric arguments are converted to a common type," the arguments
are coerced using the coercion rules listed at  Coercion rules.  If
both arguments are standard numeric types, the following coercions are
applied:

* If either argument is a complex number, the other is converted to
  complex;

* otherwise, if either argument is a floating point number, the
  other is converted to floating point;

* otherwise, if either argument is a long integer, the other is
  converted to long integer;

* otherwise, both must be plain integers and no conversion is
  necessary.

Some additional rules apply for certain operators (e.g., a string left
argument to the '%' operator). Extensions can define their own
coercions.
t���conversionss�/��
Basic customization
*******************

object.__new__(cls[, ...])

Called to create a new instance of class *cls*.  "__new__()" is a
   static method (special-cased so you need not declare it as such)
   that takes the class of which an instance was requested as its
   first argument.  The remaining arguments are those passed to the
   object constructor expression (the call to the class).  The return
   value of "__new__()" should be the new object instance (usually an
   instance of *cls*).

Typical implementations create a new instance of the class by
   invoking the superclass's "__new__()" method using
   "super(currentclass, cls).__new__(cls[, ...])" with appropriate
   arguments and then modifying the newly-created instance as
   necessary before returning it.

If "__new__()" returns an instance of *cls*, then the new
   instance's "__init__()" method will be invoked like
   "__init__(self[, ...])", where *self* is the new instance and the
   remaining arguments are the same as were passed to "__new__()".

If "__new__()" does not return an instance of *cls*, then the new
   instance's "__init__()" method will not be invoked.

"__new__()" is intended mainly to allow subclasses of immutable
   types (like int, str, or tuple) to customize instance creation.  It
   is also commonly overridden in custom metaclasses in order to
   customize class creation.

object.__init__(self[, ...])

Called after the instance has been created (by "__new__()"), but
   before it is returned to the caller.  The arguments are those
   passed to the class constructor expression.  If a base class has an
   "__init__()" method, the derived class's "__init__()" method, if
   any, must explicitly call it to ensure proper initialization of the
   base class part of the instance; for example:
   "BaseClass.__init__(self, [args...])".

Because "__new__()" and "__init__()" work together in constructing
   objects ("__new__()" to create it, and "__init__()" to customise
   it), no non-"None" value may be returned by "__init__()"; doing so
   will cause a "TypeError" to be raised at runtime.

object.__del__(self)

Called when the instance is about to be destroyed.  This is also
   called a destructor.  If a base class has a "__del__()" method, the
   derived class's "__del__()" method, if any, must explicitly call it
   to ensure proper deletion of the base class part of the instance.
   Note that it is possible (though not recommended!) for the
   "__del__()" method to postpone destruction of the instance by
   creating a new reference to it.  It may then be called at a later
   time when this new reference is deleted.  It is not guaranteed that
   "__del__()" methods are called for objects that still exist when
   the interpreter exits.

Note: "del x" doesn't directly call "x.__del__()" --- the former
     decrements the reference count for "x" by one, and the latter is
     only called when "x"'s reference count reaches zero.  Some common
     situations that may prevent the reference count of an object from
     going to zero include: circular references between objects (e.g.,
     a doubly-linked list or a tree data structure with parent and
     child pointers); a reference to the object on the stack frame of
     a function that caught an exception (the traceback stored in
     "sys.exc_traceback" keeps the stack frame alive); or a reference
     to the object on the stack frame that raised an unhandled
     exception in interactive mode (the traceback stored in
     "sys.last_traceback" keeps the stack frame alive).  The first
     situation can only be remedied by explicitly breaking the cycles;
     the latter two situations can be resolved by storing "None" in
     "sys.exc_traceback" or "sys.last_traceback".  Circular references
     which are garbage are detected when the option cycle detector is
     enabled (it's on by default), but can only be cleaned up if there
     are no Python-level "__del__()" methods involved. Refer to the
     documentation for the "gc" module for more information about how
     "__del__()" methods are handled by the cycle detector,
     particularly the description of the "garbage" value.

Warning: Due to the precarious circumstances under which
     "__del__()" methods are invoked, exceptions that occur during
     their execution are ignored, and a warning is printed to
     "sys.stderr" instead. Also, when "__del__()" is invoked in
     response to a module being deleted (e.g., when execution of the
     program is done), other globals referenced by the "__del__()"
     method may already have been deleted or in the process of being
     torn down (e.g. the import machinery shutting down).  For this
     reason, "__del__()" methods should do the absolute minimum needed
     to maintain external invariants.  Starting with version 1.5,
     Python guarantees that globals whose name begins with a single
     underscore are deleted from their module before other globals are
     deleted; if no other references to such globals exist, this may
     help in assuring that imported modules are still available at the
     time when the "__del__()" method is called.

See also:

**PEP 236** - Back to the __future__
     The original proposal for the __future__ mechanism.
t���importsO��
Membership test operations
**************************

The operators "in" and "not in" test for membership.  "x in s"
evaluates to "True" if *x* is a member of *s*, and "False" otherwise.
"x not in s" returns the negation of "x in s".  All built-in sequences
and set types support this as well as dictionary, for which "in" tests
whether the dictionary has a given key. For container types such as
list, tuple, set, frozenset, dict, or collections.deque, the
expression "x in y" is equivalent to "any(x is e or x == e for e in
y)".

For the string and bytes types, "x in y" is "True" if and only if *x*
is a substring of *y*.  An equivalent test is "y.find(x) != -1".
Empty strings are always considered to be a substring of any other
string, so """ in "abc"" will return "True".

For user-defined classes which define the "__contains__()" method, "x
in y" returns "True" if "y.__contains__(x)" returns a true value, and
"False" otherwise.

For user-defined classes which do not define "__contains__()" but do
define "__iter__()", "x in y" is "True" if some value "z" with "x ==
z" is produced while iterating over "y".  If an exception is raised
during the iteration, it is as if "in" raised that exception.

Lastly, the old-style iteration protocol is tried: if a class defines
"__getitem__()", "x in y" is "True" if and only if there is a non-
negative integer index *i* such that "x == y[i]", and all lower
integer indices do not raise "IndexError" exception. (If any other
exception is raised, it is as if "in" raised that exception).

The operator "not in" is defined to have the inverse true value of
"in".
t���inso��
Integer and long integer literals
*********************************

Integer and long integer literals are described by the following
lexical definitions:

Although both lower case "'l'" and upper case "'L'" are allowed as
suffix for long integers, it is strongly recommended to always use
"'L'", since the letter "'l'" looks too much like the digit "'1'".

Plain integer literals that are above the largest representable plain
integer (e.g., 2147483647 when using 32-bit arithmetic) are accepted
as if they were long integers instead. [1]  There is no limit for long
integer literals apart from what can be stored in available memory.

Some examples of plain integer literals (first row) and long integer
literals (second and third rows):

7     2147483647                        0177
   3L    79228162514264337593543950336L    0377L   0x100000000L
         79228162514264337593543950336             0xdeadbeef
t���integerssx��
Lambdas
*******

lambda_expr     ::= "lambda" [parameter_list]: expression
   old_lambda_expr ::= "lambda" [parameter_list]: old_expression

Lambda expressions (sometimes called lambda forms) have the same
syntactic position as expressions.  They are a shorthand to create
anonymous functions; the expression "lambda arguments: expression"
yields a function object.  The unnamed object behaves like a function
object defined with

def name(arguments):
       return expression

See section Function definitions for the syntax of parameter lists.
Note that functions created with lambda expressions cannot contain
statements.
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List displays
*************

A list display is a possibly empty series of expressions enclosed in
square brackets:

list_display        ::= "[" [expression_list | list_comprehension] "]"
   list_comprehension  ::= expression list_for
   list_for            ::= "for" target_list "in" old_expression_list [list_iter]
   old_expression_list ::= old_expression [("," old_expression)+ [","]]
   old_expression      ::= or_test | old_lambda_expr
   list_iter           ::= list_for | list_if
   list_if             ::= "if" old_expression [list_iter]

A list display yields a new list object.  Its contents are specified
by providing either a list of expressions or a list comprehension.
When a comma-separated list of expressions is supplied, its elements
are evaluated from left to right and placed into the list object in
that order.  When a list comprehension is supplied, it consists of a
single expression followed by at least one "for" clause and zero or
more "for" or "if" clauses.  In this case, the elements of the new
list are those that would be produced by considering each of the "for"
or "if" clauses a block, nesting from left to right, and evaluating
the expression to produce a list element each time the innermost block
is reached [1].
t���listss���
Naming and binding
******************

*Names* refer to objects.  Names are introduced by name binding
operations. Each occurrence of a name in the program text refers to
the *binding* of that name established in the innermost function block
containing the use.

A *block* is a piece of Python program text that is executed as a
unit. The following are blocks: a module, a function body, and a class
definition. Each command typed interactively is a block.  A script
file (a file given as standard input to the interpreter or specified
on the interpreter command line the first argument) is a code block.
A script command (a command specified on the interpreter command line
with the '**-c**' option) is a code block.  The file read by the
built-in function "execfile()" is a code block.  The string argument
passed to the built-in function "eval()" and to the "exec" statement
is a code block. The expression read and evaluated by the built-in
function "input()" is a code block.

A code block is executed in an *execution frame*.  A frame contains
some administrative information (used for debugging) and determines
where and how execution continues after the code block's execution has
completed.

A *scope* defines the visibility of a name within a block.  If a local
variable is defined in a block, its scope includes that block.  If the
definition occurs in a function block, the scope extends to any blocks
contained within the defining one, unless a contained block introduces
a different binding for the name.  The scope of names defined in a
class block is limited to the class block; it does not extend to the
code blocks of methods -- this includes generator expressions since
they are implemented using a function scope.  This means that the
following will fail:

class A:
       a = 42
       b = list(a + i for i in range(10))

When a name is used in a code block, it is resolved using the nearest
enclosing scope.  The set of all such scopes visible to a code block
is called the block's *environment*.

If a name is bound in a block, it is a local variable of that block.
If a name is bound at the module level, it is a global variable.  (The
variables of the module code block are local and global.)  If a
variable is used in a code block but not defined there, it is a *free
variable*.

When a name is not found at all, a "NameError" exception is raised.
If the name refers to a local variable that has not been bound, a
"UnboundLocalError" exception is raised.  "UnboundLocalError" is a
subclass of "NameError".

The following constructs bind names: formal parameters to functions,
"import" statements, class and function definitions (these bind the
class or function name in the defining block), and targets that are
identifiers if occurring in an assignment, "for" loop header, in the
second position of an "except" clause header or after "as" in a "with"
statement.  The "import" statement of the form "from ... import *"
binds all names defined in the imported module, except those beginning
with an underscore.  This form may only be used at the module level.

A target occurring in a "del" statement is also considered bound for
this purpose (though the actual semantics are to unbind the name).  It
is illegal to unbind a name that is referenced by an enclosing scope;
the compiler will report a "SyntaxError".

Each assignment or import statement occurs within a block defined by a
class or function definition or at the module level (the top-level
code block).

If a name binding operation occurs anywhere within a code block, all
uses of the name within the block are treated as references to the
current block.  This can lead to errors when a name is used within a
block before it is bound. This rule is subtle.  Python lacks
declarations and allows name binding operations to occur anywhere
within a code block.  The local variables of a code block can be
determined by scanning the entire text of the block for name binding
operations.

If the global statement occurs within a block, all uses of the name
specified in the statement refer to the binding of that name in the
top-level namespace. Names are resolved in the top-level namespace by
searching the global namespace, i.e. the namespace of the module
containing the code block, and the builtins namespace, the namespace
of the module "__builtin__".  The global namespace is searched first.
If the name is not found there, the builtins namespace is searched.
The global statement must precede all uses of the name.

The builtins namespace associated with the execution of a code block
is actually found by looking up the name "__builtins__" in its global
namespace; this should be a dictionary or a module (in the latter case
the module's dictionary is used).  By default, when in the "__main__"
module, "__builtins__" is the built-in module "__builtin__" (note: no
's'); when in any other module, "__builtins__" is an alias for the
dictionary of the "__builtin__" module itself.  "__builtins__" can be
set to a user-created dictionary to create a weak form of restricted
execution.

**CPython implementation detail:** Users should not touch
"__builtins__"; it is strictly an implementation detail.  Users
wanting to override values in the builtins namespace should "import"
the "__builtin__" (no 's') module and modify its attributes
appropriately.

The namespace for a module is automatically created the first time a
module is imported.  The main module for a script is always called
"__main__".

The "global" statement has the same scope as a name binding operation
in the same block.  If the nearest enclosing scope for a free variable
contains a global statement, the free variable is treated as a global.

A class definition is an executable statement that may use and define
names. These references follow the normal rules for name resolution.
The namespace of the class definition becomes the attribute dictionary
of the class.  Names defined at the class scope are not visible in
methods.

Interaction with dynamic features
=================================

There are several cases where Python statements are illegal when used
in conjunction with nested scopes that contain free variables.

If a variable is referenced in an enclosing scope, it is illegal to
delete the name.  An error will be reported at compile time.

If the wild card form of import --- "import *" --- is used in a
function and the function contains or is a nested block with free
variables, the compiler will raise a "SyntaxError".

If "exec" is used in a function and the function contains or is a
nested block with free variables, the compiler will raise a
"SyntaxError" unless the exec explicitly specifies the local namespace
for the "exec".  (In other words, "exec obj" would be illegal, but
"exec obj in ns" would be legal.)

The "eval()", "execfile()", and "input()" functions and the "exec"
statement do not have access to the full environment for resolving
names.  Names may be resolved in the local and global namespaces of
the caller.  Free variables are not resolved in the nearest enclosing
namespace, but in the global namespace. [1] The "exec" statement and
the "eval()" and "execfile()" functions have optional arguments to
override the global and local namespace.  If only one namespace is
specified, it is used for both.
t���namings���
Numeric literals
****************

There are four types of numeric literals: plain integers, long
integers, floating point numbers, and imaginary numbers.  There are no
complex literals (complex numbers can be formed by adding a real
number and an imaginary number).

Note that numeric literals do not include a sign; a phrase like "-1"
is actually an expression composed of the unary operator '"-"' and the
literal "1".
t���numberssy��
Emulating numeric types
***********************

The following methods can be defined to emulate numeric objects.
Methods corresponding to operations that are not supported by the
particular kind of number implemented (e.g., bitwise operations for
non-integral numbers) should be left undefined.

object.__add__(self, other)
object.__sub__(self, other)
object.__mul__(self, other)
object.__floordiv__(self, other)
object.__mod__(self, other)
object.__divmod__(self, other)
object.__pow__(self, other[, modulo])
object.__lshift__(self, other)
object.__rshift__(self, other)
object.__and__(self, other)
object.__xor__(self, other)
object.__or__(self, other)

These methods are called to implement the binary arithmetic
   operations ("+", "-", "*", "//", "%", "divmod()", "pow()", "**",
   "<<", ">>", "&", "^", "|").  For instance, to evaluate the
   expression "x + y", where *x* is an instance of a class that has an
   "__add__()" method, "x.__add__(y)" is called.  The "__divmod__()"
   method should be the equivalent to using "__floordiv__()" and
   "__mod__()"; it should not be related to "__truediv__()" (described
   below).  Note that "__pow__()" should be defined to accept an
   optional third argument if the ternary version of the built-in
   "pow()" function is to be supported.

If one of those methods does not support the operation with the
   supplied arguments, it should return "NotImplemented".

object.__div__(self, other)
object.__truediv__(self, other)

The division operator ("/") is implemented by these methods.  The
   "__truediv__()" method is used when "__future__.division" is in
   effect, otherwise "__div__()" is used.  If only one of these two
   methods is defined, the object will not support division in the
   alternate context; "TypeError" will be raised instead.

object.__radd__(self, other)
object.__rsub__(self, other)
object.__rmul__(self, other)
object.__rdiv__(self, other)
object.__rtruediv__(self, other)
object.__rfloordiv__(self, other)
object.__rmod__(self, other)
object.__rdivmod__(self, other)
object.__rpow__(self, other)
object.__rlshift__(self, other)
object.__rrshift__(self, other)
object.__rand__(self, other)
object.__rxor__(self, other)
object.__ror__(self, other)

These methods are called to implement the binary arithmetic
   operations ("+", "-", "*", "/", "%", "divmod()", "pow()", "**",
   "<<", ">>", "&", "^", "|") with reflected (swapped) operands.
   These functions are only called if the left operand does not
   support the corresponding operation and the operands are of
   different types. [2] For instance, to evaluate the expression "x -
   y", where *y* is an instance of a class that has an "__rsub__()"
   method, "y.__rsub__(x)" is called if "x.__sub__(y)" returns
   *NotImplemented*.

Note that ternary "pow()" will not try calling "__rpow__()" (the
   coercion rules would become too complicated).

Note: If the right operand's type is a subclass of the left
     operand's type and that subclass provides the reflected method
     for the operation, this method will be called before the left
     operand's non-reflected method.  This behavior allows subclasses
     to override their ancestors' operations.

object.__iadd__(self, other)
object.__isub__(self, other)
object.__imul__(self, other)
object.__idiv__(self, other)
object.__itruediv__(self, other)
object.__ifloordiv__(self, other)
object.__imod__(self, other)
object.__ipow__(self, other[, modulo])
object.__ilshift__(self, other)
object.__irshift__(self, other)
object.__iand__(self, other)
object.__ixor__(self, other)
object.__ior__(self, other)

These methods are called to implement the augmented arithmetic
   assignments ("+=", "-=", "*=", "/=", "//=", "%=", "**=", "<<=",
   ">>=", "&=", "^=", "|=").  These methods should attempt to do the
   operation in-place (modifying *self*) and return the result (which
   could be, but does not have to be, *self*).  If a specific method
   is not defined, the augmented assignment falls back to the normal
   methods.  For instance, to execute the statement "x += y", where
   *x* is an instance of a class that has an "__iadd__()" method,
   "x.__iadd__(y)" is called.  If *x* is an instance of a class that
   does not define a "__iadd__()" method, "x.__add__(y)" and
   "y.__radd__(x)" are considered, as with the evaluation of "x + y".

object.__neg__(self)
object.__pos__(self)
object.__abs__(self)
object.__invert__(self)

Called to implement the unary arithmetic operations ("-", "+",
   "abs()" and "~").

object.__complex__(self)
object.__int__(self)
object.__long__(self)
object.__float__(self)

Called to implement the built-in functions "complex()", "int()",
   "long()", and "float()".  Should return a value of the appropriate
   type.

object.__oct__(self)
object.__hex__(self)

Called to implement the built-in functions "oct()" and "hex()".
   Should return a string value.

object.__index__(self)

Called to implement "operator.index()".  Also called whenever
   Python needs an integer object (such as in slicing).  Must return
   an integer (int or long).

New in version 2.5.

object.__coerce__(self, other)

Called to implement "mixed-mode" numeric arithmetic.  Should either
   return a 2-tuple containing *self* and *other* converted to a
   common numeric type, or "None" if conversion is impossible.  When
   the common type would be the type of "other", it is sufficient to
   return "None", since the interpreter will also ask the other object
   to attempt a coercion (but sometimes, if the implementation of the
   other type cannot be changed, it is useful to do the conversion to
   the other type here).  A return value of "NotImplemented" is
   equivalent to returning "None".
s
���numeric-typessZ��
Objects, values and types
*************************

*Objects* are Python's abstraction for data.  All data in a Python
program is represented by objects or by relations between objects. (In
a sense, and in conformance to Von Neumann's model of a "stored
program computer," code is also represented by objects.)

Every object has an identity, a type and a value.  An object's
*identity* never changes once it has been created; you may think of it
as the object's address in memory.  The '"is"' operator compares the
identity of two objects; the "id()" function returns an integer
representing its identity (currently implemented as its address). An
object's *type* is also unchangeable. [1] An object's type determines
the operations that the object supports (e.g., "does it have a
length?") and also defines the possible values for objects of that
type.  The "type()" function returns an object's type (which is an
object itself).  The *value* of some objects can change.  Objects
whose value can change are said to be *mutable*; objects whose value
is unchangeable once they are created are called *immutable*. (The
value of an immutable container object that contains a reference to a
mutable object can change when the latter's value is changed; however
the container is still considered immutable, because the collection of
objects it contains cannot be changed.  So, immutability is not
strictly the same as having an unchangeable value, it is more subtle.)
An object's mutability is determined by its type; for instance,
numbers, strings and tuples are immutable, while dictionaries and
lists are mutable.

Objects are never explicitly destroyed; however, when they become
unreachable they may be garbage-collected.  An implementation is
allowed to postpone garbage collection or omit it altogether --- it is
a matter of implementation quality how garbage collection is
implemented, as long as no objects are collected that are still
reachable.

**CPython implementation detail:** CPython currently uses a reference-
counting scheme with (optional) delayed detection of cyclically linked
garbage, which collects most objects as soon as they become
unreachable, but is not guaranteed to collect garbage containing
circular references.  See the documentation of the "gc" module for
information on controlling the collection of cyclic garbage. Other
implementations act differently and CPython may change. Do not depend
on immediate finalization of objects when they become unreachable (ex:
always close files).

Note that the use of the implementation's tracing or debugging
facilities may keep objects alive that would normally be collectable.
Also note that catching an exception with a '"try"..."except"'
statement may keep objects alive.

Some objects contain references to "external" resources such as open
files or windows.  It is understood that these resources are freed
when the object is garbage-collected, but since garbage collection is
not guaranteed to happen, such objects also provide an explicit way to
release the external resource, usually a "close()" method. Programs
are strongly recommended to explicitly close such objects.  The
'"try"..."finally"' statement provides a convenient way to do this.

Some objects contain references to other objects; these are called
*containers*. Examples of containers are tuples, lists and
dictionaries.  The references are part of a container's value.  In
most cases, when we talk about the value of a container, we imply the
values, not the identities of the contained objects; however, when we
talk about the mutability of a container, only the identities of the
immediately contained objects are implied.  So, if an immutable
container (like a tuple) contains a reference to a mutable object, its
value changes if that mutable object is changed.

Types affect almost all aspects of object behavior.  Even the
importance of object identity is affected in some sense: for immutable
types, operations that compute new values may actually return a
reference to any existing object with the same type and value, while
for mutable objects this is not allowed.  E.g., after "a = 1; b = 1",
"a" and "b" may or may not refer to the same object with the value
one, depending on the implementation, but after "c = []; d = []", "c"
and "d" are guaranteed to refer to two different, unique, newly
created empty lists. (Note that "c = d = []" assigns the same object
to both "c" and "d".)
t���objectss��
Operator precedence
*******************

The following table summarizes the operator precedences in Python,
from lowest precedence (least binding) to highest precedence (most
binding). Operators in the same box have the same precedence.  Unless
the syntax is explicitly given, operators are binary.  Operators in
the same box group left to right (except for comparisons, including
tests, which all have the same precedence and chain from left to right
--- see section Comparisons --- and exponentiation, which groups from
right to left).

+-------------------------------------------------+---------------------------------------+
| Operator                                        | Description                           |
+=================================================+=======================================+
| "lambda"                                        | Lambda expression                     |
+-------------------------------------------------+---------------------------------------+
| "if" -- "else"                                  | Conditional expression                |
+-------------------------------------------------+---------------------------------------+
| "or"                                            | Boolean OR                            |
+-------------------------------------------------+---------------------------------------+
| "and"                                           | Boolean AND                           |
+-------------------------------------------------+---------------------------------------+
| "not" "x"                                       | Boolean NOT                           |
+-------------------------------------------------+---------------------------------------+
| "in", "not in", "is", "is not", "<", "<=", ">", | Comparisons, including membership     |
| ">=", "<>", "!=", "=="                          | tests and identity tests              |
+-------------------------------------------------+---------------------------------------+
| "|"                                             | Bitwise OR                            |
+-------------------------------------------------+---------------------------------------+
| "^"                                             | Bitwise XOR                           |
+-------------------------------------------------+---------------------------------------+
| "&"                                             | Bitwise AND                           |
+-------------------------------------------------+---------------------------------------+
| "<<", ">>"                                      | Shifts                                |
+-------------------------------------------------+---------------------------------------+
| "+", "-"                                        | Addition and subtraction              |
+-------------------------------------------------+---------------------------------------+
| "*", "/", "//", "%"                             | Multiplication, division, remainder   |
|                                                 | [7]                                   |
+-------------------------------------------------+---------------------------------------+
| "+x", "-x", "~x"                                | Positive, negative, bitwise NOT       |
+-------------------------------------------------+---------------------------------------+
| "**"                                            | Exponentiation [8]                    |
+-------------------------------------------------+---------------------------------------+
| "x[index]", "x[index:index]",                   | Subscription, slicing, call,          |
| "x(arguments...)", "x.attribute"                | attribute reference                   |
+-------------------------------------------------+---------------------------------------+
| "(expressions...)", "[expressions...]", "{key:  | Binding or tuple display, list        |
| value...}", "`expressions...`"                  | display, dictionary display, string   |
|                                                 | conversion                            |
+-------------------------------------------------+---------------------------------------+

-[ Footnotes ]-

[1] In Python 2.3 and later releases, a list comprehension "leaks"
    the control variables of each "for" it contains into the
    containing scope.  However, this behavior is deprecated, and
    relying on it will not work in Python 3.

[2] While "abs(x%y) < abs(y)" is true mathematically, for floats
    it may not be true numerically due to roundoff.  For example, and
    assuming a platform on which a Python float is an IEEE 754 double-
    precision number, in order that "-1e-100 % 1e100" have the same
    sign as "1e100", the computed result is "-1e-100 + 1e100", which
    is numerically exactly equal to "1e100".  The function
    "math.fmod()" returns a result whose sign matches the sign of the
    first argument instead, and so returns "-1e-100" in this case.
    Which approach is more appropriate depends on the application.

[3] If x is very close to an exact integer multiple of y, it's
    possible for "floor(x/y)" to be one larger than "(x-x%y)/y" due to
    rounding.  In such cases, Python returns the latter result, in
    order to preserve that "divmod(x,y)[0] * y + x % y" be very close
    to "x".

[4] The Unicode standard distinguishes between *code points* (e.g.
    U+0041) and *abstract characters* (e.g. "LATIN CAPITAL LETTER A").
    While most abstract characters in Unicode are only represented
    using one code point, there is a number of abstract characters
    that can in addition be represented using a sequence of more than
    one code point.  For example, the abstract character "LATIN
    CAPITAL LETTER C WITH CEDILLA" can be represented as a single
    *precomposed character* at code position U+00C7, or as a sequence
    of a *base character* at code position U+0043 (LATIN CAPITAL
    LETTER C), followed by a *combining character* at code position
    U+0327 (COMBINING CEDILLA).

The comparison operators on unicode strings compare at the level
    of Unicode code points. This may be counter-intuitive to humans.
    For example, "u"\u00C7" == u"\u0043\u0327"" is "False", even
    though both strings represent the same abstract character "LATIN
    CAPITAL LETTER C WITH CEDILLA".

To compare strings at the level of abstract characters (that is,
    in a way intuitive to humans), use "unicodedata.normalize()".

[5] Earlier versions of Python used lexicographic comparison of
    the sorted (key, value) lists, but this was very expensive for the
    common case of comparing for equality.  An even earlier version of
    Python compared dictionaries by identity only, but this caused
    surprises because people expected to be able to test a dictionary
    for emptiness by comparing it to "{}".

[6] Due to automatic garbage-collection, free lists, and the
    dynamic nature of descriptors, you may notice seemingly unusual
    behaviour in certain uses of the "is" operator, like those
    involving comparisons between instance methods, or constants.
    Check their documentation for more info.

[7] The "%" operator is also used for string formatting; the same
    precedence applies.

[8] The power operator "**" binds less tightly than an arithmetic
    or bitwise unary operator on its right, that is, "2**-1" is "0.5".
s���operator-summarysx��
The "pass" statement
********************

pass_stmt ::= "pass"

"pass" is a null operation --- when it is executed, nothing happens.
It is useful as a placeholder when a statement is required
syntactically, but no code needs to be executed, for example:

def f(arg): pass    # a function that does nothing (yet)

class C: pass       # a class with no methods (yet)
t���passs���
The power operator
******************

The power operator binds more tightly than unary operators on its
left; it binds less tightly than unary operators on its right.  The
syntax is:

power ::= primary ["**" u_expr]

Thus, in an unparenthesized sequence of power and unary operators, the
operators are evaluated from right to left (this does not constrain
the evaluation order for the operands): "-1**2" results in "-1".

The power operator has the same semantics as the built-in "pow()"
function, when called with two arguments: it yields its left argument
raised to the power of its right argument.  The numeric arguments are
first converted to a common type.  The result type is that of the
arguments after coercion.

With mixed operand types, the coercion rules for binary arithmetic
operators apply. For int and long int operands, the result has the
same type as the operands (after coercion) unless the second argument
is negative; in that case, all arguments are converted to float and a
float result is delivered. For example, "10**2" returns "100", but
"10**-2" returns "0.01". (This last feature was added in Python 2.2.
In Python 2.1 and before, if both arguments were of integer types and
the second argument was negative, an exception was raised).

Raising "0.0" to a negative power results in a "ZeroDivisionError".
Raising a negative number to a fractional power results in a
"ValueError".
t���powers���
The "print" statement
*********************

print_stmt ::= "print" ([expression ("," expression)* [","]]
                  | ">>" expression [("," expression)+ [","]])

"print" evaluates each expression in turn and writes the resulting
object to standard output (see below).  If an object is not a string,
it is first converted to a string using the rules for string
conversions.  The (resulting or original) string is then written.  A
space is written before each object is (converted and) written, unless
the output system believes it is positioned at the beginning of a
line.  This is the case (1) when no characters have yet been written
to standard output, (2) when the last character written to standard
output is a whitespace character except "' '", or (3) when the last
write operation on standard output was not a "print" statement. (In
some cases it may be functional to write an empty string to standard
output for this reason.)

Note: Objects which act like file objects but which are not the
  built-in file objects often do not properly emulate this aspect of
  the file object's behavior, so it is best not to rely on this.

A "'\n'" character is written at the end, unless the "print" statement
ends with a comma.  This is the only action if the statement contains
just the keyword "print".

Standard output is defined as the file object named "stdout" in the
built-in module "sys".  If no such object exists, or if it does not
have a "write()" method, a "RuntimeError" exception is raised.

"print" also has an extended form, defined by the second portion of
the syntax described above. This form is sometimes referred to as
""print" chevron." In this form, the first expression after the ">>"
must evaluate to a "file-like" object, specifically an object that has
a "write()" method as described above.  With this extended form, the
subsequent expressions are printed to this file object.  If the first
expression evaluates to "None", then "sys.stdout" is used as the file
for output.
t���prints���
The "raise" statement
*********************

raise_stmt ::= "raise" [expression ["," expression ["," expression]]]

If no expressions are present, "raise" re-raises the last exception
that was active in the current scope.  If no exception is active in
the current scope, a "TypeError" exception is raised indicating that
this is an error (if running under IDLE, a "Queue.Empty" exception is
raised instead).

Otherwise, "raise" evaluates the expressions to get three objects,
using "None" as the value of omitted expressions.  The first two
objects are used to determine the *type* and *value* of the exception.

If the first object is an instance, the type of the exception is the
class of the instance, the instance itself is the value, and the
second object must be "None".

If the first object is a class, it becomes the type of the exception.
The second object is used to determine the exception value: If it is
an instance of the class, the instance becomes the exception value. If
the second object is a tuple, it is used as the argument list for the
class constructor; if it is "None", an empty argument list is used,
and any other object is treated as a single argument to the
constructor.  The instance so created by calling the constructor is
used as the exception value.

If a third object is present and not "None", it must be a traceback
object (see section The standard type hierarchy), and it is
substituted instead of the current location as the place where the
exception occurred.  If the third object is present and not a
traceback object or "None", a "TypeError" exception is raised.  The
three-expression form of "raise" is useful to re-raise an exception
transparently in an except clause, but "raise" with no expressions
should be preferred if the exception to be re-raised was the most
recently active exception in the current scope.

Additional information on exceptions can be found in section
Exceptions, and information about handling exceptions is in section
The try statement.
t���raises���
The "return" statement
**********************

return_stmt ::= "return" [expression_list]

"return" may only occur syntactically nested in a function definition,
not within a nested class definition.

If an expression list is present, it is evaluated, else "None" is
substituted.

"return" leaves the current function call with the expression list (or
"None") as return value.

When "return" passes control out of a "try" statement with a "finally"
clause, that "finally" clause is executed before really leaving the
function.

In a generator function, the "return" statement is not allowed to
include an "expression_list".  In that context, a bare "return"
indicates that the generator is done and will cause "StopIteration" to
be raised.
t���returns���
Emulating container types
*************************

The following methods can be defined to implement container objects.
Containers usually are sequences (such as lists or tuples) or mappings
(like dictionaries), but can represent other containers as well.  The
first set of methods is used either to emulate a sequence or to
emulate a mapping; the difference is that for a sequence, the
allowable keys should be the integers *k* for which "0 <= k < N" where
*N* is the length of the sequence, or slice objects, which define a
range of items. (For backwards compatibility, the method
"__getslice__()" (see below) can also be defined to handle simple, but
not extended slices.) It is also recommended that mappings provide the
methods "keys()", "values()", "items()", "has_key()", "get()",
"clear()", "setdefault()", "iterkeys()", "itervalues()",
"iteritems()", "pop()", "popitem()", "copy()", and "update()" behaving
similar to those for Python's standard dictionary objects.  The
"UserDict" module provides a "DictMixin" class to help create those
methods from a base set of "__getitem__()", "__setitem__()",
"__delitem__()", and "keys()". Mutable sequences should provide
methods "append()", "count()", "index()", "extend()", "insert()",
"pop()", "remove()", "reverse()" and "sort()", like Python standard
list objects.  Finally, sequence types should implement addition
(meaning concatenation) and multiplication (meaning repetition) by
defining the methods "__add__()", "__radd__()", "__iadd__()",
"__mul__()", "__rmul__()" and "__imul__()" described below; they
should not define "__coerce__()" or other numerical operators.  It is
recommended that both mappings and sequences implement the
"__contains__()" method to allow efficient use of the "in" operator;
for mappings, "in" should be equivalent of "has_key()"; for sequences,
it should search through the values.  It is further recommended that
both mappings and sequences implement the "__iter__()" method to allow
efficient iteration through the container; for mappings, "__iter__()"
should be the same as "iterkeys()"; for sequences, it should iterate
through the values.

object.__len__(self)

Called to implement the built-in function "len()".  Should return
   the length of the object, an integer ">=" 0.  Also, an object that
   doesn't define a "__nonzero__()" method and whose "__len__()"
   method returns zero is considered to be false in a Boolean context.

**CPython implementation detail:** In CPython, the length is
   required to be at most "sys.maxsize". If the length is larger than
   "sys.maxsize" some features (such as "len()") may raise
   "OverflowError".  To prevent raising "OverflowError" by truth value
   testing, an object must define a "__nonzero__()" method.

object.__getitem__(self, key)

Called to implement evaluation of "self[key]". For sequence types,
   the accepted keys should be integers and slice objects.  Note that
   the special interpretation of negative indexes (if the class wishes
   to emulate a sequence type) is up to the "__getitem__()" method. If
   *key* is of an inappropriate type, "TypeError" may be raised; if of
   a value outside the set of indexes for the sequence (after any
   special interpretation of negative values), "IndexError" should be
   raised. For mapping types, if *key* is missing (not in the
   container), "KeyError" should be raised.

Note: "for" loops expect that an "IndexError" will be raised for
     illegal indexes to allow proper detection of the end of the
     sequence.

object.__missing__(self, key)

Called by "dict"."__getitem__()" to implement "self[key]" for dict
   subclasses when key is not in the dictionary.

object.__setitem__(self, key, value)

Called to implement assignment to "self[key]".  Same note as for
   "__getitem__()".  This should only be implemented for mappings if
   the objects support changes to the values for keys, or if new keys
   can be added, or for sequences if elements can be replaced.  The
   same exceptions should be raised for improper *key* values as for
   the "__getitem__()" method.

object.__delitem__(self, key)

Called to implement deletion of "self[key]".  Same note as for
   "__getitem__()".  This should only be implemented for mappings if
   the objects support removal of keys, or for sequences if elements
   can be removed from the sequence.  The same exceptions should be
   raised for improper *key* values as for the "__getitem__()" method.

object.__iter__(self)

This method is called when an iterator is required for a container.
   This method should return a new iterator object that can iterate
   over all the objects in the container.  For mappings, it should
   iterate over the keys of the container, and should also be made
   available as the method "iterkeys()".

Iterator objects also need to implement this method; they are
   required to return themselves.  For more information on iterator
   objects, see Iterator Types.

object.__reversed__(self)

Called (if present) by the "reversed()" built-in to implement
   reverse iteration.  It should return a new iterator object that
   iterates over all the objects in the container in reverse order.

If the "__reversed__()" method is not provided, the "reversed()"
   built-in will fall back to using the sequence protocol ("__len__()"
   and "__getitem__()").  Objects that support the sequence protocol
   should only provide "__reversed__()" if they can provide an
   implementation that is more efficient than the one provided by
   "reversed()".

New in version 2.6.

The membership test operators ("in" and "not in") are normally
implemented as an iteration through a sequence.  However, container
objects can supply the following special method with a more efficient
implementation, which also does not require the object be a sequence.

object.__contains__(self, item)

Called to implement membership test operators.  Should return true
   if *item* is in *self*, false otherwise.  For mapping objects, this
   should consider the keys of the mapping rather than the values or
   the key-item pairs.

For objects that don't define "__contains__()", the membership test
   first tries iteration via "__iter__()", then the old sequence
   iteration protocol via "__getitem__()", see this section in the
   language reference.
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Shifting operations
*******************

The shifting operations have lower priority than the arithmetic
operations:

shift_expr ::= a_expr | shift_expr ( "<<" | ">>" ) a_expr

These operators accept plain or long integers as arguments.  The
arguments are converted to a common type.  They shift the first
argument to the left or right by the number of bits given by the
second argument.

A right shift by *n* bits is defined as division by "pow(2, n)".  A
left shift by *n* bits is defined as multiplication with "pow(2, n)".
Negative shift counts raise a "ValueError" exception.

Note: In the current implementation, the right-hand operand is
  required to be at most "sys.maxsize".  If the right-hand operand is
  larger than "sys.maxsize" an "OverflowError" exception is raised.
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��
Slicings
********

A slicing selects a range of items in a sequence object (e.g., a
string, tuple or list).  Slicings may be used as expressions or as
targets in assignment or "del" statements.  The syntax for a slicing:

slicing          ::= simple_slicing | extended_slicing
   simple_slicing   ::= primary "[" short_slice "]"
   extended_slicing ::= primary "[" slice_list "]"
   slice_list       ::= slice_item ("," slice_item)* [","]
   slice_item       ::= expression | proper_slice | ellipsis
   proper_slice     ::= short_slice | long_slice
   short_slice      ::= [lower_bound] ":" [upper_bound]
   long_slice       ::= short_slice ":" [stride]
   lower_bound      ::= expression
   upper_bound      ::= expression
   stride           ::= expression
   ellipsis         ::= "..."

There is ambiguity in the formal syntax here: anything that looks like
an expression list also looks like a slice list, so any subscription
can be interpreted as a slicing.  Rather than further complicating the
syntax, this is disambiguated by defining that in this case the
interpretation as a subscription takes priority over the
interpretation as a slicing (this is the case if the slice list
contains no proper slice nor ellipses).  Similarly, when the slice
list has exactly one short slice and no trailing comma, the
interpretation as a simple slicing takes priority over that as an
extended slicing.

The semantics for a simple slicing are as follows.  The primary must
evaluate to a sequence object.  The lower and upper bound expressions,
if present, must evaluate to plain integers; defaults are zero and the
"sys.maxint", respectively.  If either bound is negative, the
sequence's length is added to it.  The slicing now selects all items
with index *k* such that "i <= k < j" where *i* and *j* are the
specified lower and upper bounds.  This may be an empty sequence.  It
is not an error if *i* or *j* lie outside the range of valid indexes
(such items don't exist so they aren't selected).

The semantics for an extended slicing are as follows.  The primary
must evaluate to a mapping object, and it is indexed with a key that
is constructed from the slice list, as follows.  If the slice list
contains at least one comma, the key is a tuple containing the
conversion of the slice items; otherwise, the conversion of the lone
slice item is the key.  The conversion of a slice item that is an
expression is that expression.  The conversion of an ellipsis slice
item is the built-in "Ellipsis" object.  The conversion of a proper
slice is a slice object (see section The standard type hierarchy)
whose "start", "stop" and "step" attributes are the values of the
expressions given as lower bound, upper bound and stride,
respectively, substituting "None" for missing expressions.
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Special Attributes
******************

The implementation adds a few special read-only attributes to several
object types, where they are relevant.  Some of these are not reported
by the "dir()" built-in function.

object.__dict__

A dictionary or other mapping object used to store an object's
   (writable) attributes.

object.__methods__

Deprecated since version 2.2: Use the built-in function "dir()" to
   get a list of an object's attributes. This attribute is no longer
   available.

object.__members__

Deprecated since version 2.2: Use the built-in function "dir()" to
   get a list of an object's attributes. This attribute is no longer
   available.

instance.__class__

The class to which a class instance belongs.

class.__bases__

The tuple of base classes of a class object.

definition.__name__

The name of the class, type, function, method, descriptor, or
   generator instance.

The following attributes are only supported by *new-style class*es.

class.__mro__

This attribute is a tuple of classes that are considered when
   looking for base classes during method resolution.

class.mro()

This method can be overridden by a metaclass to customize the
   method resolution order for its instances.  It is called at class
   instantiation, and its result is stored in "__mro__".

class.__subclasses__()

Each new-style class keeps a list of weak references to its
   immediate subclasses.  This method returns a list of all those
   references still alive. Example:

>>> int.__subclasses__()
      [<type 'bool'>]

-[ Footnotes ]-

[1] Additional information on these special methods may be found
    in the Python Reference Manual (Basic customization).

[2] As a consequence, the list "[1, 2]" is considered equal to
    "[1.0, 2.0]", and similarly for tuples.

[3] They must have since the parser can't tell the type of the
    operands.

[4] Cased characters are those with general category property
    being one of "Lu" (Letter, uppercase), "Ll" (Letter, lowercase),
    or "Lt" (Letter, titlecase).

[5] To format only a tuple you should therefore provide a
    singleton tuple whose only element is the tuple to be formatted.

[6] The advantage of leaving the newline on is that returning an
    empty string is then an unambiguous EOF indication.  It is also
    possible (in cases where it might matter, for example, if you want
    to make an exact copy of a file while scanning its lines) to tell
    whether the last line of a file ended in a newline or not (yes
    this happens!).
t���specialattrssa���
Special method names
********************

A class can implement certain operations that are invoked by special
syntax (such as arithmetic operations or subscripting and slicing) by
defining methods with special names. This is Python's approach to
*operator overloading*, allowing classes to define their own behavior
with respect to language operators.  For instance, if a class defines
a method named "__getitem__()", and "x" is an instance of this class,
then "x[i]" is roughly equivalent to "x.__getitem__(i)" for old-style
classes and "type(x).__getitem__(x, i)" for new-style classes.  Except
where mentioned, attempts to execute an operation raise an exception
when no appropriate method is defined (typically "AttributeError" or
"TypeError").

When implementing a class that emulates any built-in type, it is
important that the emulation only be implemented to the degree that it
makes sense for the object being modelled.  For example, some
sequences may work well with retrieval of individual elements, but
extracting a slice may not make sense.  (One example of this is the
"NodeList" interface in the W3C's Document Object Model.)

Basic customization
===================

object.__new__(cls[, ...])

Called to create a new instance of class *cls*.  "__new__()" is a
   static method (special-cased so you need not declare it as such)
   that takes the class of which an instance was requested as its
   first argument.  The remaining arguments are those passed to the
   object constructor expression (the call to the class).  The return
   value of "__new__()" should be the new object instance (usually an
   instance of *cls*).

Typical implementations create a new instance of the class by
   invoking the superclass's "__new__()" method using
   "super(currentclass, cls).__new__(cls[, ...])" with appropriate
   arguments and then modifying the newly-created instance as
   necessary before returning it.

If "__new__()" returns an instance of *cls*, then the new
   instance's "__init__()" method will be invoked like
   "__init__(self[, ...])", where *self* is the new instance and the
   remaining arguments are the same as were passed to "__new__()".

If "__new__()" does not return an instance of *cls*, then the new
   instance's "__init__()" method will not be invoked.

"__new__()" is intended mainly to allow subclasses of immutable
   types (like int, str, or tuple) to customize instance creation.  It
   is also commonly overridden in custom metaclasses in order to
   customize class creation.

object.__init__(self[, ...])

Called after the instance has been created (by "__new__()"), but
   before it is returned to the caller.  The arguments are those
   passed to the class constructor expression.  If a base class has an
   "__init__()" method, the derived class's "__init__()" method, if
   any, must explicitly call it to ensure proper initialization of the
   base class part of the instance; for example:
   "BaseClass.__init__(self, [args...])".

Because "__new__()" and "__init__()" work together in constructing
   objects ("__new__()" to create it, and "__init__()" to customise
   it), no non-"None" value may be returned by "__init__()"; doing so
   will cause a "TypeError" to be raised at runtime.

object.__del__(self)

Called when the instance is about to be destroyed.  This is also
   called a destructor.  If a base class has a "__del__()" method, the
   derived class's "__del__()" method, if any, must explicitly call it
   to ensure proper deletion of the base class part of the instance.
   Note that it is possible (though not recommended!) for the
   "__del__()" method to postpone destruction of the instance by
   creating a new reference to it.  It may then be called at a later
   time when this new reference is deleted.  It is not guaranteed that
   "__del__()" methods are called for objects that still exist when
   the interpreter exits.

Note: "del x" doesn't directly call "x.__del__()" --- the former
     decrements the reference count for "x" by one, and the latter is
     only called when "x"'s reference count reaches zero.  Some common
     situations that may prevent the reference count of an object from
     going to zero include: circular references between objects (e.g.,
     a doubly-linked list or a tree data structure with parent and
     child pointers); a reference to the object on the stack frame of
     a function that caught an exception (the traceback stored in
     "sys.exc_traceback" keeps the stack frame alive); or a reference
     to the object on the stack frame that raised an unhandled
     exception in interactive mode (the traceback stored in
     "sys.last_traceback" keeps the stack frame alive).  The first
     situation can only be remedied by explicitly breaking the cycles;
     the latter two situations can be resolved by storing "None" in
     "sys.exc_traceback" or "sys.last_traceback".  Circular references
     which are garbage are detected when the option cycle detector is
     enabled (it's on by default), but can only be cleaned up if there
     are no Python-level "__del__()" methods involved. Refer to the
     documentation for the "gc" module for more information about how
     "__del__()" methods are handled by the cycle detector,
     particularly the description of the "garbage" value.

Warning: Due to the precarious circumstances under which
     "__del__()" methods are invoked, exceptions that occur during
     their execution are ignored, and a warning is printed to
     "sys.stderr" instead. Also, when "__del__()" is invoked in
     response to a module being deleted (e.g., when execution of the
     program is done), other globals referenced by the "__del__()"
     method may already have been deleted or in the process of being
     torn down (e.g. the import machinery shutting down).  For this
     reason, "__del__()" methods should do the absolute minimum needed
     to maintain external invariants.  Starting with version 1.5,
     Python guarantees that globals whose name begins with a single
     underscore are deleted from their module before other globals are
     deleted; if no other references to such globals exist, this may
     help in assuring that imported modules are still available at the
     time when the "__del__()" method is called.

See also:

**PEP 3119** - Introducing Abstract Base Classes
     Includes the specification for customizing "isinstance()" and
     "issubclass()" behavior through "__instancecheck__()" and
     "__subclasscheck__()", with motivation for this functionality in
     the context of adding Abstract Base Classes (see the "abc"
     module) to the language.

Emulating callable objects
==========================

object.__call__(self[, args...])

Called when the instance is "called" as a function; if this method
   is defined, "x(arg1, arg2, ...)" is a shorthand for
   "x.__call__(arg1, arg2, ...)".

Emulating container types
=========================

The following methods can be defined to implement container objects.
Containers usually are sequences (such as lists or tuples) or mappings
(like dictionaries), but can represent other containers as well.  The
first set of methods is used either to emulate a sequence or to
emulate a mapping; the difference is that for a sequence, the
allowable keys should be the integers *k* for which "0 <= k < N" where
*N* is the length of the sequence, or slice objects, which define a
range of items. (For backwards compatibility, the method
"__getslice__()" (see below) can also be defined to handle simple, but
not extended slices.) It is also recommended that mappings provide the
methods "keys()", "values()", "items()", "has_key()", "get()",
"clear()", "setdefault()", "iterkeys()", "itervalues()",
"iteritems()", "pop()", "popitem()", "copy()", and "update()" behaving
similar to those for Python's standard dictionary objects.  The
"UserDict" module provides a "DictMixin" class to help create those
methods from a base set of "__getitem__()", "__setitem__()",
"__delitem__()", and "keys()". Mutable sequences should provide
methods "append()", "count()", "index()", "extend()", "insert()",
"pop()", "remove()", "reverse()" and "sort()", like Python standard
list objects.  Finally, sequence types should implement addition
(meaning concatenation) and multiplication (meaning repetition) by
defining the methods "__add__()", "__radd__()", "__iadd__()",
"__mul__()", "__rmul__()" and "__imul__()" described below; they
should not define "__coerce__()" or other numerical operators.  It is
recommended that both mappings and sequences implement the
"__contains__()" method to allow efficient use of the "in" operator;
for mappings, "in" should be equivalent of "has_key()"; for sequences,
it should search through the values.  It is further recommended that
both mappings and sequences implement the "__iter__()" method to allow
efficient iteration through the container; for mappings, "__iter__()"
should be the same as "iterkeys()"; for sequences, it should iterate
through the values.

object.__len__(self)

Called to implement the built-in function "len()".  Should return
   the length of the object, an integer ">=" 0.  Also, an object that
   doesn't define a "__nonzero__()" method and whose "__len__()"
   method returns zero is considered to be false in a Boolean context.

**CPython implementation detail:** In CPython, the length is
   required to be at most "sys.maxsize". If the length is larger than
   "sys.maxsize" some features (such as "len()") may raise
   "OverflowError".  To prevent raising "OverflowError" by truth value
   testing, an object must define a "__nonzero__()" method.

object.__getitem__(self, key)

Called to implement evaluation of "self[key]". For sequence types,
   the accepted keys should be integers and slice objects.  Note that
   the special interpretation of negative indexes (if the class wishes
   to emulate a sequence type) is up to the "__getitem__()" method. If
   *key* is of an inappropriate type, "TypeError" may be raised; if of
   a value outside the set of indexes for the sequence (after any
   special interpretation of negative values), "IndexError" should be
   raised. For mapping types, if *key* is missing (not in the
   container), "KeyError" should be raised.

Note: "for" loops expect that an "IndexError" will be raised for
     illegal indexes to allow proper detection of the end of the
     sequence.

object.__missing__(self, key)

Called by "dict"."__getitem__()" to implement "self[key]" for dict
   subclasses when key is not in the dictionary.

object.__setitem__(self, key, value)

Called to implement assignment to "self[key]".  Same note as for
   "__getitem__()".  This should only be implemented for mappings if
   the objects support changes to the values for keys, or if new keys
   can be added, or for sequences if elements can be replaced.  The
   same exceptions should be raised for improper *key* values as for
   the "__getitem__()" method.

object.__delitem__(self, key)

Called to implement deletion of "self[key]".  Same note as for
   "__getitem__()".  This should only be implemented for mappings if
   the objects support removal of keys, or for sequences if elements
   can be removed from the sequence.  The same exceptions should be
   raised for improper *key* values as for the "__getitem__()" method.

object.__iter__(self)

This method is called when an iterator is required for a container.
   This method should return a new iterator object that can iterate
   over all the objects in the container.  For mappings, it should
   iterate over the keys of the container, and should also be made
   available as the method "iterkeys()".

Iterator objects also need to implement this method; they are
   required to return themselves.  For more information on iterator
   objects, see Iterator Types.

object.__reversed__(self)

Called (if present) by the "reversed()" built-in to implement
   reverse iteration.  It should return a new iterator object that
   iterates over all the objects in the container in reverse order.

If the "__reversed__()" method is not provided, the "reversed()"
   built-in will fall back to using the sequence protocol ("__len__()"
   and "__getitem__()").  Objects that support the sequence protocol
   should only provide "__reversed__()" if they can provide an
   implementation that is more efficient than the one provided by
   "reversed()".

New in version 2.6.

The membership test operators ("in" and "not in") are normally
implemented as an iteration through a sequence.  However, container
objects can supply the following special method with a more efficient
implementation, which also does not require the object be a sequence.

object.__contains__(self, item)

Called to implement membership test operators.  Should return true
   if *item* is in *self*, false otherwise.  For mapping objects, this
   should consider the keys of the mapping rather than the values or
   the key-item pairs.

For objects that don't define "__contains__()", the membership test
   first tries iteration via "__iter__()", then the old sequence
   iteration protocol via "__getitem__()", see this section in the
   language reference.

Additional methods for emulation of sequence types
==================================================

The following optional methods can be defined to further emulate
sequence objects.  Immutable sequences methods should at most only
define "__getslice__()"; mutable sequences might define all three
methods.

object.__getslice__(self, i, j)

Deprecated since version 2.0: Support slice objects as parameters
   to the "__getitem__()" method. (However, built-in types in CPython
   currently still implement "__getslice__()".  Therefore, you have to
   override it in derived classes when implementing slicing.)

Called to implement evaluation of "self[i:j]". The returned object
   should be of the same type as *self*.  Note that missing *i* or *j*
   in the slice expression are replaced by zero or "sys.maxsize",
   respectively.  If negative indexes are used in the slice, the
   length of the sequence is added to that index. If the instance does
   not implement the "__len__()" method, an "AttributeError" is
   raised. No guarantee is made that indexes adjusted this way are not
   still negative.  Indexes which are greater than the length of the
   sequence are not modified. If no "__getslice__()" is found, a slice
   object is created instead, and passed to "__getitem__()" instead.

object.__setslice__(self, i, j, sequence)

Called to implement assignment to "self[i:j]". Same notes for *i*
   and *j* as for "__getslice__()".

This method is deprecated. If no "__setslice__()" is found, or for
   extended slicing of the form "self[i:j:k]", a slice object is
   created, and passed to "__setitem__()", instead of "__setslice__()"
   being called.

object.__delslice__(self, i, j)

Called to implement deletion of "self[i:j]". Same notes for *i* and
   *j* as for "__getslice__()". This method is deprecated. If no
   "__delslice__()" is found, or for extended slicing of the form
   "self[i:j:k]", a slice object is created, and passed to
   "__delitem__()", instead of "__delslice__()" being called.

Notice that these methods are only invoked when a single slice with a
single colon is used, and the slice method is available.  For slice
operations involving extended slice notation, or in absence of the
slice methods, "__getitem__()", "__setitem__()" or "__delitem__()" is
called with a slice object as argument.

The following example demonstrate how to make your program or module
compatible with earlier versions of Python (assuming that methods
"__getitem__()", "__setitem__()" and "__delitem__()" support slice
objects as arguments):

class MyClass:
       ...
       def __getitem__(self, index):
           ...
       def __setitem__(self, index, value):
           ...
       def __delitem__(self, index):
           ...

if sys.version_info < (2, 0):
           # They won't be defined if version is at least 2.0 final

def __getslice__(self, i, j):
               return self[max(0, i):max(0, j):]
           def __setslice__(self, i, j, seq):
               self[max(0, i):max(0, j):] = seq
           def __delslice__(self, i, j):
               del self[max(0, i):max(0, j):]
       ...

Note the calls to "max()"; these are necessary because of the handling
of negative indices before the "__*slice__()" methods are called.
When negative indexes are used, the "__*item__()" methods receive them
as provided, but the "__*slice__()" methods get a "cooked" form of the
index values.  For each negative index value, the length of the
sequence is added to the index before calling the method (which may
still result in a negative index); this is the customary handling of
negative indexes by the built-in sequence types, and the "__*item__()"
methods are expected to do this as well.  However, since they should
already be doing that, negative indexes cannot be passed in; they must
be constrained to the bounds of the sequence before being passed to
the "__*item__()" methods. Calling "max(0, i)" conveniently returns
the proper value.

Emulating numeric types
=======================