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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.
u assertuÂ#
Assignment statements
*********************
Assignment statements are used to (re)bind names to values and to
modify attributes or items of mutable objects:
assignment_stmt ::= (target_list "=")+ (expression_list | yield_expression)
target_list ::= target ("," target)* [","]
target ::= identifier
| "(" target_list ")"
| "[" target_list "]"
| attributeref
| subscription
| slicing
| "*" target
(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, optionally enclosed in
parentheses or square brackets, 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.
* If the target list contains one target prefixed with an
asterisk, called a "starred" target: The object must be a sequence
with at least as many items as there are targets in the target
list, minus one. The first items of the sequence are assigned,
from left to right, to the targets before the starred target. The
final items of the sequence are assigned to the targets after the
starred target. A list of the remaining items in the sequence is
then assigned to the starred target (the list can be empty).
* Else: The object must be a sequence 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" or "nonlocal" 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 global
namespace or the outer namespace determined by "nonlocal",
respectively.
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 an 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).
For user-defined objects, the "__setitem__()" method is called with
appropriate arguments.
* 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 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)
See also: **PEP 3132** - Extended Iterable Unpacking
The specification for the "*target" feature.
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.
u
assignmentuÑ
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.
u atom-identifiersu
Literals
********
Python supports string and bytes literals and various numeric
literals:
literal ::= stringliteral | bytesliteral
| integer | floatnumber | imagnumber
Evaluation of a literal yields an object of the given type (string,
bytes, 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.
u
atom-literalsu¢&
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 over attribute access.
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*.
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 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.
object.__dir__(self)
Called when "dir()" is called on the object. A sequence must be
returned. "dir()" converts the returned sequence to a list and
sorts it.
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.
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 an object instance, "a.x" is transformed into the
call: "type(a).__dict__['x'].__get__(a, type(a))".
Class Binding
If binding to a 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 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 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.
object.__slots__
This class variable can be assigned a string, iterable, or sequence
of strings with variable names used by instances. If defined in a
class, *__slots__* reserves space for the declared variables and
prevents the automatic creation of *__dict__* and *__weakref__* for
each instance.
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.
* 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.
* *__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 "int", "bytes" 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__*.
u attribute-accessu“
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, which most objects do. This object is then
asked to produce the attribute whose name is the identifier (which can
be customized by overriding the "__getattr__()" method). 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.
u attribute-referencesuÉ
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.
u augassignuµ
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:
m_expr ::= u_expr | m_expr "*" u_expr | m_expr "//" u_expr | m_expr "/" u_expr
| m_expr "%" u_expr
a_expr ::= m_expr | a_expr "+" m_expr | a_expr "-" m_expr
The "*" (multiplication) operator yields the product of its arguments.
The arguments must either both be numbers, or one argument must be an
integer 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. Division of integers yields a float, while
floor division of integers results in an integer; 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 [1].
The floor division and modulo operators are connected by the following
identity: "x == (x//y)*y + (x%y)". Floor division and modulo are also
connected with the built-in function "divmod()": "divmod(x, y) ==
(x//y, x%y)". [2].
In addition to performing the modulo operation on numbers, the "%"
operator is also overloaded by string objects to perform old-style
string formatting (also known as interpolation). The syntax for
string formatting is described in the Python Library Reference,
section *printf-style String Formatting*.
The floor division operator, the modulo operator, and the "divmod()"
function are not 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.
u binaryu%
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 integers.
The "^" operator yields the bitwise XOR (exclusive OR) of its
arguments, which must be integers.
The "|" operator yields the bitwise (inclusive) OR of its arguments,
which must be integers.
u bitwiseuu
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 "__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()" or "eval()" built-in functions.
See *The standard type hierarchy* for more information.
u bltin-code-objectsu1
The Ellipsis Object
*******************
This object is commonly used by slicing (see *Slicings*). It supports
no special operations. There is exactly one ellipsis object, named
"Ellipsis" (a built-in name). "type(Ellipsis)()" produces the
"Ellipsis" singleton.
It is written as "Ellipsis" or "...".
u bltin-ellipsis-objectu
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). "type(None)()" produces the
same singleton.
It is written as "None".
u bltin-null-objectu4
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: "".
u bltin-type-objectsu½
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. User-defined objects can customize their truth value by
providing a "__bool__()" method.
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 "''".)
u booleansu%
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.
u breakuù
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, ...)".
u callable-typesuC
Calls
*****
A call calls a callable object (e.g., a *function*) with a possibly
empty series of *arguments*:
call ::= primary "(" [argument_list [","] | comprehension] ")"
argument_list ::= positional_arguments ["," keyword_arguments]
["," "*" expression] ["," keyword_arguments]
["," "**" expression]
| keyword_arguments ["," "*" expression]
["," keyword_arguments] ["," "**" 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 all objects having a
"__call__()" method are callable). 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 "", line 1, in ?
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.
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.
u callsuê
Class definitions
*****************
A class definition defines a class object (see section *The standard
type hierarchy*):
classdef ::= [decorators] "class" classname [inheritance] ":" suite
inheritance ::= "(" [parameter_list] ")"
classname ::= identifier
A class definition is an executable statement. The inheritance list
usually gives a list of base classes (see *Customizing class creation*
for more advanced uses), so each item in the list should evaluate to a
class object which allows subclassing. Classes without an inheritance
list inherit, by default, from the base class "object"; hence,
class Foo:
pass
is equivalent to
class Foo(object):
pass
The class's suite is then executed in a new execution frame (see
*Naming and binding*), using a newly created local namespace and the
original global namespace. (Usually, the suite contains mostly
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.
Class creation can be customized heavily using *metaclasses*.
Classes can also be decorated: just like when decorating functions,
@f1(arg)
@f2
class Foo: pass
is equivalent to
class Foo: pass
Foo = f1(arg)(f2(Foo))
The evaluation rules for the decorator expressions are the same as for
function decorators. The result must be a class object, which is then
bound to the class name.
**Programmer's note:** Variables defined in the class definition are
class attributes; they are shared by instances. Instance attributes
can be set in a method with "self.name = value". Both class and
instance attributes are accessible through the notation ""self.name"",
and an instance attribute hides a class attribute with the same name
when accessed in this way. Class attributes can be used as defaults
for instance attributes, but using mutable values there can lead to
unexpected results. *Descriptors* can be used to create instance
variables with different implementation details.
See also: **PEP 3115** - Metaclasses in Python 3 **PEP 3129** -
Class Decorators
-[ 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*.
u classu»
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 operators "<", ">", "==", ">=", "<=", and "!=" compare the values
of two objects. The objects need not have the same type. If both are
numbers, they are converted to a common type. Otherwise, the "==" and
"!=" operators *always* consider objects of different types to be
unequal, while the "<", ">", ">=" and "<=" operators raise a
"TypeError" when comparing objects of different types that do not
implement these operators for the given pair of types. You can
control comparison behavior of objects of non-built-in types by
defining rich comparison methods like "__gt__()", described in section
*Basic customization*.
Comparison of objects of the same type depends on the type:
* Numbers are compared arithmetically.
* The values "float('NaN')" and "Decimal('NaN')" are special. The
are identical to themselves, "x is x" but are not equal to
themselves, "x != x". Additionally, comparing any value to a
not-a-number value will return "False". For example, both "3 <
float('NaN')" and "float('NaN') < 3" will return "False".
* Bytes objects are compared lexicographically using the numeric
values of their elements.
* Strings are compared lexicographically using the numeric
equivalents (the result of the built-in function "ord()") of their
characters. [3] String and bytes object can't be compared!
* Tuples and lists are compared lexicographically using comparison
of corresponding elements. This means that to compare equal, each
element must compare equal and the two sequences must be of the same
type and have the same length.
If not equal, the sequences are ordered the same as their first
differing elements. For example, "[1,2,x] <= [1,2,y]" has the same
value as "x <= y". If the corresponding element does not exist, the
shorter sequence is ordered first (for example, "[1,2] < [1,2,3]").
* Mappings (dictionaries) compare equal if and only if they have the
same "(key, value)" pairs. Order comparisons "('<', '<=', '>=',
'>')" raise "TypeError".
* Sets and frozensets define comparison operators to mean subset and
superset tests. Those relations do not define total orderings (the
two sets "{1,2}" and {2,3} are not equal, nor subsets of one
another, nor supersets of one another). Accordingly, sets are not
appropriate arguments for functions which depend on total ordering.
For example, "min()", "max()", and "sorted()" produce undefined
results given a list of sets as inputs.
* 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.
Comparison of objects of the differing types depends on whether either
of the types provide explicit support for the comparison. Most
numeric types can be compared with one another. When cross-type
comparison is not supported, the comparison method returns
"NotImplemented".
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 a 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" is true if and only if "y.__contains__(x)" is true.
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".
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. [4]
u comparisonsuèT
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, while the "with" statement allows the
execution of initialization and finalization code around a block of
code. 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()" calls are executed:
if x < y < z: print(x); print(y); print(z)
Summarizing:
compound_stmt ::= if_stmt
| while_stmt
| for_stmt
| try_stmt
| with_stmt
| funcdef
| classdef
suite ::= stmt_list NEWLINE | NEWLINE INDENT statement+ DEDENT
statement ::= stmt_list NEWLINE | compound_stmt
stmt_list ::= simple_stmt (";" simple_stmt)* [";"]
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 (see *Assignment statements*), and then the suite is
executed. When the items are exhausted (which is immediately when the
sequence is empty or an iterator raises a "StopIteration" exception),
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.
Names in the target list are 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 an
iterator of integers suitable to emulate the effect of Pascal's "for i
:= a to b do"; e.g., "list(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" target]] ":" suite)+
["else" ":" suite]
["finally" ":" suite]
try2_stmt ::= "try" ":" suite
"finally" ":" suite
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 after the "as" keyword 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.)
When an exception has been assigned using "as target", it is cleared
at the end of the except clause. This is as if
except E as N:
foo
was translated to
except E as N:
try:
foo
finally:
del N
This means the exception must be assigned to a different name to be
able to refer to it after the except clause. Exceptions are cleared
because with the traceback attached to them, they form a reference
cycle with the stack frame, keeping all locals in that frame alive
until the next garbage collection occurs.
Before an except clause's suite is executed, details about the
exception are stored in the "sys" module and can be access via
"sys.exc_info()". "sys.exc_info()" returns a 3-tuple consisting of the
exception class, the exception instance and a traceback object (see
section *The standard type hierarchy*) identifying the point in the
program where the exception occurred. "sys.exc_info()" values 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, the saved
exception is set as the context of the new exception. If the "finally"
clause 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).
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
====================
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
Changed in version 3.1: Support for multiple context expressions.
See also: **PEP 0343** - 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*):
funcdef ::= [decorators] "def" funcname "(" [parameter_list] ")" ["->" expression] ":" suite
decorators ::= decorator+
decorator ::= "@" dotted_name ["(" [parameter_list [","]] ")"] NEWLINE
dotted_name ::= identifier ("." identifier)*
parameter_list ::= (defparameter ",")*
( "*" [parameter] ("," defparameter)* ["," "**" parameter]
| "**" parameter
| defparameter [","] )
parameter ::= identifier [":" expression]
defparameter ::= parameter ["=" expression]
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 *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 up until the ""*"" 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. Parameters after ""*"" or ""*identifier"" are
keyword-only parameters and may only be passed used keyword arguments.
Parameters may have annotations of the form "": expression"" following
the parameter name. Any parameter may have an annotation even those
of the form "*identifier" or "**identifier". Functions may have
"return" annotation of the form ""-> expression"" after the parameter
list. These annotations can be any valid Python expression and are
evaluated when the function definition is executed. Annotations may
be evaluated in a different order than they appear in the source code.
The presence of annotations does not change the semantics of a
function. The annotation values are available as values of a
dictionary keyed by the parameters' names in the "__annotations__"
attribute of the function object.
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 and annotations.
**Programmer's note:** Functions are first-class objects. A ""def""
statement 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.
See also: **PEP 3107** - Function Annotations
The original specification for function annotations.
Class definitions
=================
A class definition defines a class object (see section *The standard
type hierarchy*):
classdef ::= [decorators] "class" classname [inheritance] ":" suite
inheritance ::= "(" [parameter_list] ")"
classname ::= identifier
A class definition is an executable statement. The inheritance list
usually gives a list of base classes (see *Customizing class creation*
for more advanced uses), so each item in the list should evaluate to a
class object which allows subclassing. Classes without an inheritance
list inherit, by default, from the base class "object"; hence,
class Foo:
pass
is equivalent to
class Foo(object):
pass
The class's suite is then executed in a new execution frame (see
*Naming and binding*), using a newly created local namespace and the
original global namespace. (Usually, the suite contains mostly
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.
Class creation can be customized heavily using *metaclasses*.
Classes can also be decorated: just like when decorating functions,
@f1(arg)
@f2
class Foo: pass
is equivalent to
class Foo: pass
Foo = f1(arg)(f2(Foo))
The evaluation rules for the decorator expressions are the same as for
function decorators. The result must be a class object, which is then
bound to the class name.
**Programmer's note:** Variables defined in the class definition are
class attributes; they are shared by instances. Instance attributes
can be set in a method with "self.name = value". Both class and
instance attributes are accessible through the notation ""self.name"",
and an instance attribute hides a class attribute with the same name
when accessed in this way. Class attributes can be used as defaults
for instance attributes, but using mutable values there can lead to
unexpected results. *Descriptors* can be used to create instance
variables with different implementation details.
See also: **PEP 3115** - Metaclasses in Python 3 **PEP 3129** -
Class Decorators
-[ 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*.
u compoundu¢
With Statement Context Managers
*******************************
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 0343** - The "with" statement
The specification, background, and examples for the Python "with"
statement.
u context-managersuØ
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.
u continueu•
Arithmetic conversions
**********************
When a description of an arithmetic operator below uses the phrase
"the numeric arguments are converted to a common type," this means
that the operator implementation for built-in types works that way:
* 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, both must be integers and no conversion is necessary.
Some additional rules apply for certain operators (e.g., a string left
argument to the '%' operator). Extensions must define their own
conversion behavior.
u conversionsu93
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_info()[2]" 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.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.
object.__repr__(self)
Called by the "repr()" built-in function to compute the "official"
string representation of an object. If at all possible, this
should look like a valid Python expression that could be used to
recreate an object with the same value (given an appropriate
environment). If this is not possible, a string of the form
"<...some useful description...>" should be returned. The return
value must be a string object. If a class defines "__repr__()" but
not "__str__()", then "__repr__()" is also used when an "informal"
string representation of instances of that class is required.
This is typically used for debugging, so it is important that the
representation is information-rich and unambiguous.
object.__str__(self)
Called by "str(object)" and the built-in functions "format()" and
"print()" to compute the "informal" or nicely printable string
representation of an object. The return value must be a *string*
object.
This method differs from "object.__repr__()" in that there is no
expectation that "__str__()" return a valid Python expression: a
more convenient or concise representation can be used.
The default implementation defined by the built-in type "object"
calls "object.__repr__()".
object.__bytes__(self)
Called by "bytes()" to compute a byte-string representation of an
object. This should return a "bytes" object.
object.__format__(self, format_spec)
Called by the "format()" built-in function (and by extension, the
"str.format()" method of class "str") to produce a "formatted"
string representation of an object. The "format_spec" argument is a
string that contains a description of the formatting options
desired. The interpretation of the "format_spec" argument is up to
the type implementing "__format__()", however most classes will
either delegate formatting to one of the built-in types, or use a
similar formatting option syntax.
See *Format Specification Mini-Language* for a description of the
standard formatting syntax.
The return value must be a string object.
object.__lt__(self, other)
object.__le__(self, other)
object.__eq__(self, other)
object.__ne__(self, other)
object.__gt__(self, other)
object.__ge__(self, other)
These are the so-called "rich comparison" methods. The
correspondence between operator symbols and method names is as
follows: "xy" calls
"x.__gt__(y)", and "x>=y" calls "x.__ge__(y)".
A rich comparison method may return the singleton "NotImplemented"
if it does not implement the operation for a given pair of
arguments. By convention, "False" and "True" are returned for a
successful comparison. However, these methods can return any value,
so if the comparison operator is used in a Boolean context (e.g.,
in the condition of an "if" statement), Python will call "bool()"
on the value to determine if the result is true or false.
There are no implied relationships among the comparison operators.
The truth of "x==y" does not imply that "x!=y" is false.
Accordingly, when defining "__eq__()", one should also define
"__ne__()" so that the operators will behave as expected. See the
paragraph on "__hash__()" for some important notes on creating
*hashable* objects which support custom comparison operations and
are usable as dictionary keys.
There are no swapped-argument versions of these methods (to be used
when the left argument does not support the operation but the right
argument does); rather, "__lt__()" and "__gt__()" are each other's
reflection, "__le__()" and "__ge__()" are each other's reflection,
and "__eq__()" and "__ne__()" are their own reflection.
Arguments to rich comparison methods are never coerced.
To automatically generate ordering operations from a single root
operation, see "functools.total_ordering()".
object.__hash__(self)
Called by built-in function "hash()" and for operations on members
of hashed collections including "set", "frozenset", and "dict".
"__hash__()" should return an integer. The only required property
is that objects which compare equal have the same hash value; it is
advised to somehow mix together (e.g. using exclusive or) the hash
values for the components of the object that also play a part in
comparison of objects.
Note: "hash()" truncates the value returned from an object's
custom "__hash__()" method to the size of a "Py_ssize_t". This
is typically 8 bytes on 64-bit builds and 4 bytes on 32-bit
builds. If an object's "__hash__()" must interoperate on builds
of different bit sizes, be sure to check the width on all
supported builds. An easy way to do this is with "python -c
"import sys; print(sys.hash_info.width)""
If a class does not define an "__eq__()" method it should not
define a "__hash__()" operation either; if it defines "__eq__()"
but not "__hash__()", its instances will not be usable as items in
hashable collections. If a class defines mutable objects and
implements an "__eq__()" method, it should not implement
"__hash__()", since the implementation of hashable collections
requires that a key's hash value is immutable (if the object's hash
value changes, it will be in the wrong hash bucket).
User-defined classes have "__eq__()" and "__hash__()" methods by
default; with them, all objects compare unequal (except with
themselves) and "x.__hash__()" returns an appropriate value such
that "x == y" implies both that "x is y" and "hash(x) == hash(y)".
A class that overrides "__eq__()" and does not define "__hash__()"
will have its "__hash__()" implicitly set to "None". When the
"__hash__()" method of a class is "None", instances of the class
will raise an appropriate "TypeError" when a program attempts to
retrieve their hash value, and will also be correctly identified as
unhashable when checking "isinstance(obj, collections.Hashable").
If a class that overrides "__eq__()" needs to retain the
implementation of "__hash__()" from a parent class, the interpreter
must be told this explicitly by setting "__hash__ =
.__hash__".
If a class that does not override "__eq__()" wishes to suppress
hash support, it should include "__hash__ = None" in the class
definition. A class which defines its own "__hash__()" that
explicitly raises a "TypeError" would be incorrectly identified as
hashable by an "isinstance(obj, collections.Hashable)" call.
Note: By default, the "__hash__()" values of str, bytes and
datetime objects are "salted" with an unpredictable random value.
Although they remain constant within an individual Python
process, they are not predictable between repeated invocations of
Python.This is intended to provide protection against a denial-
of-service caused by carefully-chosen inputs that exploit the
worst case performance of a dict insertion, O(n^2) complexity.
See http://www.ocert.org/advisories/ocert-2011-003.html for
details.Changing hash values affects the iteration order of
dicts, sets and other mappings. Python has never made guarantees
about this ordering (and it typically varies between 32-bit and
64-bit builds).See also "PYTHONHASHSEED".
Changed in version 3.3: Hash randomization is enabled by default.
object.__bool__(self)
Called to implement truth value testing and the built-in operation
"bool()"; should return "False" or "True". When this method is not
defined, "__len__()" is called, if it is defined, and the object is
considered true if its result is nonzero. If a class defines
neither "__len__()" nor "__bool__()", all its instances are
considered true.
u
customizationuÓD
"pdb" --- The Python Debugger
*****************************
The module "pdb" defines an interactive source code debugger for
Python programs. It supports setting (conditional) breakpoints and
single stepping at the source line level, inspection of stack frames,
source code listing, and evaluation of arbitrary Python code in the
context of any stack frame. It also supports post-mortem debugging
and can be called under program control.
The debugger is extensible -- it is actually defined as the class
"Pdb". This is currently undocumented but easily understood by reading
the source. The extension interface uses the modules "bdb" and "cmd".
The debugger's prompt is "(Pdb)". Typical usage to run a program under
control of the debugger is:
>>> import pdb
>>> import mymodule
>>> pdb.run('mymodule.test()')
> (0)?()
(Pdb) continue
> (1)?()
(Pdb) continue
NameError: 'spam'
> (1)?()
(Pdb)
Changed in version 3.3: Tab-completion via the "readline" module is
available for commands and command arguments, e.g. the current global
and local names are offered as arguments of the "print" command.
"pdb.py" can also be invoked as a script to debug other scripts. For
example:
python3 -m pdb myscript.py
When invoked as a script, pdb will automatically enter post-mortem
debugging if the program being debugged exits abnormally. After post-
mortem debugging (or after normal exit of the program), pdb will
restart the program. Automatic restarting preserves pdb's state (such
as breakpoints) and in most cases is more useful than quitting the
debugger upon program's exit.
New in version 3.2: "pdb.py" now accepts a "-c" option that executes
commands as if given in a ".pdbrc" file, see *Debugger Commands*.
The typical usage to break into the debugger from a running program is
to insert
import pdb; pdb.set_trace()
at the location you want to break into the debugger. You can then
step through the code following this statement, and continue running
without the debugger using the "continue" command.
The typical usage to inspect a crashed program is:
>>> import pdb
>>> import mymodule
>>> mymodule.test()
Traceback (most recent call last):
File "", line 1, in ?
File "./mymodule.py", line 4, in test
test2()
File "./mymodule.py", line 3, in test2
print(spam)
NameError: spam
>>> pdb.pm()
> ./mymodule.py(3)test2()
-> print(spam)
(Pdb)
The module defines the following functions; each enters the debugger
in a slightly different way:
pdb.run(statement, globals=None, locals=None)
Execute the *statement* (given as a string or a code object) under
debugger control. The debugger prompt appears before any code is
executed; you can set breakpoints and type "continue", or you can
step through the statement using "step" or "next" (all these
commands are explained below). The optional *globals* and *locals*
arguments specify the environment in which the code is executed; by
default the dictionary of the module "__main__" is used. (See the
explanation of the built-in "exec()" or "eval()" functions.)
pdb.runeval(expression, globals=None, locals=None)
Evaluate the *expression* (given as a string or a code object)
under debugger control. When "runeval()" returns, it returns the
value of the expression. Otherwise this function is similar to
"run()".
pdb.runcall(function, *args, **kwds)
Call the *function* (a function or method object, not a string)
with the given arguments. When "runcall()" returns, it returns
whatever the function call returned. The debugger prompt appears
as soon as the function is entered.
pdb.set_trace()
Enter the debugger at the calling stack frame. This is useful to
hard-code a breakpoint at a given point in a program, even if the
code is not otherwise being debugged (e.g. when an assertion
fails).
pdb.post_mortem(traceback=None)
Enter post-mortem debugging of the given *traceback* object. If no
*traceback* is given, it uses the one of the exception that is
currently being handled (an exception must be being handled if the
default is to be used).
pdb.pm()
Enter post-mortem debugging of the traceback found in
"sys.last_traceback".
The "run*" functions and "set_trace()" are aliases for instantiating
the "Pdb" class and calling the method of the same name. If you want
to access further features, you have to do this yourself:
class class pdb.Pdb(completekey='tab', stdin=None, stdout=None, skip=None, nosigint=False)
"Pdb" is the debugger class.
The *completekey*, *stdin* and *stdout* arguments are passed to the
underlying "cmd.Cmd" class; see the description there.
The *skip* argument, if given, must be an iterable of glob-style
module name patterns. The debugger will not step into frames that
originate in a module that matches one of these patterns. [1]
By default, Pdb sets a handler for the SIGINT signal (which is sent
when the user presses Ctrl-C on the console) when you give a
"continue" command. This allows you to break into the debugger
again by pressing Ctrl-C. If you want Pdb not to touch the SIGINT
handler, set *nosigint* tot true.
Example call to enable tracing with *skip*:
import pdb; pdb.Pdb(skip=['django.*']).set_trace()
New in version 3.1: The *skip* argument.
New in version 3.2: The *nosigint* argument. Previously, a SIGINT
handler was never set by Pdb.
run(statement, globals=None, locals=None)
runeval(expression, globals=None, locals=None)
runcall(function, *args, **kwds)
set_trace()
See the documentation for the functions explained above.
Debugger Commands
=================
The commands recognized by the debugger are listed below. Most
commands can be abbreviated to one or two letters as indicated; e.g.
"h(elp)" means that either "h" or "help" can be used to enter the help
command (but not "he" or "hel", nor "H" or "Help" or "HELP").
Arguments to commands must be separated by whitespace (spaces or
tabs). Optional arguments are enclosed in square brackets ("[]") in
the command syntax; the square brackets must not be typed.
Alternatives in the command syntax are separated by a vertical bar
("|").
Entering a blank line repeats the last command entered. Exception: if
the last command was a "list" command, the next 11 lines are listed.
Commands that the debugger doesn't recognize are assumed to be Python
statements and are executed in the context of the program being
debugged. Python statements can also be prefixed with an exclamation
point ("!"). This is a powerful way to inspect the program being
debugged; it is even possible to change a variable or call a function.
When an exception occurs in such a statement, the exception name is
printed but the debugger's state is not changed.
The debugger supports *aliases*. Aliases can have parameters which
allows one a certain level of adaptability to the context under
examination.
Multiple commands may be entered on a single line, separated by ";;".
(A single ";" is not used as it is the separator for multiple commands
in a line that is passed to the Python parser.) No intelligence is
applied to separating the commands; the input is split at the first
";;" pair, even if it is in the middle of a quoted string.
If a file ".pdbrc" exists in the user's home directory or in the
current directory, it is read in and executed as if it had been typed
at the debugger prompt. This is particularly useful for aliases. If
both files exist, the one in the home directory is read first and
aliases defined there can be overridden by the local file.
Changed in version 3.2: ".pdbrc" can now contain commands that
continue debugging, such as "continue" or "next". Previously, these
commands had no effect.
h(elp) [command]
Without argument, print the list of available commands. With a
*command* as argument, print help about that command. "help pdb"
displays the full documentation (the docstring of the "pdb"
module). Since the *command* argument must be an identifier, "help
exec" must be entered to get help on the "!" command.
w(here)
Print a stack trace, with the most recent frame at the bottom. An
arrow indicates the current frame, which determines the context of
most commands.
d(own) [count]
Move the current frame *count* (default one) levels down in the
stack trace (to a newer frame).
u(p) [count]
Move the current frame *count* (default one) levels up in the stack
trace (to an older frame).
b(reak) [([filename:]lineno | function) [, condition]]
With a *lineno* argument, set a break there in the current file.
With a *function* argument, set a break at the first executable
statement within that function. The line number may be prefixed
with a filename and a colon, to specify a breakpoint in another
file (probably one that hasn't been loaded yet). The file is
searched on "sys.path". Note that each breakpoint is assigned a
number to which all the other breakpoint commands refer.
If a second argument is present, it is an expression which must
evaluate to true before the breakpoint is honored.
Without argument, list all breaks, including for each breakpoint,
the number of times that breakpoint has been hit, the current
ignore count, and the associated condition if any.
tbreak [([filename:]lineno | function) [, condition]]
Temporary breakpoint, which is removed automatically when it is
first hit. The arguments are the same as for "break".
cl(ear) [filename:lineno | bpnumber [bpnumber ...]]
With a *filename:lineno* argument, clear all the breakpoints at
this line. With a space separated list of breakpoint numbers, clear
those breakpoints. Without argument, clear all breaks (but first
ask confirmation).
disable [bpnumber [bpnumber ...]]
Disable the breakpoints given as a space separated list of
breakpoint numbers. Disabling a breakpoint means it cannot cause
the program to stop execution, but unlike clearing a breakpoint, it
remains in the list of breakpoints and can be (re-)enabled.
enable [bpnumber [bpnumber ...]]
Enable the breakpoints specified.
ignore bpnumber [count]
Set the ignore count for the given breakpoint number. If count is
omitted, the ignore count is set to 0. A breakpoint becomes active
when the ignore count is zero. When non-zero, the count is
decremented each time the breakpoint is reached and the breakpoint
is not disabled and any associated condition evaluates to true.
condition bpnumber [condition]
Set a new *condition* for the breakpoint, an expression which must
evaluate to true before the breakpoint is honored. If *condition*
is absent, any existing condition is removed; i.e., the breakpoint
is made unconditional.
commands [bpnumber]
Specify a list of commands for breakpoint number *bpnumber*. The
commands themselves appear on the following lines. Type a line
containing just "end" to terminate the commands. An example:
(Pdb) commands 1
(com) print some_variable
(com) end
(Pdb)
To remove all commands from a breakpoint, type commands and follow
it immediately with "end"; that is, give no commands.
With no *bpnumber* argument, commands refers to the last breakpoint
set.
You can use breakpoint commands to start your program up again.
Simply use the continue command, or step, or any other command that
resumes execution.
Specifying any command resuming execution (currently continue,
step, next, return, jump, quit and their abbreviations) terminates
the command list (as if that command was immediately followed by
end). This is because any time you resume execution (even with a
simple next or step), you may encounter another breakpoint--which
could have its own command list, leading to ambiguities about which
list to execute.
If you use the 'silent' command in the command list, the usual
message about stopping at a breakpoint is not printed. This may be
desirable for breakpoints that are to print a specific message and
then continue. If none of the other commands print anything, you
see no sign that the breakpoint was reached.
s(tep)
Execute the current line, stop at the first possible occasion
(either in a function that is called or on the next line in the
current function).
n(ext)
Continue execution until the next line in the current function is
reached or it returns. (The difference between "next" and "step"
is that "step" stops inside a called function, while "next"
executes called functions at (nearly) full speed, only stopping at
the next line in the current function.)
unt(il) [lineno]
Without argument, continue execution until the line with a number
greater than the current one is reached.
With a line number, continue execution until a line with a number
greater or equal to that is reached. In both cases, also stop when
the current frame returns.
Changed in version 3.2: Allow giving an explicit line number.
r(eturn)
Continue execution until the current function returns.
c(ont(inue))
Continue execution, only stop when a breakpoint is encountered.
j(ump) lineno
Set the next line that will be executed. Only available in the
bottom-most frame. This lets you jump back and execute code again,
or jump forward to skip code that you don't want to run.
It should be noted that not all jumps are allowed -- for instance
it is not possible to jump into the middle of a "for" loop or out
of a "finally" clause.
l(ist) [first[, last]]
List source code for the current file. Without arguments, list 11
lines around the current line or continue the previous listing.
With "." as argument, list 11 lines around the current line. With
one argument, list 11 lines around at that line. With two
arguments, list the given range; if the second argument is less
than the first, it is interpreted as a count.
The current line in the current frame is indicated by "->". If an
exception is being debugged, the line where the exception was
originally raised or propagated is indicated by ">>", if it differs
from the current line.
New in version 3.2: The ">>" marker.
ll | longlist
List all source code for the current function or frame.
Interesting lines are marked as for "list".
New in version 3.2.
a(rgs)
Print the argument list of the current function.
p(rint) expression
Evaluate the *expression* in the current context and print its
value.
pp expression
Like the "print" command, except the value of the expression is
pretty-printed using the "pprint" module.
whatis expression
Print the type of the *expression*.
source expression
Try to get source code for the given object and display it.
New in version 3.2.
display [expression]
Display the value of the expression if it changed, each time
execution stops in the current frame.
Without expression, list all display expressions for the current
frame.
New in version 3.2.
undisplay [expression]
Do not display the expression any more in the current frame.
Without expression, clear all display expressions for the current
frame.
New in version 3.2.
interact
Start an interative interpreter (using the "code" module) whose
global namespace contains all the (global and local) names found in
the current scope.
New in version 3.2.
alias [name [command]]
Create an alias called *name* that executes *command*. The command
must *not* be enclosed in quotes. Replaceable parameters can be
indicated by "%1", "%2", and so on, while "%*" is replaced by all
the parameters. If no command is given, the current alias for
*name* is shown. If no arguments are given, all aliases are listed.
Aliases may be nested and can contain anything that can be legally
typed at the pdb prompt. Note that internal pdb commands *can* be
overridden by aliases. Such a command is then hidden until the
alias is removed. Aliasing is recursively applied to the first
word of the command line; all other words in the line are left
alone.
As an example, here are two useful aliases (especially when placed
in the ".pdbrc" file):
# Print instance variables (usage "pi classInst")
alias pi for k in %1.__dict__.keys(): print("%1.",k,"=",%1.__dict__[k])
# Print instance variables in self
alias ps pi self
unalias name
Delete the specified alias.
! statement
Execute the (one-line) *statement* in the context of the current
stack frame. The exclamation point can be omitted unless the first
word of the statement resembles a debugger command. To set a
global variable, you can prefix the assignment command with a
"global" statement on the same line, e.g.:
(Pdb) global list_options; list_options = ['-l']
(Pdb)
run [args ...]
restart [args ...]
Restart the debugged Python program. If an argument is supplied,
it is split with "shlex" and the result is used as the new
"sys.argv". History, breakpoints, actions and debugger options are
preserved. "restart" is an alias for "run".
q(uit)
Quit from the debugger. The program being executed is aborted.
-[ Footnotes ]-
[1] Whether a frame is considered to originate in a certain module
is determined by the "__name__" in the frame globals.
u debuggeruª
The "del" statement
*******************
del_stmt ::= "del" target_list
Deletion is recursively defined very similar to the way assignment is
defined. Rather than spelling it out in full details, here are some
hints.
Deletion of a target list recursively deletes each target, from left
to right.
Deletion of a name removes the binding of that name from the local or
global namespace, depending on whether the name occurs in a "global"
statement in the same code block. If the name is unbound, a
"NameError" exception will be raised.
Deletion of attribute references, subscriptions and slicings is passed
to the primary object involved; deletion of a slicing is in general
equivalent to assignment of an empty slice of the right type (but even
this is determined by the sliced object).
Changed in version 3.2: Previously it was illegal to delete a name
from the local namespace if it occurs as a free variable in a nested
block.
u delu¦
Dictionary displays
*******************
A dictionary display is a possibly empty series of key/datum pairs
enclosed in curly braces:
dict_display ::= "{" [key_datum_list | dict_comprehension] "}"
key_datum_list ::= key_datum ("," key_datum)* [","]
key_datum ::= expression ":" expression
dict_comprehension ::= expression ":" expression comp_for
A dictionary display yields a new dictionary object.
If a comma-separated sequence of key/datum pairs is given, they are
evaluated from left to right to define the entries of the dictionary:
each key object is used as a key into the dictionary to store the
corresponding datum. This means that you can specify the same key
multiple times in the key/datum list, and the final dictionary's value
for that key will be the last one given.
A dict comprehension, in contrast to list and set comprehensions,
needs two expressions separated with a colon followed by the usual
"for" and "if" clauses. When the comprehension is run, the resulting
key and value elements are inserted in the new dictionary in the order
they are produced.
Restrictions on the types of the key values are listed earlier in
section *The standard type hierarchy*. (To summarize, the key type
should be *hashable*, which excludes all mutable objects.) Clashes
between duplicate keys are not detected; the last datum (textually
rightmost in the display) stored for a given key value prevails.
u dictu»
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".
The "eval()" and "exec()" functions 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()" and "eval()" functions have optional arguments to
override the global and local namespace. If only one namespace is
specified, it is used for both.
u dynamic-featuresuG
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.
u elseu
Exceptions
**********
Exceptions are a means of breaking out of the normal flow of control
of a code block in order to handle errors or other exceptional
conditions. An exception is *raised* at the point where the error is
detected; it may be *handled* by the surrounding code block or by any
code block that directly or indirectly invoked the code block where
the error occurred.
The Python interpreter raises an exception when it detects a run-time
error (such as division by zero). A Python program can also
explicitly raise an exception with the "raise" statement. Exception
handlers are specified with the "try" ... "except" statement. The
"finally" clause of such a statement can be used to specify cleanup
code which does not handle the exception, but is executed whether an
exception occurred or not in the preceding code.
Python uses the "termination" model of error handling: an exception
handler can find out what happened and continue execution at an outer
level, but it cannot repair the cause of the error and retry the
failing operation (except by re-entering the offending piece of code
from the top).
When an exception is not handled at all, the interpreter terminates
execution of the program, or returns to its interactive main loop. In
either case, it prints a stack backtrace, except when the exception is
"SystemExit".
Exceptions are identified by class instances. The "except" clause is
selected depending on the class of the instance: it must reference the
class of the instance or a base class thereof. The instance can be
received by the handler and can carry additional information about the
exceptional condition.
Note: Exception messages are not part of the Python API. Their
contents may change from one version of Python to the next without
warning and should not be relied on by code which will run under
multiple versions of the interpreter.
See also the description of the "try" statement in section *The try
statement* and "raise" statement in section *The raise statement*.
-[ Footnotes ]-
[1] This limitation occurs because the code that is executed by
these operations is not available at the time the module is
compiled.
u
exceptionsu«"
Execution model
***************
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 string argument passed
to the built-in functions "eval()" and "exec()" 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 comprehensions and 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,
unless declared as "nonlocal". 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, or after
"as" in a "with" statement or "except" clause. 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).
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 "builtins". 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 "builtins"; when in any
other module, "__builtins__" is an alias for the dictionary of the
"builtins" 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 "builtins" 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".
The "eval()" and "exec()" functions 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()" and "eval()" functions have optional arguments to
override the global and local namespace. If only one namespace is
specified, it is used for both.
Exceptions
==========
Exceptions are a means of breaking out of the normal flow of control
of a code block in order to handle errors or other exceptional
conditions. An exception is *raised* at the point where the error is
detected; it may be *handled* by the surrounding code block or by any
code block that directly or indirectly invoked the code block where
the error occurred.
The Python interpreter raises an exception when it detects a run-time
error (such as division by zero). A Python program can also
explicitly raise an exception with the "raise" statement. Exception
handlers are specified with the "try" ... "except" statement. The
"finally" clause of such a statement can be used to specify cleanup
code which does not handle the exception, but is executed whether an
exception occurred or not in the preceding code.
Python uses the "termination" model of error handling: an exception
handler can find out what happened and continue execution at an outer
level, but it cannot repair the cause of the error and retry the
failing operation (except by re-entering the offending piece of code
from the top).
When an exception is not handled at all, the interpreter terminates
execution of the program, or returns to its interactive main loop. In
either case, it prints a stack backtrace, except when the exception is
"SystemExit".
Exceptions are identified by class instances. The "except" clause is
selected depending on the class of the instance: it must reference the
class of the instance or a base class thereof. The instance can be
received by the handler and can carry additional information about the
exceptional condition.
Note: Exception messages are not part of the Python API. Their
contents may change from one version of Python to the next without
warning and should not be relied on by code which will run under
multiple versions of the interpreter.
See also the description of the "try" statement in section *The try
statement* and "raise" statement in section *The raise statement*.
-[ Footnotes ]-
[1] This limitation occurs because the code that is executed by
these operations is not available at the time the module is
compiled.
u execmodeluK
Expression lists
****************
expression_list ::= expression ( "," expression )* [","]
An expression list containing at least one comma yields a tuple. The
length of the tuple is the number of expressions in the list. The
expressions are evaluated from left to right.
The trailing comma is required only to create a single tuple (a.k.a. a
*singleton*); it is optional in all other cases. A single expression
without a trailing comma doesn't create a tuple, but rather yields the
value of that expression. (To create an empty tuple, use an empty pair
of parentheses: "()".)
u exprlistsud
Floating point literals
***********************
Floating point literals are described by the following lexical
definitions:
floatnumber ::= pointfloat | exponentfloat
pointfloat ::= [intpart] fraction | intpart "."
exponentfloat ::= (intpart | pointfloat) exponent
intpart ::= digit+
fraction ::= "." digit+
exponent ::= ("e" | "E") ["+" | "-"] digit+
Note that the integer and exponent parts are always interpreted using
radix 10. For example, "077e010" is legal, and denotes the same number
as "77e10". The allowed range of floating point literals is
implementation-dependent. Some examples of floating point literals:
3.14 10. .001 1e100 3.14e-10 0e0
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".
u floatingu¼
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 (see *Assignment statements*), and then the suite is
executed. When the items are exhausted (which is immediately when the
sequence is empty or an iterator raises a "StopIteration" exception),
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.
Names in the target list are 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 an
iterator of integers suitable to emulate the effect of Pascal's "for i
:= a to b do"; e.g., "list(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)
u foruÃF
Format String Syntax
********************
The "str.format()" method and the "Formatter" class share the same
syntax for format strings (although in the case of "Formatter",
subclasses can define their own format string syntax).
Format strings contain "replacement fields" surrounded by curly braces
"{}". Anything that is not contained in braces is considered literal
text, which is copied unchanged to the output. If you need to include
a brace character in the literal text, it can be escaped by doubling:
"{{" and "}}".
The grammar for a replacement field is as follows:
replacement_field ::= "{" [field_name] ["!" conversion] [":" format_spec] "}"
field_name ::= arg_name ("." attribute_name | "[" element_index "]")*
arg_name ::= [identifier | integer]
attribute_name ::= identifier
element_index ::= integer | index_string
index_string ::= +
conversion ::= "r" | "s" | "a"
format_spec ::=
In less formal terms, the replacement field can start with a
*field_name* that specifies the object whose value is to be formatted
and inserted into the output instead of the replacement field. The
*field_name* is optionally followed by a *conversion* field, which is
preceded by an exclamation point "'!'", and a *format_spec*, which is
preceded by a colon "':'". These specify a non-default format for the
replacement value.
See also the *Format Specification Mini-Language* section.
The *field_name* itself begins with an *arg_name* that is either a
number or a keyword. If it's a number, it refers to a positional
argument, and if it's a keyword, it refers to a named keyword
argument. If the numerical arg_names in a format string are 0, 1, 2,
... in sequence, they can all be omitted (not just some) and the
numbers 0, 1, 2, ... will be automatically inserted in that order.
Because *arg_name* is not quote-delimited, it is not possible to
specify arbitrary dictionary keys (e.g., the strings "'10'" or
"':-]'") within a format string. The *arg_name* can be followed by any
number of index or attribute expressions. An expression of the form
"'.name'" selects the named attribute using "getattr()", while an
expression of the form "'[index]'" does an index lookup using
"__getitem__()".
Changed in version 3.1: The positional argument specifiers can be
omitted, so "'{} {}'" is equivalent to "'{0} {1}'".
Some simple format string examples:
"First, thou shalt count to {0}" # References first positional argument
"Bring me a {}" # Implicitly references the first positional argument
"From {} to {}" # Same as "From {0} to {1}"
"My quest is {name}" # References keyword argument 'name'
"Weight in tons {0.weight}" # 'weight' attribute of first positional arg
"Units destroyed: {players[0]}" # First element of keyword argument 'players'.
The *conversion* field causes a type coercion before formatting.
Normally, the job of formatting a value is done by the "__format__()"
method of the value itself. However, in some cases it is desirable to
force a type to be formatted as a string, overriding its own
definition of formatting. By converting the value to a string before
calling "__format__()", the normal formatting logic is bypassed.
Three conversion flags are currently supported: "'!s'" which calls
"str()" on the value, "'!r'" which calls "repr()" and "'!a'" which
calls "ascii()".
Some examples:
"Harold's a clever {0!s}" # Calls str() on the argument first
"Bring out the holy {name!r}" # Calls repr() on the argument first
"More {!a}" # Calls ascii() on the argument first
The *format_spec* field contains a specification of how the value
should be presented, including such details as field width, alignment,
padding, decimal precision and so on. Each value type can define its
own "formatting mini-language" or interpretation of the *format_spec*.
Most built-in types support a common formatting mini-language, which
is described in the next section.
A *format_spec* field can also include nested replacement fields
within it. These nested replacement fields can contain only a field
name; conversion flags and format specifications are not allowed. The
replacement fields within the format_spec are substituted before the
*format_spec* string is interpreted. This allows the formatting of a
value to be dynamically specified.
See the *Format examples* section for some examples.
Format Specification Mini-Language
==================================
"Format specifications" are used within replacement fields contained
within a format string to define how individual values are presented
(see *Format String Syntax*). They can also be passed directly to the
built-in "format()" function. Each formattable type may define how
the format specification is to be interpreted.
Most built-in types implement the following options for format
specifications, although some of the formatting options are only
supported by the numeric types.
A general convention is that an empty format string ("""") produces
the same result as if you had called "str()" on the value. A non-empty
format string typically modifies the result.
The general form of a *standard format specifier* is:
format_spec ::= [[fill]align][sign][#][0][width][,][.precision][type]
fill ::=
align ::= "<" | ">" | "=" | "^"
sign ::= "+" | "-" | " "
width ::= integer
precision ::= integer
type ::= "b" | "c" | "d" | "e" | "E" | "f" | "F" | "g" | "G" | "n" | "o" | "s" | "x" | "X" | "%"
If a valid *align* value is specified, it can be preceded by a *fill*
character that can be any character and defaults to a space if
omitted. Note that it is not possible to use "{" and "}" as *fill*
char while using the "str.format()" method; this limitation however
doesn't affect the "format()" function.
The meaning of the various alignment options is as follows:
+-----------+------------------------------------------------------------+
| Option | Meaning |
+===========+============================================================+
| "'<'" | Forces the field to be left-aligned within the available |
+-----------+------------------------------------------------------------+
| "'>'" | Forces the field to be right-aligned within the available |
+-----------+------------------------------------------------------------+
| "'='" | Forces the padding to be placed after the sign (if any) |
+-----------+------------------------------------------------------------+
| "'^'" | Forces the field to be centered within the available |
+-----------+------------------------------------------------------------+
Note that unless a minimum field width is defined, the field width
will always be the same size as the data to fill it, so that the
alignment option has no meaning in this case.
The *sign* option is only valid for number types, and can be one of
the following:
+-----------+------------------------------------------------------------+
| Option | Meaning |
+===========+============================================================+
| "'+'" | indicates that a sign should be used for both positive as |
+-----------+------------------------------------------------------------+
| "'-'" | indicates that a sign should be used only for negative |
+-----------+------------------------------------------------------------+
| space | indicates that a leading space should be used on positive |
+-----------+------------------------------------------------------------+
The "'#'" option causes the "alternate form" to be used for the
conversion. The alternate form is defined differently for different
types. This option is only valid for integer, float, complex and
Decimal types. For integers, when binary, octal, or hexadecimal output
is used, this option adds the prefix respective "'0b'", "'0o'", or
"'0x'" to the output value. For floats, complex and Decimal the
alternate form causes the result of the conversion to always contain a
decimal-point character, even if no digits follow it. Normally, a
decimal-point character appears in the result of these conversions
only if a digit follows it. In addition, for "'g'" and "'G'"
conversions, trailing zeros are not removed from the result.
The "','" option signals the use of a comma for a thousands separator.
For a locale aware separator, use the "'n'" integer presentation type
instead.
Changed in version 3.1: Added the "','" option (see also **PEP 378**).
*width* is a decimal integer defining the minimum field width. If not
specified, then the field width will be determined by the content.
Preceding the *width* field by a zero ("'0'") character enables sign-
aware zero-padding for numeric types. This is equivalent to a *fill*
character of "'0'" with an *alignment* type of "'='".
The *precision* is a decimal number indicating how many digits should
be displayed after the decimal point for a floating point value
formatted with "'f'" and "'F'", or before and after the decimal point
for a floating point value formatted with "'g'" or "'G'". For non-
number types the field indicates the maximum field size - in other
words, how many characters will be used from the field content. The
*precision* is not allowed for integer values.
Finally, the *type* determines how the data should be presented.
The available string presentation types are:
+-----------+------------------------------------------------------------+
| Type | Meaning |
+===========+============================================================+
| "'s'" | String format. This is the default type for strings and |
+-----------+------------------------------------------------------------+
| None | The same as "'s'". |
+-----------+------------------------------------------------------------+
The available integer presentation types are:
+-----------+------------------------------------------------------------+
| Type | Meaning |
+===========+============================================================+
| "'b'" | Binary format. Outputs the number in base 2. |
+-----------+------------------------------------------------------------+
| "'c'" | Character. Converts the integer to the corresponding |
+-----------+------------------------------------------------------------+
| "'d'" | Decimal Integer. Outputs the number in base 10. |
+-----------+------------------------------------------------------------+
| "'o'" | Octal format. Outputs the number in base 8. |
+-----------+------------------------------------------------------------+
| "'x'" | Hex format. Outputs the number in base 16, using lower- |
+-----------+------------------------------------------------------------+
| "'X'" | Hex format. Outputs the number in base 16, using upper- |
+-----------+------------------------------------------------------------+
| "'n'" | Number. This is the same as "'d'", except that it uses the |
+-----------+------------------------------------------------------------+
| None | The same as "'d'". |
+-----------+------------------------------------------------------------+
In addition to the above presentation types, integers can be formatted
with the floating point presentation types listed below (except "'n'"
and None). When doing so, "float()" is used to convert the integer to
a floating point number before formatting.
The available presentation types for floating point and decimal values
are:
+-----------+------------------------------------------------------------+
| Type | Meaning |
+===========+============================================================+
| "'e'" | Exponent notation. Prints the number in scientific |
+-----------+------------------------------------------------------------+
| "'E'" | Exponent notation. Same as "'e'" except it uses an upper |
+-----------+------------------------------------------------------------+
| "'f'" | Fixed point. Displays the number as a fixed-point number. |
+-----------+------------------------------------------------------------+
| "'F'" | Fixed point. Same as "'f'", but converts "nan" to "NAN" |
+-----------+------------------------------------------------------------+
| "'g'" | General format. For a given precision "p >= 1", this |
+-----------+------------------------------------------------------------+
| "'G'" | General format. Same as "'g'" except switches to "'E'" if |
+-----------+------------------------------------------------------------+
| "'n'" | Number. This is the same as "'g'", except that it uses the |
+-----------+------------------------------------------------------------+
| "'%'" | Percentage. Multiplies the number by 100 and displays in |
+-----------+------------------------------------------------------------+
| None | Similar to "'g'", except with at least one digit past the |
+-----------+------------------------------------------------------------+
Format examples
===============
This section contains examples of the new format syntax and comparison
with the old "%"-formatting.
In most of the cases the syntax is similar to the old "%"-formatting,
with the addition of the "{}" and with ":" used instead of "%". For
example, "'%03.2f'" can be translated to "'{:03.2f}'".
The new format syntax also supports new and different options, shown
in the follow examples.
Accessing arguments by position:
>>> '{0}, {1}, {2}'.format('a', 'b', 'c')
'a, b, c'
>>> '{}, {}, {}'.format('a', 'b', 'c') # 3.1+ only
'a, b, c'
>>> '{2}, {1}, {0}'.format('a', 'b', 'c')
'c, b, a'
>>> '{2}, {1}, {0}'.format(*'abc') # unpacking argument sequence
'c, b, a'
>>> '{0}{1}{0}'.format('abra', 'cad') # arguments' indices can be repeated
'abracadabra'
Accessing arguments by name:
>>> 'Coordinates: {latitude}, {longitude}'.format(latitude='37.24N', longitude='-115.81W')
'Coordinates: 37.24N, -115.81W'
>>> coord = {'latitude': '37.24N', 'longitude': '-115.81W'}
>>> 'Coordinates: {latitude}, {longitude}'.format(**coord)
'Coordinates: 37.24N, -115.81W'
Accessing arguments' attributes:
>>> c = 3-5j
>>> ('The complex number {0} is formed from the real part {0.real} '
... 'and the imaginary part {0.imag}.').format(c)
'The complex number (3-5j) is formed from the real part 3.0 and the imaginary part -5.0.'
>>> class Point:
... def __init__(self, x, y):
... self.x, self.y = x, y
... def __str__(self):
... return 'Point({self.x}, {self.y})'.format(self=self)
...
>>> str(Point(4, 2))
'Point(4, 2)'
Accessing arguments' items:
>>> coord = (3, 5)
>>> 'X: {0[0]}; Y: {0[1]}'.format(coord)
'X: 3; Y: 5'
Replacing "%s" and "%r":
>>> "repr() shows quotes: {!r}; str() doesn't: {!s}".format('test1', 'test2')
"repr() shows quotes: 'test1'; str() doesn't: test2"
Aligning the text and specifying a width:
>>> '{:<30}'.format('left aligned')
'left aligned '
>>> '{:>30}'.format('right aligned')
' right aligned'
>>> '{:^30}'.format('centered')
' centered '
>>> '{:*^30}'.format('centered') # use '*' as a fill char
'***********centered***********'
Replacing "%+f", "%-f", and "% f" and specifying a sign:
>>> '{:+f}; {:+f}'.format(3.14, -3.14) # show it always
'+3.140000; -3.140000'
>>> '{: f}; {: f}'.format(3.14, -3.14) # show a space for positive numbers
' 3.140000; -3.140000'
>>> '{:-f}; {:-f}'.format(3.14, -3.14) # show only the minus -- same as '{:f}; {:f}'
'3.140000; -3.140000'
Replacing "%x" and "%o" and converting the value to different bases:
>>> # format also supports binary numbers
>>> "int: {0:d}; hex: {0:x}; oct: {0:o}; bin: {0:b}".format(42)
'int: 42; hex: 2a; oct: 52; bin: 101010'
>>> # with 0x, 0o, or 0b as prefix:
>>> "int: {0:d}; hex: {0:#x}; oct: {0:#o}; bin: {0:#b}".format(42)
'int: 42; hex: 0x2a; oct: 0o52; bin: 0b101010'
Using the comma as a thousands separator:
>>> '{:,}'.format(1234567890)
'1,234,567,890'
Expressing a percentage:
>>> points = 19
>>> total = 22
>>> 'Correct answers: {:.2%}'.format(points/total)
'Correct answers: 86.36%'
Using type-specific formatting:
>>> import datetime
>>> d = datetime.datetime(2010, 7, 4, 12, 15, 58)
>>> '{:%Y-%m-%d %H:%M:%S}'.format(d)
'2010-07-04 12:15:58'
Nesting arguments and more complex examples:
>>> for align, text in zip('<^>', ['left', 'center', 'right']):
... '{0:{fill}{align}16}'.format(text, fill=align, align=align)
...
'left<<<<<<<<<<<<'
'^^^^^center^^^^^'
'>>>>>>>>>>>right'
>>>
>>> octets = [192, 168, 0, 1]
>>> '{:02X}{:02X}{:02X}{:02X}'.format(*octets)
'C0A80001'
>>> int(_, 16)
3232235521
>>>
>>> width = 5
>>> for num in range(5,12): #doctest: +NORMALIZE_WHITESPACE
... for base in 'dXob':
... print('{0:{width}{base}}'.format(num, base=base, width=width), end=' ')
... print()
...
5 5 5 101
6 6 6 110
7 7 7 111
8 8 10 1000
9 9 11 1001
10 A 12 1010
11 B 13 1011
u
formatstringsu
Function definitions
********************
A function definition defines a user-defined function object (see
section *The standard type hierarchy*):
funcdef ::= [decorators] "def" funcname "(" [parameter_list] ")" ["->" expression] ":" suite
decorators ::= decorator+
decorator ::= "@" dotted_name ["(" [parameter_list [","]] ")"] NEWLINE
dotted_name ::= identifier ("." identifier)*
parameter_list ::= (defparameter ",")*
( "*" [parameter] ("," defparameter)* ["," "**" parameter]
| "**" parameter
| defparameter [","] )
parameter ::= identifier [":" expression]
defparameter ::= parameter ["=" expression]
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 *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 up until the ""*"" 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. Parameters after ""*"" or ""*identifier"" are
keyword-only parameters and may only be passed used keyword arguments.
Parameters may have annotations of the form "": expression"" following
the parameter name. Any parameter may have an annotation even those
of the form "*identifier" or "**identifier". Functions may have
"return" annotation of the form ""-> expression"" after the parameter
list. These annotations can be any valid Python expression and are
evaluated when the function definition is executed. Annotations may
be evaluated in a different order than they appear in the source code.
The presence of annotations does not change the semantics of a
function. The annotation values are available as values of a
dictionary keyed by the parameters' names in the "__annotations__"
attribute of the function object.
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 and annotations.
**Programmer's note:** Functions are first-class objects. A ""def""
statement 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.
See also: **PEP 3107** - Function Annotations
The original specification for function annotations.
u functionu°
The "global" statement
**********************
global_stmt ::= "global" identifier ("," identifier)*
The "global" statement is a declaration which holds for the entire
current code block. It means that the listed identifiers are to be
interpreted as globals. It would be impossible to assign to a global
variable without "global", although free variables may refer to
globals without being declared global.
Names listed in a "global" statement must not be used in the same code
block textually preceding that "global" statement.
Names listed in a "global" statement must not be defined as formal
parameters or in a "for" loop control target, "class" definition,
function definition, or "import" statement.
**CPython implementation detail:** The current implementation does not
enforce the latter two restrictions, but programs should not abuse
this freedom, as future implementations may enforce them or silently
change the meaning of the program.
**Programmer's note:** the "global" is a directive to the parser. It
applies only to code parsed at the same time as the "global"
statement. In particular, a "global" statement contained in a string
or code object supplied to the built-in "exec()" function does not
affect the code block *containing* the function call, and code
contained in such a string is unaffected by "global" statements in the
code containing the function call. The same applies to the "eval()"
and "compile()" functions.
u globalu¦
Reserved classes of identifiers
*******************************
Certain classes of identifiers (besides keywords) have special
meanings. These classes are identified by the patterns of leading and
trailing underscore characters:
"_*"
Not imported by "from module import *". The special identifier "_"
is used in the interactive interpreter to store the result of the
last evaluation; it is stored in the "builtins" module. When not
in interactive mode, "_" has no special meaning and is not defined.
See section *The import statement*.
Note: The name "_" is often used in conjunction with
internationalization; refer to the documentation for the
"gettext" module for more information on this convention.
"__*__"
System-defined names. These names are defined by the interpreter
and its implementation (including the standard library). Current
system names are discussed in the *Special method names* section
and elsewhere. More will likely be defined in future versions of
Python. *Any* use of "__*__" names, in any context, that does not
follow explicitly documented use, is subject to breakage without
warning.
"__*"
Class-private names. Names in this category, when used within the
context of a class definition, are re-written to use a mangled form
to help avoid name clashes between "private" attributes of base and
derived classes. See section *Identifiers (Names)*.
u
id-classesu*
Identifiers and keywords
************************
Identifiers (also referred to as *names*) are described by the
following lexical definitions.
The syntax of identifiers in Python is based on the Unicode standard
annex UAX-31, with elaboration and changes as defined below; see also
**PEP 3131** for further details.
Within the ASCII range (U+0001..U+007F), the valid characters for
identifiers are the same as in Python 2.x: the uppercase and lowercase
letters "A" through "Z", the underscore "_" and, except for the first
character, the digits "0" through "9".
Python 3.0 introduces additional characters from outside the ASCII
range (see **PEP 3131**). For these characters, the classification
uses the version of the Unicode Character Database as included in the
"unicodedata" module.
Identifiers are unlimited in length. Case is significant.
identifier ::= xid_start xid_continue*
id_start ::=
id_continue ::=
xid_start ::=
xid_continue ::=
The Unicode category codes mentioned above stand for:
* *Lu* - uppercase letters
* *Ll* - lowercase letters
* *Lt* - titlecase letters
* *Lm* - modifier letters
* *Lo* - other letters
* *Nl* - letter numbers
* *Mn* - nonspacing marks
* *Mc* - spacing combining marks
* *Nd* - decimal numbers
* *Pc* - connector punctuations
* *Other_ID_Start* - explicit list of characters in PropList.txt to
support backwards compatibility
* *Other_ID_Continue* - likewise
All identifiers are converted into the normal form NFKC while parsing;
comparison of identifiers is based on NFKC.
A non-normative HTML file listing all valid identifier characters for
Unicode 4.1 can be found at http://www.dcl.hpi.uni-
potsdam.de/home/loewis/table-3131.html.
Keywords
========
The following identifiers are used as reserved words, or *keywords* of
the language, and cannot be used as ordinary identifiers. They must
be spelled exactly as written here:
False class finally is return
None continue for lambda try
True def from nonlocal while
and del global not with
as elif if or yield
assert else import pass
break except in raise
Reserved classes of identifiers
===============================
Certain classes of identifiers (besides keywords) have special
meanings. These classes are identified by the patterns of leading and
trailing underscore characters:
"_*"
Not imported by "from module import *". The special identifier "_"
is used in the interactive interpreter to store the result of the
last evaluation; it is stored in the "builtins" module. When not
in interactive mode, "_" has no special meaning and is not defined.
See section *The import statement*.
Note: The name "_" is often used in conjunction with
internationalization; refer to the documentation for the
"gettext" module for more information on this convention.
"__*__"
System-defined names. These names are defined by the interpreter
and its implementation (including the standard library). Current
system names are discussed in the *Special method names* section
and elsewhere. More will likely be defined in future versions of
Python. *Any* use of "__*__" names, in any context, that does not
follow explicitly documented use, is subject to breakage without
warning.
"__*"
Class-private names. Names in this category, when used within the
context of a class definition, are re-written to use a mangled form
to help avoid name clashes between "private" attributes of base and
derived classes. See section *Identifiers (Names)*.
u identifiersu ifu%
Imaginary literals
******************
Imaginary literals are described by the following lexical definitions:
imagnumber ::= (floatnumber | intpart) ("j" | "J")
An imaginary literal yields a complex number with a real part of 0.0.
Complex numbers are represented as a pair of floating point numbers
and have the same restrictions on their range. To create a complex
number with a nonzero real part, add a floating point number to it,
e.g., "(3+4j)". Some examples of imaginary literals:
3.14j 10.j 10j .001j 1e100j 3.14e-10j
u imaginaryu(!
The "import" statement
**********************
import_stmt ::= "import" module ["as" name] ( "," module ["as" name] )*
| "from" relative_module "import" identifier ["as" name]
( "," identifier ["as" name] )*
| "from" relative_module "import" "(" identifier ["as" name]
( "," identifier ["as" name] )* [","] ")"
| "from" module "import" "*"
module ::= (identifier ".")* identifier
relative_module ::= "."* module | "."+
name ::= identifier
The basic import statement (no "from" clause) is executed in two
steps:
1. find a module, loading and initializing it if necessary
2. define a name or names in the local namespace for the scope
where the "import" statement occurs.
When the statement contains multiple clauses (separated by commas) the
two steps are carried out separately for each clause, just as though
the clauses had been separated out into individiual import statements.
The details of the first step, finding and loading modules is
described in greater detail in the section on the *import system*,
which also describes the various types of packages and modules that
can be imported, as well as all the hooks that can be used to
customize the import system. Note that failures in this step may
indicate either that the module could not be located, *or* that an
error occurred while initializing the module, which includes execution
of the module's code.
If the requested module is retrieved successfully, it will be made
available in the local namespace in one of three ways:
* If the module name is followed by "as", then the name following
"as" is bound directly to the imported module.
* If no other name is specified, and the module being imported is a
top level module, the module's name is bound in the local namespace
as a reference to the imported module
* If the module being imported is *not* a top level module, then the
name of the top level package that contains the module is bound in
the local namespace as a reference to the top level package. The
imported module must be accessed using its full qualified name
rather than directly
The "from" form uses a slightly more complex process:
1. find the module specified in the "from" clause loading and
initializing it if necessary;
2. for each of the identifiers specified in the "import" clauses:
1. check if the imported module has an attribute by that name
2. if not, attempt to import a submodule with that name and then
check the imported module again for that attribute
3. if the attribute is not found, "ImportError" is raised.
4. otherwise, a reference to that value is bound in the local
namespace, using the name in the "as" clause if it is present,
otherwise using the attribute name
Examples:
import foo # foo imported and bound locally
import foo.bar.baz # foo.bar.baz imported, foo bound locally
import foo.bar.baz as fbb # foo.bar.baz imported and bound as fbb
from foo.bar import baz # foo.bar.baz imported and bound as baz
from foo import attr # foo imported and foo.attr bound as attr
If the list of identifiers is replaced by a star ("'*'"), all public
names defined in the module are bound in the local namespace for the
scope where the "import" statement occurs.
The *public names* defined by a module are determined by checking the
module's namespace for a variable named "__all__"; if defined, it must
be a sequence of strings which are names defined or imported by that
module. The names given in "__all__" are all considered public and
are required to exist. If "__all__" is not defined, the set of public
names includes all names found in the module's namespace which do not
begin with an underscore character ("'_'"). "__all__" should contain
the entire public API. It is intended to avoid accidentally exporting
items that are not part of the API (such as library modules which were
imported and used within the module).
The "from" form with "*" may only occur in a module scope. The wild
card form of import --- "import *" --- is only allowed at the module
level. Attempting to use it in class or function definitions will
raise a "SyntaxError".
When specifying what module to import you do not have to specify the
absolute name of the module. When a module or package is contained
within another package it is possible to make a relative import within
the same top package without having to mention the package name. By
using leading dots in the specified module or package after "from" you
can specify how high to traverse up the current package hierarchy
without specifying exact names. One leading dot means the current
package where the module making the import exists. Two dots means up
one package level. Three dots is up two levels, etc. So if you execute
"from . import mod" from a module in the "pkg" package then you will
end up importing "pkg.mod". If you execute "from ..subpkg2 import mod"
from within "pkg.subpkg1" you will import "pkg.subpkg2.mod". The
specification for relative imports is contained within **PEP 328**.
"importlib.import_module()" is provided to support applications that
determine which modules need to be loaded dynamically.
Future statements
=================
A *future statement* is a directive to the compiler that a particular
module should be compiled using syntax or semantics that will be
available in a specified future release of Python. The future
statement is intended to ease migration to future versions of Python
that introduce incompatible changes to the language. It allows use of
the new features on a per-module basis before the release in which the
feature becomes standard.
future_statement ::= "from" "__future__" "import" feature ["as" name]
("," feature ["as" name])*
| "from" "__future__" "import" "(" feature ["as" name]
("," feature ["as" name])* [","] ")"
feature ::= identifier
name ::= identifier
A future statement must appear near the top of the module. The only
lines that can appear before a future statement are:
* the module docstring (if any),
* comments,
* blank lines, and
* other future statements.
The features recognized by Python 3.0 are "absolute_import",
"division", "generators", "unicode_literals", "print_function",
"nested_scopes" and "with_statement". They are all redundant because
they are always enabled, and only kept for backwards compatibility.
A future statement is recognized and treated specially at compile
time: Changes to the semantics of core constructs are often
implemented by generating different code. It may even be the case
that a new feature introduces new incompatible syntax (such as a new
reserved word), in which case the compiler may need to parse the
module differently. Such decisions cannot be pushed off until
runtime.
For any given release, the compiler knows which feature names have
been defined, and raises a compile-time error if a future statement
contains a feature not known to it.
The direct runtime semantics are the same as for any import statement:
there is a standard module "__future__", described later, and it will
be imported in the usual way at the time the future statement is
executed.
The interesting runtime semantics depend on the specific feature
enabled by the future statement.
Note that there is nothing special about the statement:
import __future__ [as name]
That is not a future statement; it's an ordinary import statement with
no special semantics or syntax restrictions.
Code compiled by calls to the built-in functions "exec()" and
"compile()" that occur in a module "M" containing a future statement
will, by default, use the new syntax or semantics associated with the
future statement. This can be controlled by optional arguments to
"compile()" --- see the documentation of that function for details.
A future statement typed at an interactive interpreter prompt will
take effect for the rest of the interpreter session. If an
interpreter is started with the *-i* option, is passed a script name
to execute, and the script includes a future statement, it will be in
effect in the interactive session started after the script is
executed.
See also: **PEP 236** - Back to the __future__
The original proposal for the __future__ mechanism.
u importu inu
Integer literals
****************
Integer literals are described by the following lexical definitions:
integer ::= decimalinteger | octinteger | hexinteger | bininteger
decimalinteger ::= nonzerodigit digit* | "0"+
nonzerodigit ::= "1"..."9"
digit ::= "0"..."9"
octinteger ::= "0" ("o" | "O") octdigit+
hexinteger ::= "0" ("x" | "X") hexdigit+
bininteger ::= "0" ("b" | "B") bindigit+
octdigit ::= "0"..."7"
hexdigit ::= digit | "a"..."f" | "A"..."F"
bindigit ::= "0" | "1"
There is no limit for the length of integer literals apart from what
can be stored in available memory.
Note that leading zeros in a non-zero decimal number are not allowed.
This is for disambiguation with C-style octal literals, which Python
used before version 3.0.
Some examples of integer literals:
7 2147483647 0o177 0b100110111
3 79228162514264337593543950336 0o377 0x100000000
79228162514264337593543950336 0xdeadbeef
u integersu–
Lambdas
*******
lambda_expr ::= "lambda" [parameter_list]: expression
lambda_expr_nocond ::= "lambda" [parameter_list]: expression_nocond
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 (arguments):
return expression
See section *Function definitions* for the syntax of parameter lists.
Note that functions created with lambda expressions cannot contain
statements or annotations.
u lambdau3
List displays
*************
A list display is a possibly empty series of expressions enclosed in
square brackets:
list_display ::= "[" [expression_list | comprehension] "]"
A list display yields a new list object, the contents being specified
by either a list of expressions or a 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 comprehension is supplied, the list is constructed from the
elements resulting from the comprehension.
u listsuù
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 string argument passed
to the built-in functions "eval()" and "exec()" 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 comprehensions and 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,
unless declared as "nonlocal". 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, or after
"as" in a "with" statement or "except" clause. 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).
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 "builtins". 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 "builtins"; when in any
other module, "__builtins__" is an alias for the dictionary of the
"builtins" 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 "builtins" 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".
The "eval()" and "exec()" functions 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()" and "eval()" functions have optional arguments to
override the global and local namespace. If only one namespace is
specified, it is used for both.
u namingu“
The "nonlocal" statement
************************
nonlocal_stmt ::= "nonlocal" identifier ("," identifier)*
The "nonlocal" statement causes the listed identifiers to refer to
previously bound variables in the nearest enclosing scope. This is
important because the default behavior for binding is to search the
local namespace first. The statement allows encapsulated code to
rebind variables outside of the local scope besides the global
(module) scope.
Names listed in a "nonlocal" statement, unlike to those listed in a
"global" statement, must refer to pre-existing bindings in an
enclosing scope (the scope in which a new binding should be created
cannot be determined unambiguously).
Names listed in a "nonlocal" statement must not collide with pre-
existing bindings in the local scope.
See also: **PEP 3104** - Access to Names in Outer Scopes
The specification for the "nonlocal" statement.
u nonlocalu’
Numeric literals
****************
There are three types of numeric literals: 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".
u numbersuz
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.__truediv__(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__()". 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.__radd__(self, other)
object.__rsub__(self, other)
object.__rmul__(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.__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, if *x* is an instance of a class with an
"__iadd__()" method, "x += y" is equivalent to "x = x.__iadd__(y)"
. Otherwise, "x.__add__(y)" and "y.__radd__(x)" are considered, as
with the evaluation of "x + y". In certain situations, augmented
assignment can result in unexpected errors (see *Why does
a_tuple[i] += ['item'] raise an exception when the addition
works?*), but this behavior is in fact part of the data model.
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.__float__(self)
object.__round__(self[, n])
Called to implement the built-in functions "complex()", "int()",
"float()" and "round()". Should return a value of the appropriate
type.
object.__index__(self)
Called to implement "operator.index()". Also called whenever
Python needs an integer object (such as in slicing, or in the
built-in "bin()", "hex()" and "oct()" functions). Must return an
integer.
u
numeric-typesuÅ
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.
**CPython implementation detail:** For CPython, "id(x)" is the memory
address where "x" is stored.
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). Like its identity, an object's
*type* is also unchangeable. [1]
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 and the '"with"' statement provide
convenient ways 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".)
u objectsuï
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 |
+-------------------------------------------------+---------------------------------------+
| "+x", "-x", "~x" | Positive, negative, bitwise NOT |
+-------------------------------------------------+---------------------------------------+
| "**" | Exponentiation [6] |
+-------------------------------------------------+---------------------------------------+
| "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, set |
+-------------------------------------------------+---------------------------------------+
-[ Footnotes ]-
[1] 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.
[2] If x is very close to an exact integer multiple of y, it's
possible for "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".
[3] While comparisons between strings make sense at the byte
level, they may be counter-intuitive to users. For example, the
strings ""\u00C7"" and ""\u0327\u0043"" compare differently, even
though they both represent the same unicode character (LATIN
CAPITAL LETTER C WITH CEDILLA). To compare strings in a human
recognizable way, compare using "unicodedata.normalize()".
[4] 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.
[5] The "%" operator is also used for string formatting; the same
precedence applies.
[6] The power operator "**" binds less tightly than an arithmetic
or bitwise unary operator on its right, that is, "2**-1" is "0.5".
u operator-summaryux
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)
u passu|
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, and the result is of that type.
For int operands, the result has the same type as the operands 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".
Raising "0.0" to a negative power results in a "ZeroDivisionError".
Raising a negative number to a fractional power results in a "complex"
number. (In earlier versions it raised a "ValueError".)
u powerud
The "raise" statement
*********************
raise_stmt ::= "raise" [expression ["from" 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 "RuntimeError" exception is raised indicating
that this is an error.
Otherwise, "raise" evaluates the first expression as the exception
object. It must be either a subclass or an instance of
"BaseException". If it is a class, the exception instance will be
obtained when needed by instantiating the class with no arguments.
The *type* of the exception is the exception instance's class, the
*value* is the instance itself.
A traceback object is normally created automatically when an exception
is raised and attached to it as the "__traceback__" attribute, which
is writable. You can create an exception and set your own traceback in
one step using the "with_traceback()" exception method (which returns
the same exception instance, with its traceback set to its argument),
like so:
raise Exception("foo occurred").with_traceback(tracebackobj)
The "from" clause is used for exception chaining: if given, the second
*expression* must be another exception class or instance, which will
then be attached to the raised exception as the "__cause__" attribute
(which is writable). If the raised exception is not handled, both
exceptions will be printed:
>>> try:
... print(1 / 0)
... except Exception as exc:
... raise RuntimeError("Something bad happened") from exc
...
Traceback (most recent call last):
File "", line 2, in
ZeroDivisionError: int division or modulo by zero
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "", line 4, in
RuntimeError: Something bad happened
A similar mechanism works implicitly if an exception is raised inside
an exception handler: the previous exception is then attached as the
new exception's "__context__" attribute:
>>> try:
... print(1 / 0)
... except:
... raise RuntimeError("Something bad happened")
...
Traceback (most recent call last):
File "", line 2, in
ZeroDivisionError: int division or modulo by zero
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "", line 4, in
RuntimeError: Something bad happened
Additional information on exceptions can be found in section
*Exceptions*, and information about handling exceptions is in section
*The try statement*.
u raiseu
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 indicates that the
generator is done and will cause "StopIteration" to be raised. The
returned value (if any) is used as an argument to construct
"StopIteration" and becomes the "StopIteration.value" attribute.
u returnuö
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. It is also recommended that mappings provide the
methods "keys()", "values()", "items()", "get()", "clear()",
"setdefault()", "pop()", "popitem()", "copy()", and "update()"
behaving similar to those for Python's standard dictionary objects.
The "collections" module provides a "MutableMapping" abstract base
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 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 search the mapping's keys; 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 "keys()"; 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 "__bool__()" method and whose "__len__()" method
returns zero is considered to be false in a Boolean context.
Note: Slicing is done exclusively with the following three methods.
A call like
a[1:2] = b
is translated to
a[slice(1, 2, None)] = b
and so forth. Missing slice items are always filled in with "None".
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.__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 "keys()".
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()".
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*.
u sequence-typesu£
Shifting operations
*******************
The shifting operations have lower priority than the arithmetic
operations:
shift_expr ::= a_expr | shift_expr ( "<<" | ">>" ) a_expr
These operators accept integers as arguments. 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 floor division by "pow(2,n)".
A left shift by *n* bits is defined as multiplication with "pow(2,n)".
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.
u shiftingu¹
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 ::= primary "[" slice_list "]"
slice_list ::= slice_item ("," slice_item)* [","]
slice_item ::= expression | proper_slice
proper_slice ::= [lower_bound] ":" [upper_bound] [ ":" [stride] ]
lower_bound ::= expression
upper_bound ::= expression
stride ::= expression
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).
The semantics for a slicing are as follows. The primary must evaluate
to a mapping object, and it is indexed (using the same "__getitem__()"
method as normal subscription) 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 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.
u slicingsu
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.
instance.__class__
The class to which a class instance belongs.
class.__bases__
The tuple of base classes of a class object.
class.__name__
The name of the class or type.
class.__qualname__
The *qualified name* of the class or type.
New in version 3.3.
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 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__()
[]
-[ 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.
u specialattrsu®¶
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 "type(x).__getitem__(x, i)".
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_info()[2]" 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.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.
object.__repr__(self)
Called by the "repr()" built-in function to compute the "official"
string representation of an object. If at all possible, this
should look like a valid Python expression that could be used to
recreate an object with the same value (given an appropriate
environment). If this is not possible, a string of the form
"<...some useful description...>" should be returned. The return
value must be a string object. If a class defines "__repr__()" but
not "__str__()", then "__repr__()" is also used when an "informal"
string representation of instances of that class is required.
This is typically used for debugging, so it is important that the
representation is information-rich and unambiguous.
object.__str__(self)
Called by "str(object)" and the built-in functions "format()" and
"print()" to compute the "informal" or nicely printable string
representation of an object. The return value must be a *string*
object.
This method differs from "object.__repr__()" in that there is no
expectation that "__str__()" return a valid Python expression: a
more convenient or concise representation can be used.
The default implementation defined by the built-in type "object"
calls "object.__repr__()".
object.__bytes__(self)
Called by "bytes()" to compute a byte-string representation of an
object. This should return a "bytes" object.
object.__format__(self, format_spec)
Called by the "format()" built-in function (and by extension, the
"str.format()" method of class "str") to produce a "formatted"
string representation of an object. The "format_spec" argument is a
string that contains a description of the formatting options
desired. The interpretation of the "format_spec" argument is up to
the type implementing "__format__()", however most classes will
either delegate formatting to one of the built-in types, or use a
similar formatting option syntax.
See *Format Specification Mini-Language* for a description of the
standard formatting syntax.
The return value must be a string object.
object.__lt__(self, other)
object.__le__(self, other)
object.__eq__(self, other)
object.__ne__(self, other)
object.__gt__(self, other)
object.__ge__(self, other)
These are the so-called "rich comparison" methods. The
correspondence between operator symbols and method names is as
follows: "xy" calls
"x.__gt__(y)", and "x>=y" calls "x.__ge__(y)".
A rich comparison method may return the singleton "NotImplemented"
if it does not implement the operation for a given pair of
arguments. By convention, "False" and "True" are returned for a
successful comparison. However, these methods can return any value,
so if the comparison operator is used in a Boolean context (e.g.,
in the condition of an "if" statement), Python will call "bool()"
on the value to determine if the result is true or false.
There are no implied relationships among the comparison operators.
The truth of "x==y" does not imply that "x!=y" is false.
Accordingly, when defining "__eq__()", one should also define
"__ne__()" so that the operators will behave as expected. See the
paragraph on "__hash__()" for some important notes on creating
*hashable* objects which support custom comparison operations and
are usable as dictionary keys.
There are no swapped-argument versions of these methods (to be used
when the left argument does not support the operation but the right
argument does); rather, "__lt__()" and "__gt__()" are each other's
reflection, "__le__()" and "__ge__()" are each other's reflection,
and "__eq__()" and "__ne__()" are their own reflection.
Arguments to rich comparison methods are never coerced.
To automatically generate ordering operations from a single root
operation, see "functools.total_ordering()".
object.__hash__(self)
Called by built-in function "hash()" and for operations on members
of hashed collections including "set", "frozenset", and "dict".
"__hash__()" should return an integer. The only required property
is that objects which compare equal have the same hash value; it is
advised to somehow mix together (e.g. using exclusive or) the hash
values for the components of the object that also play a part in
comparison of objects.
Note: "hash()" truncates the value returned from an object's
custom "__hash__()" method to the size of a "Py_ssize_t". This
is typically 8 bytes on 64-bit builds and 4 bytes on 32-bit
builds. If an object's "__hash__()" must interoperate on builds
of different bit sizes, be sure to check the width on all
supported builds. An easy way to do this is with "python -c
"import sys; print(sys.hash_info.width)""
If a class does not define an "__eq__()" method it should not
define a "__hash__()" operation either; if it defines "__eq__()"
but not "__hash__()", its instances will not be usable as items in
hashable collections. If a class defines mutable objects and
implements an "__eq__()" method, it should not implement
"__hash__()", since the implementation of hashable collections
requires that a key's hash value is immutable (if the object's hash
value changes, it will be in the wrong hash bucket).
User-defined classes have "__eq__()" and "__hash__()" methods by
default; with them, all objects compare unequal (except with
themselves) and "x.__hash__()" returns an appropriate value such
that "x == y" implies both that "x is y" and "hash(x) == hash(y)".
A class that overrides "__eq__()" and does not define "__hash__()"
will have its "__hash__()" implicitly set to "None". When the
"__hash__()" method of a class is "None", instances of the class
will raise an appropriate "TypeError" when a program attempts to
retrieve their hash value, and will also be correctly identified as
unhashable when checking "isinstance(obj, collections.Hashable").
If a class that overrides "__eq__()" needs to retain the
implementation of "__hash__()" from a parent class, the interpreter
must be told this explicitly by setting "__hash__ =
.__hash__".
If a class that does not override "__eq__()" wishes to suppress
hash support, it should include "__hash__ = None" in the class
definition. A class which defines its own "__hash__()" that
explicitly raises a "TypeError" would be incorrectly identified as
hashable by an "isinstance(obj, collections.Hashable)" call.
Note: By default, the "__hash__()" values of str, bytes and
datetime objects are "salted" with an unpredictable random value.
Although they remain constant within an individual Python
process, they are not predictable between repeated invocations of
Python.This is intended to provide protection against a denial-
of-service caused by carefully-chosen inputs that exploit the
worst case performance of a dict insertion, O(n^2) complexity.
See http://www.ocert.org/advisories/ocert-2011-003.html for
details.Changing hash values affects the iteration order of
dicts, sets and other mappings. Python has never made guarantees
about this ordering (and it typically varies between 32-bit and
64-bit builds).See also "PYTHONHASHSEED".
Changed in version 3.3: Hash randomization is enabled by default.
object.__bool__(self)
Called to implement truth value testing and the built-in operation
"bool()"; should return "False" or "True". When this method is not
defined, "__len__()" is called, if it is defined, and the object is
considered true if its result is nonzero. If a class defines
neither "__len__()" nor "__bool__()", all its instances are
considered true.
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 over attribute access.
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*.
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 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.
object.__dir__(self)
Called when "dir()" is called on the object. A sequence must be
returned. "dir()" converts the returned sequence to a list and
sorts it.
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.
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 an object instance, "a.x" is transformed into the
call: "type(a).__dict__['x'].__get__(a, type(a))".
Class Binding
If binding to a 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 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 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.
object.__slots__
This class variable can be assigned a string, iterable, or sequence
of strings with variable names used by instances. If defined in a
class, *__slots__* reserves space for the declared variables and
prevents the automatic creation of *__dict__* and *__weakref__* for
each instance.
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.
* 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.
* *__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 "int", "bytes" 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__*.
Customizing class creation
==========================
By default, classes are constructed using "type()". The class body is
executed in a new namespace and the class name is bound locally to the
result of "type(name, bases, namespace)".
The class creation process can be customised by passing the
"metaclass" keyword argument in the class definition line, or by
inheriting from an existing class that included such an argument. In
the following example, both "MyClass" and "MySubclass" are instances
of "Meta":
class Meta(type):
pass
class MyClass(metaclass=Meta):
pass
class MySubclass(MyClass):
pass
Any other keyword arguments that are specified in the class definition
are passed through to all metaclass operations described below.
When a class definition is executed, the following steps occur:
* the appropriate metaclass is determined
* the class namespace is prepared
* the class body is executed
* the class object is created
Determining the appropriate metaclass
-------------------------------------
The appropriate metaclass for a class definition is determined as
follows:
* if no bases and no explicit metaclass are given, then "type()" is
used
* if an explicit metaclass is given and it is *not* an instance of
"type()", then it is used directly as the metaclass
* if an instance of "type()" is given as the explicit metaclass, or
bases are defined, then the most derived metaclass is used
The most derived metaclass is selected from the explicitly specified
metaclass (if any) and the metaclasses (i.e. "type(cls)") of all
specified base classes. The most derived metaclass is one which is a
subtype of *all* of these candidate metaclasses. If none of the
candidate metaclasses meets that criterion, then the class definition
will fail with "TypeError".
Preparing the class namespace
-----------------------------
Once the appropriate metaclass has been identified, then the class
namespace is prepared. If the metaclass has a "__prepare__" attribute,
it is called as "namespace = metaclass.__prepare__(name, bases,
**kwds)" (where the additional keyword arguments, if any, come from
the class definition).
If the metaclass has no "__prepare__" attribute, then the class
namespace is initialised as an empty "dict()" instance.
See also: **PEP 3115** - Metaclasses in Python 3000
Introduced the "__prepare__" namespace hook
Executing the class body
------------------------
The class body is executed (approximately) as "exec(body, globals(),
namespace)". The key difference from a normal call to "exec()" is that
lexical scoping allows the class body (including any methods) to
reference names from the current and outer scopes when the class
definition occurs inside a function.
However, even when the class definition occurs inside the function,
methods defined inside the class still cannot see names defined at the
class scope. Class variables must be accessed through the first
parameter of instance or class methods, and cannot be accessed at all
from static methods.
Creating the class object
-------------------------
Once the class namespace has been populated by executing the class
body, the class object is created by calling "metaclass(name, bases,
namespace, **kwds)" (the additional keywords passed here are the same
as those passed to "__prepare__").
This class object is the one that will be referenced by the zero-
argument form of "super()". "__class__" is an implicit closure
reference created by the compiler if any methods in a class body refer
to either "__class__" or "super". This allows the zero argument form
of "super()" to correctly identify the class being defined based on
lexical scoping, while the class or instance that was used to make the
current call is identified based on the first argument passed to the
method.
After the class object is created, it is passed to the class
decorators included in the class definition (if any) and the resulting
object is bound in the local namespace as the defined class.
See also: **PEP 3135** - New super
Describes the implicit "__class__" closure reference
Metaclass example
-----------------
The potential uses for metaclasses are boundless. Some ideas that have
been explored include logging, interface checking, automatic
delegation, automatic property creation, proxies, frameworks, and
automatic resource locking/synchronization.
Here is an example of a metaclass that uses an
"collections.OrderedDict" to remember the order that class members
were defined:
class OrderedClass(type):
@classmethod
def __prepare__(metacls, name, bases, **kwds):
return collections.OrderedDict()
def __new__(cls, name, bases, namespace, **kwds):
result = type.__new__(cls, name, bases, dict(namespace))
result.members = tuple(namespace)
return result
class A(metaclass=OrderedClass):
def one(self): pass
def two(self): pass
def three(self): pass
def four(self): pass
>>> A.members
('__module__', 'one', 'two', 'three', 'four')
When the class definition for *A* gets executed, the process begins
with calling the metaclass's "__prepare__()" method which returns an
empty "collections.OrderedDict". That mapping records the methods and
attributes of *A* as they are defined within the body of the class
statement. Once those definitions are executed, the ordered dictionary
is fully populated and the metaclass's "__new__()" method gets
invoked. That method builds the new type and it saves the ordered
dictionary keys in an attribute called "members".
Customizing instance and subclass checks
========================================
The following methods are used to override the default behavior of the
"isinstance()" and "issubclass()" built-in functions.
In particular, the metaclass "abc.ABCMeta" implements these methods in
order to allow the addition of Abstract Base Classes (ABCs) as
"virtual base classes" to any class or type (including built-in
types), including other ABCs.
class.__instancecheck__(self, instance)
Return true if *instance* should be considered a (direct or
indirect) instance of *class*. If defined, called to implement
"isinstance(instance, class)".
class.__subclasscheck__(self, subclass)
Return true if *subclass* should be considered a (direct or
indirect) subclass of *class*. If defined, called to implement
"issubclass(subclass, class)".
Note that these methods are looked up on the type (metaclass) of a
class. They cannot be defined as class methods in the actual class.
This is consistent with the lookup of special methods that are called
on instances, only in this case the instance is itself a class.
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. It is also recommended that mappings provide the
methods "keys()", "values()", "items()", "get()", "clear()",
"setdefault()", "pop()", "popitem()", "copy()", and "update()"
behaving similar to those for Python's standard dictionary objects.
The "collections" module provides a "MutableMapping" abstract base
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 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 search the mapping's keys; 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 "keys()"; 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 "__bool__()" method and whose "__len__()" method
returns zero is considered to be false in a Boolean context.
Note: Slicing is done exclusively with the following three methods.
A call like
a[1:2] = b
is translated to
a[slice(1, 2, None)] = b
and so forth. Missing slice items are always filled in with "None".
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.__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 "keys()".
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()".
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*.
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.__truediv__(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__()". 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.__radd__(self, other)
object.__rsub__(self, other)
object.__rmul__(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.__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, if *x* is an instance of a class with an
"__iadd__()" method, "x += y" is equivalent to "x = x.__iadd__(y)"
. Otherwise, "x.__add__(y)" and "y.__radd__(x)" are considered, as
with the evaluation of "x + y". In certain situations, augmented
assignment can result in unexpected errors (see *Why does
a_tuple[i] += ['item'] raise an exception when the addition
works?*), but this behavior is in fact part of the data model.
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.__float__(self)
object.__round__(self[, n])
Called to implement the built-in functions "complex()", "int()",
"float()" and "round()". Should return a value of the appropriate
type.
object.__index__(self)
Called to implement "operator.index()". Also called whenever
Python needs an integer object (such as in slicing, or in the
built-in "bin()", "hex()" and "oct()" functions). Must return an
integer.
With Statement Context Managers
===============================
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 0343** - The "with" statement
The specification, background, and examples for the Python "with"
statement.
Special method lookup
=====================
For custom classes, implicit invocations of special methods are only
guaranteed to work correctly if defined on an object's type, not in
the object's instance dictionary. That behaviour is the reason why
the following code raises an exception:
>>> class C:
... pass
...
>>> c = C()
>>> c.__len__ = lambda: 5
>>> len(c)
Traceback (most recent call last):
File "", line 1, in
TypeError: object of type 'C' has no len()
The rationale behind this behaviour lies with a number of special
methods such as "__hash__()" and "__repr__()" that are implemented by
all objects, including type objects. If the implicit lookup of these
methods used the conventional lookup process, they would fail when
invoked on the type object itself:
>>> 1 .__hash__() == hash(1)
True
>>> int.__hash__() == hash(int)
Traceback (most recent call last):
File "", line 1, in
TypeError: descriptor '__hash__' of 'int' object needs an argument
Incorrectly attempting to invoke an unbound method of a class in this
way is sometimes referred to as 'metaclass confusion', and is avoided
by bypassing the instance when looking up special methods:
>>> type(1).__hash__(1) == hash(1)
True
>>> type(int).__hash__(int) == hash(int)
True
In addition to bypassing any instance attributes in the interest of
correctness, implicit special method lookup generally also bypasses
the "__getattribute__()" method even of the object's metaclass:
>>> class Meta(type):
... def __getattribute__(*args):
... print("Metaclass getattribute invoked")
... return type.__getattribute__(*args)
...
>>> class C(object, metaclass=Meta):
... def __len__(self):
... return 10
... def __getattribute__(*args):
... print("Class getattribute invoked")
... return object.__getattribute__(*args)
...
>>> c = C()
>>> c.__len__() # Explicit lookup via instance
Class getattribute invoked
10
>>> type(c).__len__(c) # Explicit lookup via type
Metaclass getattribute invoked
10
>>> len(c) # Implicit lookup
10
Bypassing the "__getattribute__()" machinery in this fashion provides
significant scope for speed optimisations within the interpreter, at
the cost of some flexibility in the handling of special methods (the
special method *must* be set on the class object itself in order to be
consistently invoked by the interpreter).
-[ Footnotes ]-
[1] It *is* possible in some cases to change an object's type,
under certain controlled conditions. It generally isn't a good
idea though, since it can lead to some very strange behaviour if
it is handled incorrectly.
[2] For operands of the same type, it is assumed that if the non-
reflected method (such as "__add__()") fails the operation is not
supported, which is why the reflected method is not called.
u specialnamesu…G
String Methods
**************
Strings implement all of the *common* sequence operations, along with
the additional methods described below.
Strings also support two styles of string formatting, one providing a
large degree of flexibility and customization (see "str.format()",
*Format String Syntax* and *String Formatting*) and the other based on
C "printf" style formatting that handles a narrower range of types and
is slightly harder to use correctly, but is often faster for the cases
it can handle (*printf-style String Formatting*).
The *Text Processing Services* section of the standard library covers
a number of other modules that provide various text related utilities
(including regular expression support in the "re" module).
str.capitalize()
Return a copy of the string with its first character capitalized
and the rest lowercased.
str.casefold()
Return a casefolded copy of the string. Casefolded strings may be
used for caseless matching.
Casefolding is similar to lowercasing but more aggressive because
it is intended to remove all case distinctions in a string. For
example, the German lowercase letter "'ß'" is equivalent to ""ss"".
Since it is already lowercase, "lower()" would do nothing to "'ß'";
"casefold()" converts it to ""ss"".
The casefolding algorithm is described in section 3.13 of the
Unicode Standard.
New in version 3.3.
str.center(width[, fillchar])
Return centered in a string of length *width*. Padding is done
using the specified *fillchar* (default is a space).
str.count(sub[, start[, end]])
Return the number of non-overlapping occurrences of substring *sub*
in the range [*start*, *end*]. Optional arguments *start* and
*end* are interpreted as in slice notation.
str.encode(encoding="utf-8", errors="strict")
Return an encoded version of the string as a bytes object. Default
encoding is "'utf-8'". *errors* may be given to set a different
error handling scheme. The default for *errors* is "'strict'",
meaning that encoding errors raise a "UnicodeError". Other possible
values are "'ignore'", "'replace'", "'xmlcharrefreplace'",
"'backslashreplace'" and any other name registered via
"codecs.register_error()", see section *Codec Base Classes*. For a
list of possible encodings, see section *Standard Encodings*.
Changed in version 3.1: Support for keyword arguments added.
str.endswith(suffix[, start[, end]])
Return "True" if the string ends with the specified *suffix*,
otherwise return "False". *suffix* can also be a tuple of suffixes
to look for. With optional *start*, test beginning at that
position. With optional *end*, stop comparing at that position.
str.expandtabs([tabsize])
Return a copy of the string where all tab characters are replaced
by one or more spaces, depending on the current column and the
given tab size. Tab positions occur every *tabsize* characters
(default is 8, giving tab positions at columns 0, 8, 16 and so on).
To expand the string, the current column is set to zero and the
string is examined character by character. If the character is a
tab ("\t"), one or more space characters are inserted in the result
until the current column is equal to the next tab position. (The
tab character itself is not copied.) If the character is a newline
("\n") or return ("\r"), it is copied and the current column is
reset to zero. Any other character is copied unchanged and the
current column is incremented by one regardless of how the
character is represented when printed.
>>> '01\t012\t0123\t01234'.expandtabs()
'01 012 0123 01234'
>>> '01\t012\t0123\t01234'.expandtabs(4)
'01 012 0123 01234'
str.find(sub[, start[, end]])
Return the lowest index in the string where substring *sub* is
found, such that *sub* is contained in the slice "s[start:end]".
Optional arguments *start* and *end* are interpreted as in slice
notation. Return "-1" if *sub* is not found.
Note: The "find()" method should be used only if you need to know
the position of *sub*. To check if *sub* is a substring or not,
use the "in" operator:
>>> 'Py' in 'Python'
True
str.format(*args, **kwargs)
Perform a string formatting operation. The string on which this
method is called can contain literal text or replacement fields
delimited by braces "{}". Each replacement field contains either
the numeric index of a positional argument, or the name of a
keyword argument. Returns a copy of the string where each
replacement field is replaced with the string value of the
corresponding argument.
>>> "The sum of 1 + 2 is {0}".format(1+2)
'The sum of 1 + 2 is 3'
See *Format String Syntax* for a description of the various
formatting options that can be specified in format strings.
str.format_map(mapping)
Similar to "str.format(**mapping)", except that "mapping" is used
directly and not copied to a "dict". This is useful if for example
"mapping" is a dict subclass:
>>> class Default(dict):
... def __missing__(self, key):
... return key
...
>>> '{name} was born in {country}'.format_map(Default(name='Guido'))
'Guido was born in country'
New in version 3.2.
str.index(sub[, start[, end]])
Like "find()", but raise "ValueError" when the substring is not
found.
str.isalnum()
Return true if all characters in the string are alphanumeric and
there is at least one character, false otherwise. A character "c"
is alphanumeric if one of the following returns "True":
"c.isalpha()", "c.isdecimal()", "c.isdigit()", or "c.isnumeric()".
str.isalpha()
Return true if all characters in the string are alphabetic and
there is at least one character, false otherwise. Alphabetic
characters are those characters defined in the Unicode character
database as "Letter", i.e., those with general category property
being one of "Lm", "Lt", "Lu", "Ll", or "Lo". Note that this is
different from the "Alphabetic" property defined in the Unicode
Standard.
str.isdecimal()
Return true if all characters in the string are decimal characters
and there is at least one character, false otherwise. Decimal
characters are those from general category "Nd". This category
includes digit characters, and all characters that can be used to
form decimal-radix numbers, e.g. U+0660, ARABIC-INDIC DIGIT ZERO.
str.isdigit()
Return true if all characters in the string are digits and there is
at least one character, false otherwise. Digits include decimal
characters and digits that need special handling, such as the
compatibility superscript digits. Formally, a digit is a character
that has the property value Numeric_Type=Digit or
Numeric_Type=Decimal.
str.isidentifier()
Return true if the string is a valid identifier according to the
language definition, section *Identifiers and keywords*.
Use "keyword.iskeyword()" to test for reserved identifiers such as
"def" and "class".
str.islower()
Return true if all cased characters [4] in the string are lowercase
and there is at least one cased character, false otherwise.
str.isnumeric()
Return true if all characters in the string are numeric characters,
and there is at least one character, false otherwise. Numeric
characters include digit characters, and all characters that have
the Unicode numeric value property, e.g. U+2155, VULGAR FRACTION
ONE FIFTH. Formally, numeric characters are those with the
property value Numeric_Type=Digit, Numeric_Type=Decimal or
Numeric_Type=Numeric.
str.isprintable()
Return true if all characters in the string are printable or the
string is empty, false otherwise. Nonprintable characters are
those characters defined in the Unicode character database as
"Other" or "Separator", excepting the ASCII space (0x20) which is
considered printable. (Note that printable characters in this
context are those which should not be escaped when "repr()" is
invoked on a string. It has no bearing on the handling of strings
written to "sys.stdout" or "sys.stderr".)
str.isspace()
Return true if there are only whitespace characters in the string
and there is at least one character, false otherwise. Whitespace
characters are those characters defined in the Unicode character
database as "Other" or "Separator" and those with bidirectional
property being one of "WS", "B", or "S".
str.istitle()
Return true if the string is a titlecased string and there is at
least one character, for example uppercase characters may only
follow uncased characters and lowercase characters only cased ones.
Return false otherwise.
str.isupper()
Return true if all cased characters [4] in the string are uppercase
and there is at least one cased character, false otherwise.
str.join(iterable)
Return a string which is the concatenation of the strings in the
*iterable* *iterable*. A "TypeError" will be raised if there are
any non-string values in *iterable*, including "bytes" objects.
The separator between elements is the string providing this method.
str.ljust(width[, fillchar])
Return the string left justified in a string of length *width*.
Padding is done using the specified *fillchar* (default is a
space). The original string is returned if *width* is less than or
equal to "len(s)".
str.lower()
Return a copy of the string with all the cased characters [4]
converted to lowercase.
The lowercasing algorithm used is described in section 3.13 of the
Unicode Standard.
str.lstrip([chars])
Return a copy of the string with leading characters removed. The
*chars* argument is a string specifying the set of characters to be
removed. If omitted or "None", the *chars* argument defaults to
removing whitespace. The *chars* argument is not a prefix; rather,
all combinations of its values are stripped:
>>> ' spacious '.lstrip()
'spacious '
>>> 'www.example.com'.lstrip('cmowz.')
'example.com'
static str.maketrans(x[, y[, z]])
This static method returns a translation table usable for
"str.translate()".
If there is only one argument, it must be a dictionary mapping
Unicode ordinals (integers) or characters (strings of length 1) to
Unicode ordinals, strings (of arbitrary lengths) or None.
Character keys will then be converted to ordinals.
If there are two arguments, they must be strings of equal length,
and in the resulting dictionary, each character in x will be mapped
to the character at the same position in y. If there is a third
argument, it must be a string, whose characters will be mapped to
None in the result.
str.partition(sep)
Split the string at the first occurrence of *sep*, and return a
3-tuple containing the part before the separator, the separator
itself, and the part after the separator. If the separator is not
found, return a 3-tuple containing the string itself, followed by
two empty strings.
str.replace(old, new[, count])
Return a copy of the string with all occurrences of substring *old*
replaced by *new*. If the optional argument *count* is given, only
the first *count* occurrences are replaced.
str.rfind(sub[, start[, end]])
Return the highest index in the string where substring *sub* is
found, such that *sub* is contained within "s[start:end]".
Optional arguments *start* and *end* are interpreted as in slice
notation. Return "-1" on failure.
str.rindex(sub[, start[, end]])
Like "rfind()" but raises "ValueError" when the substring *sub* is
not found.
str.rjust(width[, fillchar])
Return the string right justified in a string of length *width*.
Padding is done using the specified *fillchar* (default is a
space). The original string is returned if *width* is less than or
equal to "len(s)".
str.rpartition(sep)
Split the string at the last occurrence of *sep*, and return a
3-tuple containing the part before the separator, the separator
itself, and the part after the separator. If the separator is not
found, return a 3-tuple containing two empty strings, followed by
the string itself.
str.rsplit(sep=None, maxsplit=-1)
Return a list of the words in the string, using *sep* as the
delimiter string. If *maxsplit* is given, at most *maxsplit* splits
are done, the *rightmost* ones. If *sep* is not specified or
"None", any whitespace string is a separator. Except for splitting
from the right, "rsplit()" behaves like "split()" which is
described in detail below.
str.rstrip([chars])
Return a copy of the string with trailing characters removed. The
*chars* argument is a string specifying the set of characters to be
removed. If omitted or "None", the *chars* argument defaults to
removing whitespace. The *chars* argument is not a suffix; rather,
all combinations of its values are stripped:
>>> ' spacious '.rstrip()
' spacious'
>>> 'mississippi'.rstrip('ipz')
'mississ'
str.split(sep=None, maxsplit=-1)
Return a list of the words in the string, using *sep* as the
delimiter string. If *maxsplit* is given, at most *maxsplit*
splits are done (thus, the list will have at most "maxsplit+1"
elements). If *maxsplit* is not specified or "-1", then there is
no limit on the number of splits (all possible splits are made).
If *sep* is given, consecutive delimiters are not grouped together
and are deemed to delimit empty strings (for example,
"'1,,2'.split(',')" returns "['1', '', '2']"). The *sep* argument
may consist of multiple characters (for example,
"'1<>2<>3'.split('<>')" returns "['1', '2', '3']"). Splitting an
empty string with a specified separator returns "['']".
If *sep* is not specified or is "None", a different splitting
algorithm is applied: runs of consecutive whitespace are regarded
as a single separator, and the result will contain no empty strings
at the start or end if the string has leading or trailing
whitespace. Consequently, splitting an empty string or a string
consisting of just whitespace with a "None" separator returns "[]".
For example, "' 1 2 3 '.split()" returns "['1', '2', '3']", and
"' 1 2 3 '.split(None, 1)" returns "['1', '2 3 ']".
str.splitlines([keepends])
Return a list of the lines in the string, breaking at line
boundaries. This method uses the *universal newlines* approach to
splitting lines. Line breaks are not included in the resulting list
unless *keepends* is given and true.
For example, "'ab c\n\nde fg\rkl\r\n'.splitlines()" returns "['ab
c', '', 'de fg', 'kl']", while the same call with
"splitlines(True)" returns "['ab c\n', '\n', 'de fg\r', 'kl\r\n']".
Unlike "split()" when a delimiter string *sep* is given, this
method returns an empty list for the empty string, and a terminal
line break does not result in an extra line.
str.startswith(prefix[, start[, end]])
Return "True" if string starts with the *prefix*, otherwise return
"False". *prefix* can also be a tuple of prefixes to look for.
With optional *start*, test string beginning at that position.
With optional *end*, stop comparing string at that position.
str.strip([chars])
Return a copy of the string with the leading and trailing
characters removed. The *chars* argument is a string specifying the
set of characters to be removed. If omitted or "None", the *chars*
argument defaults to removing whitespace. The *chars* argument is
not a prefix or suffix; rather, all combinations of its values are
stripped:
>>> ' spacious '.strip()
'spacious'
>>> 'www.example.com'.strip('cmowz.')
'example'
str.swapcase()
Return a copy of the string with uppercase characters converted to
lowercase and vice versa. Note that it is not necessarily true that
"s.swapcase().swapcase() == s".
str.title()
Return a titlecased version of the string where words start with an
uppercase character and the remaining characters are lowercase.
The algorithm uses a simple language-independent definition of a
word as groups of consecutive letters. The definition works in
many contexts but it means that apostrophes in contractions and
possessives form word boundaries, which may not be the desired
result:
>>> "they're bill's friends from the UK".title()
"They'Re Bill'S Friends From The Uk"
A workaround for apostrophes can be constructed using regular
expressions:
>>> import re
>>> def titlecase(s):
... return re.sub(r"[A-Za-z]+('[A-Za-z]+)?",
... lambda mo: mo.group(0)[0].upper() +
... mo.group(0)[1:].lower(),
... s)
...
>>> titlecase("they're bill's friends.")
"They're Bill's Friends."
str.translate(map)
Return a copy of the *s* where all characters have been mapped
through the *map* which must be a dictionary of Unicode ordinals
(integers) to Unicode ordinals, strings or "None". Unmapped
characters are left untouched. Characters mapped to "None" are
deleted.
You can use "str.maketrans()" to create a translation map from
character-to-character mappings in different formats.
Note: An even more flexible approach is to create a custom
character mapping codec using the "codecs" module (see
"encodings.cp1251" for an example).
str.upper()
Return a copy of the string with all the cased characters [4]
converted to uppercase. Note that "str.upper().isupper()" might be
"False" if "s" contains uncased characters or if the Unicode
category of the resulting character(s) is not "Lu" (Letter,
uppercase), but e.g. "Lt" (Letter, titlecase).
The uppercasing algorithm used is described in section 3.13 of the
Unicode Standard.
str.zfill(width)
Return the numeric string left filled with zeros in a string of
length *width*. A sign prefix is handled correctly. The original
string is returned if *width* is less than or equal to "len(s)".
u string-methodsu¯
String and Bytes literals
*************************
String literals are described by the following lexical definitions:
stringliteral ::= [stringprefix](shortstring | longstring)
stringprefix ::= "r" | "u" | "R" | "U"
shortstring ::= "'" shortstringitem* "'" | '"' shortstringitem* '"'
longstring ::= "'''" longstringitem* "'''" | '"""' longstringitem* '"""'
shortstringitem ::= shortstringchar | stringescapeseq
longstringitem ::= longstringchar | stringescapeseq
shortstringchar ::=
longstringchar ::=
stringescapeseq ::= "\"
bytesliteral ::= bytesprefix(shortbytes | longbytes)
bytesprefix ::= "b" | "B" | "br" | "Br" | "bR" | "BR" | "rb" | "rB" | "Rb" | "RB"
shortbytes ::= "'" shortbytesitem* "'" | '"' shortbytesitem* '"'
longbytes ::= "'''" longbytesitem* "'''" | '"""' longbytesitem* '"""'
shortbytesitem ::= shortbyteschar | bytesescapeseq
longbytesitem ::= longbyteschar | bytesescapeseq
shortbyteschar ::=
longbyteschar ::=
bytesescapeseq ::= "\"
One syntactic restriction not indicated by these productions is that
whitespace is not allowed between the "stringprefix" or "bytesprefix"
and the rest of the literal. The source character set is defined by
the encoding declaration; it is UTF-8 if no encoding declaration is
given in the source file; see section *Encoding declarations*.
In plain English: Both types of literals can be enclosed in matching
single quotes ("'") or double quotes ("""). They can also be enclosed
in matching groups of three single or double quotes (these are
generally referred to as *triple-quoted strings*). The backslash
("\") character is used to escape characters that otherwise have a
special meaning, such as newline, backslash itself, or the quote
character.
Bytes literals are always prefixed with "'b'" or "'B'"; they produce
an instance of the "bytes" type instead of the "str" type. They may
only contain ASCII characters; bytes with a numeric value of 128 or
greater must be expressed with escapes.
As of Python 3.3 it is possible again to prefix unicode strings with a
"u" prefix to simplify maintenance of dual 2.x and 3.x codebases.
Both string and bytes literals may optionally be prefixed with a
letter "'r'" or "'R'"; such strings are called *raw strings* and treat
backslashes as literal characters. As a result, in string literals,
"'\U'" and "'\u'" escapes in raw strings are not treated specially.
Given that Python 2.x's raw unicode literals behave differently than
Python 3.x's the "'ur'" syntax is not supported.
New in version 3.3: The "'rb'" prefix of raw bytes literals has
been added as a synonym of "'br'".
New in version 3.3: Support for the unicode legacy literal
("u'value'") was reintroduced to simplify the maintenance of dual
Python 2.x and 3.x codebases. See **PEP 414** for more information.
In triple-quoted strings, unescaped newlines and quotes are allowed
(and are retained), except that three unescaped quotes in a row
terminate the string. (A "quote" is the character used to open the
string, i.e. either "'" or """.)
Unless an "'r'" or "'R'" prefix is present, escape sequences in
strings are interpreted according to rules similar to those used by
Standard C. The recognized escape sequences are:
+-------------------+-----------------------------------+---------+
| Escape Sequence | Meaning | Notes |
+===================+===================================+=========+
+-------------------+-----------------------------------+---------+
+-------------------+-----------------------------------+---------+
+-------------------+-----------------------------------+---------+
+-------------------+-----------------------------------+---------+
+-------------------+-----------------------------------+---------+
+-------------------+-----------------------------------+---------+
+-------------------+-----------------------------------+---------+
+-------------------+-----------------------------------+---------+
+-------------------+-----------------------------------+---------+
+-------------------+-----------------------------------+---------+
+-------------------+-----------------------------------+---------+
| "\ooo" | Character with octal value *ooo* | (1,3) |
+-------------------+-----------------------------------+---------+
| "\xhh" | Character with hex value *hh* | (2,3) |
+-------------------+-----------------------------------+---------+
Escape sequences only recognized in string literals are:
+-------------------+-----------------------------------+---------+
| Escape Sequence | Meaning | Notes |
+===================+===================================+=========+
| "\N{name}" | Character named *name* in the | (4) |
+-------------------+-----------------------------------+---------+
| "\uxxxx" | Character with 16-bit hex value | (5) |
+-------------------+-----------------------------------+---------+
| "\Uxxxxxxxx" | Character with 32-bit hex value | (6) |
+-------------------+-----------------------------------+---------+
Notes:
1. As in Standard C, up to three octal digits are accepted.
2. Unlike in Standard C, exactly two hex digits are required.
3. In a bytes literal, hexadecimal and octal escapes denote the
byte with the given value. In a string literal, these escapes
denote a Unicode character with the given value.
4. Changed in version 3.3: Support for name aliases [1] has been
added.
5. Individual code units which form parts of a surrogate pair can
be encoded using this escape sequence. Exactly four hex digits are
required.
6. Any Unicode character can be encoded this way. Exactly eight
hex digits are required.
Unlike Standard C, all unrecognized escape sequences are left in the
string unchanged, i.e., *the backslash is left in the string*. (This
behavior is useful when debugging: if an escape sequence is mistyped,
the resulting output is more easily recognized as broken.) It is also
important to note that the escape sequences only recognized in string
literals fall into the category of unrecognized escapes for bytes
literals.
Even in a raw string, string quotes can be escaped with a backslash,
but the backslash remains in the string; for example, "r"\""" is a
valid string literal consisting of two characters: a backslash and a
double quote; "r"\"" is not a valid string literal (even a raw string
cannot end in an odd number of backslashes). Specifically, *a raw
string cannot end in a single backslash* (since the backslash would
escape the following quote character). Note also that a single
backslash followed by a newline is interpreted as those two characters
as part of the string, *not* as a line continuation.
u stringsuC
Subscriptions
*************
A subscription selects an item of a sequence (string, tuple or list)
or mapping (dictionary) object:
subscription ::= primary "[" expression_list "]"
The primary must evaluate to an object that supports subscription,
e.g. a list or dictionary. User-defined objects can support
subscription by defining a "__getitem__()" method.
For built-in objects, there are two types of objects that support
subscription:
If the primary is a mapping, the expression list must evaluate to an
object whose value is one of the keys of the mapping, and the
subscription selects the value in the mapping that corresponds to that
key. (The expression list is a tuple except if it has exactly one
item.)
If the primary is a sequence, the expression (list) must evaluate to
an integer or a slice (as discussed in the following section).
The formal syntax makes no special provision for negative indices in
sequences; however, built-in sequences all provide a "__getitem__()"
method that interprets negative indices by adding the length of the
sequence to the index (so that "x[-1]" selects the last item of "x").
The resulting value must be a nonnegative integer less than the number
of items in the sequence, and the subscription selects the item whose
index is that value (counting from zero). Since the support for
negative indices and slicing occurs in the object's "__getitem__()"
method, subclasses overriding this method will need to explicitly add
that support.
A string's items are characters. A character is not a separate data
type but a string of exactly one character.
u
subscriptionsu‚
Truth Value Testing
*******************
Any object can be tested for truth value, for use in an "if" or
"while" condition or as operand of the Boolean operations below. The
following values are considered false:
* "None"
* "False"
* zero of any numeric type, for example, "0", "0.0", "0j".
* any empty sequence, for example, "''", "()", "[]".
* any empty mapping, for example, "{}".
* instances of user-defined classes, if the class defines a
"__bool__()" or "__len__()" method, when that method returns the
integer zero or "bool" value "False". [1]
All other values are considered true --- so objects of many types are
always true.
Operations and built-in functions that have a Boolean result always
return "0" or "False" for false and "1" or "True" for true, unless
otherwise stated. (Important exception: the Boolean operations "or"
and "and" always return one of their operands.)
u truthuY
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" target]] ":" suite)+
["else" ":" suite]
["finally" ":" suite]
try2_stmt ::= "try" ":" suite
"finally" ":" suite
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 after the "as" keyword 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.)
When an exception has been assigned using "as target", it is cleared
at the end of the except clause. This is as if
except E as N:
foo
was translated to
except E as N:
try:
foo
finally:
del N
This means the exception must be assigned to a different name to be
able to refer to it after the except clause. Exceptions are cleared
because with the traceback attached to them, they form a reference
cycle with the stack frame, keeping all locals in that frame alive
until the next garbage collection occurs.
Before an except clause's suite is executed, details about the
exception are stored in the "sys" module and can be access via
"sys.exc_info()". "sys.exc_info()" returns a 3-tuple consisting of the
exception class, the exception instance and a traceback object (see
section *The standard type hierarchy*) identifying the point in the
program where the exception occurred. "sys.exc_info()" values 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, the saved
exception is set as the context of the new exception. If the "finally"
clause 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).
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*.
u tryu³}
The standard type hierarchy
***************************
Below is a list of the types that are built into Python. Extension
modules (written in C, Java, or other languages, depending on the
implementation) can define additional types. Future versions of
Python may add types to the type hierarchy (e.g., rational numbers,
efficiently stored arrays of integers, etc.), although such additions
will often be provided via the standard library instead.
Some of the type descriptions below contain a paragraph listing
'special attributes.' These are attributes that provide access to the
implementation and are not intended for general use. Their definition
may change in the future.
None
This type has a single value. There is a single object with this
value. This object is accessed through the built-in name "None". It
is used to signify the absence of a value in many situations, e.g.,
it is returned from functions that don't explicitly return
anything. Its truth value is false.
NotImplemented
This type has a single value. There is a single object with this
value. This object is accessed through the built-in name
"NotImplemented". Numeric methods and rich comparison methods may
return this value if they do not implement the operation for the
operands provided. (The interpreter will then try the reflected
operation, or some other fallback, depending on the operator.) Its
truth value is true.
Ellipsis
This type has a single value. There is a single object with this
value. This object is accessed through the literal "..." or the
built-in name "Ellipsis". Its truth value is true.
"numbers.Number"
These are created by numeric literals and returned as results by
arithmetic operators and arithmetic built-in functions. Numeric
objects are immutable; once created their value never changes.
Python numbers are of course strongly related to mathematical
numbers, but subject to the limitations of numerical representation
in computers.
Python distinguishes between integers, floating point numbers, and
complex numbers:
"numbers.Integral"
These represent elements from the mathematical set of integers
(positive and negative).
There are two types of integers:
Integers ("int")
These represent numbers in an unlimited range, subject to
available (virtual) memory only. For the purpose of shift
and mask operations, a binary representation is assumed, and
negative numbers are represented in a variant of 2's
complement which gives the illusion of an infinite string of
sign bits extending to the left.
Booleans ("bool")
These represent the truth values False and True. The two
objects representing the values "False" and "True" are the
only Boolean objects. The Boolean type is a subtype of the
integer type, and Boolean values behave like the values 0 and
1, respectively, in almost all contexts, the exception being
that when converted to a string, the strings ""False"" or
""True"" are returned, respectively.
The rules for integer representation are intended to give the
most meaningful interpretation of shift and mask operations
involving negative integers.
"numbers.Real" ("float")
These represent machine-level double precision floating point
numbers. You are at the mercy of the underlying machine
architecture (and C or Java implementation) for the accepted
range and handling of overflow. Python does not support single-
precision floating point numbers; the savings in processor and
memory usage that are usually the reason for using these is
dwarfed by the overhead of using objects in Python, so there is
no reason to complicate the language with two kinds of floating
point numbers.
"numbers.Complex" ("complex")
These represent complex numbers as a pair of machine-level
double precision floating point numbers. The same caveats apply
as for floating point numbers. The real and imaginary parts of a
complex number "z" can be retrieved through the read-only
attributes "z.real" and "z.imag".
Sequences
These represent finite ordered sets indexed by non-negative
numbers. The built-in function "len()" returns the number of items
of a sequence. When the length of a sequence is *n*, the index set
contains the numbers 0, 1, ..., *n*-1. Item *i* of sequence *a* is
selected by "a[i]".
Sequences also support slicing: "a[i:j]" selects all items with
index *k* such that *i* "<=" *k* "<" *j*. When used as an
expression, a slice is a sequence of the same type. This implies
that the index set is renumbered so that it starts at 0.
Some sequences also support "extended slicing" with a third "step"
parameter: "a[i:j:k]" selects all items of *a* with index *x* where
"x = i + n*k", *n* ">=" "0" and *i* "<=" *x* "<" *j*.
Sequences are distinguished according to their mutability:
Immutable sequences
An object of an immutable sequence type cannot change once it is
created. (If the object contains references to other objects,
these other objects may be mutable and may be changed; however,
the collection of objects directly referenced by an immutable
object cannot change.)
The following types are immutable sequences:
Strings
A string is a sequence of values that represent Unicode
codepoints. All the codepoints in range "U+0000 - U+10FFFF"
can be represented in a string. Python doesn't have a "chr"
type, and every character in the string is represented as a
string object with length "1". The built-in function "ord()"
converts a character to its codepoint (as an integer);
"chr()" converts an integer in range "0 - 10FFFF" to the
corresponding character. "str.encode()" can be used to
convert a "str" to "bytes" using the given encoding, and
"bytes.decode()" can be used to achieve the opposite.
Tuples
The items of a tuple are arbitrary Python objects. Tuples of
two or more items are formed by comma-separated lists of
expressions. A tuple of one item (a 'singleton') can be
formed by affixing a comma to an expression (an expression by
itself does not create a tuple, since parentheses must be
usable for grouping of expressions). An empty tuple can be
formed by an empty pair of parentheses.
Bytes
A bytes object is an immutable array. The items are 8-bit
bytes, represented by integers in the range 0 <= x < 256.
Bytes literals (like "b'abc'") and the built-in function
"bytes()" can be used to construct bytes objects. Also,
bytes objects can be decoded to strings via the "decode()"
method.
Mutable sequences
Mutable sequences can be changed after they are created. The
subscription and slicing notations can be used as the target of
assignment and "del" (delete) statements.
There are currently two intrinsic mutable sequence types:
Lists
The items of a list are arbitrary Python objects. Lists are
formed by placing a comma-separated list of expressions in
square brackets. (Note that there are no special cases needed
to form lists of length 0 or 1.)
Byte Arrays
A bytearray object is a mutable array. They are created by
the built-in "bytearray()" constructor. Aside from being
mutable (and hence unhashable), byte arrays otherwise provide
the same interface and functionality as immutable bytes
objects.
The extension module "array" provides an additional example of a
mutable sequence type, as does the "collections" module.
Set types
These represent unordered, finite sets of unique, immutable
objects. As such, they cannot be indexed by any subscript. However,
they can be iterated over, and the built-in function "len()"
returns the number of items in a set. Common uses for sets are fast
membership testing, removing duplicates from a sequence, and
computing mathematical operations such as intersection, union,
difference, and symmetric difference.
For set elements, the same immutability rules apply as for
dictionary keys. Note that numeric types obey the normal rules for
numeric comparison: if two numbers compare equal (e.g., "1" and
"1.0"), only one of them can be contained in a set.
There are currently two intrinsic set types:
Sets
These represent a mutable set. They are created by the built-in
"set()" constructor and can be modified afterwards by several
methods, such as "add()".
Frozen sets
These represent an immutable set. They are created by the
built-in "frozenset()" constructor. As a frozenset is immutable
and *hashable*, it can be used again as an element of another
set, or as a dictionary key.
Mappings
These represent finite sets of objects indexed by arbitrary index
sets. The subscript notation "a[k]" selects the item indexed by "k"
from the mapping "a"; this can be used in expressions and as the
target of assignments or "del" statements. The built-in function
"len()" returns the number of items in a mapping.
There is currently a single intrinsic mapping type:
Dictionaries
These represent finite sets of objects indexed by nearly
arbitrary values. The only types of values not acceptable as
keys are values containing lists or dictionaries or other
mutable types that are compared by value rather than by object
identity, the reason being that the efficient implementation of
dictionaries requires a key's hash value to remain constant.
Numeric types used for keys obey the normal rules for numeric
comparison: if two numbers compare equal (e.g., "1" and "1.0")
then they can be used interchangeably to index the same
dictionary entry.
Dictionaries are mutable; they can be created by the "{...}"
notation (see section *Dictionary displays*).
The extension modules "dbm.ndbm" and "dbm.gnu" provide
additional examples of mapping types, as does the "collections"
module.
Callable types
These are the types to which the function call operation (see
section *Calls*) can be applied:
User-defined functions
A user-defined function object is created by a function
definition (see section *Function definitions*). It should be
called with an argument list containing the same number of items
as the function's formal parameter list.
Special attributes:
+---------------------------+---------------------------------+-------------+
+===========================+=================================+=============+
| "__doc__" | The function's documentation | Writable |
+---------------------------+---------------------------------+-------------+
| "__name__" | The function's name | Writable |
+---------------------------+---------------------------------+-------------+
| "__qualname__" | The function's *qualified name* | Writable |
+---------------------------+---------------------------------+-------------+
| "__module__" | The name of the module the | Writable |
+---------------------------+---------------------------------+-------------+
| "__defaults__" | A tuple containing default | Writable |
+---------------------------+---------------------------------+-------------+
| "__code__" | The code object representing | Writable |
+---------------------------+---------------------------------+-------------+
| "__globals__" | A reference to the dictionary | Read-only |
+---------------------------+---------------------------------+-------------+
| "__dict__" | The namespace supporting | Writable |
+---------------------------+---------------------------------+-------------+
| "__closure__" | "None" or a tuple of cells that | Read-only |
+---------------------------+---------------------------------+-------------+
| "__annotations__" | A dict containing annotations | Writable |
+---------------------------+---------------------------------+-------------+
| "__kwdefaults__" | A dict containing defaults for | Writable |
+---------------------------+---------------------------------+-------------+
Most of the attributes labelled "Writable" check the type of the
assigned value.
Function objects also support getting and setting arbitrary
attributes, which can be used, for example, to attach metadata
to functions. Regular attribute dot-notation is used to get and
set such attributes. *Note that the current implementation only
supports function attributes on user-defined functions. Function
attributes on built-in functions may be supported in the
future.*
Additional information about a function's definition can be
retrieved from its code object; see the description of internal
types below.
Instance methods
An instance method object combines a class, a class instance and
any callable object (normally a user-defined function).
Special read-only attributes: "__self__" is the class instance
object, "__func__" is the function object; "__doc__" is the
method's documentation (same as "__func__.__doc__"); "__name__"
is the method name (same as "__func__.__name__"); "__module__"
is the name of the module the method was defined in, or "None"
if unavailable.
Methods also support accessing (but not setting) the arbitrary
function attributes on the underlying function object.
User-defined method objects may be created when getting an
attribute of a class (perhaps via an instance of that class), if
that attribute is a user-defined function object or a class
method object.
When an instance method object is created by retrieving a user-
defined function object from a class via one of its instances,
its "__self__" attribute is the instance, and the method object
is said to be bound. The new method's "__func__" attribute is
the original function object.
When a user-defined method object is created by retrieving
another method object from a class or instance, the behaviour is
the same as for a function object, except that the "__func__"
attribute of the new instance is not the original method object
but its "__func__" attribute.
When an instance method object is created by retrieving a class
method object from a class or instance, its "__self__" attribute
is the class itself, and its "__func__" attribute is the
function object underlying the class method.
When an instance method object is called, the underlying
function ("__func__") is called, inserting the class instance
("__self__") in front of the argument list. For instance, when
"C" is a class which contains a definition for a function "f()",
and "x" is an instance of "C", calling "x.f(1)" is equivalent to
calling "C.f(x, 1)".
When an instance method object is derived from a class method
object, the "class instance" stored in "__self__" will actually
be the class itself, so that calling either "x.f(1)" or "C.f(1)"
is equivalent to calling "f(C,1)" where "f" is the underlying
function.
Note that the transformation from function object to instance
method object happens each time the attribute is retrieved from
the instance. In some cases, a fruitful optimization is to
assign the attribute to a local variable and call that local
variable. Also notice that this transformation only happens for
user-defined functions; other callable objects (and all non-
callable objects) are retrieved without transformation. It is
also important to note that user-defined functions which are
attributes of a class instance are not converted to bound
methods; this *only* happens when the function is an attribute
of the class.
Generator functions
A function or method which uses the "yield" statement (see
section *The yield statement*) is called a *generator function*.
Such a function, when called, always returns an iterator object
which can be used to execute the body of the function: calling
the iterator's "iterator.__next__()" method will cause the
function to execute until it provides a value using the "yield"
statement. When the function executes a "return" statement or
falls off the end, a "StopIteration" exception is raised and the
iterator will have reached the end of the set of values to be
returned.
Built-in functions
A built-in function object is a wrapper around a C function.
Examples of built-in functions are "len()" and "math.sin()"
("math" is a standard built-in module). The number and type of
the arguments are determined by the C function. Special read-
only attributes: "__doc__" is the function's documentation
string, or "None" if unavailable; "__name__" is the function's
name; "__self__" is set to "None" (but see the next item);
"__module__" is the name of the module the function was defined
in or "None" if unavailable.
Built-in methods
This is really a different disguise of a built-in function, this
time containing an object passed to the C function as an
implicit extra argument. An example of a built-in method is
"alist.append()", assuming *alist* is a list object. In this
case, the special read-only attribute "__self__" is set to the
object denoted by *alist*.
Classes
Classes are callable. These objects normally act as factories
for new instances of themselves, but variations are possible for
class types that override "__new__()". The arguments of the
call are passed to "__new__()" and, in the typical case, to
"__init__()" to initialize the new instance.
Class Instances
Instances of arbitrary classes can be made callable by defining
a "__call__()" method in their class.
Modules
Modules are a basic organizational unit of Python code, and are
created by the *import system* as invoked either by the "import"
statement (see "import"), or by calling functions such as
"importlib.import_module()" and built-in "__import__()". A module
object has a namespace implemented by a dictionary object (this is
the dictionary referenced by the "__globals__" attribute of
functions defined in the module). Attribute references are
translated to lookups in this dictionary, e.g., "m.x" is equivalent
to "m.__dict__["x"]". A module object does not contain the code
object used to initialize the module (since it isn't needed once
the initialization is done).
Attribute assignment updates the module's namespace dictionary,
e.g., "m.x = 1" is equivalent to "m.__dict__["x"] = 1".
Special read-only attribute: "__dict__" is the module's namespace
as a dictionary object.
**CPython implementation detail:** Because of the way CPython
clears module dictionaries, the module dictionary will be cleared
when the module falls out of scope even if the dictionary still has
live references. To avoid this, copy the dictionary or keep the
module around while using its dictionary directly.
Predefined (writable) attributes: "__name__" is the module's name;
"__doc__" is the module's documentation string, or "None" if
unavailable; "__file__" is the pathname of the file from which the
module was loaded, if it was loaded from a file. The "__file__"
attribute may be missing for certain types of modules, such as C
modules that are statically linked into the interpreter; for
extension modules loaded dynamically from a shared library, it is
the pathname of the shared library file.
Custom classes
Custom class types are typically created by class definitions (see
section *Class definitions*). A class has a namespace implemented
by a dictionary object. Class attribute references are translated
to lookups in this dictionary, e.g., "C.x" is translated to
"C.__dict__["x"]" (although there are a number of hooks which allow
for other means of locating attributes). When the attribute name is
not found there, the attribute search continues in the base
classes. This search of the base classes uses the C3 method
resolution order which behaves correctly even in the presence of
'diamond' inheritance structures where there are multiple
inheritance paths leading back to a common ancestor. Additional
details on the C3 MRO used by Python can be found in the
documentation accompanying the 2.3 release at
http://www.python.org/download/releases/2.3/mro/.
When a class attribute reference (for class "C", say) would yield a
class method object, it is transformed into an instance method
object whose "__self__" attributes is "C". When it would yield a
static method object, it is transformed into the object wrapped by
the static method object. See section *Implementing Descriptors*
for another way in which attributes retrieved from a class may
differ from those actually contained in its "__dict__".
Class attribute assignments update the class's dictionary, never
the dictionary of a base class.
A class object can be called (see above) to yield a class instance
(see below).
Special attributes: "__name__" is the class name; "__module__" is
the module name in which the class was defined; "__dict__" is the
dictionary containing the class's namespace; "__bases__" is a tuple
(possibly empty or a singleton) containing the base classes, in the
order of their occurrence in the base class list; "__doc__" is the
class's documentation string, or None if undefined.
Class instances
A class instance is created by calling a class object (see above).
A class instance has a namespace implemented as a dictionary which
is the first place in which attribute references are searched.
When an attribute is not found there, and the instance's class has
an attribute by that name, the search continues with the class
attributes. If a class attribute is found that is a user-defined
function object, it is transformed into an instance method object
whose "__self__" attribute is the instance. Static method and
class method objects are also transformed; see above under
"Classes". See section *Implementing Descriptors* for another way
in which attributes of a class retrieved via its instances may
differ from the objects actually stored in the class's "__dict__".
If no class attribute is found, and the object's class has a
"__getattr__()" method, that is called to satisfy the lookup.
Attribute assignments and deletions update the instance's
dictionary, never a class's dictionary. If the class has a
"__setattr__()" or "__delattr__()" method, this is called instead
of updating the instance dictionary directly.
Class instances can pretend to be numbers, sequences, or mappings
if they have methods with certain special names. See section
*Special method names*.
Special attributes: "__dict__" is the attribute dictionary;
"__class__" is the instance's class.
I/O objects (also known as file objects)
A *file object* represents an open file. Various shortcuts are
available to create file objects: the "open()" built-in function,
and also "os.popen()", "os.fdopen()", and the "makefile()" method
of socket objects (and perhaps by other functions or methods
provided by extension modules).
The objects "sys.stdin", "sys.stdout" and "sys.stderr" are
initialized to file objects corresponding to the interpreter's
standard input, output and error streams; they are all open in text
mode and therefore follow the interface defined by the
"io.TextIOBase" abstract class.
Internal types
A few types used internally by the interpreter are exposed to the
user. Their definitions may change with future versions of the
interpreter, but they are mentioned here for completeness.
Code objects
Code objects represent *byte-compiled* executable Python code,
or *bytecode*. The difference between a code object and a
function object is that the function object contains an explicit
reference to the function's globals (the module in which it was
defined), while a code object contains no context; also the
default argument values are stored in the function object, not
in the code object (because they represent values calculated at
run-time). Unlike function objects, code objects are immutable
and contain no references (directly or indirectly) to mutable
objects.
Special read-only attributes: "co_name" gives the function name;
"co_argcount" is the number of positional arguments (including
arguments with default values); "co_nlocals" is the number of
local variables used by the function (including arguments);
"co_varnames" is a tuple containing the names of the local
variables (starting with the argument names); "co_cellvars" is a
tuple containing the names of local variables that are
referenced by nested functions; "co_freevars" is a tuple
containing the names of free variables; "co_code" is a string
representing the sequence of bytecode instructions; "co_consts"
is a tuple containing the literals used by the bytecode;
"co_names" is a tuple containing the names used by the bytecode;
"co_filename" is the filename from which the code was compiled;
"co_firstlineno" is the first line number of the function;
"co_lnotab" is a string encoding the mapping from bytecode
offsets to line numbers (for details see the source code of the
interpreter); "co_stacksize" is the required stack size
(including local variables); "co_flags" is an integer encoding a
number of flags for the interpreter.
The following flag bits are defined for "co_flags": bit "0x04"
is set if the function uses the "*arguments" syntax to accept an
arbitrary number of positional arguments; bit "0x08" is set if
the function uses the "**keywords" syntax to accept arbitrary
keyword arguments; bit "0x20" is set if the function is a
generator.
Future feature declarations ("from __future__ import division")
also use bits in "co_flags" to indicate whether a code object
was compiled with a particular feature enabled: bit "0x2000" is
set if the function was compiled with future division enabled;
bits "0x10" and "0x1000" were used in earlier versions of
Python.
Other bits in "co_flags" are reserved for internal use.
If a code object represents a function, the first item in
"co_consts" is the documentation string of the function, or
"None" if undefined.
Frame objects
Frame objects represent execution frames. They may occur in
traceback objects (see below).
Special read-only attributes: "f_back" is to the previous stack
frame (towards the caller), or "None" if this is the bottom
stack frame; "f_code" is the code object being executed in this
frame; "f_locals" is the dictionary used to look up local
variables; "f_globals" is used for global variables;
"f_builtins" is used for built-in (intrinsic) names; "f_lasti"
gives the precise instruction (this is an index into the
bytecode string of the code object).
Special writable attributes: "f_trace", if not "None", is a
function called at the start of each source code line (this is
used by the debugger); "f_lineno" is the current line number of
the frame --- writing to this from within a trace function jumps
to the given line (only for the bottom-most frame). A debugger
can implement a Jump command (aka Set Next Statement) by writing
to f_lineno.
Traceback objects
Traceback objects represent a stack trace of an exception. A
traceback object is created when an exception occurs. When the
search for an exception handler unwinds the execution stack, at
each unwound level a traceback object is inserted in front of
the current traceback. When an exception handler is entered,
the stack trace is made available to the program. (See section
*The try statement*.) It is accessible as the third item of the
tuple returned by "sys.exc_info()". When the program contains no
suitable handler, the stack trace is written (nicely formatted)
to the standard error stream; if the interpreter is interactive,
it is also made available to the user as "sys.last_traceback".
Special read-only attributes: "tb_next" is the next level in the
stack trace (towards the frame where the exception occurred), or
"None" if there is no next level; "tb_frame" points to the
execution frame of the current level; "tb_lineno" gives the line
number where the exception occurred; "tb_lasti" indicates the
precise instruction. The line number and last instruction in
the traceback may differ from the line number of its frame
object if the exception occurred in a "try" statement with no
matching except clause or with a finally clause.
Slice objects
Slice objects are used to represent slices for "__getitem__()"
methods. They are also created by the built-in "slice()"
function.
Special read-only attributes: "start" is the lower bound; "stop"
is the upper bound; "step" is the step value; each is "None" if
omitted. These attributes can have any type.
Slice objects support one method:
slice.indices(self, length)
This method takes a single integer argument *length* and
computes information about the slice that the slice object
would describe if applied to a sequence of *length* items.
It returns a tuple of three integers; respectively these are
the *start* and *stop* indices and the *step* or stride
length of the slice. Missing or out-of-bounds indices are
handled in a manner consistent with regular slices.
Static method objects
Static method objects provide a way of defeating the
transformation of function objects to method objects described
above. A static method object is a wrapper around any other
object, usually a user-defined method object. When a static
method object is retrieved from a class or a class instance, the
object actually returned is the wrapped object, which is not
subject to any further transformation. Static method objects are
not themselves callable, although the objects they wrap usually
are. Static method objects are created by the built-in
"staticmethod()" constructor.
Class method objects
A class method object, like a static method object, is a wrapper
around another object that alters the way in which that object
is retrieved from classes and class instances. The behaviour of
class method objects upon such retrieval is described above,
under "User-defined methods". Class method objects are created
by the built-in "classmethod()" constructor.
u typesu¯
Functions
*********
Function objects are created by function definitions. The only
operation on a function object is to call it: "func(argument-list)".
There are really two flavors of function objects: built-in functions
and user-defined functions. Both support the same operation (to call
the function), but the implementation is different, hence the
different object types.
See *Function definitions* for more information.
u typesfunctionsu¨#
Mapping Types --- "dict"
************************
A *mapping* object maps *hashable* values to arbitrary objects.
Mappings are mutable objects. There is currently only one standard
mapping type, the *dictionary*. (For other containers see the built-
in "list", "set", and "tuple" classes, and the "collections" module.)
A dictionary's keys are *almost* arbitrary values. Values that are
not *hashable*, that is, values containing lists, dictionaries or
other mutable types (that are compared by value rather than by object
identity) may not be used as keys. Numeric types used for keys obey
the normal rules for numeric comparison: if two numbers compare equal
(such as "1" and "1.0") then they can be used interchangeably to index
the same dictionary entry. (Note however, that since computers store
floating-point numbers as approximations it is usually unwise to use
them as dictionary keys.)
Dictionaries can be created by placing a comma-separated list of "key:
value" pairs within braces, for example: "{'jack': 4098, 'sjoerd':
4127}" or "{4098: 'jack', 4127: 'sjoerd'}", or by the "dict"
constructor.
class class dict(**kwarg)
class class dict(mapping, **kwarg)
class class dict(iterable, **kwarg)
Return a new dictionary initialized from an optional positional
argument and a possibly empty set of keyword arguments.
If no positional argument is given, an empty dictionary is created.
If a positional argument is given and it is a mapping object, a
dictionary is created with the same key-value pairs as the mapping
object. Otherwise, the positional argument must be an *iterator*
object. Each item in the iterable must itself be an iterator with
exactly two objects. The first object of each item becomes a key
in the new dictionary, and the second object the corresponding
value. If a key occurs more than once, the last value for that key
becomes the corresponding value in the new dictionary.
If keyword arguments are given, the keyword arguments and their
values are added to the dictionary created from the positional
argument. If a key being added is already present, the value from
the keyword argument replaces the value from the positional
argument.
To illustrate, the following examples all return a dictionary equal
to "{"one": 1, "two": 2, "three": 3}":
>>> a = dict(one=1, two=2, three=3)
>>> b = {'one': 1, 'two': 2, 'three': 3}
>>> c = dict(zip(['one', 'two', 'three'], [1, 2, 3]))
>>> d = dict([('two', 2), ('one', 1), ('three', 3)])
>>> e = dict({'three': 3, 'one': 1, 'two': 2})
>>> a == b == c == d == e
True
Providing keyword arguments as in the first example only works for
keys that are valid Python identifiers. Otherwise, any valid keys
can be used.
These are the operations that dictionaries support (and therefore,
custom mapping types should support too):
len(d)
Return the number of items in the dictionary *d*.
d[key]
Return the item of *d* with key *key*. Raises a "KeyError" if
*key* is not in the map.
If a subclass of dict defines a method "__missing__()", if the
key *key* is not present, the "d[key]" operation calls that
method with the key *key* as argument. The "d[key]" operation
then returns or raises whatever is returned or raised by the
"__missing__(key)" call if the key is not present. No other
operations or methods invoke "__missing__()". If "__missing__()"
is not defined, "KeyError" is raised. "__missing__()" must be a
method; it cannot be an instance variable:
>>> class Counter(dict):
... def __missing__(self, key):
... return 0
>>> c = Counter()
>>> c['red']
0
>>> c['red'] += 1
>>> c['red']
1
See "collections.Counter" for a complete implementation
including other methods helpful for accumulating and managing
tallies.
d[key] = value
Set "d[key]" to *value*.
del d[key]
Remove "d[key]" from *d*. Raises a "KeyError" if *key* is not
in the map.
key in d
Return "True" if *d* has a key *key*, else "False".
key not in d
Equivalent to "not key in d".
iter(d)
Return an iterator over the keys of the dictionary. This is a
shortcut for "iter(d.keys())".
clear()
Remove all items from the dictionary.
copy()
Return a shallow copy of the dictionary.
classmethod fromkeys(seq[, value])
Create a new dictionary with keys from *seq* and values set to
*value*.
"fromkeys()" is a class method that returns a new dictionary.
*value* defaults to "None".
get(key[, default])
Return the value for *key* if *key* is in the dictionary, else
*default*. If *default* is not given, it defaults to "None", so
that this method never raises a "KeyError".
items()
Return a new view of the dictionary's items ("(key, value)"
pairs). See the *documentation of view objects*.
keys()
Return a new view of the dictionary's keys. See the
*documentation of view objects*.
pop(key[, default])
If *key* is in the dictionary, remove it and return its value,
else return *default*. If *default* is not given and *key* is
not in the dictionary, a "KeyError" is raised.
popitem()
Remove and return an arbitrary "(key, value)" pair from the
dictionary.
"popitem()" is useful to destructively iterate over a
dictionary, as often used in set algorithms. If the dictionary
is empty, calling "popitem()" raises a "KeyError".
setdefault(key[, default])
If *key* is in the dictionary, return its value. If not, insert
*key* with a value of *default* and return *default*. *default*
defaults to "None".
update([other])
Update the dictionary with the key/value pairs from *other*,
overwriting existing keys. Return "None".
"update()" accepts either another dictionary object or an
iterable of key/value pairs (as tuples or other iterables of
length two). If keyword arguments are specified, the dictionary
is then updated with those key/value pairs: "d.update(red=1,
blue=2)".
values()
Return a new view of the dictionary's values. See the
*documentation of view objects*.
See also: "types.MappingProxyType" can be used to create a read-only
view of a "dict".
Dictionary view objects
=======================
The objects returned by "dict.keys()", "dict.values()" and
"dict.items()" are *view objects*. They provide a dynamic view on the
dictionary's entries, which means that when the dictionary changes,
the view reflects these changes.
Dictionary views can be iterated over to yield their respective data,
and support membership tests:
len(dictview)
Return the number of entries in the dictionary.
iter(dictview)
Return an iterator over the keys, values or items (represented as
tuples of "(key, value)") in the dictionary.
Keys and values are iterated over in an arbitrary order which is
non-random, varies across Python implementations, and depends on
the dictionary's history of insertions and deletions. If keys,
values and items views are iterated over with no intervening
modifications to the dictionary, the order of items will directly
correspond. This allows the creation of "(value, key)" pairs using
"zip()": "pairs = zip(d.values(), d.keys())". Another way to
create the same list is "pairs = [(v, k) for (k, v) in d.items()]".
Iterating views while adding or deleting entries in the dictionary
may raise a "RuntimeError" or fail to iterate over all entries.
x in dictview
Return "True" if *x* is in the underlying dictionary's keys, values
or items (in the latter case, *x* should be a "(key, value)"
tuple).
Keys views are set-like since their entries are unique and hashable.
If all values are hashable, so that "(key, value)" pairs are unique
and hashable, then the items view is also set-like. (Values views are
not treated as set-like since the entries are generally not unique.)
For set-like views, all of the operations defined for the abstract
base class "collections.abc.Set" are available (for example, "==",
"<", or "^").
An example of dictionary view usage:
>>> dishes = {'eggs': 2, 'sausage': 1, 'bacon': 1, 'spam': 500}
>>> keys = dishes.keys()
>>> values = dishes.values()
>>> # iteration
>>> n = 0
>>> for val in values:
... n += val
>>> print(n)
504
>>> # keys and values are iterated over in the same order
>>> list(keys)
['eggs', 'bacon', 'sausage', 'spam']
>>> list(values)
[2, 1, 1, 500]
>>> # view objects are dynamic and reflect dict changes
>>> del dishes['eggs']
>>> del dishes['sausage']
>>> list(keys)
['spam', 'bacon']
>>> # set operations
>>> keys & {'eggs', 'bacon', 'salad'}
{'bacon'}
>>> keys ^ {'sausage', 'juice'}
{'juice', 'sausage', 'bacon', 'spam'}
u typesmappingu
Methods
*******
Methods are functions that are called using the attribute notation.
There are two flavors: built-in methods (such as "append()" on lists)
and class instance methods. Built-in methods are described with the
types that support them.
If you access a method (a function defined in a class namespace)
through an instance, you get a special object: a *bound method* (also
called *instance method*) object. When called, it will add the "self"
argument to the argument list. Bound methods have two special read-
only attributes: "m.__self__" is the object on which the method
operates, and "m.__func__" is the function implementing the method.
Calling "m(arg-1, arg-2, ..., arg-n)" is completely equivalent to
calling "m.__func__(m.__self__, arg-1, arg-2, ..., arg-n)".
Like function objects, bound method objects support getting arbitrary
attributes. However, since method attributes are actually stored on
the underlying function object ("meth.__func__"), setting method
attributes on bound methods is disallowed. Attempting to set an
attribute on a method results in an "AttributeError" being raised. In
order to set a method attribute, you need to explicitly set it on the
underlying function object:
>>> class C:
... def method(self):
... pass
...
>>> c = C()
>>> c.method.whoami = 'my name is method' # can't set on the method
Traceback (most recent call last):
File "", line 1, in
AttributeError: 'method' object has no attribute 'whoami'
>>> c.method.__func__.whoami = 'my name is method'
>>> c.method.whoami
'my name is method'
See *The standard type hierarchy* for more information.
u typesmethodsu
Modules
*******
The only special operation on a module is attribute access: "m.name",
where *m* is a module and *name* accesses a name defined in *m*'s
symbol table. Module attributes can be assigned to. (Note that the
"import" statement is not, strictly speaking, an operation on a module
object; "import foo" does not require a module object named *foo* to
exist, rather it requires an (external) *definition* for a module
named *foo* somewhere.)
A special attribute of every module is "__dict__". This is the
dictionary containing the module's symbol table. Modifying this
dictionary will actually change the module's symbol table, but direct
assignment to the "__dict__" attribute is not possible (you can write
"m.__dict__['a'] = 1", which defines "m.a" to be "1", but you can't
write "m.__dict__ = {}"). Modifying "__dict__" directly is not
recommended.
Modules built into the interpreter are written like this: "". If loaded from a file, they are written as
"".
u typesmodulesu•I
Sequence Types --- "list", "tuple", "range"
*******************************************
There are three basic sequence types: lists, tuples, and range
objects. Additional sequence types tailored for processing of *binary
data* and *text strings* are described in dedicated sections.
Common Sequence Operations
==========================
The operations in the following table are supported by most sequence
types, both mutable and immutable. The "collections.abc.Sequence" ABC
is provided to make it easier to correctly implement these operations
on custom sequence types.
This table lists the sequence operations sorted in ascending priority
(operations in the same box have the same priority). In the table,
*s* and *t* are sequences of the same type, *n*, *i*, *j* and *k* are
integers and *x* is an arbitrary object that meets any type and value
restrictions imposed by *s*.
The "in" and "not in" operations have the same priorities as the
comparison operations. The "+" (concatenation) and "*" (repetition)
operations have the same priority as the corresponding numeric
operations.
+----------------------------+----------------------------------+------------+
| Operation | Result | Notes |
+============================+==================================+============+
| "x in s" | "True" if an item of *s* is | (1) |
+----------------------------+----------------------------------+------------+
| "x not in s" | "False" if an item of *s* is | (1) |
+----------------------------+----------------------------------+------------+
| "s + t" | the concatenation of *s* and *t* | (6)(7) |
+----------------------------+----------------------------------+------------+
| "s * n" or "n * s" | *n* shallow copies of *s* | (2)(7) |
+----------------------------+----------------------------------+------------+
| "s[i]" | *i*th item of *s*, origin 0 | (3) |
+----------------------------+----------------------------------+------------+
| "s[i:j]" | slice of *s* from *i* to *j* | (3)(4) |
+----------------------------+----------------------------------+------------+
| "s[i:j:k]" | slice of *s* from *i* to *j* | (3)(5) |
+----------------------------+----------------------------------+------------+
+----------------------------+----------------------------------+------------+
+----------------------------+----------------------------------+------------+
+----------------------------+----------------------------------+------------+
| "s.index(x[, i[, j]])" | index of the first occurrence of | (8) |
+----------------------------+----------------------------------+------------+
+----------------------------+----------------------------------+------------+
Sequences of the same type also support comparisons. In particular,
tuples and lists are compared lexicographically by comparing
corresponding elements. This means that to compare equal, every
element must compare equal and the two sequences must be of the same
type and have the same length. (For full details see *Comparisons* in
the language reference.)
Notes:
1. While the "in" and "not in" operations are used only for simple
containment testing in the general case, some specialised sequences
(such as "str", "bytes" and "bytearray") also use them for
subsequence testing:
>>> "gg" in "eggs"
True
2. Values of *n* less than "0" are treated as "0" (which yields an
empty sequence of the same type as *s*). Note also that the copies
are shallow; nested structures are not copied. This often haunts
new Python programmers; consider:
>>> lists = [[]] * 3
>>> lists
[[], [], []]
>>> lists[0].append(3)
>>> lists
[[3], [3], [3]]
What has happened is that "[[]]" is a one-element list containing
an empty list, so all three elements of "[[]] * 3" are (pointers
to) this single empty list. Modifying any of the elements of
"lists" modifies this single list. You can create a list of
different lists this way:
>>> lists = [[] for i in range(3)]
>>> lists[0].append(3)
>>> lists[1].append(5)
>>> lists[2].append(7)
>>> lists
[[3], [5], [7]]
3. If *i* or *j* is negative, the index is relative to the end of
the string: "len(s) + i" or "len(s) + j" is substituted. But note
that "-0" is still "0".
4. The slice of *s* from *i* to *j* is defined as the sequence of
items with index *k* such that "i <= k < j". If *i* or *j* is
greater than "len(s)", use "len(s)". If *i* is omitted or "None",
use "0". If *j* is omitted or "None", use "len(s)". If *i* is
greater than or equal to *j*, the slice is empty.
5. The slice of *s* from *i* to *j* with step *k* is defined as the
sequence of items with index "x = i + n*k" such that "0 <= n <
(j-i)/k". In other words, the indices are "i", "i+k", "i+2*k",
"i+3*k" and so on, stopping when *j* is reached (but never
including *j*). If *i* or *j* is greater than "len(s)", use
"len(s)". If *i* or *j* are omitted or "None", they become "end"
values (which end depends on the sign of *k*). Note, *k* cannot be
zero. If *k* is "None", it is treated like "1".
6. Concatenating immutable sequences always results in a new
object. This means that building up a sequence by repeated
concatenation will have a quadratic runtime cost in the total
sequence length. To get a linear runtime cost, you must switch to
one of the alternatives below:
* if concatenating "str" objects, you can build a list and use
"str.join()" at the end or else write to a "io.StringIO" instance
and retrieve its value when complete
* if concatenating "bytes" objects, you can similarly use
"bytes.join()" or "io.BytesIO", or you can do in-place
concatenation with a "bytearray" object. "bytearray" objects are
mutable and have an efficient overallocation mechanism
* if concatenating "tuple" objects, extend a "list" instead
* for other types, investigate the relevant class documentation
7. Some sequence types (such as "range") only support item
sequences that follow specific patterns, and hence don't support
sequence concatenation or repetition.
8. "index" raises "ValueError" when *x* is not found in *s*. When
supported, the additional arguments to the index method allow
efficient searching of subsections of the sequence. Passing the
extra arguments is roughly equivalent to using "s[i:j].index(x)",
only without copying any data and with the returned index being
relative to the start of the sequence rather than the start of the
slice.
Immutable Sequence Types
========================
The only operation that immutable sequence types generally implement
that is not also implemented by mutable sequence types is support for
the "hash()" built-in.
This support allows immutable sequences, such as "tuple" instances, to
be used as "dict" keys and stored in "set" and "frozenset" instances.
Attempting to hash an immutable sequence that contains unhashable
values will result in "TypeError".
Mutable Sequence Types
======================
The operations in the following table are defined on mutable sequence
types. The "collections.abc.MutableSequence" ABC is provided to make
it easier to correctly implement these operations on custom sequence
types.
In the table *s* is an instance of a mutable sequence type, *t* is any
iterable object and *x* is an arbitrary object that meets any type and
value restrictions imposed by *s* (for example, "bytearray" only
accepts integers that meet the value restriction "0 <= x <= 255").
+--------------------------------+----------------------------------+-----------------------+
| Operation | Result | Notes |
+================================+==================================+=======================+
+--------------------------------+----------------------------------+-----------------------+
+--------------------------------+----------------------------------+-----------------------+
+--------------------------------+----------------------------------+-----------------------+
| "s[i:j:k] = t" | the elements of "s[i:j:k]" are | (1) |
+--------------------------------+----------------------------------+-----------------------+
+--------------------------------+----------------------------------+-----------------------+
+--------------------------------+----------------------------------+-----------------------+
| "s.clear()" | removes all items from "s" (same | (5) |
+--------------------------------+----------------------------------+-----------------------+
| "s.copy()" | creates a shallow copy of "s" | (5) |
+--------------------------------+----------------------------------+-----------------------+
+--------------------------------+----------------------------------+-----------------------+
+--------------------------------+----------------------------------+-----------------------+
| "s.pop([i])" | retrieves the item at *i* and | (2) |
+--------------------------------+----------------------------------+-----------------------+
| "s.remove(x)" | remove the first item from *s* | (3) |
+--------------------------------+----------------------------------+-----------------------+
| "s.reverse()" | reverses the items of *s* in | (4) |
+--------------------------------+----------------------------------+-----------------------+
Notes:
1. *t* must have the same length as the slice it is replacing.
2. The optional argument *i* defaults to "-1", so that by default
the last item is removed and returned.
3. "remove" raises "ValueError" when *x* is not found in *s*.
4. The "reverse()" method modifies the sequence in place for
economy of space when reversing a large sequence. To remind users
that it operates by side effect, it does not return the reversed
sequence.
5. "clear()" and "copy()" are included for consistency with the
interfaces of mutable containers that don't support slicing
operations (such as "dict" and "set")
New in version 3.3: "clear()" and "copy()" methods.
Lists
=====
Lists are mutable sequences, typically used to store collections of
homogeneous items (where the precise degree of similarity will vary by
application).
class class list([iterable])
Lists may be constructed in several ways:
* Using a pair of square brackets to denote the empty list: "[]"
* Using square brackets, separating items with commas: "[a]",
"[a, b, c]"
* Using a list comprehension: "[x for x in iterable]"
* Using the type constructor: "list()" or "list(iterable)"
The constructor builds a list whose items are the same and in the
same order as *iterable*'s items. *iterable* may be either a
sequence, a container that supports iteration, or an iterator
object. If *iterable* is already a list, a copy is made and
returned, similar to "iterable[:]". For example, "list('abc')"
returns "['a', 'b', 'c']" and "list( (1, 2, 3) )" returns "[1, 2,
3]". If no argument is given, the constructor creates a new empty
list, "[]".
Many other operations also produce lists, including the "sorted()"
built-in.
Lists implement all of the *common* and *mutable* sequence
operations. Lists also provide the following additional method:
sort(*, key=None, reverse=None)
This method sorts the list in place, using only "<" comparisons
between items. Exceptions are not suppressed - if any comparison
operations fail, the entire sort operation will fail (and the
list will likely be left in a partially modified state).
"sort()" accepts two arguments that can only be passed by
keyword (*keyword-only arguments*):
*key* specifies a function of one argument that is used to
extract a comparison key from each list element (for example,
"key=str.lower"). The key corresponding to each item in the list
is calculated once and then used for the entire sorting process.
The default value of "None" means that list items are sorted
directly without calculating a separate key value.
The "functools.cmp_to_key()" utility is available to convert a
2.x style *cmp* function to a *key* function.
*reverse* is a boolean value. If set to "True", then the list
elements are sorted as if each comparison were reversed.
This method modifies the sequence in place for economy of space
when sorting a large sequence. To remind users that it operates
by side effect, it does not return the sorted sequence (use
"sorted()" to explicitly request a new sorted list instance).
The "sort()" method is guaranteed to be stable. A sort is
stable if it guarantees not to change the relative order of
elements that compare equal --- this is helpful for sorting in
multiple passes (for example, sort by department, then by salary
grade).
**CPython implementation detail:** While a list is being sorted,
the effect of attempting to mutate, or even inspect, the list is
undefined. The C implementation of Python makes the list appear
empty for the duration, and raises "ValueError" if it can detect
that the list has been mutated during a sort.
Tuples
======
Tuples are immutable sequences, typically used to store collections of
heterogeneous data (such as the 2-tuples produced by the "enumerate()"
built-in). Tuples are also used for cases where an immutable sequence
of homogeneous data is needed (such as allowing storage in a "set" or
"dict" instance).
class class tuple([iterable])
Tuples may be constructed in a number of ways:
* Using a pair of parentheses to denote the empty tuple: "()"
* Using a trailing comma for a singleton tuple: "a," or "(a,)"
* Separating items with commas: "a, b, c" or "(a, b, c)"
* Using the "tuple()" built-in: "tuple()" or "tuple(iterable)"
The constructor builds a tuple whose items are the same and in the
same order as *iterable*'s items. *iterable* may be either a
sequence, a container that supports iteration, or an iterator
object. If *iterable* is already a tuple, it is returned
unchanged. For example, "tuple('abc')" returns "('a', 'b', 'c')"
and "tuple( [1, 2, 3] )" returns "(1, 2, 3)". If no argument is
given, the constructor creates a new empty tuple, "()".
Note that it is actually the comma which makes a tuple, not the
parentheses. The parentheses are optional, except in the empty
tuple case, or when they are needed to avoid syntactic ambiguity.
For example, "f(a, b, c)" is a function call with three arguments,
while "f((a, b, c))" is a function call with a 3-tuple as the sole
argument.
Tuples implement all of the *common* sequence operations.
For heterogeneous collections of data where access by name is clearer
than access by index, "collections.namedtuple()" may be a more
appropriate choice than a simple tuple object.
Ranges
======
The "range" type represents an immutable sequence of numbers and is
commonly used for looping a specific number of times in "for" loops.
class class range(stop)
class class range(start, stop[, step])
The arguments to the range constructor must be integers (either
built-in "int" or any object that implements the "__index__"
special method). If the *step* argument is omitted, it defaults to
"1". If the *start* argument is omitted, it defaults to "0". If
*step* is zero, "ValueError" is raised.
For a positive *step*, the contents of a range "r" are determined
by the formula "r[i] = start + step*i" where "i >= 0" and "r[i] <
stop".
For a negative *step*, the contents of the range are still
determined by the formula "r[i] = start + step*i", but the
constraints are "i >= 0" and "r[i] > stop".
A range object will be empty if "r[0]" does not meet the value
constraint. Ranges do support negative indices, but these are
interpreted as indexing from the end of the sequence determined by
the positive indices.
Ranges containing absolute values larger than "sys.maxsize" are
permitted but some features (such as "len()") may raise
"OverflowError".
Range examples:
>>> list(range(10))
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> list(range(1, 11))
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
>>> list(range(0, 30, 5))
[0, 5, 10, 15, 20, 25]
>>> list(range(0, 10, 3))
[0, 3, 6, 9]
>>> list(range(0, -10, -1))
[0, -1, -2, -3, -4, -5, -6, -7, -8, -9]
>>> list(range(0))
[]
>>> list(range(1, 0))
[]
Ranges implement all of the *common* sequence operations except
concatenation and repetition (due to the fact that range objects
can only represent sequences that follow a strict pattern and
repetition and concatenation will usually violate that pattern).
The advantage of the "range" type over a regular "list" or "tuple" is
that a "range" object will always take the same (small) amount of
memory, no matter the size of the range it represents (as it only
stores the "start", "stop" and "step" values, calculating individual
items and subranges as needed).
Range objects implement the "collections.abc.Sequence" ABC, and
provide features such as containment tests, element index lookup,
slicing and support for negative indices (see *Sequence Types ---
list, tuple, range*):
>>> r = range(0, 20, 2)
>>> r
range(0, 20, 2)
>>> 11 in r
False
>>> 10 in r
True
>>> r.index(10)
5
>>> r[5]
10
>>> r[:5]
range(0, 10, 2)
>>> r[-1]
18
Testing range objects for equality with "==" and "!=" compares them as
sequences. That is, two range objects are considered equal if they
represent the same sequence of values. (Note that two range objects
that compare equal might have different "start", "stop" and "step"
attributes, for example "range(0) == range(2, 1, 3)" or "range(0, 3,
2) == range(0, 4, 2)".)
Changed in version 3.2: Implement the Sequence ABC. Support slicing
and negative indices. Test "int" objects for membership in constant
time instead of iterating through all items.
Changed in version 3.3: Define '==' and '!=' to compare range objects
based on the sequence of values they define (instead of comparing
based on object identity).
New in version 3.3: The "start", "stop" and "step" attributes.
u typessequÙ
Mutable Sequence Types
**********************
The operations in the following table are defined on mutable sequence
types. The "collections.abc.MutableSequence" ABC is provided to make
it easier to correctly implement these operations on custom sequence
types.
In the table *s* is an instance of a mutable sequence type, *t* is any
iterable object and *x* is an arbitrary object that meets any type and
value restrictions imposed by *s* (for example, "bytearray" only
accepts integers that meet the value restriction "0 <= x <= 255").
+--------------------------------+----------------------------------+-----------------------+
| Operation | Result | Notes |
+================================+==================================+=======================+
+--------------------------------+----------------------------------+-----------------------+
+--------------------------------+----------------------------------+-----------------------+
+--------------------------------+----------------------------------+-----------------------+
| "s[i:j:k] = t" | the elements of "s[i:j:k]" are | (1) |
+--------------------------------+----------------------------------+-----------------------+
+--------------------------------+----------------------------------+-----------------------+
+--------------------------------+----------------------------------+-----------------------+
| "s.clear()" | removes all items from "s" (same | (5) |
+--------------------------------+----------------------------------+-----------------------+
| "s.copy()" | creates a shallow copy of "s" | (5) |
+--------------------------------+----------------------------------+-----------------------+
+--------------------------------+----------------------------------+-----------------------+
+--------------------------------+----------------------------------+-----------------------+
| "s.pop([i])" | retrieves the item at *i* and | (2) |
+--------------------------------+----------------------------------+-----------------------+
| "s.remove(x)" | remove the first item from *s* | (3) |
+--------------------------------+----------------------------------+-----------------------+
| "s.reverse()" | reverses the items of *s* in | (4) |
+--------------------------------+----------------------------------+-----------------------+
Notes:
1. *t* must have the same length as the slice it is replacing.
2. The optional argument *i* defaults to "-1", so that by default
the last item is removed and returned.
3. "remove" raises "ValueError" when *x* is not found in *s*.
4. The "reverse()" method modifies the sequence in place for
economy of space when reversing a large sequence. To remind users
that it operates by side effect, it does not return the reversed
sequence.
5. "clear()" and "copy()" are included for consistency with the
interfaces of mutable containers that don't support slicing
operations (such as "dict" and "set")
New in version 3.3: "clear()" and "copy()" methods.
u typesseq-mutableu
Unary arithmetic and bitwise operations
***************************************
All unary arithmetic and bitwise operations have the same priority:
u_expr ::= power | "-" u_expr | "+" u_expr | "~" u_expr
The unary "-" (minus) operator yields the negation of its numeric
argument.
The unary "+" (plus) operator yields its numeric argument unchanged.
The unary "~" (invert) operator yields the bitwise inversion of its
integer argument. The bitwise inversion of "x" is defined as
"-(x+1)". It only applies to integral numbers.
In all three cases, if the argument does not have the proper type, a
"TypeError" exception is raised.
u unaryu°
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.
u whileu
The "with" statement
********************
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
Changed in version 3.1: Support for multiple context expressions.
See also: **PEP 0343** - The "with" statement
The specification, background, and examples for the Python "with"
statement.
u withu1
The "yield" statement
*********************
yield_stmt ::= yield_expression
A "yield" statement is semantically equivalent to a *yield
expression*. The yield statement can be used to omit the parentheses
that would otherwise be required in the equivalent yield expression
statement. For example, the yield statements
yield
yield from
are equivalent to the yield expression statements
(yield )
(yield from )
Yield expressions and statements are only used when defining a
*generator* function, and are only used in the body of the generator
function. Using yield in a function definition is sufficient to cause
that definition to create a generator function instead of a normal
function.
For full details of "yield" semantics, refer to the *Yield
expressions* section.
u yieldN( u topics( ( ( u6 /opt/alt/python33/lib64/python3.3/pydoc_data/topics.pyu s˜