Файловый менеджер

eaiovnaovbqoebvqoeavibavo Файловый менеджер

Файловый менеджер - Редактировать - /opt/alt/python27/lib64/python2.7/pydoc_data/topics.pyc

Назад
� �V~gc��@��s3��iO�d��d�6d�d�6d�d�6d�d�6d�d �6d �d�6d�d �6d�d�6d�d�6d�d�6d�d�6d�d�6d�d�6d�d�6d�d�6d�d�6d �d!�6d"�d#�6d$�d%�6d&�d'�6d(�d)�6d�d+�6d,�d-�6d.�d/�6d0�d1�6d2�d3�6d4�d5�6d6�d7�6d8�d9�6d:�d;�6d<�d=�6d>�d?�6d@�dA�6dB�dC�6dD�dE�6dF�dG�6dH�dI�6dJ�dK�6dL�dM�6dN�dO�6dP�dQ�6d:�dR�6dS�dT�6dU�dV�6dW�dX�6dY�dZ�6d[�d\�6d]�d^�6d_�d`�6da�db�6dc�dd�6de�df�6dg�dh�6di�dj�6dk�dl�6dm�dn�6do�dp�6dq�dr�6ds�dt�6du�dv�6dw�dx�6dy�dz�6d{�d\|�6d}�d~�6d�d��6d��d��6d��d��6d��d��6d��d��6d��d��6d��d��6d��d��6d��d��6d��d��6d��d��6d��d��6d��d��6d��d��6d��d��6Z��d��S(��st�� The "assert" statement ******************* Assert statements are a convenient way to insert debugging assertions into a program: assert_stmt ::= "assert" expression ["," expression] The simple form, "assert expression", is equivalent to if __debug__: if not expression: raise AssertionError The extended form, "assert expression1, expression2", is equivalent to if __debug__: if not expression1: raise AssertionError(expression2) These equivalences assume that "__debug__" and "AssertionError" refer to the built-in variables with those names. In the current implementation, the built-in variable "__debug__" is "True" under normal circumstances, "False" when optimization is requested (command line option -O). The current code generator emits no code for an assert statement when optimization is requested at compile time. Note that it is unnecessary to include the source code for the expression that failed in the error message; it will be displayed as part of the stack trace. Assignments to "__debug__" are illegal. The value for the built-in variable is determined when the interpreter starts. t��asserts�� Assignment statements ******************* Assignment statements are used to (re)bind names to values and to modify attributes or items of mutable objects: assignment_stmt ::= (target_list "=")+ (expression_list \| yield_expression) target_list ::= target ("," target)* [","] target ::= identifier \| "(" target_list ")" \| "[" [target_list] "]" \| attributeref \| subscription \| slicing (See section Primaries for the syntax definitions for the last three symbols.) An assignment statement evaluates the expression list (remember that this can be a single expression or a comma-separated list, the latter yielding a tuple) and assigns the single resulting object to each of the target lists, from left to right. Assignment is defined recursively depending on the form of the target (list). When a target is part of a mutable object (an attribute reference, subscription or slicing), the mutable object must ultimately perform the assignment and decide about its validity, and may raise an exception if the assignment is unacceptable. The rules observed by various types and the exceptions raised are given with the definition of the object types (see section The standard type hierarchy). Assignment of an object to a target list is recursively defined as follows. * If the target list is a single target: The object is assigned to that target. * If the target list is a comma-separated list of targets: The object must be an iterable with the same number of items as there are targets in the target list, and the items are assigned, from left to right, to the corresponding targets. Assignment of an object to a single target is recursively defined as follows. * If the target is an identifier (name): * If the name does not occur in a "global" statement in the current code block: the name is bound to the object in the current local namespace. * Otherwise: the name is bound to the object in the current global namespace. The name is rebound if it was already bound. This may cause the reference count for the object previously bound to the name to reach zero, causing the object to be deallocated and its destructor (if it has one) to be called. * If the target is a target list enclosed in parentheses or in square brackets: The object must be an iterable with the same number of items as there are targets in the target list, and its items are assigned, from left to right, to the corresponding targets. * If the target is an attribute reference: The primary expression in the reference is evaluated. It should yield an object with assignable attributes; if this is not the case, "TypeError" is raised. That object is then asked to assign the assigned object to the given attribute; if it cannot perform the assignment, it raises an exception (usually but not necessarily "AttributeError"). Note: If the object is a class instance and the attribute reference occurs on both sides of the assignment operator, the RHS expression, "a.x" can access either an instance attribute or (if no instance attribute exists) a class attribute. The LHS target "a.x" is always set as an instance attribute, creating it if necessary. Thus, the two occurrences of "a.x" do not necessarily refer to the same attribute: if the RHS expression refers to a class attribute, the LHS creates a new instance attribute as the target of the assignment: class Cls: x = 3 # class variable inst = Cls() inst.x = inst.x + 1 # writes inst.x as 4 leaving Cls.x as 3 This description does not necessarily apply to descriptor attributes, such as properties created with "property()". * If the target is a subscription: The primary expression in the reference is evaluated. It should yield either a mutable sequence object (such as a list) or a mapping object (such as a dictionary). Next, the subscript expression is evaluated. If the primary is a mutable sequence object (such as a list), the subscript must yield a plain integer. If it is negative, the sequence's length is added to it. The resulting value must be a nonnegative integer less than the sequence's length, and the sequence is asked to assign the assigned object to its item with that index. If the index is out of range, "IndexError" is raised (assignment to a subscripted sequence cannot add new items to a list). If the primary is a mapping object (such as a dictionary), the subscript must have a type compatible with the mapping's key type, and the mapping is then asked to create a key/datum pair which maps the subscript to the assigned object. This can either replace an existing key/value pair with the same key value, or insert a new key/value pair (if no key with the same value existed). * If the target is a slicing: The primary expression in the reference is evaluated. It should yield a mutable sequence object (such as a list). The assigned object should be a sequence object of the same type. Next, the lower and upper bound expressions are evaluated, insofar they are present; defaults are zero and the sequence's length. The bounds should evaluate to (small) integers. If either bound is negative, the sequence's length is added to it. The resulting bounds are clipped to lie between zero and the sequence's length, inclusive. Finally, the sequence object is asked to replace the slice with the items of the assigned sequence. The length of the slice may be different from the length of the assigned sequence, thus changing the length of the target sequence, if the object allows it. CPython implementation detail: In the current implementation, the syntax for targets is taken to be the same as for expressions, and invalid syntax is rejected during the code generation phase, causing less detailed error messages. WARNING: Although the definition of assignment implies that overlaps between the left-hand side and the right-hand side are 'safe' (for example "a, b = b, a" swaps two variables), overlaps within the collection of assigned-to variables are not safe! For instance, the following program prints "[0, 2]": x = [0, 1] i = 0 i, x[i] = 1, 2 print x Augmented assignment statements =============================== Augmented assignment is the combination, in a single statement, of a binary operation and an assignment statement: augmented_assignment_stmt ::= augtarget augop (expression_list \| yield_expression) augtarget ::= identifier \| attributeref \| subscription \| slicing augop ::= "+=" \| "-=" \| "=" \| "/=" \| "//=" \| "%=" \| "=" \| ">>=" \| "<<=" \| "&=" \| "^=" \| "\|=" (See section Primaries for the syntax definitions for the last three symbols.) An augmented assignment evaluates the target (which, unlike normal assignment statements, cannot be an unpacking) and the expression list, performs the binary operation specific to the type of assignment on the two operands, and assigns the result to the original target. The target is only evaluated once. An augmented assignment expression like "x += 1" can be rewritten as "x = x + 1" to achieve a similar, but not exactly equal effect. In the augmented version, "x" is only evaluated once. Also, when possible, the actual operation is performed in-place, meaning that rather than creating a new object and assigning that to the target, the old object is modified instead. With the exception of assigning to tuples and multiple targets in a single statement, the assignment done by augmented assignment statements is handled the same way as normal assignments. Similarly, with the exception of the possible in-place* behavior, the binary operation performed by augmented assignment is the same as the normal binary operations. For targets which are attribute references, the same caveat about class and instance attributes applies as for regular assignments. t ��assignments�� Identifiers (Names) ***************** An identifier occurring as an atom is a name. See section Identifiers and keywords for lexical definition and section Naming and binding for documentation of naming and binding. When the name is bound to an object, evaluation of the atom yields that object. When a name is not bound, an attempt to evaluate it raises a "NameError" exception. Private name mangling:** When an identifier that textually occurs in a class definition begins with two or more underscore characters and does not end in two or more underscores, it is considered a private name of that class. Private names are transformed to a longer form before code is generated for them. The transformation inserts the class name, with leading underscores removed and a single underscore inserted, in front of the name. For example, the identifier "__spam" occurring in a class named "Ham" will be transformed to "_Ham__spam". This transformation is independent of the syntactical context in which the identifier is used. If the transformed name is extremely long (longer than 255 characters), implementation defined truncation may happen. If the class name consists only of underscores, no transformation is done. s��atom-identifierss�� Literals ******** Python supports string literals and various numeric literals: literal ::= stringliteral \| integer \| longinteger \| floatnumber \| imagnumber Evaluation of a literal yields an object of the given type (string, integer, long integer, floating point number, complex number) with the given value. The value may be approximated in the case of floating point and imaginary (complex) literals. See section Literals for details. All literals correspond to immutable data types, and hence the object's identity is less important than its value. Multiple evaluations of literals with the same value (either the same occurrence in the program text or a different occurrence) may obtain the same object or a different object with the same value. s ��atom-literalssU�� Customizing attribute access *************************** The following methods can be defined to customize the meaning of attribute access (use of, assignment to, or deletion of "x.name") for class instances. object.__getattr__(self, name) Called when an attribute lookup has not found the attribute in the usual places (i.e. it is not an instance attribute nor is it found in the class tree for "self"). "name" is the attribute name. This method should return the (computed) attribute value or raise an "AttributeError" exception. Note that if the attribute is found through the normal mechanism, "__getattr__()" is not called. (This is an intentional asymmetry between "__getattr__()" and "__setattr__()".) This is done both for efficiency reasons and because otherwise "__getattr__()" would have no way to access other attributes of the instance. Note that at least for instance variables, you can fake total control by not inserting any values in the instance attribute dictionary (but instead inserting them in another object). See the "__getattribute__()" method below for a way to actually get total control in new-style classes. object.__setattr__(self, name, value) Called when an attribute assignment is attempted. This is called instead of the normal mechanism (i.e. store the value in the instance dictionary). name is the attribute name, value is the value to be assigned to it. If "__setattr__()" wants to assign to an instance attribute, it should not simply execute "self.name = value" --- this would cause a recursive call to itself. Instead, it should insert the value in the dictionary of instance attributes, e.g., "self.__dict__[name] = value". For new-style classes, rather than accessing the instance dictionary, it should call the base class method with the same name, for example, "object.__setattr__(self, name, value)". object.__delattr__(self, name) Like "__setattr__()" but for attribute deletion instead of assignment. This should only be implemented if "del obj.name" is meaningful for the object. More attribute access for new-style classes =========================================== The following methods only apply to new-style classes. object.__getattribute__(self, name) Called unconditionally to implement attribute accesses for instances of the class. If the class also defines "__getattr__()", the latter will not be called unless "__getattribute__()" either calls it explicitly or raises an "AttributeError". This method should return the (computed) attribute value or raise an "AttributeError" exception. In order to avoid infinite recursion in this method, its implementation should always call the base class method with the same name to access any attributes it needs, for example, "object.__getattribute__(self, name)". Note: This method may still be bypassed when looking up special methods as the result of implicit invocation via language syntax or built-in functions. See Special method lookup for new-style classes. Implementing Descriptors ======================== The following methods only apply when an instance of the class containing the method (a so-called descriptor class) appears in an owner class (the descriptor must be in either the owner's class dictionary or in the class dictionary for one of its parents). In the examples below, "the attribute" refers to the attribute whose name is the key of the property in the owner class' "__dict__". object.__get__(self, instance, owner) Called to get the attribute of the owner class (class attribute access) or of an instance of that class (instance attribute access). owner is always the owner class, while instance is the instance that the attribute was accessed through, or "None" when the attribute is accessed through the owner. This method should return the (computed) attribute value or raise an "AttributeError" exception. object.__set__(self, instance, value) Called to set the attribute on an instance instance of the owner class to a new value, value. object.__delete__(self, instance) Called to delete the attribute on an instance instance of the owner class. Invoking Descriptors ==================== In general, a descriptor is an object attribute with "binding behavior", one whose attribute access has been overridden by methods in the descriptor protocol: "__get__()", "__set__()", and "__delete__()". If any of those methods are defined for an object, it is said to be a descriptor. The default behavior for attribute access is to get, set, or delete the attribute from an object's dictionary. For instance, "a.x" has a lookup chain starting with "a.__dict__['x']", then "type(a).__dict__['x']", and continuing through the base classes of "type(a)" excluding metaclasses. However, if the looked-up value is an object defining one of the descriptor methods, then Python may override the default behavior and invoke the descriptor method instead. Where this occurs in the precedence chain depends on which descriptor methods were defined and how they were called. Note that descriptors are only invoked for new style objects or classes (ones that subclass "object()" or "type()"). The starting point for descriptor invocation is a binding, "a.x". How the arguments are assembled depends on "a": Direct Call The simplest and least common call is when user code directly invokes a descriptor method: "x.__get__(a)". Instance Binding If binding to a new-style object instance, "a.x" is transformed into the call: "type(a).__dict__['x'].__get__(a, type(a))". Class Binding If binding to a new-style class, "A.x" is transformed into the call: "A.__dict__['x'].__get__(None, A)". Super Binding If "a" is an instance of "super", then the binding "super(B, obj).m()" searches "obj.__class__.__mro__" for the base class "A" immediately preceding "B" and then invokes the descriptor with the call: "A.__dict__['m'].__get__(obj, obj.__class__)". For instance bindings, the precedence of descriptor invocation depends on the which descriptor methods are defined. A descriptor can define any combination of "__get__()", "__set__()" and "__delete__()". If it does not define "__get__()", then accessing the attribute will return the descriptor object itself unless there is a value in the object's instance dictionary. If the descriptor defines "__set__()" and/or "__delete__()", it is a data descriptor; if it defines neither, it is a non-data descriptor. Normally, data descriptors define both "__get__()" and "__set__()", while non-data descriptors have just the "__get__()" method. Data descriptors with "__set__()" and "__get__()" defined always override a redefinition in an instance dictionary. In contrast, non-data descriptors can be overridden by instances. Python methods (including "staticmethod()" and "classmethod()") are implemented as non-data descriptors. Accordingly, instances can redefine and override methods. This allows individual instances to acquire behaviors that differ from other instances of the same class. The "property()" function is implemented as a data descriptor. Accordingly, instances cannot override the behavior of a property. __slots__ ========= By default, instances of both old and new-style classes have a dictionary for attribute storage. This wastes space for objects having very few instance variables. The space consumption can become acute when creating large numbers of instances. The default can be overridden by defining __slots__ in a new-style class definition. The __slots__ declaration takes a sequence of instance variables and reserves just enough space in each instance to hold a value for each variable. Space is saved because __dict__ is not created for each instance. __slots__ This class variable can be assigned a string, iterable, or sequence of strings with variable names used by instances. If defined in a new-style class, __slots__ reserves space for the declared variables and prevents the automatic creation of __dict__ and __weakref__ for each instance. New in version 2.2. Notes on using __slots__ * When inheriting from a class without __slots__, the __dict__ attribute of that class will always be accessible, so a __slots__ definition in the subclass is meaningless. * Without a __dict__ variable, instances cannot be assigned new variables not listed in the __slots__ definition. Attempts to assign to an unlisted variable name raises "AttributeError". If dynamic assignment of new variables is desired, then add "'__dict__'" to the sequence of strings in the __slots__ declaration. Changed in version 2.3: Previously, adding "'__dict__'" to the __slots__ declaration would not enable the assignment of new attributes not specifically listed in the sequence of instance variable names. * Without a __weakref__ variable for each instance, classes defining __slots__ do not support weak references to its instances. If weak reference support is needed, then add "'__weakref__'" to the sequence of strings in the __slots__ declaration. Changed in version 2.3: Previously, adding "'__weakref__'" to the __slots__ declaration would not enable support for weak references. * __slots__ are implemented at the class level by creating descriptors (Implementing Descriptors) for each variable name. As a result, class attributes cannot be used to set default values for instance variables defined by __slots__; otherwise, the class attribute would overwrite the descriptor assignment. * The action of a __slots__ declaration is limited to the class where it is defined. As a result, subclasses will have a __dict__ unless they also define __slots__ (which must only contain names of any additional slots). * If a class defines a slot also defined in a base class, the instance variable defined by the base class slot is inaccessible (except by retrieving its descriptor directly from the base class). This renders the meaning of the program undefined. In the future, a check may be added to prevent this. * Nonempty __slots__ does not work for classes derived from "variable-length" built-in types such as "long", "str" and "tuple". * Any non-string iterable may be assigned to __slots__. Mappings may also be used; however, in the future, special meaning may be assigned to the values corresponding to each key. * __class__ assignment works only if both classes have the same __slots__. Changed in version 2.6: Previously, __class__ assignment raised an error if either new or old class had __slots__. s��attribute-accesss_�� Attribute references ****************** An attribute reference is a primary followed by a period and a name: attributeref ::= primary "." identifier The primary must evaluate to an object of a type that supports attribute references, e.g., a module, list, or an instance. This object is then asked to produce the attribute whose name is the identifier. If this attribute is not available, the exception "AttributeError" is raised. Otherwise, the type and value of the object produced is determined by the object. Multiple evaluations of the same attribute reference may yield different objects. s��attribute-referencess�� Augmented assignment statements ***************************** Augmented assignment is the combination, in a single statement, of a binary operation and an assignment statement: augmented_assignment_stmt ::= augtarget augop (expression_list \| yield_expression) augtarget ::= identifier \| attributeref \| subscription \| slicing augop ::= "+=" \| "-=" \| "=" \| "/=" \| "//=" \| "%=" \| "=" \| ">>=" \| "<<=" \| "&=" \| "^=" \| "\|=" (See section Primaries for the syntax definitions for the last three symbols.) An augmented assignment evaluates the target (which, unlike normal assignment statements, cannot be an unpacking) and the expression list, performs the binary operation specific to the type of assignment on the two operands, and assigns the result to the original target. The target is only evaluated once. An augmented assignment expression like "x += 1" can be rewritten as "x = x + 1" to achieve a similar, but not exactly equal effect. In the augmented version, "x" is only evaluated once. Also, when possible, the actual operation is performed in-place, meaning that rather than creating a new object and assigning that to the target, the old object is modified instead. With the exception of assigning to tuples and multiple targets in a single statement, the assignment done by augmented assignment statements is handled the same way as normal assignments. Similarly, with the exception of the possible in-place* behavior, the binary operation performed by augmented assignment is the same as the normal binary operations. For targets which are attribute references, the same caveat about class and instance attributes applies as for regular assignments. t ��augassignsn�� Binary arithmetic operations **************************** The binary arithmetic operations have the conventional priority levels. Note that some of these operations also apply to certain non- numeric types. Apart from the power operator, there are only two levels, one for multiplicative operators and one for additive operators: 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 (plain or long) and the other must be a sequence. In the former case, the numbers are converted to a common type and then multiplied together. In the latter case, sequence repetition is performed; a negative repetition factor yields an empty sequence. The "/" (division) and "//" (floor division) operators yield the quotient of their arguments. The numeric arguments are first converted to a common type. Plain or long integer division yields an integer of the same type; the result is that of mathematical division with the 'floor' function applied to the result. Division by zero raises the "ZeroDivisionError" exception. The "%" (modulo) operator yields the remainder from the division of the first argument by the second. The numeric arguments are first converted to a common type. A zero right argument raises the "ZeroDivisionError" exception. The arguments may be floating point numbers, e.g., "3.14%0.7" equals "0.34" (since "3.14" equals "40.7 + 0.34".) The modulo operator always yields a result with the same sign as its second operand (or zero); the absolute value of the result is strictly smaller than the absolute value of the second operand [2]. The integer division and modulo operators are connected by the following identity: "x == (x/y)y + (x%y)". Integer division and modulo are also connected with the built-in function "divmod()": "divmod(x, y) == (x/y, x%y)". These identities don't hold for floating point numbers; there similar identities hold approximately where "x/y" is replaced by "floor(x/y)" or "floor(x/y) - 1" [3]. In addition to performing the modulo operation on numbers, the "%" operator is also overloaded by string and unicode objects to perform string formatting (also known as interpolation). The syntax for string formatting is described in the Python Library Reference, section String Formatting Operations. Deprecated since version 2.3: The floor division operator, the modulo operator, and the "divmod()" function are no longer defined for complex numbers. Instead, convert to a floating point number using the "abs()" function if appropriate. The "+" (addition) operator yields the sum of its arguments. The arguments must either both be numbers or both sequences of the same type. In the former case, the numbers are converted to a common type and then added together. In the latter case, the sequences are concatenated. The "-" (subtraction) operator yields the difference of its arguments. The numeric arguments are first converted to a common type. t��binarys�� Binary bitwise operations *********************** Each of the three bitwise operations has a different priority level: and_expr ::= shift_expr \| and_expr "&" shift_expr xor_expr ::= and_expr \| xor_expr "^" and_expr or_expr ::= xor_expr \| or_expr "\|" xor_expr The "&" operator yields the bitwise AND of its arguments, which must be plain or long integers. The arguments are converted to a common type. The "^" operator yields the bitwise XOR (exclusive OR) of its arguments, which must be plain or long integers. The arguments are converted to a common type. The "\|" operator yields the bitwise (inclusive) OR of its arguments, which must be plain or long integers. The arguments are converted to a common type. t��bitwises~�� Code Objects ******** Code objects are used by the implementation to represent "pseudo- compiled" executable Python code such as a function body. They differ from function objects because they don't contain a reference to their global execution environment. Code objects are returned by the built- in "compile()" function and can be extracted from function objects through their "func_code" attribute. See also the "code" module. A code object can be executed or evaluated by passing it (instead of a source string) to the "exec" statement or the built-in "eval()" function. See The standard type hierarchy for more information. s��bltin-code-objectssE�� The Ellipsis Object *************** This object is used by extended slice notation (see Slicings). It supports no special operations. There is exactly one ellipsis object, named "Ellipsis" (a built-in name). It is written as "Ellipsis". When in a subscript, it can also be written as "...", for example "seq[...]". s��bltin-ellipsis-objects�+�� File Objects ********** File objects are implemented using C's "stdio" package and can be created with the built-in "open()" function. File objects are also returned by some other built-in functions and methods, such as "os.popen()" and "os.fdopen()" and the "makefile()" method of socket objects. Temporary files can be created using the "tempfile" module, and high-level file operations such as copying, moving, and deleting files and directories can be achieved with the "shutil" module. When a file operation fails for an I/O-related reason, the exception "IOError" is raised. This includes situations where the operation is not defined for some reason, like "seek()" on a tty device or writing a file opened for reading. Files have the following methods: file.close() Close the file. A closed file cannot be read or written any more. Any operation which requires that the file be open will raise a "ValueError" after the file has been closed. Calling "close()" more than once is allowed. As of Python 2.5, you can avoid having to call this method explicitly if you use the "with" statement. For example, the following code will automatically close f when the "with" block is exited: from __future__ import with_statement # This isn't required in Python 2.6 with open("hello.txt") as f: for line in f: print line, In older versions of Python, you would have needed to do this to get the same effect: f = open("hello.txt") try: for line in f: print line, finally: f.close() Note: Not all "file-like" types in Python support use as a context manager for the "with" statement. If your code is intended to work with any file-like object, you can use the function "contextlib.closing()" instead of using the object directly. file.flush() Flush the internal buffer, like "stdio"'s "fflush()". This may be a no-op on some file-like objects. Note: "flush()" does not necessarily write the file's data to disk. Use "flush()" followed by "os.fsync()" to ensure this behavior. file.fileno() Return the integer "file descriptor" that is used by the underlying implementation to request I/O operations from the operating system. This can be useful for other, lower level interfaces that use file descriptors, such as the "fcntl" module or "os.read()" and friends. Note: File-like objects which do not have a real file descriptor should not provide this method! file.isatty() Return "True" if the file is connected to a tty(-like) device, else "False". Note: If a file-like object is not associated with a real file, this method should not be implemented. file.next() A file object is its own iterator, for example "iter(f)" returns f (unless f is closed). When a file is used as an iterator, typically in a "for" loop (for example, "for line in f: print line.strip()"), the "next()" method is called repeatedly. This method returns the next input line, or raises "StopIteration" when EOF is hit when the file is open for reading (behavior is undefined when the file is open for writing). In order to make a "for" loop the most efficient way of looping over the lines of a file (a very common operation), the "next()" method uses a hidden read-ahead buffer. As a consequence of using a read-ahead buffer, combining "next()" with other file methods (like "readline()") does not work right. However, using "seek()" to reposition the file to an absolute position will flush the read-ahead buffer. New in version 2.3. file.read([size]) Read at most size bytes from the file (less if the read hits EOF before obtaining size bytes). If the size argument is negative or omitted, read all data until EOF is reached. The bytes are returned as a string object. An empty string is returned when EOF is encountered immediately. (For certain files, like ttys, it makes sense to continue reading after an EOF is hit.) Note that this method may call the underlying C function "fread()" more than once in an effort to acquire as close to size bytes as possible. Also note that when in non-blocking mode, less data than was requested may be returned, even if no size parameter was given. Note: This function is simply a wrapper for the underlying "fread()" C function, and will behave the same in corner cases, such as whether the EOF value is cached. file.readline([size]) Read one entire line from the file. A trailing newline character is kept in the string (but may be absent when a file ends with an incomplete line). [6] If the size argument is present and non- negative, it is a maximum byte count (including the trailing newline) and an incomplete line may be returned. When size is not 0, an empty string is returned only when EOF is encountered immediately. Note: Unlike "stdio"'s "fgets()", the returned string contains null characters ("'\0'") if they occurred in the input. file.readlines([sizehint]) Read until EOF using "readline()" and return a list containing the lines thus read. If the optional sizehint argument is present, instead of reading up to EOF, whole lines totalling approximately sizehint bytes (possibly after rounding up to an internal buffer size) are read. Objects implementing a file-like interface may choose to ignore sizehint if it cannot be implemented, or cannot be implemented efficiently. file.xreadlines() This method returns the same thing as "iter(f)". New in version 2.1. Deprecated since version 2.3: Use "for line in file" instead. file.seek(offset[, whence]) Set the file's current position, like "stdio"'s "fseek()". The whence argument is optional and defaults to "os.SEEK_SET" or "0" (absolute file positioning); other values are "os.SEEK_CUR" or "1" (seek relative to the current position) and "os.SEEK_END" or "2" (seek relative to the file's end). There is no return value. For example, "f.seek(2, os.SEEK_CUR)" advances the position by two and "f.seek(-3, os.SEEK_END)" sets the position to the third to last. Note that if the file is opened for appending (mode "'a'" or "'a+'"), any "seek()" operations will be undone at the next write. If the file is only opened for writing in append mode (mode "'a'"), this method is essentially a no-op, but it remains useful for files opened in append mode with reading enabled (mode "'a+'"). If the file is opened in text mode (without "'b'"), only offsets returned by "tell()" are legal. Use of other offsets causes undefined behavior. Note that not all file objects are seekable. Changed in version 2.6: Passing float values as offset has been deprecated. file.tell() Return the file's current position, like "stdio"'s "ftell()". Note: On Windows, "tell()" can return illegal values (after an "fgets()") when reading files with Unix-style line-endings. Use binary mode ("'rb'") to circumvent this problem. file.truncate([size]) Truncate the file's size. If the optional size argument is present, the file is truncated to (at most) that size. The size defaults to the current position. The current file position is not changed. Note that if a specified size exceeds the file's current size, the result is platform-dependent: possibilities include that the file may remain unchanged, increase to the specified size as if zero-filled, or increase to the specified size with undefined new content. Availability: Windows, many Unix variants. file.write(str) Write a string to the file. There is no return value. Due to buffering, the string may not actually show up in the file until the "flush()" or "close()" method is called. file.writelines(sequence) Write a sequence of strings to the file. The sequence can be any iterable object producing strings, typically a list of strings. There is no return value. (The name is intended to match "readlines()"; "writelines()" does not add line separators.) Files support the iterator protocol. Each iteration returns the same result as "readline()", and iteration ends when the "readline()" method returns an empty string. File objects also offer a number of other interesting attributes. These are not required for file-like objects, but should be implemented if they make sense for the particular object. file.closed bool indicating the current state of the file object. This is a read-only attribute; the "close()" method changes the value. It may not be available on all file-like objects. file.encoding The encoding that this file uses. When Unicode strings are written to a file, they will be converted to byte strings using this encoding. In addition, when the file is connected to a terminal, the attribute gives the encoding that the terminal is likely to use (that information might be incorrect if the user has misconfigured the terminal). The attribute is read-only and may not be present on all file-like objects. It may also be "None", in which case the file uses the system default encoding for converting Unicode strings. New in version 2.3. file.errors The Unicode error handler used along with the encoding. New in version 2.6. file.mode The I/O mode for the file. If the file was created using the "open()" built-in function, this will be the value of the mode parameter. This is a read-only attribute and may not be present on all file-like objects. file.name If the file object was created using "open()", the name of the file. Otherwise, some string that indicates the source of the file object, of the form "<...>". This is a read-only attribute and may not be present on all file-like objects. file.newlines If Python was built with universal newlines enabled (the default) this read-only attribute exists, and for files opened in universal newline read mode it keeps track of the types of newlines encountered while reading the file. The values it can take are "'\r'", "'\n'", "'\r\n'", "None" (unknown, no newlines read yet) or a tuple containing all the newline types seen, to indicate that multiple newline conventions were encountered. For files not opened in universal newlines read mode the value of this attribute will be "None". file.softspace Boolean that indicates whether a space character needs to be printed before another value when using the "print" statement. Classes that are trying to simulate a file object should also have a writable "softspace" attribute, which should be initialized to zero. This will be automatic for most classes implemented in Python (care may be needed for objects that override attribute access); types implemented in C will have to provide a writable "softspace" attribute. Note: This attribute is not used to control the "print" statement, but to allow the implementation of "print" to keep track of its internal state. s��bltin-file-objectss�� The Null Object ************* This object is returned by functions that don't explicitly return a value. It supports no special operations. There is exactly one null object, named "None" (a built-in name). It is written as "None". s��bltin-null-objects3�� Type Objects ******** Type objects represent the various object types. An object's type is accessed by the built-in function "type()". There are no special operations on types. The standard module "types" defines names for all standard built-in types. Types are written like this: "<type 'int'>". s��bltin-type-objectss�� Boolean operations **************** or_test ::= and_test \| or_test "or" and_test and_test ::= not_test \| and_test "and" not_test not_test ::= comparison \| "not" not_test In the context of Boolean operations, and also when expressions are used by control flow statements, the following values are interpreted as false: "False", "None", numeric zero of all types, and empty strings and containers (including strings, tuples, lists, dictionaries, sets and frozensets). All other values are interpreted as true. (See the "__nonzero__()" special method for a way to change this.) The operator "not" yields "True" if its argument is false, "False" otherwise. The expression "x and y" first evaluates x; if x is false, its value is returned; otherwise, y is evaluated and the resulting value is returned. The expression "x or y" first evaluates x; if x is true, its value is returned; otherwise, y is evaluated and the resulting value is returned. (Note that neither "and" nor "or" restrict the value and type they return to "False" and "True", but rather return the last evaluated argument. This is sometimes useful, e.g., if "s" is a string that should be replaced by a default value if it is empty, the expression "s or 'foo'" yields the desired value. Because "not" has to invent a value anyway, it does not bother to return a value of the same type as its argument, so e.g., "not 'foo'" yields "False", not "''".) t��booleanss%�� 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. t��breaks�� 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, ...)". s��callable-typess�� Calls *** A call calls a callable object (e.g., a function) with a possibly empty series of arguments: call ::= primary "(" [argument_list [","] \| expression genexpr_for] ")" argument_list ::= positional_arguments ["," keyword_arguments] ["," "" expression] ["," keyword_arguments] ["," "" expression] \| keyword_arguments ["," "" expression] ["," "*" expression] \| "" expression ["," keyword_arguments] ["," "" expression] \| "" expression positional_arguments ::= expression ("," expression)* keyword_arguments ::= keyword_item ("," keyword_item)* keyword_item ::= identifier "=" expression A trailing comma may be present after the positional and keyword arguments but does not affect the semantics. The primary must evaluate to a callable object (user-defined functions, built-in functions, methods of built-in objects, class objects, methods of class instances, and certain class instances themselves are callable; extensions may define additional callable object types). All argument expressions are evaluated before the call is attempted. Please refer to section Function definitions for the syntax of formal parameter lists. If keyword arguments are present, they are first converted to positional arguments, as follows. First, a list of unfilled slots is created for the formal parameters. If there are N positional arguments, they are placed in the first N slots. Next, for each keyword argument, the identifier is used to determine the corresponding slot (if the identifier is the same as the first formal parameter name, the first slot is used, and so on). If the slot is already filled, a "TypeError" exception is raised. Otherwise, the value of the argument is placed in the slot, filling it (even if the expression is "None", it fills the slot). When all arguments have been processed, the slots that are still unfilled are filled with the corresponding default value from the function definition. (Default values are calculated, once, when the function is defined; thus, a mutable object such as a list or dictionary used as default value will be shared by all calls that don't specify an argument value for the corresponding slot; this should usually be avoided.) If there are any unfilled slots for which no default value is specified, a "TypeError" exception is raised. Otherwise, the list of filled slots is used as the argument list for the call. CPython implementation detail: An implementation may provide built-in functions whose positional parameters do not have names, even if they are 'named' for the purpose of documentation, and which therefore cannot be supplied by keyword. In CPython, this is the case for functions implemented in C that use "PyArg_ParseTuple()" to parse their arguments. If there are more positional arguments than there are formal parameter slots, a "TypeError" exception is raised, unless a formal parameter using the syntax "identifier" is present; in this case, that formal parameter receives a tuple containing the excess positional arguments (or an empty tuple if there were no excess positional arguments). If any keyword argument does not correspond to a formal parameter name, a "TypeError" exception is raised, unless a formal parameter using the syntax "identifier" is present; in this case, that formal parameter receives a dictionary containing the excess keyword arguments (using the keywords as keys and the argument values as corresponding values), or a (new) empty dictionary if there were no excess keyword arguments. If the syntax "expression" appears in the function call, "expression" must evaluate to an iterable. Elements from this iterable are treated as if they were additional positional arguments; if there are positional arguments x1, ..., xN, and "expression" evaluates to a sequence y1, ..., yM, this is equivalent to a call with M+N positional arguments x1, ..., xN, y1, ..., yM. A consequence of this is that although the "expression" syntax may appear after* some keyword arguments, it is processed before the keyword arguments (and the "*expression" argument, if any -- see below). So: >>> def f(a, b): ... print a, b ... >>> f(b=1, (2,)) 2 1 >>> f(a=1, (2,)) Traceback (most recent call last): File "<stdin>", line 1, in <module> TypeError: f() got multiple values for keyword argument 'a' >>> f(1, (2,)) 1 2 It is unusual for both keyword arguments and the "expression" syntax to be used in the same call, so in practice this confusion does not arise. If the syntax "expression" appears in the function call, "expression" must evaluate to a mapping, the contents of which are treated as additional keyword arguments. In the case of a keyword appearing in both "expression" and as an explicit keyword argument, a "TypeError" exception is raised. Formal parameters using the syntax "identifier" or "identifier" cannot be used as positional argument slots or as keyword argument names. Formal parameters using the syntax "(sublist)" cannot be used as keyword argument names; the outermost sublist corresponds to a single unnamed argument slot, and the argument value is assigned to the sublist using the usual tuple assignment rules after all other parameter processing is done. A call always returns some value, possibly "None", unless it raises an exception. How this value is computed depends on the type of the callable object. If it is--- a user-defined function: The code block for the function is executed, passing it the argument list. The first thing the code block will do is bind the formal parameters to the arguments; this is described in section Function definitions. When the code block executes a "return" statement, this specifies the return value of the function call. a built-in function or method: The result is up to the interpreter; see Built-in Functions for the descriptions of built-in functions and methods. a class object: A new instance of that class is returned. a class instance method: The corresponding user-defined function is called, with an argument list that is one longer than the argument list of the call: the instance becomes the first argument. a class instance: The class must define a "__call__()" method; the effect is then the same as if that method was called. t��callssJ �� Class definitions ************* A class definition defines a class object (see section The standard type hierarchy): classdef ::= "class" classname [inheritance] ":" suite inheritance ::= "(" [expression_list] ")" classname ::= identifier A class definition is an executable statement. It first evaluates the inheritance list, if present. Each item in the inheritance list should evaluate to a class object or class type which allows subclassing. The class's suite is then executed in a new execution frame (see section Naming and binding), using a newly created local namespace and the original global namespace. (Usually, the suite contains only function definitions.) When the class's suite finishes execution, its execution frame is discarded but its local namespace is saved. [4] A class object is then created using the inheritance list for the base classes and the saved local namespace for the attribute dictionary. The class name is bound to this class object in the original local namespace. Programmer's note:** Variables defined in the class definition are class variables; they are shared by all instances. To create instance variables, they can be set in a method with "self.name = value". Both class and instance variables are accessible through the notation ""self.name"", and an instance variable hides a class variable with the same name when accessed in this way. Class variables can be used as defaults for instance variables, but using mutable values there can lead to unexpected results. For new-style classes, descriptors can be used to create instance variables with different implementation details. Class definitions, like function definitions, may be wrapped by one or more decorator expressions. The evaluation rules for the decorator expressions are the same as for functions. The result must be a class object, which is then bound to the class name. -[ Footnotes ]- [1] The exception is propagated to the invocation stack unless there is a "finally" clause which happens to raise another exception. That new exception causes the old one to be lost. [2] Currently, control "flows off the end" except in the case of an exception or the execution of a "return", "continue", or "break" statement. [3] A string literal appearing as the first statement in the function body is transformed into the function's "__doc__" attribute and therefore the function's docstring. [4] A string literal appearing as the first statement in the class body is transformed into the namespace's "__doc__" item and therefore the class's docstring. t��classs$�� Comparisons *********** Unlike C, all comparison operations in Python have the same priority, which is lower than that of any arithmetic, shifting or bitwise operation. Also unlike C, expressions like "a < b < c" have the interpretation that is conventional in mathematics: comparison ::= or_expr ( comp_operator or_expr )* comp_operator ::= "<" \| ">" \| "==" \| ">=" \| "<=" \| "<>" \| "!=" \| "is" ["not"] \| ["not"] "in" Comparisons yield boolean values: "True" or "False". Comparisons can be chained arbitrarily, e.g., "x < y <= z" is equivalent to "x < y and y <= z", except that "y" is evaluated only once (but in both cases "z" is not evaluated at all when "x < y" is found to be false). Formally, if a, b, c, ..., y, z are expressions and op1, op2, ..., opN are comparison operators, then "a op1 b op2 c ... y opN z" is equivalent to "a op1 b and b op2 c and ... y opN z", except that each expression is evaluated at most once. Note that "a op1 b op2 c" doesn't imply any kind of comparison between a and c, so that, e.g., "x < y > z" is perfectly legal (though perhaps not pretty). The forms "<>" and "!=" are equivalent; for consistency with C, "!=" is preferred; where "!=" is mentioned below "<>" is also accepted. The "<>" spelling is considered obsolescent. Value comparisons ================= The operators "<", ">", "==", ">=", "<=", and "!=" compare the values of two objects. The objects do not need to have the same type. Chapter Objects, values and types states that objects have a value (in addition to type and identity). The value of an object is a rather abstract notion in Python: For example, there is no canonical access method for an object's value. Also, there is no requirement that the value of an object should be constructed in a particular way, e.g. comprised of all its data attributes. Comparison operators implement a particular notion of what the value of an object is. One can think of them as defining the value of an object indirectly, by means of their comparison implementation. Types can customize their comparison behavior by implementing a "__cmp__()" method or rich comparison methods like "__lt__()", described in Basic customization. The default behavior for equality comparison ("==" and "!=") is based on the identity of the objects. Hence, equality comparison of instances with the same identity results in equality, and equality comparison of instances with different identities results in inequality. A motivation for this default behavior is the desire that all objects should be reflexive (i.e. "x is y" implies "x == y"). The default order comparison ("<", ">", "<=", and ">=") gives a consistent but arbitrary order. (This unusual definition of comparison was used to simplify the definition of operations like sorting and the "in" and "not in" operators. In the future, the comparison rules for objects of different types are likely to change.) The behavior of the default equality comparison, that instances with different identities are always unequal, may be in contrast to what types will need that have a sensible definition of object value and value-based equality. Such types will need to customize their comparison behavior, and in fact, a number of built-in types have done that. The following list describes the comparison behavior of the most important built-in types. * Numbers of built-in numeric types (Numeric Types --- int, float, long, complex) and of the standard library types "fractions.Fraction" and "decimal.Decimal" can be compared within and across their types, with the restriction that complex numbers do not support order comparison. Within the limits of the types involved, they compare mathematically (algorithmically) correct without loss of precision. * Strings (instances of "str" or "unicode") compare lexicographically using the numeric equivalents (the result of the built-in function "ord()") of their characters. [4] When comparing an 8-bit string and a Unicode string, the 8-bit string is converted to Unicode. If the conversion fails, the strings are considered unequal. * Instances of "tuple" or "list" can be compared only within each of their types. Equality comparison across these types results in unequality, and ordering comparison across these types gives an arbitrary order. These sequences compare lexicographically using comparison of corresponding elements, whereby reflexivity of the elements is enforced. In enforcing reflexivity of elements, the comparison of collections assumes that for a collection element "x", "x == x" is always true. Based on that assumption, element identity is compared first, and element comparison is performed only for distinct elements. This approach yields the same result as a strict element comparison would, if the compared elements are reflexive. For non-reflexive elements, the result is different than for strict element comparison. Lexicographical comparison between built-in collections works as follows: * For two collections to compare equal, they must be of the same type, have the same length, and each pair of corresponding elements must compare equal (for example, "[1,2] == (1,2)" is false because the type is not the same). * Collections are ordered the same as their first unequal elements (for example, "cmp([1,2,x], [1,2,y])" returns the same as "cmp(x,y)"). If a corresponding element does not exist, the shorter collection is ordered first (for example, "[1,2] < [1,2,3]" is true). * Mappings (instances of "dict") compare equal if and only if they have equal (key, value) pairs. Equality comparison of the keys and values enforces reflexivity. Outcomes other than equality are resolved consistently, but are not otherwise defined. [5] * Most other objects of built-in types compare unequal unless they are the same object; the choice whether one object is considered smaller or larger than another one is made arbitrarily but consistently within one execution of a program. User-defined classes that customize their comparison behavior should follow some consistency rules, if possible: * Equality comparison should be reflexive. In other words, identical objects should compare equal: "x is y" implies "x == y" * Comparison should be symmetric. In other words, the following expressions should have the same result: "x == y" and "y == x" "x != y" and "y != x" "x < y" and "y > x" "x <= y" and "y >= x" * Comparison should be transitive. The following (non-exhaustive) examples illustrate that: "x > y and y > z" implies "x > z" "x < y and y <= z" implies "x < z" * Inverse comparison should result in the boolean negation. In other words, the following expressions should have the same result: "x == y" and "not x != y" "x < y" and "not x >= y" (for total ordering) "x > y" and "not x <= y" (for total ordering) The last two expressions apply to totally ordered collections (e.g. to sequences, but not to sets or mappings). See also the "total_ordering()" decorator. * The "hash()" result should be consistent with equality. Objects that are equal should either have the same hash value, or be marked as unhashable. Python does not enforce these consistency rules. Membership test operations ========================== The operators "in" and "not in" test for membership. "x in s" evaluates to "True" if x is a member of s, and "False" otherwise. "x not in s" returns the negation of "x in s". All built-in sequences and set types support this as well as dictionary, for which "in" tests whether the dictionary has a given key. For container types such as list, tuple, set, frozenset, dict, or collections.deque, the expression "x in y" is equivalent to "any(x is e or x == e for e in y)". For the string and bytes types, "x in y" is "True" if and only if x is a substring of y. An equivalent test is "y.find(x) != -1". Empty strings are always considered to be a substring of any other string, so """ in "abc"" will return "True". For user-defined classes which define the "__contains__()" method, "x in y" returns "True" if "y.__contains__(x)" returns a true value, and "False" otherwise. For user-defined classes which do not define "__contains__()" but do define "__iter__()", "x in y" is "True" if some value "z" with "x == z" is produced while iterating over "y". If an exception is raised during the iteration, it is as if "in" raised that exception. Lastly, the old-style iteration protocol is tried: if a class defines "__getitem__()", "x in y" is "True" if and only if there is a non- negative integer index i such that "x == y[i]", and all lower integer indices do not raise "IndexError" exception. (If any other exception is raised, it is as if "in" raised that exception). The operator "not in" is defined to have the inverse true value of "in". Identity comparisons ==================== The operators "is" and "is not" test for object identity: "x is y" is true if and only if x and y are the same object. "x is not y" yields the inverse truth value. [6] t��comparisonsspP�� Compound statements ******************* Compound statements contain (groups of) other statements; they affect or control the execution of those other statements in some way. In general, compound statements span multiple lines, although in simple incarnations a whole compound statement may be contained in one line. The "if", "while" and "for" statements implement traditional control flow constructs. "try" specifies exception handlers and/or cleanup code for a group of statements. Function and class definitions are also syntactically compound statements. Compound statements consist of one or more 'clauses.' A clause consists of a header and a 'suite.' The clause headers of a particular compound statement are all at the same indentation level. Each clause header begins with a uniquely identifying keyword and ends with a colon. A suite is a group of statements controlled by a clause. A suite can be one or more semicolon-separated simple statements on the same line as the header, following the header's colon, or it can be one or more indented statements on subsequent lines. Only the latter form of suite can contain nested compound statements; the following is illegal, mostly because it wouldn't be clear to which "if" clause a following "else" clause would belong: if test1: if test2: print x Also note that the semicolon binds tighter than the colon in this context, so that in the following example, either all or none of the "print" statements are executed: if x < y < z: print x; print y; print z Summarizing: compound_stmt ::= if_stmt \| while_stmt \| for_stmt \| try_stmt \| with_stmt \| funcdef \| classdef \| decorated 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, and then the suite is executed. When the items are exhausted (which is immediately when the sequence is empty), the suite in the "else" clause, if present, is executed, and the loop terminates. A "break" statement executed in the first suite terminates the loop without executing the "else" clause's suite. A "continue" statement executed in the first suite skips the rest of the suite and continues with the next item, or with the "else" clause if there was no next item. The suite may assign to the variable(s) in the target list; this does not affect the next item assigned to it. The target list is not deleted when the loop is finished, but if the sequence is empty, it will not have been assigned to at all by the loop. Hint: the built-in function "range()" returns a sequence of integers suitable to emulate the effect of Pascal's "for i := a to b do"; e.g., "range(3)" returns the list "[0, 1, 2]". Note: There is a subtlety when the sequence is being modified by the loop (this can only occur for mutable sequences, i.e. lists). An internal counter is used to keep track of which item is used next, and this is incremented on each iteration. When this counter has reached the length of the sequence the loop terminates. This means that if the suite deletes the current (or a previous) item from the sequence, the next item will be skipped (since it gets the index of the current item which has already been treated). Likewise, if the suite inserts an item in the sequence before the current item, the current item will be treated again the next time through the loop. This can lead to nasty bugs that can be avoided by making a temporary copy using a slice of the whole sequence, e.g., for x in a[:]: if x < 0: a.remove(x) The "try" statement =================== The "try" statement specifies exception handlers and/or cleanup code for a group of statements: try_stmt ::= try1_stmt \| try2_stmt try1_stmt ::= "try" ":" suite ("except" [expression [("as" \| ",") identifier]] ":" suite)+ ["else" ":" suite] ["finally" ":" suite] try2_stmt ::= "try" ":" suite "finally" ":" suite Changed in version 2.5: In previous versions of Python, "try"..."except"..."finally" did not work. "try"..."except" had to be nested in "try"..."finally". The "except" clause(s) specify one or more exception handlers. When no exception occurs in the "try" clause, no exception handler is executed. When an exception occurs in the "try" suite, a search for an exception handler is started. This search inspects the except clauses in turn until one is found that matches the exception. An expression- less except clause, if present, must be last; it matches any exception. For an except clause with an expression, that expression is evaluated, and the clause matches the exception if the resulting object is "compatible" with the exception. An object is compatible with an exception if it is the class or a base class of the exception object, or a tuple containing an item compatible with the exception. If no except clause matches the exception, the search for an exception handler continues in the surrounding code and on the invocation stack. [1] If the evaluation of an expression in the header of an except clause raises an exception, the original search for a handler is canceled and a search starts for the new exception in the surrounding code and on the call stack (it is treated as if the entire "try" statement raised the exception). When a matching except clause is found, the exception is assigned to the target specified in that except clause, if present, and the except clause's suite is executed. All except clauses must have an executable block. When the end of this block is reached, execution continues normally after the entire try statement. (This means that if two nested handlers exist for the same exception, and the exception occurs in the try clause of the inner handler, the outer handler will not handle the exception.) Before an except clause's suite is executed, details about the exception are assigned to three variables in the "sys" module: "sys.exc_type" receives the object identifying the exception; "sys.exc_value" receives the exception's parameter; "sys.exc_traceback" receives a traceback object (see section The standard type hierarchy) identifying the point in the program where the exception occurred. These details are also available through the "sys.exc_info()" function, which returns a tuple "(exc_type, exc_value, exc_traceback)". Use of the corresponding variables is deprecated in favor of this function, since their use is unsafe in a threaded program. As of Python 1.5, the variables are restored to their previous values (before the call) when returning from a function that handled an exception. The optional "else" clause is executed if and when control flows off the end of the "try" clause. [2] Exceptions in the "else" clause are not handled by the preceding "except" clauses. If "finally" is present, it specifies a 'cleanup' handler. The "try" clause is executed, including any "except" and "else" clauses. If an exception occurs in any of the clauses and is not handled, the exception is temporarily saved. The "finally" clause is executed. If there is a saved exception, it is re-raised at the end of the "finally" clause. If the "finally" clause raises another exception or executes a "return" or "break" statement, the saved exception is discarded: >>> def f(): ... try: ... 1/0 ... finally: ... return 42 ... >>> f() 42 The exception information is not available to the program during execution of the "finally" clause. When a "return", "break" or "continue" statement is executed in the "try" suite of a "try"..."finally" statement, the "finally" clause is also executed 'on the way out.' A "continue" statement is illegal in the "finally" clause. (The reason is a problem with the current implementation --- this restriction may be lifted in the future). The return value of a function is determined by the last "return" statement executed. Since the "finally" clause always executes, a "return" statement executed in the "finally" clause will always be the last one executed: >>> def foo(): ... try: ... return 'try' ... finally: ... return 'finally' ... >>> foo() 'finally' Additional information on exceptions can be found in section Exceptions, and information on using the "raise" statement to generate exceptions may be found in section The raise statement. The "with" statement ==================== New in version 2.5. The "with" statement is used to wrap the execution of a block with methods defined by a context manager (see section With Statement Context Managers). This allows common "try"..."except"..."finally" usage patterns to be encapsulated for convenient reuse. with_stmt ::= "with" with_item ("," with_item)* ":" suite with_item ::= expression ["as" target] The execution of the "with" statement with one "item" proceeds as follows: 1. The context expression (the expression given in the "with_item") is evaluated to obtain a context manager. 2. The context manager's "__exit__()" is loaded for later use. 3. The context manager's "__enter__()" method is invoked. 4. If a target was included in the "with" statement, the return value from "__enter__()" is assigned to it. Note: The "with" statement guarantees that if the "__enter__()" method returns without an error, then "__exit__()" will always be called. Thus, if an error occurs during the assignment to the target list, it will be treated the same as an error occurring within the suite would be. See step 6 below. 5. The suite is executed. 6. The context manager's "__exit__()" method is invoked. If an exception caused the suite to be exited, its type, value, and traceback are passed as arguments to "__exit__()". Otherwise, three "None" arguments are supplied. If the suite was exited due to an exception, and the return value from the "__exit__()" method was false, the exception is reraised. If the return value was true, the exception is suppressed, and execution continues with the statement following the "with" statement. If the suite was exited for any reason other than an exception, the return value from "__exit__()" is ignored, and execution proceeds at the normal location for the kind of exit that was taken. With more than one item, the context managers are processed as if multiple "with" statements were nested: with A() as a, B() as b: suite is equivalent to with A() as a: with B() as b: suite Note: In Python 2.5, the "with" statement is only allowed when the "with_statement" feature has been enabled. It is always enabled in Python 2.6. Changed in version 2.7: Support for multiple context expressions. See also: PEP 343 - The "with" statement The specification, background, and examples for the Python "with" statement. Function definitions ==================== A function definition defines a user-defined function object (see section The standard type hierarchy): decorated ::= decorators (classdef \| funcdef) decorators ::= decorator+ decorator ::= "@" dotted_name ["(" [argument_list [","]] ")"] NEWLINE funcdef ::= "def" funcname "(" [parameter_list] ")" ":" suite dotted_name ::= identifier ("." identifier)* parameter_list ::= (defparameter ",")* ( "" identifier ["," "" identifier] \| "" identifier \| defparameter [","] ) defparameter ::= parameter ["=" expression] sublist ::= parameter ("," parameter) [","] parameter ::= identifier \| "(" sublist ")" funcname ::= identifier A function definition is an executable statement. Its execution binds the function name in the current local namespace to a function object (a wrapper around the executable code for the function). This function object contains a reference to the current global namespace as the global namespace to be used when the function is called. The function definition does not execute the function body; this gets executed only when the function is called. [3] A function definition may be wrapped by one or more decorator expressions. Decorator expressions are evaluated when the function is defined, in the scope that contains the function definition. The result must be a callable, which is invoked with the function object as the only argument. The returned value is bound to the function name instead of the function object. Multiple decorators are applied in nested fashion. For example, the following code: @f1(arg) @f2 def func(): pass is equivalent to: def func(): pass func = f1(arg)(f2(func)) When one or more top-level parameters have the form parameter "=" expression, the function is said to have "default parameter values." For a parameter with a default value, the corresponding argument may be omitted from a call, in which case the parameter's default value is substituted. If a parameter has a default value, all following parameters must also have a default value --- this is a syntactic restriction that is not expressed by the grammar. Default parameter values are evaluated when the function definition is executed. This means that the expression is evaluated once, when the function is defined, and that the same "pre-computed" value is used for each call. This is especially important to understand when a default parameter is a mutable object, such as a list or a dictionary: if the function modifies the object (e.g. by appending an item to a list), the default value is in effect modified. This is generally not what was intended. A way around this is to use "None" as the default, and explicitly test for it in the body of the function, e.g.: def whats_on_the_telly(penguin=None): if penguin is None: penguin = [] penguin.append("property of the zoo") return penguin Function call semantics are described in more detail in section Calls. A function call always assigns values to all parameters mentioned in the parameter list, either from position arguments, from keyword arguments, or from default values. If the form ""identifier"" is present, it is initialized to a tuple receiving any excess positional parameters, defaulting to the empty tuple. If the form ""identifier"" is present, it is initialized to a new dictionary receiving any excess keyword arguments, defaulting to a new empty dictionary. It is also possible to create anonymous functions (functions not bound to a name), for immediate use in expressions. This uses lambda expressions, described in section Lambdas. Note that the lambda expression is merely a shorthand for a simplified function definition; a function defined in a ""def"" statement can be passed around or assigned to another name just like a function defined by a lambda expression. The ""def"" form is actually more powerful since it allows the execution of multiple statements. Programmer's note:* Functions are first-class objects. A ""def"" form executed inside a function definition defines a local function that can be returned or passed around. Free variables used in the nested function can access the local variables of the function containing the def. See section Naming and binding for details. Class definitions ================= A class definition defines a class object (see section The standard type hierarchy): classdef ::= "class" classname [inheritance] ":" suite inheritance ::= "(" [expression_list] ")" classname ::= identifier A class definition is an executable statement. It first evaluates the inheritance list, if present. Each item in the inheritance list should evaluate to a class object or class type which allows subclassing. The class's suite is then executed in a new execution frame (see section Naming and binding), using a newly created local namespace and the original global namespace. (Usually, the suite contains only function definitions.) When the class's suite finishes execution, its execution frame is discarded but its local namespace is saved. [4] A class object is then created using the inheritance list for the base classes and the saved local namespace for the attribute dictionary. The class name is bound to this class object in the original local namespace. Programmer's note: Variables defined in the class definition are class variables; they are shared by all instances. To create instance variables, they can be set in a method with "self.name = value". Both class and instance variables are accessible through the notation ""self.name"", and an instance variable hides a class variable with the same name when accessed in this way. Class variables can be used as defaults for instance variables, but using mutable values there can lead to unexpected results. For new-style classes, descriptors can be used to create instance variables with different implementation details. Class definitions, like function definitions, may be wrapped by one or more decorator expressions. The evaluation rules for the decorator expressions are the same as for functions. The result must be a class object, which is then bound to the class name. -[ Footnotes ]- [1] The exception is propagated to the invocation stack unless there is a "finally" clause which happens to raise another exception. That new exception causes the old one to be lost. [2] Currently, control "flows off the end" except in the case of an exception or the execution of a "return", "continue", or "break" statement. [3] A string literal appearing as the first statement in the function body is transformed into the function's "__doc__" attribute and therefore the function's docstring. [4] A string literal appearing as the first statement in the class body is transformed into the namespace's "__doc__" item and therefore the class's docstring. t��compounds�� With Statement Context Managers ******************************* New in version 2.5. A context manager is an object that defines the runtime context to be established when executing a "with" statement. The context manager handles the entry into, and the exit from, the desired runtime context for the execution of the block of code. Context managers are normally invoked using the "with" statement (described in section The with statement), but can also be used by directly invoking their methods. Typical uses of context managers include saving and restoring various kinds of global state, locking and unlocking resources, closing opened files, etc. For more information on context managers, see Context Manager Types. object.__enter__(self) Enter the runtime context related to this object. The "with" statement will bind this method's return value to the target(s) specified in the "as" clause of the statement, if any. object.__exit__(self, exc_type, exc_value, traceback) Exit the runtime context related to this object. The parameters describe the exception that caused the context to be exited. If the context was exited without an exception, all three arguments will be "None". If an exception is supplied, and the method wishes to suppress the exception (i.e., prevent it from being propagated), it should return a true value. Otherwise, the exception will be processed normally upon exit from this method. Note that "__exit__()" methods should not reraise the passed-in exception; this is the caller's responsibility. See also: PEP 343 - The "with" statement The specification, background, and examples for the Python "with" statement. s��context-managerss�� The "continue" statement ********************** continue_stmt ::= "continue" "continue" may only occur syntactically nested in a "for" or "while" loop, but not nested in a function or class definition or "finally" clause within that loop. It continues with the next cycle of the nearest enclosing loop. When "continue" passes control out of a "try" statement with a "finally" clause, that "finally" clause is executed before really starting the next loop cycle. t��continuesB�� Arithmetic conversions ******************** When a description of an arithmetic operator below uses the phrase "the numeric arguments are converted to a common type," the arguments are coerced using the coercion rules listed at Coercion rules. If both arguments are standard numeric types, the following coercions are applied: * If either argument is a complex number, the other is converted to complex; * otherwise, if either argument is a floating point number, the other is converted to floating point; * otherwise, if either argument is a long integer, the other is converted to long integer; * otherwise, both must be plain integers and no conversion is necessary. Some additional rules apply for certain operators (e.g., a string left argument to the '%' operator). Extensions can define their own coercions. t��conversionss�/�� Basic customization ******************* object.__new__(cls[, ...]) Called to create a new instance of class cls. "__new__()" is a static method (special-cased so you need not declare it as such) that takes the class of which an instance was requested as its first argument. The remaining arguments are those passed to the object constructor expression (the call to the class). The return value of "__new__()" should be the new object instance (usually an instance of cls). Typical implementations create a new instance of the class by invoking the superclass's "__new__()" method using "super(currentclass, cls).__new__(cls[, ...])" with appropriate arguments and then modifying the newly-created instance as necessary before returning it. If "__new__()" returns an instance of cls, then the new instance's "__init__()" method will be invoked like "__init__(self[, ...])", where self is the new instance and the remaining arguments are the same as were passed to "__new__()". If "__new__()" does not return an instance of cls, then the new instance's "__init__()" method will not be invoked. "__new__()" is intended mainly to allow subclasses of immutable types (like int, str, or tuple) to customize instance creation. It is also commonly overridden in custom metaclasses in order to customize class creation. object.__init__(self[, ...]) Called after the instance has been created (by "__new__()"), but before it is returned to the caller. The arguments are those passed to the class constructor expression. If a base class has an "__init__()" method, the derived class's "__init__()" method, if any, must explicitly call it to ensure proper initialization of the base class part of the instance; for example: "BaseClass.__init__(self, [args...])". Because "__new__()" and "__init__()" work together in constructing objects ("__new__()" to create it, and "__init__()" to customise it), no non-"None" value may be returned by "__init__()"; doing so will cause a "TypeError" to be raised at runtime. object.__del__(self) Called when the instance is about to be destroyed. This is also called a destructor. If a base class has a "__del__()" method, the derived class's "__del__()" method, if any, must explicitly call it to ensure proper deletion of the base class part of the instance. Note that it is possible (though not recommended!) for the "__del__()" method to postpone destruction of the instance by creating a new reference to it. It may then be called at a later time when this new reference is deleted. It is not guaranteed that "__del__()" methods are called for objects that still exist when the interpreter exits. Note: "del x" doesn't directly call "x.__del__()" --- the former decrements the reference count for "x" by one, and the latter is only called when "x"'s reference count reaches zero. Some common situations that may prevent the reference count of an object from going to zero include: circular references between objects (e.g., a doubly-linked list or a tree data structure with parent and child pointers); a reference to the object on the stack frame of a function that caught an exception (the traceback stored in "sys.exc_traceback" keeps the stack frame alive); or a reference to the object on the stack frame that raised an unhandled exception in interactive mode (the traceback stored in "sys.last_traceback" keeps the stack frame alive). The first situation can only be remedied by explicitly breaking the cycles; the latter two situations can be resolved by storing "None" in "sys.exc_traceback" or "sys.last_traceback". Circular references which are garbage are detected when the option cycle detector is enabled (it's on by default), but can only be cleaned up if there are no Python-level "__del__()" methods involved. Refer to the documentation for the "gc" module for more information about how "__del__()" methods are handled by the cycle detector, particularly the description of the "garbage" value. Warning: Due to the precarious circumstances under which "__del__()" methods are invoked, exceptions that occur during their execution are ignored, and a warning is printed to "sys.stderr" instead. Also, when "__del__()" is invoked in response to a module being deleted (e.g., when execution of the program is done), other globals referenced by the "__del__()" method may already have been deleted or in the process of being torn down (e.g. the import machinery shutting down). For this reason, "__del__()" methods should do the absolute minimum needed to maintain external invariants. Starting with version 1.5, Python guarantees that globals whose name begins with a single underscore are deleted from their module before other globals are deleted; if no other references to such globals exist, this may help in assuring that imported modules are still available at the time when the "__del__()" method is called. See also the "-R" command-line option. object.__repr__(self) Called by the "repr()" built-in function and by string conversions (reverse quotes) 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 the "str()" built-in function and by the "print" statement to compute the "informal" string representation of an object. This differs from "__repr__()" in that it does not have to be a valid Python expression: a more convenient or concise representation may be used instead. 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) New in version 2.1. These are the so-called "rich comparison" methods, and are called for comparison operators in preference to "__cmp__()" below. The correspondence between operator symbols and method names is as follows: "x<y" calls "x.__lt__(y)", "x<=y" calls "x.__le__(y)", "x==y" calls "x.__eq__(y)", "x!=y" and "x<>y" call "x.__ne__(y)", "x>y" 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.__cmp__(self, other) Called by comparison operations if rich comparison (see above) is not defined. Should return a negative integer if "self < other", zero if "self == other", a positive integer if "self > other". If no "__cmp__()", "__eq__()" or "__ne__()" operation is defined, class instances are compared by object identity ("address"). See also the description of "__hash__()" for some important notes on creating hashable objects which support custom comparison operations and are usable as dictionary keys. (Note: the restriction that exceptions are not propagated by "__cmp__()" has been removed since Python 1.5.) object.__rcmp__(self, other) Changed in version 2.1: No longer supported. 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 mix together the hash values of the components of the object that also play a part in comparison of objects by packing them into a tuple and hashing the tuple. Example: def __hash__(self): return hash((self.name, self.nick, self.color)) If a class does not define a "__cmp__()" or "__eq__()" method it should not define a "__hash__()" operation either; if it defines "__cmp__()" or "__eq__()" but not "__hash__()", its instances will not be usable in hashed collections. If a class defines mutable objects and implements a "__cmp__()" or "__eq__()" method, it should not implement "__hash__()", since hashable collection implementations require that an object's hash value is immutable (if the object's hash value changes, it will be in the wrong hash bucket). User-defined classes have "__cmp__()" and "__hash__()" methods by default; with them, all objects compare unequal (except with themselves) and "x.__hash__()" returns a result derived from "id(x)". Classes which inherit a "__hash__()" method from a parent class but change the meaning of "__cmp__()" or "__eq__()" such that the hash value returned is no longer appropriate (e.g. by switching to a value-based concept of equality instead of the default identity based equality) can explicitly flag themselves as being unhashable by setting "__hash__ = None" in the class definition. Doing so means that not only will instances of the class raise an appropriate "TypeError" when a program attempts to retrieve their hash value, but they will also be correctly identified as unhashable when checking "isinstance(obj, collections.Hashable)" (unlike classes which define their own "__hash__()" to explicitly raise "TypeError"). Changed in version 2.5: "__hash__()" may now also return a long integer object; the 32-bit integer is then derived from the hash of that object. Changed in version 2.6: "__hash__" may now be set to "None" to explicitly flag instances of a class as unhashable. object.__nonzero__(self) Called to implement truth value testing and the built-in operation "bool()"; should return "False" or "True", or their integer equivalents "0" or "1". 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 "__nonzero__()", all its instances are considered true. object.__unicode__(self) Called to implement "unicode()" built-in; should return a Unicode object. When this method is not defined, string conversion is attempted, and the result of string conversion is converted to Unicode using the system default encoding. t ��customizations�� "pdb" --- The Python Debugger *************************** Source code:** Lib/pdb.py ====================================================================== 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()') > <string>(0)?() (Pdb) continue > <string>(1)?() (Pdb) continue NameError: 'spam' > <string>(1)?() (Pdb) "pdb.py" can also be invoked as a script to debug other scripts. For example: python -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 2.4: Restarting post-mortem behavior added. 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 "c" command. The typical usage to inspect a crashed program is: >>> import pdb >>> import mymodule >>> mymodule.test() Traceback (most recent call last): File "<stdin>", line 1, in <module> 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[, locals]]) Execute the statement (given as a string) 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 "exec" statement or the "eval()" built-in function.) pdb.runeval(expression[, globals[, locals]]) Evaluate the expression (given as a string) under debugger control. When "runeval()" returns, it returns the value of the expression. Otherwise this function is similar to "run()". pdb.runcall(function[, argument, ...]) 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]) 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 pdb.Pdb(completekey='tab', stdin=None, stdout=None, skip=None) "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] Example call to enable tracing with skip: import pdb; pdb.Pdb(skip=['django.']).set_trace() New in version 2.7: The skip* argument. run(statement[, globals[, locals]]) runeval(expression[, globals[, locals]]) runcall(function[, argument, ...]) set_trace() See the documentation for the functions explained above. t��debuggers�� 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. It is illegal to delete a name from the local namespace if it occurs as a free variable in a nested block. 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). t��dels�� 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. t��dicts+�� Interaction with dynamic features ********************************* There are several cases where Python statements are illegal when used in conjunction with nested scopes that contain free variables. If a variable is referenced in an enclosing scope, it is illegal to delete the name. An error will be reported at compile time. If the wild card form of import --- "import " --- is used in a function and the function contains or is a nested block with free variables, the compiler will raise a "SyntaxError". If "exec" is used in a function and the function contains or is a nested block with free variables, the compiler will raise a "SyntaxError" unless the exec explicitly specifies the local namespace for the "exec". (In other words, "exec obj" would be illegal, but "exec obj in ns" would be legal.) The "eval()", "execfile()", and "input()" functions and the "exec" statement do not have access to the full environment for resolving names. Names may be resolved in the local and global namespaces of the caller. Free variables are not resolved in the nearest enclosing namespace, but in the global namespace. [1] The "exec" statement and the "eval()" and "execfile()" functions have optional arguments to override the global and local namespace. If only one namespace is specified, it is used for both. s��dynamic-featuressE�� 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. t��elsesh �� 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. Exceptions can also be identified by strings, in which case the "except" clause is selected by object identity. An arbitrary value can be raised along with the identifying string which can be passed to the handler. Note: Messages to exceptions 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. t ��exceptionss� �� The "exec" statement ******************** exec_stmt ::= "exec" or_expr ["in" expression ["," expression]] This statement supports dynamic execution of Python code. The first expression should evaluate to either a Unicode string, a Latin-1 encoded string, an open file object, a code object, or a tuple. If it is a string, the string is parsed as a suite of Python statements which is then executed (unless a syntax error occurs). [1] If it is an open file, the file is parsed until EOF and executed. If it is a code object, it is simply executed. For the interpretation of a tuple, see below. In all cases, the code that's executed is expected to be valid as file input (see section File input). Be aware that the "return" and "yield" statements may not be used outside of function definitions even within the context of code passed to the "exec" statement. In all cases, if the optional parts are omitted, the code is executed in the current scope. If only the first expression after "in" is specified, it should be a dictionary, which will be used for both the global and the local variables. If two expressions are given, they are used for the global and local variables, respectively. If provided, locals can be any mapping object. Remember that at module level, globals and locals are the same dictionary. If two separate objects are given as globals and locals, the code will be executed as if it were embedded in a class definition. The first expression may also be a tuple of length 2 or 3. In this case, the optional parts must be omitted. The form "exec(expr, globals)" is equivalent to "exec expr in globals", while the form "exec(expr, globals, locals)" is equivalent to "exec expr in globals, locals". The tuple form of "exec" provides compatibility with Python 3, where "exec" is a function rather than a statement. Changed in version 2.4: Formerly, locals was required to be a dictionary. As a side effect, an implementation may insert additional keys into the dictionaries given besides those corresponding to variable names set by the executed code. For example, the current implementation may add a reference to the dictionary of the built-in module "__builtin__" under the key "__builtins__" (!). Programmer's hints: dynamic evaluation of expressions is supported by the built-in function "eval()". The built-in functions "globals()" and "locals()" return the current global and local dictionary, respectively, which may be useful to pass around for use by "exec". -[ Footnotes ]- [1] Note that the parser only accepts the Unix-style end of line convention. If you are reading the code from a file, make sure to use universal newlines mode to convert Windows or Mac-style newlines. t��execs&�� 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 file read by the built-in function "execfile()" is a code block. The string argument passed to the built-in function "eval()" and to the "exec" statement is a code block. The expression read and evaluated by the built-in function "input()" is a code block. A code block is executed in an execution frame. A frame contains some administrative information (used for debugging) and determines where and how execution continues after the code block's execution has completed. A scope defines the visibility of a name within a block. If a local variable is defined in a block, its scope includes that block. If the definition occurs in a function block, the scope extends to any blocks contained within the defining one, unless a contained block introduces a different binding for the name. The scope of names defined in a class block is limited to the class block; it does not extend to the code blocks of methods -- this includes generator expressions since they are implemented using a function scope. This means that the following will fail: class A: a = 42 b = list(a + i for i in range(10)) When a name is used in a code block, it is resolved using the nearest enclosing scope. The set of all such scopes visible to a code block is called the block's environment. If a name is bound in a block, it is a local variable of that block. If a name is bound at the module level, it is a global variable. (The variables of the module code block are local and global.) If a variable is used in a code block but not defined there, it is a free variable. When a name is not found at all, a "NameError" exception is raised. If the name refers to a local variable that has not been bound, a "UnboundLocalError" exception is raised. "UnboundLocalError" is a subclass of "NameError". The following constructs bind names: formal parameters to functions, "import" statements, class and function definitions (these bind the class or function name in the defining block), and targets that are identifiers if occurring in an assignment, "for" loop header, in the second position of an "except" clause header or after "as" in a "with" statement. The "import" statement of the form "from ... import " binds all names defined in the imported module, except those beginning with an underscore. This form may only be used at the module level. A target occurring in a "del" statement is also considered bound for this purpose (though the actual semantics are to unbind the name). It is illegal to unbind a name that is referenced by an enclosing scope; the compiler will report a "SyntaxError". Each assignment or import statement occurs within a block defined by a class or function definition or at the module level (the top-level code block). If a name binding operation occurs anywhere within a code block, all uses of the name within the block are treated as references to the current block. This can lead to errors when a name is used within a block before it is bound. This rule is subtle. Python lacks declarations and allows name binding operations to occur anywhere within a code block. The local variables of a code block can be determined by scanning the entire text of the block for name binding operations. If the global statement occurs within a block, all uses of the name specified in the statement refer to the binding of that name in the top-level namespace. Names are resolved in the top-level namespace by searching the global namespace, i.e. the namespace of the module containing the code block, and the builtins namespace, the namespace of the module "__builtin__". The global namespace is searched first. If the name is not found there, the builtins namespace is searched. The global statement must precede all uses of the name. The builtins namespace associated with the execution of a code block is actually found by looking up the name "__builtins__" in its global namespace; this should be a dictionary or a module (in the latter case the module's dictionary is used). By default, when in the "__main__" module, "__builtins__" is the built-in module "__builtin__" (note: no 's'); when in any other module, "__builtins__" is an alias for the dictionary of the "__builtin__" module itself. "__builtins__" can be set to a user-created dictionary to create a weak form of restricted execution. CPython implementation detail:* Users should not touch "__builtins__"; it is strictly an implementation detail. Users wanting to override values in the builtins namespace should "import" the "__builtin__" (no 's') module and modify its attributes appropriately. The namespace for a module is automatically created the first time a module is imported. The main module for a script is always called "__main__". The "global" statement has the same scope as a name binding operation in the same block. If the nearest enclosing scope for a free variable contains a global statement, the free variable is treated as a global. A class definition is an executable statement that may use and define names. These references follow the normal rules for name resolution. The namespace of the class definition becomes the attribute dictionary of the class. Names defined at the class scope are not visible in methods. Interaction with dynamic features --------------------------------- There are several cases where Python statements are illegal when used in conjunction with nested scopes that contain free variables. If a variable is referenced in an enclosing scope, it is illegal to delete the name. An error will be reported at compile time. If the wild card form of import --- "import " --- is used in a function and the function contains or is a nested block with free variables, the compiler will raise a "SyntaxError". If "exec" is used in a function and the function contains or is a nested block with free variables, the compiler will raise a "SyntaxError" unless the exec explicitly specifies the local namespace for the "exec". (In other words, "exec obj" would be illegal, but "exec obj in ns" would be legal.) The "eval()", "execfile()", and "input()" functions and the "exec" statement do not have access to the full environment for resolving names. Names may be resolved in the local and global namespaces of the caller. Free variables are not resolved in the nearest enclosing namespace, but in the global namespace. [1] The "exec" statement and the "eval()" and "execfile()" functions have optional arguments to override the global and local namespace. If only one namespace is specified, it is used for both. 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. Exceptions can also be identified by strings, in which case the "except" clause is selected by object identity. An arbitrary value can be raised along with the identifying string which can be passed to the handler. Note: Messages to exceptions 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. t ��execmodelsK�� 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: "()".) t ��exprlistss�� 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 of floating point numbers can look like octal integers, but are 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". t��floatingsZ �� The "for" statement *************** The "for" statement is used to iterate over the elements of a sequence (such as a string, tuple or list) or other iterable object: for_stmt ::= "for" target_list "in" expression_list ":" suite ["else" ":" suite] The expression list is evaluated once; it should yield an iterable object. An iterator is created for the result of the "expression_list". The suite is then executed once for each item provided by the iterator, in the order of ascending indices. Each item in turn is assigned to the target list using the standard rules for assignments, and then the suite is executed. When the items are exhausted (which is immediately when the sequence is empty), the suite in the "else" clause, if present, is executed, and the loop terminates. A "break" statement executed in the first suite terminates the loop without executing the "else" clause's suite. A "continue" statement executed in the first suite skips the rest of the suite and continues with the next item, or with the "else" clause if there was no next item. The suite may assign to the variable(s) in the target list; this does not affect the next item assigned to it. The target list is not deleted when the loop is finished, but if the sequence is empty, it will not have been assigned to at all by the loop. Hint: the built-in function "range()" returns a sequence of integers suitable to emulate the effect of Pascal's "for i := a to b do"; e.g., "range(3)" returns the list "[0, 1, 2]". Note: There is a subtlety when the sequence is being modified by the loop (this can only occur for mutable sequences, i.e. lists). An internal counter is used to keep track of which item is used next, and this is incremented on each iteration. When this counter has reached the length of the sequence the loop terminates. This means that if the suite deletes the current (or a previous) item from the sequence, the next item will be skipped (since it gets the index of the current item which has already been treated). Likewise, if the suite inserts an item in the sequence before the current item, the current item will be treated again the next time through the loop. This can lead to nasty bugs that can be avoided by making a temporary copy using a slice of the whole sequence, e.g., for x in a[:]: if x < 0: a.remove(x) t��fors�Q�� 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 ::= <any source character except "]"> + conversion ::= "r" \| "s" format_spec ::= <described in the next section> 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 2.7: 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. Two conversion flags are currently supported: "'!s'" which calls "str()" on the value, and "'!r'" which calls "repr()". 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 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 may contain a field name, conversion flag and format specification, but deeper nesting is 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 ::= <any character> 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. It is not possible to use a literal curly brace (""{"" or ""}"") as the fill character when using the "str.format()" method. However, it is possible to insert a curly brace with a nested replacement field. This limitation 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 \| \| \| space (this is the default for most objects). \| +-----------+------------------------------------------------------------+ \| "'>'" \| Forces the field to be right-aligned within the available \| \| \| space (this is the default for numbers). \| +-----------+------------------------------------------------------------+ \| "'='" \| Forces the padding to be placed after the sign (if any) \| \| \| but before the digits. This is used for printing fields \| \| \| in the form '+000000120'. This alignment option is only \| \| \| valid for numeric types. It becomes the default when '0' \| \| \| immediately precedes the field width. \| +-----------+------------------------------------------------------------+ \| "'^'" \| Forces the field to be centered within the available \| \| \| space. \| +-----------+------------------------------------------------------------+ 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 \| \| \| well as negative numbers. \| +-----------+------------------------------------------------------------+ \| "'-'" \| indicates that a sign should be used only for negative \| \| \| numbers (this is the default behavior). \| +-----------+------------------------------------------------------------+ \| space \| indicates that a leading space should be used on positive \| \| \| numbers, and a minus sign on negative numbers. \| +-----------+------------------------------------------------------------+ The "'#'" option is only valid for integers, and only for binary, octal, or hexadecimal output. If present, it specifies that the output will be prefixed by "'0b'", "'0o'", or "'0x'", respectively. 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 2.7: 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. When no explicit alignment is given, 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 \| \| \| may be omitted. \| +-----------+------------------------------------------------------------+ \| 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 \| \| \| unicode character before printing. \| +-----------+------------------------------------------------------------+ \| "'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- \| \| \| case letters for the digits above 9. \| +-----------+------------------------------------------------------------+ \| "'X'" \| Hex format. Outputs the number in base 16, using upper- \| \| \| case letters for the digits above 9. \| +-----------+------------------------------------------------------------+ \| "'n'" \| Number. This is the same as "'d'", except that it uses the \| \| \| current locale setting to insert the appropriate number \| \| \| separator characters. \| +-----------+------------------------------------------------------------+ \| 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 \| \| \| notation using the letter 'e' to indicate the exponent. \| \| \| The default precision is "6". \| +-----------+------------------------------------------------------------+ \| "'E'" \| Exponent notation. Same as "'e'" except it uses an upper \| \| \| case 'E' as the separator character. \| +-----------+------------------------------------------------------------+ \| "'f'" \| Fixed point. Displays the number as a fixed-point number. \| \| \| The default precision is "6". \| +-----------+------------------------------------------------------------+ \| "'F'" \| Fixed point. Same as "'f'". \| +-----------+------------------------------------------------------------+ \| "'g'" \| General format. For a given precision "p >= 1", this \| \| \| rounds the number to "p" significant digits and then \| \| \| formats the result in either fixed-point format or in \| \| \| scientific notation, depending on its magnitude. The \| \| \| precise rules are as follows: suppose that the result \| \| \| formatted with presentation type "'e'" and precision "p-1" \| \| \| would have exponent "exp". Then if "-4 <= exp < p", the \| \| \| number is formatted with presentation type "'f'" and \| \| \| precision "p-1-exp". Otherwise, the number is formatted \| \| \| with presentation type "'e'" and precision "p-1". In both \| \| \| cases insignificant trailing zeros are removed from the \| \| \| significand, and the decimal point is also removed if \| \| \| there are no remaining digits following it. Positive and \| \| \| negative infinity, positive and negative zero, and nans, \| \| \| are formatted as "inf", "-inf", "0", "-0" and "nan" \| \| \| respectively, regardless of the precision. A precision of \| \| \| "0" is treated as equivalent to a precision of "1". The \| \| \| default precision is "6". \| +-----------+------------------------------------------------------------+ \| "'G'" \| General format. Same as "'g'" except switches to "'E'" if \| \| \| the number gets too large. The representations of infinity \| \| \| and NaN are uppercased, too. \| +-----------+------------------------------------------------------------+ \| "'n'" \| Number. This is the same as "'g'", except that it uses the \| \| \| current locale setting to insert the appropriate number \| \| \| separator characters. \| +-----------+------------------------------------------------------------+ \| "'%'" \| Percentage. Multiplies the number by 100 and displays in \| \| \| fixed ("'f'") format, followed by a percent sign. \| +-----------+------------------------------------------------------------+ \| None \| The same as "'g'". \| +-----------+------------------------------------------------------------+ Format examples =============== This section contains examples of the "str.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') # 2.7+ 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(object): ... 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 = 19.5 >>> total = 22 >>> 'Correct answers: {:.2%}'.format(points/total) 'Correct answers: 88.64%' 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): ... for base in 'dXob': ... print '{0:{width}{base}}'.format(num, base=base, width=width), ... 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 t ��formatstringssz�� Function definitions ******************** A function definition defines a user-defined function object (see section The standard type hierarchy): decorated ::= decorators (classdef \| funcdef) decorators ::= decorator+ decorator ::= "@" dotted_name ["(" [argument_list [","]] ")"] NEWLINE funcdef ::= "def" funcname "(" [parameter_list] ")" ":" suite dotted_name ::= identifier ("." identifier)* parameter_list ::= (defparameter ",")* ( "" identifier ["," "" identifier] \| "" identifier \| defparameter [","] ) defparameter ::= parameter ["=" expression] sublist ::= parameter ("," parameter) [","] parameter ::= identifier \| "(" sublist ")" funcname ::= identifier A function definition is an executable statement. Its execution binds the function name in the current local namespace to a function object (a wrapper around the executable code for the function). This function object contains a reference to the current global namespace as the global namespace to be used when the function is called. The function definition does not execute the function body; this gets executed only when the function is called. [3] A function definition may be wrapped by one or more decorator expressions. Decorator expressions are evaluated when the function is defined, in the scope that contains the function definition. The result must be a callable, which is invoked with the function object as the only argument. The returned value is bound to the function name instead of the function object. Multiple decorators are applied in nested fashion. For example, the following code: @f1(arg) @f2 def func(): pass is equivalent to: def func(): pass func = f1(arg)(f2(func)) When one or more top-level parameters have the form parameter "=" expression, the function is said to have "default parameter values." For a parameter with a default value, the corresponding argument may be omitted from a call, in which case the parameter's default value is substituted. If a parameter has a default value, all following parameters must also have a default value --- this is a syntactic restriction that is not expressed by the grammar. Default parameter values are evaluated when the function definition is executed. This means that the expression is evaluated once, when the function is defined, and that the same "pre-computed" value is used for each call. This is especially important to understand when a default parameter is a mutable object, such as a list or a dictionary: if the function modifies the object (e.g. by appending an item to a list), the default value is in effect modified. This is generally not what was intended. A way around this is to use "None" as the default, and explicitly test for it in the body of the function, e.g.: def whats_on_the_telly(penguin=None): if penguin is None: penguin = [] penguin.append("property of the zoo") return penguin Function call semantics are described in more detail in section Calls. A function call always assigns values to all parameters mentioned in the parameter list, either from position arguments, from keyword arguments, or from default values. If the form ""identifier"" is present, it is initialized to a tuple receiving any excess positional parameters, defaulting to the empty tuple. If the form ""identifier"" is present, it is initialized to a new dictionary receiving any excess keyword arguments, defaulting to a new empty dictionary. It is also possible to create anonymous functions (functions not bound to a name), for immediate use in expressions. This uses lambda expressions, described in section Lambdas. Note that the lambda expression is merely a shorthand for a simplified function definition; a function defined in a ""def"" statement can be passed around or assigned to another name just like a function defined by a lambda expression. The ""def"" form is actually more powerful since it allows the execution of multiple statements. Programmer's note:* Functions are first-class objects. A ""def"" form executed inside a function definition defines a local function that can be returned or passed around. Free variables used in the nested function can access the local variables of the function containing the def. See section Naming and binding for details. t��functions�� 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: "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 an "exec" statement does not affect the code block containing the "exec" statement, and code contained in an "exec" statement is unaffected by "global" statements in the code containing the "exec" statement. The same applies to the "eval()", "execfile()" and "compile()" functions. t��globals�� 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 "__builtin__" 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). s ��id-classess� �� Identifiers and keywords ************************ Identifiers (also referred to as names) are described by the following lexical definitions: identifier ::= (letter\|"_") (letter \| digit \| "_")* letter ::= lowercase \| uppercase lowercase ::= "a"..."z" uppercase ::= "A"..."Z" digit ::= "0"..."9" Identifiers are unlimited in length. Case is significant. 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: and del from not while as elif global or with assert else if pass yield break except import print class exec in raise continue finally is return def for lambda try Changed in version 2.4: "None" became a constant and is now recognized by the compiler as a name for the built-in object "None". Although it is not a keyword, you cannot assign a different object to it. Changed in version 2.5: Using "as" and "with" as identifiers triggers a warning. To use them as keywords, enable the "with_statement" future feature . Changed in version 2.6: "as" and "with" are full keywords. 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 "__builtin__" 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). t��identifierst��ifs%�� 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 t ��imaginarysK.�� 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 Import statements are executed in two steps: (1) find a module, and initialize it if necessary; (2) define a name or names in the local namespace (of the scope where the "import" statement occurs). The statement comes in two forms differing on whether it uses the "from" keyword. The first form (without "from") repeats these steps for each identifier in the list. The form with "from" performs step (1) once, and then performs step (2) repeatedly. To understand how step (1) occurs, one must first understand how Python handles hierarchical naming of modules. To help organize modules and provide a hierarchy in naming, Python has a concept of packages. A package can contain other packages and modules while modules cannot contain other modules or packages. From a file system perspective, packages are directories and modules are files. Once the name of the module is known (unless otherwise specified, the term "module" will refer to both packages and modules), searching for the module or package can begin. The first place checked is "sys.modules", the cache of all modules that have been imported previously. If the module is found there then it is used in step (2) of import. If the module is not found in the cache, then "sys.meta_path" is searched (the specification for "sys.meta_path" can be found in PEP 302). The object is a list of finder objects which are queried in order as to whether they know how to load the module by calling their "find_module()" method with the name of the module. If the module happens to be contained within a package (as denoted by the existence of a dot in the name), then a second argument to "find_module()" is given as the value of the "__path__" attribute from the parent package (everything up to the last dot in the name of the module being imported). If a finder can find the module it returns a loader (discussed later) or returns "None". If none of the finders on "sys.meta_path" are able to find the module then some implicitly defined finders are queried. Implementations of Python vary in what implicit meta path finders are defined. The one they all do define, though, is one that handles "sys.path_hooks", "sys.path_importer_cache", and "sys.path". The implicit finder searches for the requested module in the "paths" specified in one of two places ("paths" do not have to be file system paths). If the module being imported is supposed to be contained within a package then the second argument passed to "find_module()", "__path__" on the parent package, is used as the source of paths. If the module is not contained in a package then "sys.path" is used as the source of paths. Once the source of paths is chosen it is iterated over to find a finder that can handle that path. The dict at "sys.path_importer_cache" caches finders for paths and is checked for a finder. If the path does not have a finder cached then "sys.path_hooks" is searched by calling each object in the list with a single argument of the path, returning a finder or raises "ImportError". If a finder is returned then it is cached in "sys.path_importer_cache" and then used for that path entry. If no finder can be found but the path exists then a value of "None" is stored in "sys.path_importer_cache" to signify that an implicit, file- based finder that handles modules stored as individual files should be used for that path. If the path does not exist then a finder which always returns "None" is placed in the cache for the path. If no finder can find the module then "ImportError" is raised. Otherwise some finder returned a loader whose "load_module()" method is called with the name of the module to load (see PEP 302 for the original definition of loaders). A loader has several responsibilities to perform on a module it loads. First, if the module already exists in "sys.modules" (a possibility if the loader is called outside of the import machinery) then it is to use that module for initialization and not a new module. But if the module does not exist in "sys.modules" then it is to be added to that dict before initialization begins. If an error occurs during loading of the module and it was added to "sys.modules" it is to be removed from the dict. If an error occurs but the module was already in "sys.modules" it is left in the dict. The loader must set several attributes on the module. "__name__" is to be set to the name of the module. "__file__" is to be the "path" to the file unless the module is built-in (and thus listed in "sys.builtin_module_names") in which case the attribute is not set. If what is being imported is a package then "__path__" is to be set to a list of paths to be searched when looking for modules and packages contained within the package being imported. "__package__" is optional but should be set to the name of package that contains the module or package (the empty string is used for module not contained in a package). "__loader__" is also optional but should be set to the loader object that is loading the module. If an error occurs during loading then the loader raises "ImportError" if some other exception is not already being propagated. Otherwise the loader returns the module that was loaded and initialized. When step (1) finishes without raising an exception, step (2) can begin. The first form of "import" statement binds the module name in the local namespace to the module object, and then goes on to import the next identifier, if any. If the module name is followed by "as", the name following "as" is used as the local name for the module. The "from" form does not bind the module name: it goes through the list of identifiers, looks each one of them up in the module found in step (1), and binds the name in the local namespace to the object thus found. As with the first form of "import", an alternate local name can be supplied by specifying ""as" localname". If a name is not found, "ImportError" is raised. If the list of identifiers is replaced by a star ("''"), all public names defined in the module are bound in the local namespace of the "import" statement.. 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. 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". 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 2.6 are "unicode_literals", "print_function", "absolute_import", "division", "generators", "nested_scopes" and "with_statement". "generators", "with_statement", "nested_scopes" are redundant in Python version 2.6 and above because they are always enabled. 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 an "exec" statement or calls to the built-in functions "compile()" and "execfile()" 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, starting with Python 2.2 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. t��importsO�� Membership test operations ************************** The operators "in" and "not in" test for membership. "x in s" evaluates to "True" if x is a member of s, and "False" otherwise. "x not in s" returns the negation of "x in s". All built-in sequences and set types support this as well as dictionary, for which "in" tests whether the dictionary has a given key. For container types such as list, tuple, set, frozenset, dict, or collections.deque, the expression "x in y" is equivalent to "any(x is e or x == e for e in y)". For the string and bytes types, "x in y" is "True" if and only if x is a substring of y. An equivalent test is "y.find(x) != -1". Empty strings are always considered to be a substring of any other string, so """ in "abc"" will return "True". For user-defined classes which define the "__contains__()" method, "x in y" returns "True" if "y.__contains__(x)" returns a true value, and "False" otherwise. For user-defined classes which do not define "__contains__()" but do define "__iter__()", "x in y" is "True" if some value "z" with "x == z" is produced while iterating over "y". If an exception is raised during the iteration, it is as if "in" raised that exception. Lastly, the old-style iteration protocol is tried: if a class defines "__getitem__()", "x in y" is "True" if and only if there is a non- negative integer index i such that "x == y[i]", and all lower integer indices do not raise "IndexError" exception. (If any other exception is raised, it is as if "in" raised that exception). The operator "not in" is defined to have the inverse true value of "in". t��inso�� Integer and long integer literals ********************************* Integer and long integer literals are described by the following lexical definitions: longinteger ::= integer ("l" \| "L") integer ::= decimalinteger \| octinteger \| hexinteger \| bininteger decimalinteger ::= nonzerodigit digit* \| "0" octinteger ::= "0" ("o" \| "O") octdigit+ \| "0" octdigit+ hexinteger ::= "0" ("x" \| "X") hexdigit+ bininteger ::= "0" ("b" \| "B") bindigit+ nonzerodigit ::= "1"..."9" octdigit ::= "0"..."7" bindigit ::= "0" \| "1" hexdigit ::= digit \| "a"..."f" \| "A"..."F" Although both lower case "'l'" and upper case "'L'" are allowed as suffix for long integers, it is strongly recommended to always use "'L'", since the letter "'l'" looks too much like the digit "'1'". Plain integer literals that are above the largest representable plain integer (e.g., 2147483647 when using 32-bit arithmetic) are accepted as if they were long integers instead. [1] There is no limit for long integer literals apart from what can be stored in available memory. Some examples of plain integer literals (first row) and long integer literals (second and third rows): 7 2147483647 0177 3L 79228162514264337593543950336L 0377L 0x100000000L 79228162514264337593543950336 0xdeadbeef t��integerssx�� Lambdas ***** lambda_expr ::= "lambda" [parameter_list]: expression old_lambda_expr ::= "lambda" [parameter_list]: old_expression Lambda expressions (sometimes called lambda forms) have the same syntactic position as expressions. They are a shorthand to create anonymous functions; the expression "lambda arguments: expression" yields a function object. The unnamed object behaves like a function object defined with def name(arguments): return expression See section Function definitions for the syntax of parameter lists. Note that functions created with lambda expressions cannot contain statements. t��lambdas�� List displays ********* A list display is a possibly empty series of expressions enclosed in square brackets: list_display ::= "[" [expression_list \| list_comprehension] "]" list_comprehension ::= expression list_for list_for ::= "for" target_list "in" old_expression_list [list_iter] old_expression_list ::= old_expression [("," old_expression)+ [","]] old_expression ::= or_test \| old_lambda_expr list_iter ::= list_for \| list_if list_if ::= "if" old_expression [list_iter] A list display yields a new list object. Its contents are specified by providing either a list of expressions or a list comprehension. When a comma-separated list of expressions is supplied, its elements are evaluated from left to right and placed into the list object in that order. When a list comprehension is supplied, it consists of a single expression followed by at least one "for" clause and zero or more "for" or "if" clauses. In this case, the elements of the new list are those that would be produced by considering each of the "for" or "if" clauses a block, nesting from left to right, and evaluating the expression to produce a list element each time the innermost block is reached [1]. t��listss�� Naming and binding **************** Names refer to objects. Names are introduced by name binding operations. Each occurrence of a name in the program text refers to the binding of that name established in the innermost function block containing the use. A block is a piece of Python program text that is executed as a unit. The following are blocks: a module, a function body, and a class definition. Each command typed interactively is a block. A script file (a file given as standard input to the interpreter or specified on the interpreter command line the first argument) is a code block. A script command (a command specified on the interpreter command line with the '-c' option) is a code block. The file read by the built-in function "execfile()" is a code block. The string argument passed to the built-in function "eval()" and to the "exec" statement is a code block. The expression read and evaluated by the built-in function "input()" is a code block. A code block is executed in an execution frame. A frame contains some administrative information (used for debugging) and determines where and how execution continues after the code block's execution has completed. A scope defines the visibility of a name within a block. If a local variable is defined in a block, its scope includes that block. If the definition occurs in a function block, the scope extends to any blocks contained within the defining one, unless a contained block introduces a different binding for the name. The scope of names defined in a class block is limited to the class block; it does not extend to the code blocks of methods -- this includes generator expressions since they are implemented using a function scope. This means that the following will fail: class A: a = 42 b = list(a + i for i in range(10)) When a name is used in a code block, it is resolved using the nearest enclosing scope. The set of all such scopes visible to a code block is called the block's environment. If a name is bound in a block, it is a local variable of that block. If a name is bound at the module level, it is a global variable. (The variables of the module code block are local and global.) If a variable is used in a code block but not defined there, it is a free variable. When a name is not found at all, a "NameError" exception is raised. If the name refers to a local variable that has not been bound, a "UnboundLocalError" exception is raised. "UnboundLocalError" is a subclass of "NameError". The following constructs bind names: formal parameters to functions, "import" statements, class and function definitions (these bind the class or function name in the defining block), and targets that are identifiers if occurring in an assignment, "for" loop header, in the second position of an "except" clause header or after "as" in a "with" statement. The "import" statement of the form "from ... import " binds all names defined in the imported module, except those beginning with an underscore. This form may only be used at the module level. A target occurring in a "del" statement is also considered bound for this purpose (though the actual semantics are to unbind the name). It is illegal to unbind a name that is referenced by an enclosing scope; the compiler will report a "SyntaxError". Each assignment or import statement occurs within a block defined by a class or function definition or at the module level (the top-level code block). If a name binding operation occurs anywhere within a code block, all uses of the name within the block are treated as references to the current block. This can lead to errors when a name is used within a block before it is bound. This rule is subtle. Python lacks declarations and allows name binding operations to occur anywhere within a code block. The local variables of a code block can be determined by scanning the entire text of the block for name binding operations. If the global statement occurs within a block, all uses of the name specified in the statement refer to the binding of that name in the top-level namespace. Names are resolved in the top-level namespace by searching the global namespace, i.e. the namespace of the module containing the code block, and the builtins namespace, the namespace of the module "__builtin__". The global namespace is searched first. If the name is not found there, the builtins namespace is searched. The global statement must precede all uses of the name. The builtins namespace associated with the execution of a code block is actually found by looking up the name "__builtins__" in its global namespace; this should be a dictionary or a module (in the latter case the module's dictionary is used). By default, when in the "__main__" module, "__builtins__" is the built-in module "__builtin__" (note: no 's'); when in any other module, "__builtins__" is an alias for the dictionary of the "__builtin__" module itself. "__builtins__" can be set to a user-created dictionary to create a weak form of restricted execution. CPython implementation detail:* Users should not touch "__builtins__"; it is strictly an implementation detail. Users wanting to override values in the builtins namespace should "import" the "__builtin__" (no 's') module and modify its attributes appropriately. The namespace for a module is automatically created the first time a module is imported. The main module for a script is always called "__main__". The "global" statement has the same scope as a name binding operation in the same block. If the nearest enclosing scope for a free variable contains a global statement, the free variable is treated as a global. A class definition is an executable statement that may use and define names. These references follow the normal rules for name resolution. The namespace of the class definition becomes the attribute dictionary of the class. Names defined at the class scope are not visible in methods. Interaction with dynamic features ================================= There are several cases where Python statements are illegal when used in conjunction with nested scopes that contain free variables. If a variable is referenced in an enclosing scope, it is illegal to delete the name. An error will be reported at compile time. If the wild card form of import --- "import " --- is used in a function and the function contains or is a nested block with free variables, the compiler will raise a "SyntaxError". If "exec" is used in a function and the function contains or is a nested block with free variables, the compiler will raise a "SyntaxError" unless the exec explicitly specifies the local namespace for the "exec". (In other words, "exec obj" would be illegal, but "exec obj in ns" would be legal.) The "eval()", "execfile()", and "input()" functions and the "exec" statement do not have access to the full environment for resolving names. Names may be resolved in the local and global namespaces of the caller. Free variables are not resolved in the nearest enclosing namespace, but in the global namespace. [1] The "exec" statement and the "eval()" and "execfile()" functions have optional arguments to override the global and local namespace. If only one namespace is specified, it is used for both. t��namings�� Numeric literals ************* There are four types of numeric literals: plain integers, long integers, floating point numbers, and imaginary numbers. There are no complex literals (complex numbers can be formed by adding a real number and an imaginary number). Note that numeric literals do not include a sign; a phrase like "-1" is actually an expression composed of the unary operator '"-"' and the literal "1". t��numberssy�� Emulating numeric types ********************* The following methods can be defined to emulate numeric objects. Methods corresponding to operations that are not supported by the particular kind of number implemented (e.g., bitwise operations for non-integral numbers) should be left undefined. object.__add__(self, other) object.__sub__(self, other) object.__mul__(self, other) object.__floordiv__(self, other) object.__mod__(self, other) object.__divmod__(self, other) object.__pow__(self, other[, modulo]) object.__lshift__(self, other) object.__rshift__(self, other) object.__and__(self, other) object.__xor__(self, other) object.__or__(self, other) These methods are called to implement the binary arithmetic operations ("+", "-", "", "//", "%", "divmod()", "pow()", "", "<<", ">>", "&", "^", "\|"). For instance, to evaluate the expression "x + y", where x* is an instance of a class that has an "__add__()" method, "x.__add__(y)" is called. The "__divmod__()" method should be the equivalent to using "__floordiv__()" and "__mod__()"; it should not be related to "__truediv__()" (described below). Note that "__pow__()" should be defined to accept an optional third argument if the ternary version of the built-in "pow()" function is to be supported. If one of those methods does not support the operation with the supplied arguments, it should return "NotImplemented". object.__div__(self, other) object.__truediv__(self, other) The division operator ("/") is implemented by these methods. The "__truediv__()" method is used when "__future__.division" is in effect, otherwise "__div__()" is used. If only one of these two methods is defined, the object will not support division in the alternate context; "TypeError" will be raised instead. object.__radd__(self, other) object.__rsub__(self, other) object.__rmul__(self, other) object.__rdiv__(self, other) object.__rtruediv__(self, other) object.__rfloordiv__(self, other) object.__rmod__(self, other) object.__rdivmod__(self, other) object.__rpow__(self, other) object.__rlshift__(self, other) object.__rrshift__(self, other) object.__rand__(self, other) object.__rxor__(self, other) object.__ror__(self, other) These methods are called to implement the binary arithmetic operations ("+", "-", "", "/", "%", "divmod()", "pow()", "", "<<", ">>", "&", "^", "\|") with reflected (swapped) operands. These functions are only called if the left operand does not support the corresponding operation and the operands are of different types. [2] For instance, to evaluate the expression "x - y", where y* is an instance of a class that has an "__rsub__()" method, "y.__rsub__(x)" is called if "x.__sub__(y)" returns NotImplemented. Note that ternary "pow()" will not try calling "__rpow__()" (the coercion rules would become too complicated). Note: If the right operand's type is a subclass of the left operand's type and that subclass provides the reflected method for the operation, this method will be called before the left operand's non-reflected method. This behavior allows subclasses to override their ancestors' operations. object.__iadd__(self, other) object.__isub__(self, other) object.__imul__(self, other) object.__idiv__(self, other) object.__itruediv__(self, other) object.__ifloordiv__(self, other) object.__imod__(self, other) object.__ipow__(self, other[, modulo]) object.__ilshift__(self, other) object.__irshift__(self, other) object.__iand__(self, other) object.__ixor__(self, other) object.__ior__(self, other) These methods are called to implement the augmented arithmetic assignments ("+=", "-=", "=", "/=", "//=", "%=", "=", "<<=", ">>=", "&=", "^=", "\|="). These methods should attempt to do the operation in-place (modifying self) and return the result (which could be, but does not have to be, self). If a specific method is not defined, the augmented assignment falls back to the normal methods. For instance, to execute the statement "x += y", where x* is an instance of a class that has an "__iadd__()" method, "x.__iadd__(y)" is called. If x is an instance of a class that does not define a "__iadd__()" method, "x.__add__(y)" and "y.__radd__(x)" are considered, as with the evaluation of "x + y". object.__neg__(self) object.__pos__(self) object.__abs__(self) object.__invert__(self) Called to implement the unary arithmetic operations ("-", "+", "abs()" and "~"). object.__complex__(self) object.__int__(self) object.__long__(self) object.__float__(self) Called to implement the built-in functions "complex()", "int()", "long()", and "float()". Should return a value of the appropriate type. object.__oct__(self) object.__hex__(self) Called to implement the built-in functions "oct()" and "hex()". Should return a string value. object.__index__(self) Called to implement "operator.index()". Also called whenever Python needs an integer object (such as in slicing). Must return an integer (int or long). New in version 2.5. object.__coerce__(self, other) Called to implement "mixed-mode" numeric arithmetic. Should either return a 2-tuple containing self and other converted to a common numeric type, or "None" if conversion is impossible. When the common type would be the type of "other", it is sufficient to return "None", since the interpreter will also ask the other object to attempt a coercion (but sometimes, if the implementation of the other type cannot be changed, it is useful to do the conversion to the other type here). A return value of "NotImplemented" is equivalent to returning "None". s ��numeric-typessZ�� Objects, values and types ************************* Objects are Python's abstraction for data. All data in a Python program is represented by objects or by relations between objects. (In a sense, and in conformance to Von Neumann's model of a "stored program computer," code is also represented by objects.) Every object has an identity, a type and a value. An object's identity never changes once it has been created; you may think of it as the object's address in memory. The '"is"' operator compares the identity of two objects; the "id()" function returns an integer representing its identity (currently implemented as its address). An object's type is also unchangeable. [1] An object's type determines the operations that the object supports (e.g., "does it have a length?") and also defines the possible values for objects of that type. The "type()" function returns an object's type (which is an object itself). The value of some objects can change. Objects whose value can change are said to be mutable; objects whose value is unchangeable once they are created are called immutable. (The value of an immutable container object that contains a reference to a mutable object can change when the latter's value is changed; however the container is still considered immutable, because the collection of objects it contains cannot be changed. So, immutability is not strictly the same as having an unchangeable value, it is more subtle.) An object's mutability is determined by its type; for instance, numbers, strings and tuples are immutable, while dictionaries and lists are mutable. Objects are never explicitly destroyed; however, when they become unreachable they may be garbage-collected. An implementation is allowed to postpone garbage collection or omit it altogether --- it is a matter of implementation quality how garbage collection is implemented, as long as no objects are collected that are still reachable. CPython implementation detail: CPython currently uses a reference- counting scheme with (optional) delayed detection of cyclically linked garbage, which collects most objects as soon as they become unreachable, but is not guaranteed to collect garbage containing circular references. See the documentation of the "gc" module for information on controlling the collection of cyclic garbage. Other implementations act differently and CPython may change. Do not depend on immediate finalization of objects when they become unreachable (ex: always close files). Note that the use of the implementation's tracing or debugging facilities may keep objects alive that would normally be collectable. Also note that catching an exception with a '"try"..."except"' statement may keep objects alive. Some objects contain references to "external" resources such as open files or windows. It is understood that these resources are freed when the object is garbage-collected, but since garbage collection is not guaranteed to happen, such objects also provide an explicit way to release the external resource, usually a "close()" method. Programs are strongly recommended to explicitly close such objects. The '"try"..."finally"' statement provides a convenient way to do this. Some objects contain references to other objects; these are called containers. Examples of containers are tuples, lists and dictionaries. The references are part of a container's value. In most cases, when we talk about the value of a container, we imply the values, not the identities of the contained objects; however, when we talk about the mutability of a container, only the identities of the immediately contained objects are implied. So, if an immutable container (like a tuple) contains a reference to a mutable object, its value changes if that mutable object is changed. Types affect almost all aspects of object behavior. Even the importance of object identity is affected in some sense: for immutable types, operations that compute new values may actually return a reference to any existing object with the same type and value, while for mutable objects this is not allowed. E.g., after "a = 1; b = 1", "a" and "b" may or may not refer to the same object with the value one, depending on the implementation, but after "c = []; d = []", "c" and "d" are guaranteed to refer to two different, unique, newly created empty lists. (Note that "c = d = []" assigns the same object to both "c" and "d".) t��objectss�� Operator precedence ******************* The following table summarizes the operator precedences in Python, from lowest precedence (least binding) to highest precedence (most binding). Operators in the same box have the same precedence. Unless the syntax is explicitly given, operators are binary. Operators in the same box group left to right (except for comparisons, including tests, which all have the same precedence and chain from left to right --- see section Comparisons --- and exponentiation, which groups from right to left). +-------------------------------------------------+---------------------------------------+ \| Operator \| Description \| +=================================================+=======================================+ \| "lambda" \| Lambda expression \| +-------------------------------------------------+---------------------------------------+ \| "if" -- "else" \| Conditional expression \| +-------------------------------------------------+---------------------------------------+ \| "or" \| Boolean OR \| +-------------------------------------------------+---------------------------------------+ \| "and" \| Boolean AND \| +-------------------------------------------------+---------------------------------------+ \| "not" "x" \| Boolean NOT \| +-------------------------------------------------+---------------------------------------+ \| "in", "not in", "is", "is not", "<", "<=", ">", \| Comparisons, including membership \| \| ">=", "<>", "!=", "==" \| tests and identity tests \| +-------------------------------------------------+---------------------------------------+ \| "\|" \| Bitwise OR \| +-------------------------------------------------+---------------------------------------+ \| "^" \| Bitwise XOR \| +-------------------------------------------------+---------------------------------------+ \| "&" \| Bitwise AND \| +-------------------------------------------------+---------------------------------------+ \| "<<", ">>" \| Shifts \| +-------------------------------------------------+---------------------------------------+ \| "+", "-" \| Addition and subtraction \| +-------------------------------------------------+---------------------------------------+ \| "", "/", "//", "%" \| Multiplication, division, remainder \| \| \| [7] \| +-------------------------------------------------+---------------------------------------+ \| "+x", "-x", "~x" \| Positive, negative, bitwise NOT \| +-------------------------------------------------+---------------------------------------+ \| "" \| Exponentiation [8] \| +-------------------------------------------------+---------------------------------------+ \| "x[index]", "x[index:index]", \| Subscription, slicing, call, \| \| "x(arguments...)", "x.attribute" \| attribute reference \| +-------------------------------------------------+---------------------------------------+ \| "(expressions...)", "[expressions...]", "{key: \| Binding or tuple display, list \| \| value...}", "`expressions...`" \| display, dictionary display, string \| \| \| conversion \| +-------------------------------------------------+---------------------------------------+ -[ Footnotes ]- [1] In Python 2.3 and later releases, a list comprehension "leaks" the control variables of each "for" it contains into the containing scope. However, this behavior is deprecated, and relying on it will not work in Python 3. [2] While "abs(x%y) < abs(y)" is true mathematically, for floats it may not be true numerically due to roundoff. For example, and assuming a platform on which a Python float is an IEEE 754 double- precision number, in order that "-1e-100 % 1e100" have the same sign as "1e100", the computed result is "-1e-100 + 1e100", which is numerically exactly equal to "1e100". The function "math.fmod()" returns a result whose sign matches the sign of the first argument instead, and so returns "-1e-100" in this case. Which approach is more appropriate depends on the application. [3] If x is very close to an exact integer multiple of y, it's possible for "floor(x/y)" to be one larger than "(x-x%y)/y" due to rounding. In such cases, Python returns the latter result, in order to preserve that "divmod(x,y)[0] y + x % y" be very close to "x". [4] The Unicode standard distinguishes between code points (e.g. U+0041) and abstract characters (e.g. "LATIN CAPITAL LETTER A"). While most abstract characters in Unicode are only represented using one code point, there is a number of abstract characters that can in addition be represented using a sequence of more than one code point. For example, the abstract character "LATIN CAPITAL LETTER C WITH CEDILLA" can be represented as a single precomposed character at code position U+00C7, or as a sequence of a base character at code position U+0043 (LATIN CAPITAL LETTER C), followed by a combining character at code position U+0327 (COMBINING CEDILLA). The comparison operators on unicode strings compare at the level of Unicode code points. This may be counter-intuitive to humans. For example, "u"\u00C7" == u"\u0043\u0327"" is "False", even though both strings represent the same abstract character "LATIN CAPITAL LETTER C WITH CEDILLA". To compare strings at the level of abstract characters (that is, in a way intuitive to humans), use "unicodedata.normalize()". [5] Earlier versions of Python used lexicographic comparison of the sorted (key, value) lists, but this was very expensive for the common case of comparing for equality. An even earlier version of Python compared dictionaries by identity only, but this caused surprises because people expected to be able to test a dictionary for emptiness by comparing it to "{}". [6] Due to automatic garbage-collection, free lists, and the dynamic nature of descriptors, you may notice seemingly unusual behaviour in certain uses of the "is" operator, like those involving comparisons between instance methods, or constants. Check their documentation for more info. [7] The "%" operator is also used for string formatting; the same precedence applies. [8] The power operator "" binds less tightly than an arithmetic or bitwise unary operator on its right, that is, "2-1" is "0.5". s��operator-summarysx�� The "pass" statement ****************** pass_stmt ::= "pass" "pass" is a null operation --- when it is executed, nothing happens. It is useful as a placeholder when a statement is required syntactically, but no code needs to be executed, for example: def f(arg): pass # a function that does nothing (yet) class C: pass # a class with no methods (yet) t��passs�� The power operator ************** The power operator binds more tightly than unary operators on its left; it binds less tightly than unary operators on its right. The syntax is: power ::= primary ["" u_expr] Thus, in an unparenthesized sequence of power and unary operators, the operators are evaluated from right to left (this does not constrain the evaluation order for the operands): "-12" results in "-1". The power operator has the same semantics as the built-in "pow()" function, when called with two arguments: it yields its left argument raised to the power of its right argument. The numeric arguments are first converted to a common type. The result type is that of the arguments after coercion. With mixed operand types, the coercion rules for binary arithmetic operators apply. For int and long int operands, the result has the same type as the operands (after coercion) unless the second argument is negative; in that case, all arguments are converted to float and a float result is delivered. For example, "102" returns "100", but "10-2" returns "0.01". (This last feature was added in Python 2.2. In Python 2.1 and before, if both arguments were of integer types and the second argument was negative, an exception was raised). Raising "0.0" to a negative power results in a "ZeroDivisionError". Raising a negative number to a fractional power results in a "ValueError". t��powers�� The "print" statement ******************* print_stmt ::= "print" ([expression ("," expression)* [","]] \| ">>" expression [("," expression)+ [","]]) "print" evaluates each expression in turn and writes the resulting object to standard output (see below). If an object is not a string, it is first converted to a string using the rules for string conversions. The (resulting or original) string is then written. A space is written before each object is (converted and) written, unless the output system believes it is positioned at the beginning of a line. This is the case (1) when no characters have yet been written to standard output, (2) when the last character written to standard output is a whitespace character except "' '", or (3) when the last write operation on standard output was not a "print" statement. (In some cases it may be functional to write an empty string to standard output for this reason.) Note: Objects which act like file objects but which are not the built-in file objects often do not properly emulate this aspect of the file object's behavior, so it is best not to rely on this. A "'\n'" character is written at the end, unless the "print" statement ends with a comma. This is the only action if the statement contains just the keyword "print". Standard output is defined as the file object named "stdout" in the built-in module "sys". If no such object exists, or if it does not have a "write()" method, a "RuntimeError" exception is raised. "print" also has an extended form, defined by the second portion of the syntax described above. This form is sometimes referred to as ""print" chevron." In this form, the first expression after the ">>" must evaluate to a "file-like" object, specifically an object that has a "write()" method as described above. With this extended form, the subsequent expressions are printed to this file object. If the first expression evaluates to "None", then "sys.stdout" is used as the file for output. t��prints�� The "raise" statement ********************* raise_stmt ::= "raise" [expression ["," expression ["," expression]]] If no expressions are present, "raise" re-raises the last exception that was active in the current scope. If no exception is active in the current scope, a "TypeError" exception is raised indicating that this is an error (if running under IDLE, a "Queue.Empty" exception is raised instead). Otherwise, "raise" evaluates the expressions to get three objects, using "None" as the value of omitted expressions. The first two objects are used to determine the type and value of the exception. If the first object is an instance, the type of the exception is the class of the instance, the instance itself is the value, and the second object must be "None". If the first object is a class, it becomes the type of the exception. The second object is used to determine the exception value: If it is an instance of the class, the instance becomes the exception value. If the second object is a tuple, it is used as the argument list for the class constructor; if it is "None", an empty argument list is used, and any other object is treated as a single argument to the constructor. The instance so created by calling the constructor is used as the exception value. If a third object is present and not "None", it must be a traceback object (see section The standard type hierarchy), and it is substituted instead of the current location as the place where the exception occurred. If the third object is present and not a traceback object or "None", a "TypeError" exception is raised. The three-expression form of "raise" is useful to re-raise an exception transparently in an except clause, but "raise" with no expressions should be preferred if the exception to be re-raised was the most recently active exception in the current scope. Additional information on exceptions can be found in section Exceptions, and information about handling exceptions is in section The try statement. t��raises�� The "return" statement ******************** return_stmt ::= "return" [expression_list] "return" may only occur syntactically nested in a function definition, not within a nested class definition. If an expression list is present, it is evaluated, else "None" is substituted. "return" leaves the current function call with the expression list (or "None") as return value. When "return" passes control out of a "try" statement with a "finally" clause, that "finally" clause is executed before really leaving the function. In a generator function, the "return" statement is not allowed to include an "expression_list". In that context, a bare "return" indicates that the generator is done and will cause "StopIteration" to be raised. t��returns�� Emulating container types *********************** The following methods can be defined to implement container objects. Containers usually are sequences (such as lists or tuples) or mappings (like dictionaries), but can represent other containers as well. The first set of methods is used either to emulate a sequence or to emulate a mapping; the difference is that for a sequence, the allowable keys should be the integers k for which "0 <= k < N" where N is the length of the sequence, or slice objects, which define a range of items. (For backwards compatibility, the method "__getslice__()" (see below) can also be defined to handle simple, but not extended slices.) It is also recommended that mappings provide the methods "keys()", "values()", "items()", "has_key()", "get()", "clear()", "setdefault()", "iterkeys()", "itervalues()", "iteritems()", "pop()", "popitem()", "copy()", and "update()" behaving similar to those for Python's standard dictionary objects. The "UserDict" module provides a "DictMixin" class to help create those methods from a base set of "__getitem__()", "__setitem__()", "__delitem__()", and "keys()". Mutable sequences should provide methods "append()", "count()", "index()", "extend()", "insert()", "pop()", "remove()", "reverse()" and "sort()", like Python standard list objects. Finally, sequence types should implement addition (meaning concatenation) and multiplication (meaning repetition) by defining the methods "__add__()", "__radd__()", "__iadd__()", "__mul__()", "__rmul__()" and "__imul__()" described below; they should not define "__coerce__()" or other numerical operators. It is recommended that both mappings and sequences implement the "__contains__()" method to allow efficient use of the "in" operator; for mappings, "in" should be equivalent of "has_key()"; for sequences, it should search through the values. It is further recommended that both mappings and sequences implement the "__iter__()" method to allow efficient iteration through the container; for mappings, "__iter__()" should be the same as "iterkeys()"; for sequences, it should iterate through the values. object.__len__(self) Called to implement the built-in function "len()". Should return the length of the object, an integer ">=" 0. Also, an object that doesn't define a "__nonzero__()" method and whose "__len__()" method returns zero is considered to be false in a Boolean context. CPython implementation detail: In CPython, the length is required to be at most "sys.maxsize". If the length is larger than "sys.maxsize" some features (such as "len()") may raise "OverflowError". To prevent raising "OverflowError" by truth value testing, an object must define a "__nonzero__()" method. object.__getitem__(self, key) Called to implement evaluation of "self[key]". For sequence types, the accepted keys should be integers and slice objects. Note that the special interpretation of negative indexes (if the class wishes to emulate a sequence type) is up to the "__getitem__()" method. If key is of an inappropriate type, "TypeError" may be raised; if of a value outside the set of indexes for the sequence (after any special interpretation of negative values), "IndexError" should be raised. For mapping types, if key is missing (not in the container), "KeyError" should be raised. Note: "for" loops expect that an "IndexError" will be raised for illegal indexes to allow proper detection of the end of the sequence. object.__missing__(self, key) Called by "dict"."__getitem__()" to implement "self[key]" for dict subclasses when key is not in the dictionary. object.__setitem__(self, key, value) Called to implement assignment to "self[key]". Same note as for "__getitem__()". This should only be implemented for mappings if the objects support changes to the values for keys, or if new keys can be added, or for sequences if elements can be replaced. The same exceptions should be raised for improper key values as for the "__getitem__()" method. object.__delitem__(self, key) Called to implement deletion of "self[key]". Same note as for "__getitem__()". This should only be implemented for mappings if the objects support removal of keys, or for sequences if elements can be removed from the sequence. The same exceptions should be raised for improper key values as for the "__getitem__()" method. object.__iter__(self) This method is called when an iterator is required for a container. This method should return a new iterator object that can iterate over all the objects in the container. For mappings, it should iterate over the keys of the container, and should also be made available as the method "iterkeys()". Iterator objects also need to implement this method; they are required to return themselves. For more information on iterator objects, see Iterator Types. object.__reversed__(self) Called (if present) by the "reversed()" built-in to implement reverse iteration. It should return a new iterator object that iterates over all the objects in the container in reverse order. If the "__reversed__()" method is not provided, the "reversed()" built-in will fall back to using the sequence protocol ("__len__()" and "__getitem__()"). Objects that support the sequence protocol should only provide "__reversed__()" if they can provide an implementation that is more efficient than the one provided by "reversed()". New in version 2.6. The membership test operators ("in" and "not in") are normally implemented as an iteration through a sequence. However, container objects can supply the following special method with a more efficient implementation, which also does not require the object be a sequence. object.__contains__(self, item) Called to implement membership test operators. Should return true if item is in self, false otherwise. For mapping objects, this should consider the keys of the mapping rather than the values or the key-item pairs. For objects that don't define "__contains__()", the membership test first tries iteration via "__iter__()", then the old sequence iteration protocol via "__getitem__()", see this section in the language reference. s��sequence-typess�� Shifting operations ******************* The shifting operations have lower priority than the arithmetic operations: shift_expr ::= a_expr \| shift_expr ( "<<" \| ">>" ) a_expr These operators accept plain or long integers as arguments. The arguments are converted to a common type. They shift the first argument to the left or right by the number of bits given by the second argument. A right shift by n bits is defined as division by "pow(2, n)". A left shift by n bits is defined as multiplication with "pow(2, n)". Negative shift counts raise a "ValueError" exception. Note: In the current implementation, the right-hand operand is required to be at most "sys.maxsize". If the right-hand operand is larger than "sys.maxsize" an "OverflowError" exception is raised. t��shiftings� �� Slicings ******** A slicing selects a range of items in a sequence object (e.g., a string, tuple or list). Slicings may be used as expressions or as targets in assignment or "del" statements. The syntax for a slicing: slicing ::= simple_slicing \| extended_slicing simple_slicing ::= primary "[" short_slice "]" extended_slicing ::= primary "[" slice_list "]" slice_list ::= slice_item ("," slice_item)* [","] slice_item ::= expression \| proper_slice \| ellipsis proper_slice ::= short_slice \| long_slice short_slice ::= [lower_bound] ":" [upper_bound] long_slice ::= short_slice ":" [stride] lower_bound ::= expression upper_bound ::= expression stride ::= expression ellipsis ::= "..." There is ambiguity in the formal syntax here: anything that looks like an expression list also looks like a slice list, so any subscription can be interpreted as a slicing. Rather than further complicating the syntax, this is disambiguated by defining that in this case the interpretation as a subscription takes priority over the interpretation as a slicing (this is the case if the slice list contains no proper slice nor ellipses). Similarly, when the slice list has exactly one short slice and no trailing comma, the interpretation as a simple slicing takes priority over that as an extended slicing. The semantics for a simple slicing are as follows. The primary must evaluate to a sequence object. The lower and upper bound expressions, if present, must evaluate to plain integers; defaults are zero and the "sys.maxint", respectively. If either bound is negative, the sequence's length is added to it. The slicing now selects all items with index k such that "i <= k < j" where i and j are the specified lower and upper bounds. This may be an empty sequence. It is not an error if i or j lie outside the range of valid indexes (such items don't exist so they aren't selected). The semantics for an extended slicing are as follows. The primary must evaluate to a mapping object, and it is indexed with a key that is constructed from the slice list, as follows. If the slice list contains at least one comma, the key is a tuple containing the conversion of the slice items; otherwise, the conversion of the lone slice item is the key. The conversion of a slice item that is an expression is that expression. The conversion of an ellipsis slice item is the built-in "Ellipsis" object. The conversion of a proper slice is a slice object (see section The standard type hierarchy) whose "start", "stop" and "step" attributes are the values of the expressions given as lower bound, upper bound and stride, respectively, substituting "None" for missing expressions. t��slicingss� �� Special Attributes ****************** The implementation adds a few special read-only attributes to several object types, where they are relevant. Some of these are not reported by the "dir()" built-in function. object.__dict__ A dictionary or other mapping object used to store an object's (writable) attributes. object.__methods__ Deprecated since version 2.2: Use the built-in function "dir()" to get a list of an object's attributes. This attribute is no longer available. object.__members__ Deprecated since version 2.2: Use the built-in function "dir()" to get a list of an object's attributes. This attribute is no longer available. instance.__class__ The class to which a class instance belongs. class.__bases__ The tuple of base classes of a class object. definition.__name__ The name of the class, type, function, method, descriptor, or generator instance. The following attributes are only supported by new-style classes. class.__mro__ This attribute is a tuple of classes that are considered when looking for base classes during method resolution. class.mro() This method can be overridden by a metaclass to customize the method resolution order for its instances. It is called at class instantiation, and its result is stored in "__mro__". class.__subclasses__() Each new-style class keeps a list of weak references to its immediate subclasses. This method returns a list of all those references still alive. Example: >>> int.__subclasses__() [<type 'bool'>] -[ Footnotes ]- [1] Additional information on these special methods may be found in the Python Reference Manual (Basic customization). [2] As a consequence, the list "[1, 2]" is considered equal to "[1.0, 2.0]", and similarly for tuples. [3] They must have since the parser can't tell the type of the operands. [4] Cased characters are those with general category property being one of "Lu" (Letter, uppercase), "Ll" (Letter, lowercase), or "Lt" (Letter, titlecase). [5] To format only a tuple you should therefore provide a singleton tuple whose only element is the tuple to be formatted. [6] The advantage of leaving the newline on is that returning an empty string is then an unambiguous EOF indication. It is also possible (in cases where it might matter, for example, if you want to make an exact copy of a file while scanning its lines) to tell whether the last line of a file ended in a newline or not (yes this happens!). t��specialattrssa�� Special method names ******************** A class can implement certain operations that are invoked by special syntax (such as arithmetic operations or subscripting and slicing) by defining methods with special names. This is Python's approach to operator overloading, allowing classes to define their own behavior with respect to language operators. For instance, if a class defines a method named "__getitem__()", and "x" is an instance of this class, then "x[i]" is roughly equivalent to "x.__getitem__(i)" for old-style classes and "type(x).__getitem__(x, i)" for new-style classes. Except where mentioned, attempts to execute an operation raise an exception when no appropriate method is defined (typically "AttributeError" or "TypeError"). When implementing a class that emulates any built-in type, it is important that the emulation only be implemented to the degree that it makes sense for the object being modelled. For example, some sequences may work well with retrieval of individual elements, but extracting a slice may not make sense. (One example of this is the "NodeList" interface in the W3C's Document Object Model.) Basic customization =================== object.__new__(cls[, ...]) Called to create a new instance of class cls. "__new__()" is a static method (special-cased so you need not declare it as such) that takes the class of which an instance was requested as its first argument. The remaining arguments are those passed to the object constructor expression (the call to the class). The return value of "__new__()" should be the new object instance (usually an instance of cls). Typical implementations create a new instance of the class by invoking the superclass's "__new__()" method using "super(currentclass, cls).__new__(cls[, ...])" with appropriate arguments and then modifying the newly-created instance as necessary before returning it. If "__new__()" returns an instance of cls, then the new instance's "__init__()" method will be invoked like "__init__(self[, ...])", where self is the new instance and the remaining arguments are the same as were passed to "__new__()". If "__new__()" does not return an instance of cls, then the new instance's "__init__()" method will not be invoked. "__new__()" is intended mainly to allow subclasses of immutable types (like int, str, or tuple) to customize instance creation. It is also commonly overridden in custom metaclasses in order to customize class creation. object.__init__(self[, ...]) Called after the instance has been created (by "__new__()"), but before it is returned to the caller. The arguments are those passed to the class constructor expression. If a base class has an "__init__()" method, the derived class's "__init__()" method, if any, must explicitly call it to ensure proper initialization of the base class part of the instance; for example: "BaseClass.__init__(self, [args...])". Because "__new__()" and "__init__()" work together in constructing objects ("__new__()" to create it, and "__init__()" to customise it), no non-"None" value may be returned by "__init__()"; doing so will cause a "TypeError" to be raised at runtime. object.__del__(self) Called when the instance is about to be destroyed. This is also called a destructor. If a base class has a "__del__()" method, the derived class's "__del__()" method, if any, must explicitly call it to ensure proper deletion of the base class part of the instance. Note that it is possible (though not recommended!) for the "__del__()" method to postpone destruction of the instance by creating a new reference to it. It may then be called at a later time when this new reference is deleted. It is not guaranteed that "__del__()" methods are called for objects that still exist when the interpreter exits. Note: "del x" doesn't directly call "x.__del__()" --- the former decrements the reference count for "x" by one, and the latter is only called when "x"'s reference count reaches zero. Some common situations that may prevent the reference count of an object from going to zero include: circular references between objects (e.g., a doubly-linked list or a tree data structure with parent and child pointers); a reference to the object on the stack frame of a function that caught an exception (the traceback stored in "sys.exc_traceback" keeps the stack frame alive); or a reference to the object on the stack frame that raised an unhandled exception in interactive mode (the traceback stored in "sys.last_traceback" keeps the stack frame alive). The first situation can only be remedied by explicitly breaking the cycles; the latter two situations can be resolved by storing "None" in "sys.exc_traceback" or "sys.last_traceback". Circular references which are garbage are detected when the option cycle detector is enabled (it's on by default), but can only be cleaned up if there are no Python-level "__del__()" methods involved. Refer to the documentation for the "gc" module for more information about how "__del__()" methods are handled by the cycle detector, particularly the description of the "garbage" value. Warning: Due to the precarious circumstances under which "__del__()" methods are invoked, exceptions that occur during their execution are ignored, and a warning is printed to "sys.stderr" instead. Also, when "__del__()" is invoked in response to a module being deleted (e.g., when execution of the program is done), other globals referenced by the "__del__()" method may already have been deleted or in the process of being torn down (e.g. the import machinery shutting down). For this reason, "__del__()" methods should do the absolute minimum needed to maintain external invariants. Starting with version 1.5, Python guarantees that globals whose name begins with a single underscore are deleted from their module before other globals are deleted; if no other references to such globals exist, this may help in assuring that imported modules are still available at the time when the "__del__()" method is called. See also the "-R" command-line option. object.__repr__(self) Called by the "repr()" built-in function and by string conversions (reverse quotes) 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 the "str()" built-in function and by the "print" statement to compute the "informal" string representation of an object. This differs from "__repr__()" in that it does not have to be a valid Python expression: a more convenient or concise representation may be used instead. 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) New in version 2.1. These are the so-called "rich comparison" methods, and are called for comparison operators in preference to "__cmp__()" below. The correspondence between operator symbols and method names is as follows: "x<y" calls "x.__lt__(y)", "x<=y" calls "x.__le__(y)", "x==y" calls "x.__eq__(y)", "x!=y" and "x<>y" call "x.__ne__(y)", "x>y" 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.__cmp__(self, other) Called by comparison operations if rich comparison (see above) is not defined. Should return a negative integer if "self < other", zero if "self == other", a positive integer if "self > other". If no "__cmp__()", "__eq__()" or "__ne__()" operation is defined, class instances are compared by object identity ("address"). See also the description of "__hash__()" for some important notes on creating hashable objects which support custom comparison operations and are usable as dictionary keys. (Note: the restriction that exceptions are not propagated by "__cmp__()" has been removed since Python 1.5.) object.__rcmp__(self, other) Changed in version 2.1: No longer supported. 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 mix together the hash values of the components of the object that also play a part in comparison of objects by packing them into a tuple and hashing the tuple. Example: def __hash__(self): return hash((self.name, self.nick, self.color)) If a class does not define a "__cmp__()" or "__eq__()" method it should not define a "__hash__()" operation either; if it defines "__cmp__()" or "__eq__()" but not "__hash__()", its instances will not be usable in hashed collections. If a class defines mutable objects and implements a "__cmp__()" or "__eq__()" method, it should not implement "__hash__()", since hashable collection implementations require that an object's hash value is immutable (if the object's hash value changes, it will be in the wrong hash bucket). User-defined classes have "__cmp__()" and "__hash__()" methods by default; with them, all objects compare unequal (except with themselves) and "x.__hash__()" returns a result derived from "id(x)". Classes which inherit a "__hash__()" method from a parent class but change the meaning of "__cmp__()" or "__eq__()" such that the hash value returned is no longer appropriate (e.g. by switching to a value-based concept of equality instead of the default identity based equality) can explicitly flag themselves as being unhashable by setting "__hash__ = None" in the class definition. Doing so means that not only will instances of the class raise an appropriate "TypeError" when a program attempts to retrieve their hash value, but they will also be correctly identified as unhashable when checking "isinstance(obj, collections.Hashable)" (unlike classes which define their own "__hash__()" to explicitly raise "TypeError"). Changed in version 2.5: "__hash__()" may now also return a long integer object; the 32-bit integer is then derived from the hash of that object. Changed in version 2.6: "__hash__" may now be set to "None" to explicitly flag instances of a class as unhashable. object.__nonzero__(self) Called to implement truth value testing and the built-in operation "bool()"; should return "False" or "True", or their integer equivalents "0" or "1". 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 "__nonzero__()", all its instances are considered true. object.__unicode__(self) Called to implement "unicode()" built-in; should return a Unicode object. When this method is not defined, string conversion is attempted, and the result of string conversion is converted to Unicode using the system default encoding. Customizing attribute access ============================ The following methods can be defined to customize the meaning of attribute access (use of, assignment to, or deletion of "x.name") for class instances. object.__getattr__(self, name) Called when an attribute lookup has not found the attribute in the usual places (i.e. it is not an instance attribute nor is it found in the class tree for "self"). "name" is the attribute name. This method should return the (computed) attribute value or raise an "AttributeError" exception. Note that if the attribute is found through the normal mechanism, "__getattr__()" is not called. (This is an intentional asymmetry between "__getattr__()" and "__setattr__()".) This is done both for efficiency reasons and because otherwise "__getattr__()" would have no way to access other attributes of the instance. Note that at least for instance variables, you can fake total control by not inserting any values in the instance attribute dictionary (but instead inserting them in another object). See the "__getattribute__()" method below for a way to actually get total control in new-style classes. object.__setattr__(self, name, value) Called when an attribute assignment is attempted. This is called instead of the normal mechanism (i.e. store the value in the instance dictionary). name is the attribute name, value is the value to be assigned to it. If "__setattr__()" wants to assign to an instance attribute, it should not simply execute "self.name = value" --- this would cause a recursive call to itself. Instead, it should insert the value in the dictionary of instance attributes, e.g., "self.__dict__[name] = value". For new-style classes, rather than accessing the instance dictionary, it should call the base class method with the same name, for example, "object.__setattr__(self, name, value)". object.__delattr__(self, name) Like "__setattr__()" but for attribute deletion instead of assignment. This should only be implemented if "del obj.name" is meaningful for the object. More attribute access for new-style classes ------------------------------------------- The following methods only apply to new-style classes. object.__getattribute__(self, name) Called unconditionally to implement attribute accesses for instances of the class. If the class also defines "__getattr__()", the latter will not be called unless "__getattribute__()" either calls it explicitly or raises an "AttributeError". This method should return the (computed) attribute value or raise an "AttributeError" exception. In order to avoid infinite recursion in this method, its implementation should always call the base class method with the same name to access any attributes it needs, for example, "object.__getattribute__(self, name)". Note: This method may still be bypassed when looking up special methods as the result of implicit invocation via language syntax or built-in functions. See Special method lookup for new-style classes. Implementing Descriptors ------------------------ The following methods only apply when an instance of the class containing the method (a so-called descriptor class) appears in an owner class (the descriptor must be in either the owner's class dictionary or in the class dictionary for one of its parents). In the examples below, "the attribute" refers to the attribute whose name is the key of the property in the owner class' "__dict__". object.__get__(self, instance, owner) Called to get the attribute of the owner class (class attribute access) or of an instance of that class (instance attribute access). owner is always the owner class, while instance is the instance that the attribute was accessed through, or "None" when the attribute is accessed through the owner. This method should return the (computed) attribute value or raise an "AttributeError" exception. object.__set__(self, instance, value) Called to set the attribute on an instance instance of the owner class to a new value, value. object.__delete__(self, instance) Called to delete the attribute on an instance instance of the owner class. Invoking Descriptors -------------------- In general, a descriptor is an object attribute with "binding behavior", one whose attribute access has been overridden by methods in the descriptor protocol: "__get__()", "__set__()", and "__delete__()". If any of those methods are defined for an object, it is said to be a descriptor. The default behavior for attribute access is to get, set, or delete the attribute from an object's dictionary. For instance, "a.x" has a lookup chain starting with "a.__dict__['x']", then "type(a).__dict__['x']", and continuing through the base classes of "type(a)" excluding metaclasses. However, if the looked-up value is an object defining one of the descriptor methods, then Python may override the default behavior and invoke the descriptor method instead. Where this occurs in the precedence chain depends on which descriptor methods were defined and how they were called. Note that descriptors are only invoked for new style objects or classes (ones that subclass "object()" or "type()"). The starting point for descriptor invocation is a binding, "a.x". How the arguments are assembled depends on "a": Direct Call The simplest and least common call is when user code directly invokes a descriptor method: "x.__get__(a)". Instance Binding If binding to a new-style object instance, "a.x" is transformed into the call: "type(a).__dict__['x'].__get__(a, type(a))". Class Binding If binding to a new-style class, "A.x" is transformed into the call: "A.__dict__['x'].__get__(None, A)". Super Binding If "a" is an instance of "super", then the binding "super(B, obj).m()" searches "obj.__class__.__mro__" for the base class "A" immediately preceding "B" and then invokes the descriptor with the call: "A.__dict__['m'].__get__(obj, obj.__class__)". For instance bindings, the precedence of descriptor invocation depends on the which descriptor methods are defined. A descriptor can define any combination of "__get__()", "__set__()" and "__delete__()". If it does not define "__get__()", then accessing the attribute will return the descriptor object itself unless there is a value in the object's instance dictionary. If the descriptor defines "__set__()" and/or "__delete__()", it is a data descriptor; if it defines neither, it is a non-data descriptor. Normally, data descriptors define both "__get__()" and "__set__()", while non-data descriptors have just the "__get__()" method. Data descriptors with "__set__()" and "__get__()" defined always override a redefinition in an instance dictionary. In contrast, non-data descriptors can be overridden by instances. Python methods (including "staticmethod()" and "classmethod()") are implemented as non-data descriptors. Accordingly, instances can redefine and override methods. This allows individual instances to acquire behaviors that differ from other instances of the same class. The "property()" function is implemented as a data descriptor. Accordingly, instances cannot override the behavior of a property. __slots__ --------- By default, instances of both old and new-style classes have a dictionary for attribute storage. This wastes space for objects having very few instance variables. The space consumption can become acute when creating large numbers of instances. The default can be overridden by defining __slots__ in a new-style class definition. The __slots__ declaration takes a sequence of instance variables and reserves just enough space in each instance to hold a value for each variable. Space is saved because __dict__ is not created for each instance. __slots__ This class variable can be assigned a string, iterable, or sequence of strings with variable names used by instances. If defined in a new-style class, __slots__ reserves space for the declared variables and prevents the automatic creation of __dict__ and __weakref__ for each instance. New in version 2.2. Notes on using __slots__ * When inheriting from a class without __slots__, the __dict__ attribute of that class will always be accessible, so a __slots__ definition in the subclass is meaningless. * Without a __dict__ variable, instances cannot be assigned new variables not listed in the __slots__ definition. Attempts to assign to an unlisted variable name raises "AttributeError". If dynamic assignment of new variables is desired, then add "'__dict__'" to the sequence of strings in the __slots__ declaration. Changed in version 2.3: Previously, adding "'__dict__'" to the __slots__ declaration would not enable the assignment of new attributes not specifically listed in the sequence of instance variable names. * Without a __weakref__ variable for each instance, classes defining __slots__ do not support weak references to its instances. If weak reference support is needed, then add "'__weakref__'" to the sequence of strings in the __slots__ declaration. Changed in version 2.3: Previously, adding "'__weakref__'" to the __slots__ declaration would not enable support for weak references. * __slots__ are implemented at the class level by creating descriptors (Implementing Descriptors) for each variable name. As a result, class attributes cannot be used to set default values for instance variables defined by __slots__; otherwise, the class attribute would overwrite the descriptor assignment. * The action of a __slots__ declaration is limited to the class where it is defined. As a result, subclasses will have a __dict__ unless they also define __slots__ (which must only contain names of any additional slots). * If a class defines a slot also defined in a base class, the instance variable defined by the base class slot is inaccessible (except by retrieving its descriptor directly from the base class). This renders the meaning of the program undefined. In the future, a check may be added to prevent this. * Nonempty __slots__ does not work for classes derived from "variable-length" built-in types such as "long", "str" and "tuple". * Any non-string iterable may be assigned to __slots__. Mappings may also be used; however, in the future, special meaning may be assigned to the values corresponding to each key. * __class__ assignment works only if both classes have the same __slots__. Changed in version 2.6: Previously, __class__ assignment raised an error if either new or old class had __slots__. Customizing class creation ========================== By default, new-style classes are constructed using "type()". A class definition is read into a separate namespace and the value of class name is bound to the result of "type(name, bases, dict)". When the class definition is read, if __metaclass__ is defined then the callable assigned to it will be called instead of "type()". This allows classes or functions to be written which monitor or alter the class creation process: * Modifying the class dictionary prior to the class being created. * Returning an instance of another class -- essentially performing the role of a factory function. These steps will have to be performed in the metaclass's "__new__()" method -- "type.__new__()" can then be called from this method to create a class with different properties. This example adds a new element to the class dictionary before creating the class: class metacls(type): def __new__(mcs, name, bases, dict): dict['foo'] = 'metacls was here' return type.__new__(mcs, name, bases, dict) You can of course also override other class methods (or add new methods); for example defining a custom "__call__()" method in the metaclass allows custom behavior when the class is called, e.g. not always creating a new instance. __metaclass__ This variable can be any callable accepting arguments for "name", "bases", and "dict". Upon class creation, the callable is used instead of the built-in "type()". New in version 2.2. The appropriate metaclass is determined by the following precedence rules: * If "dict['__metaclass__']" exists, it is used. * Otherwise, if there is at least one base class, its metaclass is used (this looks for a __class__ attribute first and if not found, uses its type). * Otherwise, if a global variable named __metaclass__ exists, it is used. * Otherwise, the old-style, classic metaclass (types.ClassType) is used. The potential uses for metaclasses are boundless. Some ideas that have been explored including logging, interface checking, automatic delegation, automatic property creation, proxies, frameworks, and automatic resource locking/synchronization. Customizing instance and subclass checks ======================================== New in version 2.6. 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. (For backwards compatibility, the method "__getslice__()" (see below) can also be defined to handle simple, but not extended slices.) It is also recommended that mappings provide the methods "keys()", "values()", "items()", "has_key()", "get()", "clear()", "setdefault()", "iterkeys()", "itervalues()", "iteritems()", "pop()", "popitem()", "copy()", and "update()" behaving similar to those for Python's standard dictionary objects. The "UserDict" module provides a "DictMixin" class to help create those methods from a base set of "__getitem__()", "__setitem__()", "__delitem__()", and "keys()". Mutable sequences should provide methods "append()", "count()", "index()", "extend()", "insert()", "pop()", "remove()", "reverse()" and "sort()", like Python standard list objects. Finally, sequence types should implement addition (meaning concatenation) and multiplication (meaning repetition) by defining the methods "__add__()", "__radd__()", "__iadd__()", "__mul__()", "__rmul__()" and "__imul__()" described below; they should not define "__coerce__()" or other numerical operators. It is recommended that both mappings and sequences implement the "__contains__()" method to allow efficient use of the "in" operator; for mappings, "in" should be equivalent of "has_key()"; for sequences, it should search through the values. It is further recommended that both mappings and sequences implement the "__iter__()" method to allow efficient iteration through the container; for mappings, "__iter__()" should be the same as "iterkeys()"; for sequences, it should iterate through the values. object.__len__(self) Called to implement the built-in function "len()". Should return the length of the object, an integer ">=" 0. Also, an object that doesn't define a "__nonzero__()" method and whose "__len__()" method returns zero is considered to be false in a Boolean context. CPython implementation detail: In CPython, the length is required to be at most "sys.maxsize". If the length is larger than "sys.maxsize" some features (such as "len()") may raise "OverflowError". To prevent raising "OverflowError" by truth value testing, an object must define a "__nonzero__()" method. object.__getitem__(self, key) Called to implement evaluation of "self[key]". For sequence types, the accepted keys should be integers and slice objects. Note that the special interpretation of negative indexes (if the class wishes to emulate a sequence type) is up to the "__getitem__()" method. If key is of an inappropriate type, "TypeError" may be raised; if of a value outside the set of indexes for the sequence (after any special interpretation of negative values), "IndexError" should be raised. For mapping types, if key is missing (not in the container), "KeyError" should be raised. Note: "for" loops expect that an "IndexError" will be raised for illegal indexes to allow proper detection of the end of the sequence. object.__missing__(self, key) Called by "dict"."__getitem__()" to implement "self[key]" for dict subclasses when key is not in the dictionary. object.__setitem__(self, key, value) Called to implement assignment to "self[key]". Same note as for "__getitem__()". This should only be implemented for mappings if the objects support changes to the values for keys, or if new keys can be added, or for sequences if elements can be replaced. The same exceptions should be raised for improper key values as for the "__getitem__()" method. object.__delitem__(self, key) Called to implement deletion of "self[key]". Same note as for "__getitem__()". This should only be implemented for mappings if the objects support removal of keys, or for sequences if elements can be removed from the sequence. The same exceptions should be raised for improper key values as for the "__getitem__()" method. object.__iter__(self) This method is called when an iterator is required for a container. This method should return a new iterator object that can iterate over all the objects in the container. For mappings, it should iterate over the keys of the container, and should also be made available as the method "iterkeys()". Iterator objects also need to implement this method; they are required to return themselves. For more information on iterator objects, see Iterator Types. object.__reversed__(self) Called (if present) by the "reversed()" built-in to implement reverse iteration. It should return a new iterator object that iterates over all the objects in the container in reverse order. If the "__reversed__()" method is not provided, the "reversed()" built-in will fall back to using the sequence protocol ("__len__()" and "__getitem__()"). Objects that support the sequence protocol should only provide "__reversed__()" if they can provide an implementation that is more efficient than the one provided by "reversed()". New in version 2.6. The membership test operators ("in" and "not in") are normally implemented as an iteration through a sequence. However, container objects can supply the following special method with a more efficient implementation, which also does not require the object be a sequence. object.__contains__(self, item) Called to implement membership test operators. Should return true if item is in self, false otherwise. For mapping objects, this should consider the keys of the mapping rather than the values or the key-item pairs. For objects that don't define "__contains__()", the membership test first tries iteration via "__iter__()", then the old sequence iteration protocol via "__getitem__()", see this section in the language reference. Additional methods for emulation of sequence types ================================================== The following optional methods can be defined to further emulate sequence objects. Immutable sequences methods should at most only define "__getslice__()"; mutable sequences might define all three methods. object.__getslice__(self, i, j) Deprecated since version 2.0: Support slice objects as parameters to the "__getitem__()" method. (However, built-in types in CPython currently still implement "__getslice__()". Therefore, you have to override it in derived classes when implementing slicing.) Called to implement evaluation of "self[i:j]". The returned object should be of the same type as self. Note that missing i or j in the slice expression are replaced by zero or "sys.maxsize", respectively. If negative indexes are used in the slice, the length of the sequence is added to that index. If the instance does not implement the "__len__()" method, an "AttributeError" is raised. No guarantee is made that indexes adjusted this way are not still negative. Indexes which are greater than the length of the sequence are not modified. If no "__getslice__()" is found, a slice object is created instead, and passed to "__getitem__()" instead. object.__setslice__(self, i, j, sequence) Called to implement assignment to "self[i:j]". Same notes for i and j as for "__getslice__()". This method is deprecated. If no "__setslice__()" is found, or for extended slicing of the form "self[i:j:k]", a slice object is created, and passed to "__setitem__()", instead of "__setslice__()" being called. object.__delslice__(self, i, j) Called to implement deletion of "self[i:j]". Same notes for i and j as for "__getslice__()". This method is deprecated. If no "__delslice__()" is found, or for extended slicing of the form "self[i:j:k]", a slice object is created, and passed to "__delitem__()", instead of "__delslice__()" being called. Notice that these methods are only invoked when a single slice with a single colon is used, and the slice method is available. For slice operations involving extended slice notation, or in absence of the slice methods, "__getitem__()", "__setitem__()" or "__delitem__()" is called with a slice object as argument. The following example demonstrate how to make your program or module compatible with earlier versions of Python (assuming that methods "__getitem__()", "__setitem__()" and "__delitem__()" support slice objects as arguments): class MyClass: ... def __getitem__(self, index): ... def __setitem__(self, index, value): ... def __delitem__(self, index): ... if sys.version_info < (2, 0): # They won't be defined if version is at least 2.0 final def __getslice__(self, i, j): return self[max(0, i):max(0, j):] def __setslice__(self, i, j, seq): self[max(0, i):max(0, j):] = seq def __delslice__(self, i, j): del self[max(0, i):max(0, j):] ... Note the calls to "max()"; these are necessary because of the handling of negative indices before the "__slice__()" methods are called. When negative indexes are used, the "__item__()" methods receive them as provided, but the "__slice__()" methods get a "cooked" form of the index values. For each negative index value, the length of the sequence is added to the index before calling the method (which may still result in a negative index); this is the customary handling of negative indexes by the built-in sequence types, and the "__item__()" methods are expected to do this as well. However, since they should already be doing that, negative indexes cannot be passed in; they must be constrained to the bounds of the sequence before being passed to the "__item__()" methods. Calling "max(0, i)" conveniently returns the proper value. Emulating numeric types ======================= The following methods can be defined to emulate numeric objects. Methods corresponding to operations that are not supported by the particular kind of number implemented (e.g., bitwise operations for non-integral numbers) should be left undefined. object.__add__(self, other) object.__sub__(self, other) object.__mul__(self, other) object.__floordiv__(self, other) object.__mod__(self, other) object.__divmod__(self, other) object.__pow__(self, other[, modulo]) object.__lshift__(self, other) object.__rshift__(self, other) object.__and__(self, other) object.__xor__(self, other) object.__or__(self, other) These methods are called to implement the binary arithmetic operations ("+", "-", "", "//", "%", "divmod()", "pow()", "*", "<<", ">>", "&", "^", "\|"). For instance, to evaluate the expression "x + y", where x* is an instance of a class that has an "__add__()" method, "x.__add__(y)" is called. The "__divmod__()" method should be the equivalent to using "__floordiv__()" and "__mod__()"; it should not be related to "__truediv__()" (described below). Note that "__pow__()" should be defined to accept an optional third argument if the ternary version of the built-in "pow()" function is to be supported. If one of those methods does not support the operation with the supplied arguments, it should return "NotImplemented". object.__div__(self, other) object.__truediv__(self, other) The division operator ("/") is implemented by these methods. The "__truediv__()" method is used when "__future__.division" is in effect, otherwise "__div__()" is used. If only one of these two methods is defined, the object will not support division in the alternate context; "TypeError" will be raised instead. object.__radd__(self, other) object.__rsub__(self, other) object.__rmul__(self, other) object.__rdiv__(self, other) object.__rtruediv__(self, other) object.__rfloordiv__(self, other) object.__rmod__(self, other) object.__rdivmod__(self, other) object.__rpow__(self, other) object.__rlshift__(self, other) object.__rrshift__(self, other) object.__rand__(self, other) object.__rxor__(self, other) object.__ror__(self, other) These methods are called to implement the binary arithmetic operations ("+", "-", "", "/", "%", "divmod()", "pow()", "", "<<", ">>", "&", "^", "\|") with reflected (swapped) operands. These functions are only called if the left operand does not support the corresponding operation and the operands are of different types. [2] For instance, to evaluate the expression "x - y", where y* is an instance of a class that has an "__rsub__()" method, "y.__rsub__(x)" is called if "x.__sub__(y)" returns NotImplemented. Note that ternary "pow()" will not try calling "__rpow__()" (the coercion rules would become too complicated). Note: If the right operand's type is a subclass of the left operand's type and that subclass provides the reflected method for the operation, this method will be called before the left operand's non-reflected method. This behavior allows subclasses to override their ancestors' operations. object.__iadd__(self, other) object.__isub__(self, other) object.__imul__(self, other) object.__idiv__(self, other) object.__itruediv__(self, other) object.__ifloordiv__(self, other) object.__imod__(self, other) object.__ipow__(self, other[, modulo]) object.__ilshift__(self, other) object.__irshift__(self, other) object.__iand__(self, other) object.__ixor__(self, other) object.__ior__(self, other) These methods are called to implement the augmented arithmetic assignments ("+=", "-=", "=", "/=", "//=", "%=", "=", "<<=", ">>=", "&=", "^=", "\|="). These methods should attempt to do the operation in-place (modifying self) and return the result (which could be, but does not have to be, self). If a specific method is not defined, the augmented assignment falls back to the normal methods. For instance, to execute the statement "x += y", where x* is an instance of a class that has an "__iadd__()" method, "x.__iadd__(y)" is called. If x is an instance of a class that does not define a "__iadd__()" method, "x.__add__(y)" and "y.__radd__(x)" are considered, as with the evaluation of "x + y". object.__neg__(self) object.__pos__(self) object.__abs__(self) object.__invert__(self) Called to implement the unary arithmetic operations ("-", "+", "abs()" and "~"). object.__complex__(self) object.__int__(self) object.__long__(self) object.__float__(self) Called to implement the built-in functions "complex()", "int()", "long()", and "float()". Should return a value of the appropriate type. object.__oct__(self) object.__hex__(self) Called to implement the built-in functions "oct()" and "hex()". Should return a string value. object.__index__(self) Called to implement "operator.index()". Also called whenever Python needs an integer object (such as in slicing). Must return an integer (int or long). New in version 2.5. object.__coerce__(self, other) Called to implement "mixed-mode" numeric arithmetic. Should either return a 2-tuple containing self and other converted to a common numeric type, or "None" if conversion is impossible. When the common type would be the type of "other", it is sufficient to return "None", since the interpreter will also ask the other object to attempt a coercion (but sometimes, if the implementation of the other type cannot be changed, it is useful to do the conversion to the other type here). A return value of "NotImplemented" is equivalent to returning "None". Coercion rules ============== This section used to document the rules for coercion. As the language has evolved, the coercion rules have become hard to document precisely; documenting what one version of one particular implementation does is undesirable. Instead, here are some informal guidelines regarding coercion. In Python 3, coercion will not be supported. * If the left operand of a % operator is a string or Unicode object, no coercion takes place and the string formatting operation is invoked instead. * It is no longer recommended to define a coercion operation. Mixed- mode operations on types that don't define coercion pass the original arguments to the operation. * New-style classes (those derived from "object") never invoke the "__coerce__()" method in response to a binary operator; the only time "__coerce__()" is invoked is when the built-in function "coerce()" is called. * For most intents and purposes, an operator that returns "NotImplemented" is treated the same as one that is not implemented at all. * Below, "__op__()" and "__rop__()" are used to signify the generic method names corresponding to an operator; "__iop__()" is used for the corresponding in-place operator. For example, for the operator '"+"', "__add__()" and "__radd__()" are used for the left and right variant of the binary operator, and "__iadd__()" for the in-place variant. * For objects x and y, first "x.__op__(y)" is tried. If this is not implemented or returns "NotImplemented", "y.__rop__(x)" is tried. If this is also not implemented or returns "NotImplemented", a "TypeError" exception is raised. But see the following exception: * Exception to the previous item: if the left operand is an instance of a built-in type or a new-style class, and the right operand is an instance of a proper subclass of that type or class and overrides the base's "__rop__()" method, the right operand's "__rop__()" method is tried before the left operand's "__op__()" method. This is done so that a subclass can completely override binary operators. Otherwise, the left operand's "__op__()" method would always accept the right operand: when an instance of a given class is expected, an instance of a subclass of that class is always acceptable. * When either operand type defines a coercion, this coercion is called before that type's "__op__()" or "__rop__()" method is called, but no sooner. If the coercion returns an object of a different type for the operand whose coercion is invoked, part of the process is redone using the new object. * When an in-place operator (like '"+="') is used, if the left operand implements "__iop__()", it is invoked without any coercion. When the operation falls back to "__op__()" and/or "__rop__()", the normal coercion rules apply. * In "x + y", if x is a sequence that implements sequence concatenation, sequence concatenation is invoked. * In "x * y", if one operand is a sequence that implements sequence repetition, and the other is an integer ("int" or "long"), sequence repetition is invoked. * Rich comparisons (implemented by methods "__eq__()" and so on) never use coercion. Three-way comparison (implemented by "__cmp__()") does use coercion under the same conditions as other binary operations use it. * In the current implementation, the built-in numeric types "int", "long", "float", and "complex" do not use coercion. All these types implement a "__coerce__()" method, for use by the built-in "coerce()" function. Changed in version 2.7: The complex type no longer makes implicit calls to the "__coerce__()" method for mixed-type binary arithmetic operations. With Statement Context Managers =============================== New in version 2.5. A context manager is an object that defines the runtime context to be established when executing a "with" statement. The context manager handles the entry into, and the exit from, the desired runtime context for the execution of the block of code. Context managers are normally invoked using the "with" statement (described in section The with statement), but can also be used by directly invoking their methods. Typical uses of context managers include saving and restoring various kinds of global state, locking and unlocking resources, closing opened files, etc. For more information on context managers, see Context Manager Types. object.__enter__(self) Enter the runtime context related to this object. The "with" statement will bind this method's return value to the target(s) specified in the "as" clause of the statement, if any. object.__exit__(self, exc_type, exc_value, traceback) Exit the runtime context related to this object. The parameters describe the exception that caused the context to be exited. If the context was exited without an exception, all three arguments will be "None". If an exception is supplied, and the method wishes to suppress the exception (i.e., prevent it from being propagated), it should return a true value. Otherwise, the exception will be processed normally upon exit from this method. Note that "__exit__()" methods should not reraise the passed-in exception; this is the caller's responsibility. See also: PEP 343 - The "with" statement The specification, background, and examples for the Python "with" statement. Special method lookup for old-style classes =========================================== For old-style classes, special methods are always looked up in exactly the same way as any other method or attribute. This is the case regardless of whether the method is being looked up explicitly as in "x.__getitem__(i)" or implicitly as in "x[i]". This behaviour means that special methods may exhibit different behaviour for different instances of a single old-style class if the appropriate special attributes are set differently: >>> class C: ... pass ... >>> c1 = C() >>> c2 = C() >>> c1.__len__ = lambda: 5 >>> c2.__len__ = lambda: 9 >>> len(c1) 5 >>> len(c2) 9 Special method lookup for new-style classes =========================================== For new-style 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 (unlike the equivalent example with old-style classes): >>> class C(object): ... pass ... >>> c = C() >>> c.__len__ = lambda: 5 >>> len(c) Traceback (most recent call last): File "<stdin>", line 1, in <module> 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 "<stdin>", line 1, in <module> 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. t��specialnamess�K�� String Methods ************** Below are listed the string methods which both 8-bit strings and Unicode objects support. Some of them are also available on "bytearray" objects. In addition, Python's strings support the sequence type methods described in the Sequence Types --- str, unicode, list, tuple, bytearray, buffer, xrange section. To output formatted strings use template strings or the "%" operator described in the String Formatting Operations section. Also, see the "re" module for string functions based on regular expressions. str.capitalize() Return a copy of the string with its first character capitalized and the rest lowercased. For 8-bit strings, this method is locale-dependent. str.center(width[, fillchar]) Return centered in a string of length width. Padding is done using the specified fillchar (default is a space). Changed in version 2.4: Support for the fillchar argument. 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.decode([encoding[, errors]]) Decodes the string using the codec registered for encoding. encoding defaults to the default string encoding. errors may be given to set a different error handling scheme. The default is "'strict'", meaning that encoding errors raise "UnicodeError". Other possible values are "'ignore'", "'replace'" and any other name registered via "codecs.register_error()", see section Codec Base Classes. New in version 2.2. Changed in version 2.3: Support for other error handling schemes added. Changed in version 2.7: Support for keyword arguments added. str.encode([encoding[, errors]]) Return an encoded version of the string. Default encoding is the current default string encoding. 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. New in version 2.0. Changed in version 2.3: Support for "'xmlcharrefreplace'" and "'backslashreplace'" and other error handling schemes added. Changed in version 2.7: 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. Changed in version 2.5: Accept tuples as suffix. 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 within 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. This method of string formatting is the new standard in Python 3, and should be preferred to the "%" formatting described in String Formatting Operations in new code. New in version 2.6. 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. For 8-bit strings, this method is locale-dependent. str.isalpha() Return true if all characters in the string are alphabetic and there is at least one character, false otherwise. For 8-bit strings, this method is locale-dependent. str.isdigit() Return true if all characters in the string are digits and there is at least one character, false otherwise. For 8-bit strings, this method is locale-dependent. str.islower() Return true if all cased characters [4] in the string are lowercase and there is at least one cased character, false otherwise. For 8-bit strings, this method is locale-dependent. str.isspace() Return true if there are only whitespace characters in the string and there is at least one character, false otherwise. For 8-bit strings, this method is locale-dependent. 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. For 8-bit strings, this method is locale-dependent. str.isupper() Return true if all cased characters [4] in the string are uppercase and there is at least one cased character, false otherwise. For 8-bit strings, this method is locale-dependent. str.join(iterable) Return a string which is the concatenation of the strings in 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)". Changed in version 2.4: Support for the fillchar argument. str.lower() Return a copy of the string with all the cased characters [4] converted to lowercase. For 8-bit strings, this method is locale-dependent. 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' Changed in version 2.2.2: Support for the chars argument. 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. New in version 2.5. 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)". Changed in version 2.4: Support for the fillchar argument. 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. New in version 2.5. str.rsplit([sep[, maxsplit]]) 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. New in version 2.4. 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' Changed in version 2.2.2: Support for the chars argument. str.split([sep[, maxsplit]]) 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. Python recognizes ""\r"", ""\n"", and ""\r\n"" as line boundaries for 8-bit strings. For example: >>> 'ab c\n\nde fg\rkl\r\n'.splitlines() ['ab c', '', 'de fg', 'kl'] >>> 'ab c\n\nde fg\rkl\r\n'.splitlines(True) ['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: >>> "".splitlines() [] >>> "One line\n".splitlines() ['One line'] For comparison, "split('\n')" gives: >>> ''.split('\n') [''] >>> 'Two lines\n'.split('\n') ['Two lines', ''] unicode.splitlines([keepends]) Return a list of the lines in the string, like "str.splitlines()". However, the Unicode method splits on the following line boundaries, which are a superset of the universal newlines recognized for 8-bit strings. +-------------------------+-------------------------------+ \| Representation \| Description \| +=========================+===============================+ \| "\n" \| Line Feed \| +-------------------------+-------------------------------+ \| "\r" \| Carriage Return \| +-------------------------+-------------------------------+ \| "\r\n" \| Carriage Return + Line Feed \| +-------------------------+-------------------------------+ \| "\v" or "\x0b" \| Line Tabulation \| +-------------------------+-------------------------------+ \| "\f" or "\x0c" \| Form Feed \| +-------------------------+-------------------------------+ \| "\x1c" \| File Separator \| +-------------------------+-------------------------------+ \| "\x1d" \| Group Separator \| +-------------------------+-------------------------------+ \| "\x1e" \| Record Separator \| +-------------------------+-------------------------------+ \| "\x85" \| Next Line (C1 Control Code) \| +-------------------------+-------------------------------+ \| "\u2028" \| Line Separator \| +-------------------------+-------------------------------+ \| "\u2029" \| Paragraph Separator \| +-------------------------+-------------------------------+ Changed in version 2.7: "\v" and "\f" added to list of line boundaries. 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. Changed in version 2.5: Accept tuples as prefix. 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' Changed in version 2.2.2: Support for the chars argument. str.swapcase() Return a copy of the string with uppercase characters converted to lowercase and vice versa. For 8-bit strings, this method is locale-dependent. 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." For 8-bit strings, this method is locale-dependent. str.translate(table[, deletechars]) Return a copy of the string where all characters occurring in the optional argument deletechars are removed, and the remaining characters have been mapped through the given translation table, which must be a string of length 256. You can use the "maketrans()" helper function in the "string" module to create a translation table. For string objects, set the table argument to "None" for translations that only delete characters: >>> 'read this short text'.translate(None, 'aeiou') 'rd ths shrt txt' New in version 2.6: Support for a "None" table argument. For Unicode objects, the "translate()" method does not accept the optional deletechars argument. Instead, it returns a copy of the s where all characters have been mapped through the given translation table which must be a mapping of Unicode ordinals to Unicode ordinals, Unicode strings or "None". Unmapped characters are left untouched. Characters mapped to "None" are deleted. Note, a 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). For 8-bit strings, this method is locale-dependent. 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)". New in version 2.2.2. The following methods are present only on unicode objects: unicode.isnumeric() Return "True" if there are only numeric characters in S, "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. unicode.isdecimal() Return "True" if there are only decimal characters in S, "False" otherwise. Decimal characters include digit characters, and all characters that can be used to form decimal-radix numbers, e.g. U+0660, ARABIC-INDIC DIGIT ZERO. s��string-methodssF�� String literals *************** String literals are described by the following lexical definitions: stringliteral ::= [stringprefix](shortstring \| longstring) stringprefix ::= "r" \| "u" \| "ur" \| "R" \| "U" \| "UR" \| "Ur" \| "uR" \| "b" \| "B" \| "br" \| "Br" \| "bR" \| "BR" shortstring ::= "'" shortstringitem* "'" \| '"' shortstringitem* '"' longstring ::= "'''" longstringitem* "'''" \| '"""' longstringitem* '"""' shortstringitem ::= shortstringchar \| escapeseq longstringitem ::= longstringchar \| escapeseq shortstringchar ::= <any source character except "\" or newline or the quote> longstringchar ::= <any source character except "\"> escapeseq ::= "\" <any ASCII character> One syntactic restriction not indicated by these productions is that whitespace is not allowed between the "stringprefix" and the rest of the string literal. The source character set is defined by the encoding declaration; it is ASCII if no encoding declaration is given in the source file; see section Encoding declarations. In plain English: String 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. String literals may optionally be prefixed with a letter "'r'" or "'R'"; such strings are called raw strings and use different rules for interpreting backslash escape sequences. A prefix of "'u'" or "'U'" makes the string a Unicode string. Unicode strings use the Unicode character set as defined by the Unicode Consortium and ISO 10646. Some additional escape sequences, described below, are available in Unicode strings. A prefix of "'b'" or "'B'" is ignored in Python 2; it indicates that the literal should become a bytes literal in Python 3 (e.g. when code is automatically converted with 2to3). A "'u'" or "'b'" prefix may be followed by an "'r'" prefix. 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 \| +===================+===================================+=========+ \| "\newline" \| Ignored \| \| +-------------------+-----------------------------------+---------+ \| "\\" \| Backslash ("\") \| \| +-------------------+-----------------------------------+---------+ \| "\'" \| Single quote ("'") \| \| +-------------------+-----------------------------------+---------+ \| "\"" \| Double quote (""") \| \| +-------------------+-----------------------------------+---------+ \| "\a" \| ASCII Bell (BEL) \| \| +-------------------+-----------------------------------+---------+ \| "\b" \| ASCII Backspace (BS) \| \| +-------------------+-----------------------------------+---------+ \| "\f" \| ASCII Formfeed (FF) \| \| +-------------------+-----------------------------------+---------+ \| "\n" \| ASCII Linefeed (LF) \| \| +-------------------+-----------------------------------+---------+ \| "\N{name}" \| Character named name in the \| \| \| \| Unicode database (Unicode only) \| \| +-------------------+-----------------------------------+---------+ \| "\r" \| ASCII Carriage Return (CR) \| \| +-------------------+-----------------------------------+---------+ \| "\t" \| ASCII Horizontal Tab (TAB) \| \| +-------------------+-----------------------------------+---------+ \| "\uxxxx" \| Character with 16-bit hex value \| (1) \| \| \| xxxx (Unicode only) \| \| +-------------------+-----------------------------------+---------+ \| "\Uxxxxxxxx" \| Character with 32-bit hex value \| (2) \| \| \| xxxxxxxx (Unicode only) \| \| +-------------------+-----------------------------------+---------+ \| "\v" \| ASCII Vertical Tab (VT) \| \| +-------------------+-----------------------------------+---------+ \| "\ooo" \| Character with octal value ooo \| (3,5) \| +-------------------+-----------------------------------+---------+ \| "\xhh" \| Character with hex value hh \| (4,5) \| +-------------------+-----------------------------------+---------+ Notes: 1. Individual code units which form parts of a surrogate pair can be encoded using this escape sequence. 2. Any Unicode character can be encoded this way, but characters outside the Basic Multilingual Plane (BMP) will be encoded using a surrogate pair if Python is compiled to use 16-bit code units (the default). 3. As in Standard C, up to three octal digits are accepted. 4. Unlike in Standard C, exactly two hex digits are required. 5. In a string literal, hexadecimal and octal escapes denote the byte with the given value; it is not necessary that the byte encodes a character in the source character set. In a Unicode literal, these escapes denote a Unicode character with the given value. 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 marked as "(Unicode only)" in the table above fall into the category of unrecognized escapes for non-Unicode string literals. When an "'r'" or "'R'" prefix is present, a character following a backslash is included in the string without change, and all backslashes are left in the string. For example, the string literal "r"\n"" consists of two characters: a backslash and a lowercase "'n'". 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. When an "'r'" or "'R'" prefix is used in conjunction with a "'u'" or "'U'" prefix, then the "\uXXXX" and "\UXXXXXXXX" escape sequences are processed while all other backslashes are left in the string. For example, the string literal "ur"\u0062\n"" consists of three Unicode characters: 'LATIN SMALL LETTER B', 'REVERSE SOLIDUS', and 'LATIN SMALL LETTER N'. Backslashes can be escaped with a preceding backslash; however, both remain in the string. As a result, "\uXXXX" escape sequences are only recognized when there are an odd number of backslashes. t��stringss�� 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 of a sequence or mapping type. 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 a plain integer. If this value is negative, the length of the sequence is added to it (so that, e.g., "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). A string's items are characters. A character is not a separate data type but a string of exactly one character. t ��subscriptionss�� 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", "0L", "0.0", "0j". * any empty sequence, for example, "''", "()", "[]". * any empty mapping, for example, "{}". * instances of user-defined classes, if the class defines a "__nonzero__()" 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.) t��truths�� The "try" statement ***************** The "try" statement specifies exception handlers and/or cleanup code for a group of statements: try_stmt ::= try1_stmt \| try2_stmt try1_stmt ::= "try" ":" suite ("except" [expression [("as" \| ",") identifier]] ":" suite)+ ["else" ":" suite] ["finally" ":" suite] try2_stmt ::= "try" ":" suite "finally" ":" suite Changed in version 2.5: In previous versions of Python, "try"..."except"..."finally" did not work. "try"..."except" had to be nested in "try"..."finally". The "except" clause(s) specify one or more exception handlers. When no exception occurs in the "try" clause, no exception handler is executed. When an exception occurs in the "try" suite, a search for an exception handler is started. This search inspects the except clauses in turn until one is found that matches the exception. An expression- less except clause, if present, must be last; it matches any exception. For an except clause with an expression, that expression is evaluated, and the clause matches the exception if the resulting object is "compatible" with the exception. An object is compatible with an exception if it is the class or a base class of the exception object, or a tuple containing an item compatible with the exception. If no except clause matches the exception, the search for an exception handler continues in the surrounding code and on the invocation stack. [1] If the evaluation of an expression in the header of an except clause raises an exception, the original search for a handler is canceled and a search starts for the new exception in the surrounding code and on the call stack (it is treated as if the entire "try" statement raised the exception). When a matching except clause is found, the exception is assigned to the target specified in that except clause, if present, and the except clause's suite is executed. All except clauses must have an executable block. When the end of this block is reached, execution continues normally after the entire try statement. (This means that if two nested handlers exist for the same exception, and the exception occurs in the try clause of the inner handler, the outer handler will not handle the exception.) Before an except clause's suite is executed, details about the exception are assigned to three variables in the "sys" module: "sys.exc_type" receives the object identifying the exception; "sys.exc_value" receives the exception's parameter; "sys.exc_traceback" receives a traceback object (see section The standard type hierarchy) identifying the point in the program where the exception occurred. These details are also available through the "sys.exc_info()" function, which returns a tuple "(exc_type, exc_value, exc_traceback)". Use of the corresponding variables is deprecated in favor of this function, since their use is unsafe in a threaded program. As of Python 1.5, the variables are restored to their previous values (before the call) when returning from a function that handled an exception. The optional "else" clause is executed if and when control flows off the end of the "try" clause. [2] Exceptions in the "else" clause are not handled by the preceding "except" clauses. If "finally" is present, it specifies a 'cleanup' handler. The "try" clause is executed, including any "except" and "else" clauses. If an exception occurs in any of the clauses and is not handled, the exception is temporarily saved. The "finally" clause is executed. If there is a saved exception, it is re-raised at the end of the "finally" clause. If the "finally" clause raises another exception or executes a "return" or "break" statement, the saved exception is discarded: >>> def f(): ... try: ... 1/0 ... finally: ... return 42 ... >>> f() 42 The exception information is not available to the program during execution of the "finally" clause. When a "return", "break" or "continue" statement is executed in the "try" suite of a "try"..."finally" statement, the "finally" clause is also executed 'on the way out.' A "continue" statement is illegal in the "finally" clause. (The reason is a problem with the current implementation --- this restriction may be lifted in the future). The return value of a function is determined by the last "return" statement executed. Since the "finally" clause always executes, a "return" statement executed in the "finally" clause will always be the last one executed: >>> def foo(): ... try: ... return 'try' ... finally: ... return 'finally' ... >>> foo() 'finally' Additional information on exceptions can be found in section Exceptions, and information on using the "raise" statement to generate exceptions may be found in section The raise statement. t��trys�� 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.). 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 built-in name "Ellipsis". It is used to indicate the presence of the "..." syntax in a slice. 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 three types of integers: Plain integers These represent numbers in the range -2147483648 through 2147483647. (The range may be larger on machines with a larger natural word size, but not smaller.) When the result of an operation would fall outside this range, the result is normally returned as a long integer (in some cases, the exception "OverflowError" is raised instead). For the purpose of shift and mask operations, integers are assumed to have a binary, 2's complement notation using 32 or more bits, and hiding no bits from the user (i.e., all 4294967296 different bit patterns correspond to different values). Long integers 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 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 plain integers, 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 and the least surprises when switching between the plain and long integer domains. Any operation, if it yields a result in the plain integer domain, will yield the same result in the long integer domain or when using mixed operands. The switch between domains is transparent to the programmer. "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 are 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" 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 + nk", 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 The items of a string are characters. There is no separate character type; a character is represented by a string of one item. Characters represent (at least) 8-bit bytes. The built-in functions "chr()" and "ord()" convert between characters and nonnegative integers representing the byte values. Bytes with the values 0--127 usually represent the corresponding ASCII values, but the interpretation of values is up to the program. The string data type is also used to represent arrays of bytes, e.g., to hold data read from a file. (On systems whose native character set is not ASCII, strings may use EBCDIC in their internal representation, provided the functions "chr()" and "ord()" implement a mapping between ASCII and EBCDIC, and string comparison preserves the ASCII order. Or perhaps someone can propose a better rule?) Unicode The items of a Unicode object are Unicode code units. A Unicode code unit is represented by a Unicode object of one item and can hold either a 16-bit or 32-bit value representing a Unicode ordinal (the maximum value for the ordinal is given in "sys.maxunicode", and depends on how Python is configured at compile time). Surrogate pairs may be present in the Unicode object, and will be reported as two separate items. The built-in functions "unichr()" and "ord()" convert between code units and nonnegative integers representing the Unicode ordinals as defined in the Unicode Standard 3.0. Conversion from and to other encodings are possible through the Unicode method "encode()" and the built- in function "unicode()". 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. 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. 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", "gdbm", and "bsddb" provide additional examples of mapping types. 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: +-------------------------+---------------------------------+-------------+ \| Attribute \| Meaning \| \| +=========================+=================================+=============+ \| "__doc__" "func_doc" \| The function's documentation \| Writable \| \| \| string, or "None" if \| \| \| \| unavailable. \| \| +-------------------------+---------------------------------+-------------+ \| "__name__" "func_name" \| The function's name \| Writable \| +-------------------------+---------------------------------+-------------+ \| "__module__" \| The name of the module the \| Writable \| \| \| function was defined in, or \| \| \| \| "None" if unavailable. \| \| +-------------------------+---------------------------------+-------------+ \| "__defaults__" \| A tuple containing default \| Writable \| \| "func_defaults" \| argument values for those \| \| \| \| arguments that have defaults, \| \| \| \| or "None" if no arguments have \| \| \| \| a default value. \| \| +-------------------------+---------------------------------+-------------+ \| "__code__" "func_code" \| The code object representing \| Writable \| \| \| the compiled function body. \| \| +-------------------------+---------------------------------+-------------+ \| "__globals__" \| A reference to the dictionary \| Read-only \| \| "func_globals" \| that holds the function's \| \| \| \| global variables --- the global \| \| \| \| namespace of the module in \| \| \| \| which the function was defined. \| \| +-------------------------+---------------------------------+-------------+ \| "__dict__" "func_dict" \| The namespace supporting \| Writable \| \| \| arbitrary function attributes. \| \| +-------------------------+---------------------------------+-------------+ \| "__closure__" \| "None" or a tuple of cells that \| Read-only \| \| "func_closure" \| contain bindings for the \| \| \| \| function's free variables. \| \| +-------------------------+---------------------------------+-------------+ Most of the attributes labelled "Writable" check the type of the assigned value. Changed in version 2.4: "func_name" is now writable. Changed in version 2.6: The double-underscore attributes "__closure__", "__code__", "__defaults__", and "__globals__" were introduced as aliases for the corresponding "func_" attributes for forwards compatibility with Python 3. 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. User-defined methods A user-defined method object combines a class, a class instance (or "None") and any callable object (normally a user-defined function). Special read-only attributes: "im_self" is the class instance object, "im_func" is the function object; "im_class" is the class of "im_self" for bound methods or the class that asked for the method for unbound methods; "__doc__" is the method's documentation (same as "im_func.__doc__"); "__name__" is the method name (same as "im_func.__name__"); "__module__" is the name of the module the method was defined in, or "None" if unavailable. Changed in version 2.2: "im_self" used to refer to the class that defined the method. Changed in version 2.6: For Python 3 forward-compatibility, "im_func" is also available as "__func__", and "im_self" as "__self__". 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, an unbound user-defined method object, or a class method object. When the attribute is a user-defined method object, a new method object is only created if the class from which it is being retrieved is the same as, or a derived class of, the class stored in the original method object; otherwise, the original method object is used as it is. When a user-defined method object is created by retrieving a user-defined function object from a class, its "im_self" attribute is "None" and the method object is said to be unbound. When one is created by retrieving a user-defined function object from a class via one of its instances, its "im_self" attribute is the instance, and the method object is said to be bound. In either case, the new method's "im_class" attribute is the class from which the retrieval takes place, and its "im_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 "im_func" attribute of the new instance is not the original method object but its "im_func" attribute. When a user-defined method object is created by retrieving a class method object from a class or instance, its "im_self" attribute is the class itself, and its "im_func" attribute is the function object underlying the class method. When an unbound user-defined method object is called, the underlying function ("im_func") is called, with the restriction that the first argument must be an instance of the proper class ("im_class") or of a derived class thereof. When a bound user-defined method object is called, the underlying function ("im_func") is called, inserting the class instance ("im_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 a user-defined method object is derived from a class method object, the "class instance" stored in "im_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 (unbound or bound) method object happens each time the attribute is retrieved from the class or 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 "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. Class Types Class types, or "new-style 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. Classic Classes Class objects are described below. When a class object is called, a new class instance (also described below) is created and returned. This implies a call to the class's "__init__()" method if it has one. Any arguments are passed on to the "__init__()" method. If there is no "__init__()" method, the class must be called without arguments. Class instances Class instances are described below. Class instances are callable only when the class has a "__call__()" method; "x(arguments)" is a shorthand for "x.__call__(arguments)". Modules Modules are imported by the "import" statement (see section The import statement). A module object has a namespace implemented by a dictionary object (this is the dictionary referenced by the func_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 is not present for 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. Classes Both class types (new-style classes) and class objects (old- style/classic classes) 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 for new-style classes in particular 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. For old-style classes, the search is depth-first, left-to-right in the order of occurrence in the base class list. New-style classes use the more complex 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 new-style classes can be found in the documentation accompanying the 2.3 release at https://www.python.org/download/releases/2.3/mro/. When a class attribute reference (for class "C", say) would yield a user-defined function object or an unbound user-defined method object whose associated class is either "C" or one of its base classes, it is transformed into an unbound user-defined method object whose "im_class" attribute is "C". When it would yield a class method object, it is transformed into a bound user-defined method object whose "im_self" attribute 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__" (note that only new-style classes support descriptors). 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 or an unbound user-defined method object whose associated class is the class (call it "C") of the instance for which the attribute reference was initiated or one of its bases, it is transformed into a bound user-defined method object whose "im_class" attribute is "C" and whose "im_self" attribute is the instance. Static method and class method objects are also transformed, as if they had been retrieved from class "C"; 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. Files A file object represents an open file. File objects are created by the "open()" built-in function, and also by "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. See File Objects for complete documentation of file objects. 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_restricted" is a flag indicating whether the function is executing in restricted execution mode; "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_exc_type", "f_exc_value", "f_exc_traceback" represent the last exception raised in the parent frame provided another exception was ever raised in the current frame (in all other cases they are "None"); "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 "sys.exc_traceback", and also as the third item of the tuple returned by "sys.exc_info()". The latter is the preferred interface, since it works correctly when the program is using multiple threads. 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 when extended slice syntax* is used. This is a slice using two colons, or multiple slices or ellipses separated by commas, e.g., "a[i:j:step]", "a[i:j, k:l]", or "a[..., i:j]". 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 extended 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. New in version 2.3. 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. t��typess�� 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. t��typesfunctionss�/�� 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 dict(kwarg) class dict(mapping, kwarg) 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 iterable* object. Each item in the iterable must itself be an iterable 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. New in version 2.2. Changed in version 2.3: Support for building a dictionary from keyword arguments added. 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__()" and 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. 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 The example above shows part of the implementation of "collections.Counter". A different "__missing__" method is used by "collections.defaultdict". New in version 2.5: Recognition of __missing__ methods of dict subclasses. 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". New in version 2.2. key not in d Equivalent to "not key in d". New in version 2.2. iter(d) Return an iterator over the keys of the dictionary. This is a shortcut for "iterkeys()". clear() Remove all items from the dictionary. copy() Return a shallow copy of the dictionary. 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". New in version 2.3. 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". has_key(key) Test for the presence of key in the dictionary. "has_key()" is deprecated in favor of "key in d". items() Return a copy of the dictionary's list of "(key, value)" pairs. CPython implementation detail: Keys and values are listed in an arbitrary order which is non-random, varies across Python implementations, and depends on the dictionary's history of insertions and deletions. If "items()", "keys()", "values()", "iteritems()", "iterkeys()", and "itervalues()" are called with no intervening modifications to the dictionary, the lists will directly correspond. This allows the creation of "(value, key)" pairs using "zip()": "pairs = zip(d.values(), d.keys())". The same relationship holds for the "iterkeys()" and "itervalues()" methods: "pairs = zip(d.itervalues(), d.iterkeys())" provides the same value for "pairs". Another way to create the same list is "pairs = [(v, k) for (k, v) in d.iteritems()]". iteritems() Return an iterator over the dictionary's "(key, value)" pairs. See the note for "dict.items()". Using "iteritems()" while adding or deleting entries in the dictionary may raise a "RuntimeError" or fail to iterate over all entries. New in version 2.2. iterkeys() Return an iterator over the dictionary's keys. See the note for "dict.items()". Using "iterkeys()" while adding or deleting entries in the dictionary may raise a "RuntimeError" or fail to iterate over all entries. New in version 2.2. itervalues() Return an iterator over the dictionary's values. See the note for "dict.items()". Using "itervalues()" while adding or deleting entries in the dictionary may raise a "RuntimeError" or fail to iterate over all entries. New in version 2.2. keys() Return a copy of the dictionary's list of keys. See the note for "dict.items()". 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. New in version 2.3. 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)". Changed in version 2.4: Allowed the argument to be an iterable of key/value pairs and allowed keyword arguments. values() Return a copy of the dictionary's list of values. See the note for "dict.items()". viewitems() Return a new view of the dictionary's items ("(key, value)" pairs). See below for documentation of view objects. New in version 2.7. viewkeys() Return a new view of the dictionary's keys. See below for documentation of view objects. New in version 2.7. viewvalues() Return a new view of the dictionary's values. See below for documentation of view objects. New in version 2.7. Dictionaries compare equal if and only if they have the same "(key, value)" pairs. Dictionary view objects ======================= The objects returned by "dict.viewkeys()", "dict.viewvalues()" and "dict.viewitems()" 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.) Then these set operations are available ("other" refers either to another view or a set): dictview & other Return the intersection of the dictview and the other object as a new set. dictview \| other Return the union of the dictview and the other object as a new set. dictview - other Return the difference between the dictview and the other object (all elements in dictview that aren't in other) as a new set. dictview ^ other Return the symmetric difference (all elements either in dictview or other, but not in both) of the dictview and the other object as a new set. An example of dictionary view usage: >>> dishes = {'eggs': 2, 'sausage': 1, 'bacon': 1, 'spam': 500} >>> keys = dishes.viewkeys() >>> values = dishes.viewvalues() >>> # 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'} t��typesmappingsz�� 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. The implementation adds two special read-only attributes to class instance methods: "m.im_self" is the object on which the method operates, and "m.im_func" is the function implementing the method. Calling "m(arg-1, arg-2, ..., arg-n)" is completely equivalent to calling "m.im_func(m.im_self, arg-1, arg-2, ..., arg-n)". Class instance methods are either bound or unbound, referring to whether the method was accessed through an instance or a class, respectively. When a method is unbound, its "im_self" attribute will be "None" and if called, an explicit "self" object must be passed as the first argument. In this case, "self" must be an instance of the unbound method's class (or a subclass of that class), otherwise a "TypeError" is raised. Like function objects, methods objects support getting arbitrary attributes. However, since method attributes are actually stored on the underlying function object ("meth.im_func"), setting method attributes on either bound or unbound 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 "<stdin>", line 1, in <module> AttributeError: 'instancemethod' object has no attribute 'whoami' >>> c.method.im_func.whoami = 'my name is method' >>> c.method.whoami 'my name is method' See The standard type hierarchy for more information. t��typesmethodss�� 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: "<module 'sys' (built-in)>". If loaded from a file, they are written as "<module 'os' from '/usr/local/lib/pythonX.Y/os.pyc'>". t��typesmodulessy�� Sequence Types --- "str", "unicode", "list", "tuple", "bytearray", "buffer", "xrange" ************************************************************************************* There are seven sequence types: strings, Unicode strings, lists, tuples, bytearrays, buffers, and xrange objects. For other containers see the built in "dict" and "set" classes, and the "collections" module. String literals are written in single or double quotes: "'xyzzy'", ""frobozz"". See String literals for more about string literals. Unicode strings are much like strings, but are specified in the syntax using a preceding "'u'" character: "u'abc'", "u"def"". In addition to the functionality described here, there are also string-specific methods described in the String Methods section. Lists are constructed with square brackets, separating items with commas: "[a, b, c]". Tuples are constructed by the comma operator (not within square brackets), with or without enclosing parentheses, but an empty tuple must have the enclosing parentheses, such as "a, b, c" or "()". A single item tuple must have a trailing comma, such as "(d,)". Bytearray objects are created with the built-in function "bytearray()". Buffer objects are not directly supported by Python syntax, but can be created by calling the built-in function "buffer()". They don't support concatenation or repetition. Objects of type xrange are similar to buffers in that there is no specific syntax to create them, but they are created using the "xrange()" function. They don't support slicing, concatenation or repetition, and using "in", "not in", "min()" or "max()" on them is inefficient. Most sequence types support the following operations. The "in" and "not in" operations have the same priorities as the comparison operations. The "+" and "" operations have the same priority as the corresponding numeric operations. [3] Additional methods are provided for Mutable Sequence Types. This table lists the sequence operations sorted in ascending priority. In the table, s* and t are sequences of the same type; n, i and j are integers: +--------------------+----------------------------------+------------+ \| Operation \| Result \| Notes \| +====================+==================================+============+ \| "x in s" \| "True" if an item of s is \| (1) \| \| \| equal to x, else "False" \| \| +--------------------+----------------------------------+------------+ \| "x not in s" \| "False" if an item of s is \| (1) \| \| \| equal to x, else "True" \| \| +--------------------+----------------------------------+------------+ \| "s + t" \| the concatenation of s and t \| (6) \| +--------------------+----------------------------------+------------+ \| "s * n, n * s" \| equivalent to adding s to \| (2) \| \| \| itself n times \| \| +--------------------+----------------------------------+------------+ \| "s[i]" \| ith 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) \| \| \| with step k \| \| +--------------------+----------------------------------+------------+ \| "len(s)" \| length of s \| \| +--------------------+----------------------------------+------------+ \| "min(s)" \| smallest item of s \| \| +--------------------+----------------------------------+------------+ \| "max(s)" \| largest item of s \| \| +--------------------+----------------------------------+------------+ \| "s.index(x)" \| index of the first occurrence of \| \| \| \| x in s \| \| +--------------------+----------------------------------+------------+ \| "s.count(x)" \| total number of occurrences of \| \| \| \| x in s \| \| +--------------------+----------------------------------+------------+ Sequence types 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. When s is a string or Unicode string object the "in" and "not in" operations act like a substring test. In Python versions before 2.3, x had to be a string of length 1. In Python 2.3 and beyond, x may be a string of any length. 2. Values of n less than "0" are treated as "0" (which yields an empty sequence of the same type as s). Note that items in the sequence s are not copied; they are referenced multiple times. 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 references 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]] Further explanation is available in the FAQ entry How do I create a multidimensional list?. 3. If i or j is negative, the index is relative to the end of sequence s: "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 + nk" such that "0 <= n < (j-i)/k". In other words, the indices are "i", "i+k", "i+2k", "i+3k" and so on, stopping when j* is reached (but never including j). When k is positive, i and j are reduced to "len(s)" if they are greater. When k is negative, i and j are reduced to "len(s) - 1" if they are greater. 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. CPython implementation detail: If s and t are both strings, some Python implementations such as CPython can usually perform an in-place optimization for assignments of the form "s = s + t" or "s += t". When applicable, this optimization makes quadratic run-time much less likely. This optimization is both version and implementation dependent. For performance sensitive code, it is preferable to use the "str.join()" method which assures consistent linear concatenation performance across versions and implementations. Changed in version 2.4: Formerly, string concatenation never occurred in-place. String Methods ============== Below are listed the string methods which both 8-bit strings and Unicode objects support. Some of them are also available on "bytearray" objects. In addition, Python's strings support the sequence type methods described in the Sequence Types --- str, unicode, list, tuple, bytearray, buffer, xrange section. To output formatted strings use template strings or the "%" operator described in the String Formatting Operations section. Also, see the "re" module for string functions based on regular expressions. str.capitalize() Return a copy of the string with its first character capitalized and the rest lowercased. For 8-bit strings, this method is locale-dependent. str.center(width[, fillchar]) Return centered in a string of length width. Padding is done using the specified fillchar (default is a space). Changed in version 2.4: Support for the fillchar argument. 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.decode([encoding[, errors]]) Decodes the string using the codec registered for encoding. encoding defaults to the default string encoding. errors may be given to set a different error handling scheme. The default is "'strict'", meaning that encoding errors raise "UnicodeError". Other possible values are "'ignore'", "'replace'" and any other name registered via "codecs.register_error()", see section Codec Base Classes. New in version 2.2. Changed in version 2.3: Support for other error handling schemes added. Changed in version 2.7: Support for keyword arguments added. str.encode([encoding[, errors]]) Return an encoded version of the string. Default encoding is the current default string encoding. 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. New in version 2.0. Changed in version 2.3: Support for "'xmlcharrefreplace'" and "'backslashreplace'" and other error handling schemes added. Changed in version 2.7: 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. Changed in version 2.5: Accept tuples as suffix. 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 within 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. This method of string formatting is the new standard in Python 3, and should be preferred to the "%" formatting described in String Formatting Operations in new code. New in version 2.6. 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. For 8-bit strings, this method is locale-dependent. str.isalpha() Return true if all characters in the string are alphabetic and there is at least one character, false otherwise. For 8-bit strings, this method is locale-dependent. str.isdigit() Return true if all characters in the string are digits and there is at least one character, false otherwise. For 8-bit strings, this method is locale-dependent. str.islower() Return true if all cased characters [4] in the string are lowercase and there is at least one cased character, false otherwise. For 8-bit strings, this method is locale-dependent. str.isspace() Return true if there are only whitespace characters in the string and there is at least one character, false otherwise. For 8-bit strings, this method is locale-dependent. 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. For 8-bit strings, this method is locale-dependent. str.isupper() Return true if all cased characters [4] in the string are uppercase and there is at least one cased character, false otherwise. For 8-bit strings, this method is locale-dependent. str.join(iterable) Return a string which is the concatenation of the strings in 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)". Changed in version 2.4: Support for the fillchar argument. str.lower() Return a copy of the string with all the cased characters [4] converted to lowercase. For 8-bit strings, this method is locale-dependent. 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' Changed in version 2.2.2: Support for the chars argument. 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. New in version 2.5. 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)". Changed in version 2.4: Support for the fillchar argument. 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. New in version 2.5. str.rsplit([sep[, maxsplit]]) 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. New in version 2.4. 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' Changed in version 2.2.2: Support for the chars argument. str.split([sep[, maxsplit]]) 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. Python recognizes ""\r"", ""\n"", and ""\r\n"" as line boundaries for 8-bit strings. For example: >>> 'ab c\n\nde fg\rkl\r\n'.splitlines() ['ab c', '', 'de fg', 'kl'] >>> 'ab c\n\nde fg\rkl\r\n'.splitlines(True) ['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: >>> "".splitlines() [] >>> "One line\n".splitlines() ['One line'] For comparison, "split('\n')" gives: >>> ''.split('\n') [''] >>> 'Two lines\n'.split('\n') ['Two lines', ''] unicode.splitlines([keepends]) Return a list of the lines in the string, like "str.splitlines()". However, the Unicode method splits on the following line boundaries, which are a superset of the universal newlines recognized for 8-bit strings. +-------------------------+-------------------------------+ \| Representation \| Description \| +=========================+===============================+ \| "\n" \| Line Feed \| +-------------------------+-------------------------------+ \| "\r" \| Carriage Return \| +-------------------------+-------------------------------+ \| "\r\n" \| Carriage Return + Line Feed \| +-------------------------+-------------------------------+ \| "\v" or "\x0b" \| Line Tabulation \| +-------------------------+-------------------------------+ \| "\f" or "\x0c" \| Form Feed \| +-------------------------+-------------------------------+ \| "\x1c" \| File Separator \| +-------------------------+-------------------------------+ \| "\x1d" \| Group Separator \| +-------------------------+-------------------------------+ \| "\x1e" \| Record Separator \| +-------------------------+-------------------------------+ \| "\x85" \| Next Line (C1 Control Code) \| +-------------------------+-------------------------------+ \| "\u2028" \| Line Separator \| +-------------------------+-------------------------------+ \| "\u2029" \| Paragraph Separator \| +-------------------------+-------------------------------+ Changed in version 2.7: "\v" and "\f" added to list of line boundaries. 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. Changed in version 2.5: Accept tuples as prefix. 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' Changed in version 2.2.2: Support for the chars argument. str.swapcase() Return a copy of the string with uppercase characters converted to lowercase and vice versa. For 8-bit strings, this method is locale-dependent. 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." For 8-bit strings, this method is locale-dependent. str.translate(table[, deletechars]) Return a copy of the string where all characters occurring in the optional argument deletechars are removed, and the remaining characters have been mapped through the given translation table, which must be a string of length 256. You can use the "maketrans()" helper function in the "string" module to create a translation table. For string objects, set the table argument to "None" for translations that only delete characters: >>> 'read this short text'.translate(None, 'aeiou') 'rd ths shrt txt' New in version 2.6: Support for a "None" table argument. For Unicode objects, the "translate()" method does not accept the optional deletechars argument. Instead, it returns a copy of the s where all characters have been mapped through the given translation table which must be a mapping of Unicode ordinals to Unicode ordinals, Unicode strings or "None". Unmapped characters are left untouched. Characters mapped to "None" are deleted. Note, a 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). For 8-bit strings, this method is locale-dependent. 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)". New in version 2.2.2. The following methods are present only on unicode objects: unicode.isnumeric() Return "True" if there are only numeric characters in S, "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. unicode.isdecimal() Return "True" if there are only decimal characters in S, "False" otherwise. Decimal characters include digit characters, and all characters that can be used to form decimal-radix numbers, e.g. U+0660, ARABIC-INDIC DIGIT ZERO. String Formatting Operations ============================ String and Unicode objects have one unique built-in operation: the "%" operator (modulo). This is also known as the string formatting or interpolation operator. Given "format % values" (where format is a string or Unicode object), "%" conversion specifications in format are replaced with zero or more elements of values. The effect is similar to the using "sprintf()" in the C language. If format is a Unicode object, or if any of the objects being converted using the "%s" conversion are Unicode objects, the result will also be a Unicode object. If format requires a single argument, values may be a single non- tuple object. [5] Otherwise, values must be a tuple with exactly the number of items specified by the format string, or a single mapping object (for example, a dictionary). A conversion specifier contains two or more characters and has the following components, which must occur in this order: 1. The "'%'" character, which marks the start of the specifier. 2. Mapping key (optional), consisting of a parenthesised sequence of characters (for example, "(somename)"). 3. Conversion flags (optional), which affect the result of some conversion types. 4. Minimum field width (optional). If specified as an "''" (asterisk), the actual width is read from the next element of the tuple in values, and the object to convert comes after the minimum field width and optional precision. 5. Precision (optional), given as a "'.'" (dot) followed by the precision. If specified as "''" (an asterisk), the actual width is read from the next element of the tuple in values, and the value to convert comes after the precision. 6. Length modifier (optional). 7. Conversion type. When the right argument is a dictionary (or other mapping type), then the formats in the string must include a parenthesised mapping key into that dictionary inserted immediately after the "'%'" character. The mapping key selects the value to be formatted from the mapping. For example: >>> print '%(language)s has %(number)03d quote types.' % \ ... {"language": "Python", "number": 2} Python has 002 quote types. In this case no "" specifiers may occur in a format (since they require a sequential parameter list). The conversion flag characters are: +-----------+-----------------------------------------------------------------------+ \| Flag \| Meaning \| +===========+=======================================================================+ \| "'#'" \| The value conversion will use the "alternate form" (where defined \| \| \| below). \| +-----------+-----------------------------------------------------------------------+ \| "'0'" \| The conversion will be zero padded for numeric values. \| +-----------+-----------------------------------------------------------------------+ \| "'-'" \| The converted value is left adjusted (overrides the "'0'" conversion \| \| \| if both are given). \| +-----------+-----------------------------------------------------------------------+ \| "' '" \| (a space) A blank should be left before a positive number (or empty \| \| \| string) produced by a signed conversion. \| +-----------+-----------------------------------------------------------------------+ \| "'+'" \| A sign character ("'+'" or "'-'") will precede the conversion \| \| \| (overrides a "space" flag). \| +-----------+-----------------------------------------------------------------------+ A length modifier ("h", "l", or "L") may be present, but is ignored as it is not necessary for Python -- so e.g. "%ld" is identical to "%d". The conversion types are: +--------------+-------------------------------------------------------+---------+ \| Conversion \| Meaning \| Notes \| +==============+=======================================================+=========+ \| "'d'" \| Signed integer decimal. \| \| +--------------+-------------------------------------------------------+---------+ \| "'i'" \| Signed integer decimal. \| \| +--------------+-------------------------------------------------------+---------+ \| "'o'" \| Signed octal value. \| (1) \| +--------------+-------------------------------------------------------+---------+ \| "'u'" \| Obsolete type -- it is identical to "'d'". \| (7) \| +--------------+-------------------------------------------------------+---------+ \| "'x'" \| Signed hexadecimal (lowercase). \| (2) \| +--------------+-------------------------------------------------------+---------+ \| "'X'" \| Signed hexadecimal (uppercase). \| (2) \| +--------------+-------------------------------------------------------+---------+ \| "'e'" \| Floating point exponential format (lowercase). \| (3) \| +--------------+-------------------------------------------------------+---------+ \| "'E'" \| Floating point exponential format (uppercase). \| (3) \| +--------------+-------------------------------------------------------+---------+ \| "'f'" \| Floating point decimal format. \| (3) \| +--------------+-------------------------------------------------------+---------+ \| "'F'" \| Floating point decimal format. \| (3) \| +--------------+-------------------------------------------------------+---------+ \| "'g'" \| Floating point format. Uses lowercase exponential \| (4) \| \| \| format if exponent is less than -4 or not less than \| \| \| \| precision, decimal format otherwise. \| \| +--------------+-------------------------------------------------------+---------+ \| "'G'" \| Floating point format. Uses uppercase exponential \| (4) \| \| \| format if exponent is less than -4 or not less than \| \| \| \| precision, decimal format otherwise. \| \| +--------------+-------------------------------------------------------+---------+ \| "'c'" \| Single character (accepts integer or single character \| \| \| \| string). \| \| +--------------+-------------------------------------------------------+---------+ \| "'r'" \| String (converts any Python object using repr()). \| (5) \| +--------------+-------------------------------------------------------+---------+ \| "'s'" \| String (converts any Python object using "str()"). \| (6) \| +--------------+-------------------------------------------------------+---------+ \| "'%'" \| No argument is converted, results in a "'%'" \| \| \| \| character in the result. \| \| +--------------+-------------------------------------------------------+---------+ Notes: 1. The alternate form causes a leading zero ("'0'") to be inserted between left-hand padding and the formatting of the number if the leading character of the result is not already a zero. 2. The alternate form causes a leading "'0x'" or "'0X'" (depending on whether the "'x'" or "'X'" format was used) to be inserted before the first digit. 3. The alternate form causes the result to always contain a decimal point, even if no digits follow it. The precision determines the number of digits after the decimal point and defaults to 6. 4. The alternate form causes the result to always contain a decimal point, and trailing zeroes are not removed as they would otherwise be. The precision determines the number of significant digits before and after the decimal point and defaults to 6. 5. The "%r" conversion was added in Python 2.0. The precision determines the maximal number of characters used. 6. If the object or format provided is a "unicode" string, the resulting string will also be "unicode". The precision determines the maximal number of characters used. 7. See PEP 237. Since Python strings have an explicit length, "%s" conversions do not assume that "'\0'" is the end of the string. Changed in version 2.7: "%f" conversions for numbers whose absolute value is over 1e50 are no longer replaced by "%g" conversions. Additional string operations are defined in standard modules "string" and "re". XRange Type =========== The "xrange" type is an immutable sequence which is commonly used for looping. The advantage of the "xrange" type is that an "xrange" object will always take the same amount of memory, no matter the size of the range it represents. There are no consistent performance advantages. XRange objects have very little behavior: they only support indexing, iteration, and the "len()" function. Mutable Sequence Types ====================== List and "bytearray" objects support additional operations that allow in-place modification of the object. Other mutable sequence types (when added to the language) should also support these operations. Strings and tuples are immutable sequence types: such objects cannot be modified once created. The following operations are defined on mutable sequence types (where x* is an arbitrary object): +--------------------------------+----------------------------------+-----------------------+ \| Operation \| Result \| Notes \| +================================+==================================+=======================+ \| "s[i] = x" \| item i of s is replaced by \| \| \| \| x \| \| +--------------------------------+----------------------------------+-----------------------+ \| "s[i:j] = t" \| slice of s from i to j is \| \| \| \| replaced by the contents of the \| \| \| \| iterable t \| \| +--------------------------------+----------------------------------+-----------------------+ \| "del s[i:j]" \| same as "s[i:j] = []" \| \| +--------------------------------+----------------------------------+-----------------------+ \| "s[i:j:k] = t" \| the elements of "s[i:j:k]" are \| (1) \| \| \| replaced by those of t \| \| +--------------------------------+----------------------------------+-----------------------+ \| "del s[i:j:k]" \| removes the elements of \| \| \| \| "s[i:j:k]" from the list \| \| +--------------------------------+----------------------------------+-----------------------+ \| "s.append(x)" \| same as "s[len(s):len(s)] = [x]" \| (2) \| +--------------------------------+----------------------------------+-----------------------+ \| "s.extend(t)" or "s += t" \| for the most part the same as \| (3) \| \| \| "s[len(s):len(s)] = t" \| \| +--------------------------------+----------------------------------+-----------------------+ \| "s = n" \| updates s* with its contents \| (11) \| \| \| repeated n times \| \| +--------------------------------+----------------------------------+-----------------------+ \| "s.count(x)" \| return number of i's for which \| \| \| \| "s[i] == x" \| \| +--------------------------------+----------------------------------+-----------------------+ \| "s.index(x[, i[, j]])" \| return smallest k such that \| (4) \| \| \| "s[k] == x" and "i <= k < j" \| \| +--------------------------------+----------------------------------+-----------------------+ \| "s.insert(i, x)" \| same as "s[i:i] = [x]" \| (5) \| +--------------------------------+----------------------------------+-----------------------+ \| "s.pop([i])" \| same as "x = s[i]; del s[i]; \| (6) \| \| \| return x" \| \| +--------------------------------+----------------------------------+-----------------------+ \| "s.remove(x)" \| same as "del s[s.index(x)]" \| (4) \| +--------------------------------+----------------------------------+-----------------------+ \| "s.reverse()" \| reverses the items of s in \| (7) \| \| \| place \| \| +--------------------------------+----------------------------------+-----------------------+ \| "s.sort([cmp[, key[, \| sort the items of s in place \| (7)(8)(9)(10) \| \| reverse]]])" \| \| \| +--------------------------------+----------------------------------+-----------------------+ Notes: 1. t must have the same length as the slice it is replacing. 2. The C implementation of Python has historically accepted multiple parameters and implicitly joined them into a tuple; this no longer works in Python 2.0. Use of this misfeature has been deprecated since Python 1.4. 3. t can be any iterable object. 4. Raises "ValueError" when x is not found in s. When a negative index is passed as the second or third parameter to the "index()" method, the list length is added, as for slice indices. If it is still negative, it is truncated to zero, as for slice indices. Changed in version 2.3: Previously, "index()" didn't have arguments for specifying start and stop positions. 5. When a negative index is passed as the first parameter to the "insert()" method, the list length is added, as for slice indices. If it is still negative, it is truncated to zero, as for slice indices. Changed in version 2.3: Previously, all negative indices were truncated to zero. 6. The "pop()" method's optional argument i defaults to "-1", so that by default the last item is removed and returned. 7. The "sort()" and "reverse()" methods modify the list in place for economy of space when sorting or reversing a large list. To remind you that they operate by side effect, they don't return the sorted or reversed list. 8. The "sort()" method takes optional arguments for controlling the comparisons. cmp specifies a custom comparison function of two arguments (list items) which should return a negative, zero or positive number depending on whether the first argument is considered smaller than, equal to, or larger than the second argument: "cmp=lambda x,y: cmp(x.lower(), y.lower())". The default value is "None". key specifies a function of one argument that is used to extract a comparison key from each list element: "key=str.lower". The default value is "None". reverse is a boolean value. If set to "True", then the list elements are sorted as if each comparison were reversed. In general, the key and reverse conversion processes are much faster than specifying an equivalent cmp function. This is because cmp is called multiple times for each list element while key and reverse touch each element only once. Use "functools.cmp_to_key()" to convert an old-style cmp function to a key function. Changed in version 2.3: Support for "None" as an equivalent to omitting cmp was added. Changed in version 2.4: Support for key and reverse was added. 9. Starting with Python 2.3, 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). 10. 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 2.3 and newer makes the list appear empty for the duration, and raises "ValueError" if it can detect that the list has been mutated during a sort. 11. The value n is an integer, or an object implementing "__index__()". Zero and negative values of n clear the sequence. Items in the sequence are not copied; they are referenced multiple times, as explained for "s * n" under Sequence Types --- str, unicode, list, tuple, bytearray, buffer, xrange. t��typesseqsI �� Mutable Sequence Types ********************** List and "bytearray" objects support additional operations that allow in-place modification of the object. Other mutable sequence types (when added to the language) should also support these operations. Strings and tuples are immutable sequence types: such objects cannot be modified once created. The following operations are defined on mutable sequence types (where x is an arbitrary object): +--------------------------------+----------------------------------+-----------------------+ \| Operation \| Result \| Notes \| +================================+==================================+=======================+ \| "s[i] = x" \| item i of s is replaced by \| \| \| \| x \| \| +--------------------------------+----------------------------------+-----------------------+ \| "s[i:j] = t" \| slice of s from i to j is \| \| \| \| replaced by the contents of the \| \| \| \| iterable t \| \| +--------------------------------+----------------------------------+-----------------------+ \| "del s[i:j]" \| same as "s[i:j] = []" \| \| +--------------------------------+----------------------------------+-----------------------+ \| "s[i:j:k] = t" \| the elements of "s[i:j:k]" are \| (1) \| \| \| replaced by those of t \| \| +--------------------------------+----------------------------------+-----------------------+ \| "del s[i:j:k]" \| removes the elements of \| \| \| \| "s[i:j:k]" from the list \| \| +--------------------------------+----------------------------------+-----------------------+ \| "s.append(x)" \| same as "s[len(s):len(s)] = [x]" \| (2) \| +--------------------------------+----------------------------------+-----------------------+ \| "s.extend(t)" or "s += t" \| for the most part the same as \| (3) \| \| \| "s[len(s):len(s)] = t" \| \| +--------------------------------+----------------------------------+-----------------------+ \| "s = n" \| updates s* with its contents \| (11) \| \| \| repeated n times \| \| +--------------------------------+----------------------------------+-----------------------+ \| "s.count(x)" \| return number of i's for which \| \| \| \| "s[i] == x" \| \| +--------------------------------+----------------------------------+-----------------------+ \| "s.index(x[, i[, j]])" \| return smallest k such that \| (4) \| \| \| "s[k] == x" and "i <= k < j" \| \| +--------------------------------+----------------------------------+-----------------------+ \| "s.insert(i, x)" \| same as "s[i:i] = [x]" \| (5) \| +--------------------------------+----------------------------------+-----------------------+ \| "s.pop([i])" \| same as "x = s[i]; del s[i]; \| (6) \| \| \| return x" \| \| +--------------------------------+----------------------------------+-----------------------+ \| "s.remove(x)" \| same as "del s[s.index(x)]" \| (4) \| +--------------------------------+----------------------------------+-----------------------+ \| "s.reverse()" \| reverses the items of s in \| (7) \| \| \| place \| \| +--------------------------------+----------------------------------+-----------------------+ \| "s.sort([cmp[, key[, \| sort the items of s in place \| (7)(8)(9)(10) \| \| reverse]]])" \| \| \| +--------------------------------+----------------------------------+-----------------------+ Notes: 1. t must have the same length as the slice it is replacing. 2. The C implementation of Python has historically accepted multiple parameters and implicitly joined them into a tuple; this no longer works in Python 2.0. Use of this misfeature has been deprecated since Python 1.4. 3. t can be any iterable object. 4. Raises "ValueError" when x is not found in s. When a negative index is passed as the second or third parameter to the "index()" method, the list length is added, as for slice indices. If it is still negative, it is truncated to zero, as for slice indices. Changed in version 2.3: Previously, "index()" didn't have arguments for specifying start and stop positions. 5. When a negative index is passed as the first parameter to the "insert()" method, the list length is added, as for slice indices. If it is still negative, it is truncated to zero, as for slice indices. Changed in version 2.3: Previously, all negative indices were truncated to zero. 6. The "pop()" method's optional argument i defaults to "-1", so that by default the last item is removed and returned. 7. The "sort()" and "reverse()" methods modify the list in place for economy of space when sorting or reversing a large list. To remind you that they operate by side effect, they don't return the sorted or reversed list. 8. The "sort()" method takes optional arguments for controlling the comparisons. cmp specifies a custom comparison function of two arguments (list items) which should return a negative, zero or positive number depending on whether the first argument is considered smaller than, equal to, or larger than the second argument: "cmp=lambda x,y: cmp(x.lower(), y.lower())". The default value is "None". key specifies a function of one argument that is used to extract a comparison key from each list element: "key=str.lower". The default value is "None". reverse is a boolean value. If set to "True", then the list elements are sorted as if each comparison were reversed. In general, the key and reverse conversion processes are much faster than specifying an equivalent cmp function. This is because cmp is called multiple times for each list element while key and reverse touch each element only once. Use "functools.cmp_to_key()" to convert an old-style cmp function to a key function. Changed in version 2.3: Support for "None" as an equivalent to omitting cmp was added. Changed in version 2.4: Support for key and reverse was added. 9. Starting with Python 2.3, 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). 10. 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 2.3 and newer makes the list appear empty for the duration, and raises "ValueError" if it can detect that the list has been mutated during a sort. 11. The value n is an integer, or an object implementing "__index__()". Zero and negative values of n clear the sequence. Items in the sequence are not copied; they are referenced multiple times, as explained for "s * n" under Sequence Types --- str, unicode, list, tuple, bytearray, buffer, xrange. s��typesseq-mutables�� 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 plain or long 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. t��unarys�� 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. t��whiles� �� The "with" statement ****************** New in version 2.5. The "with" statement is used to wrap the execution of a block with methods defined by a context manager (see section With Statement Context Managers). This allows common "try"..."except"..."finally" usage patterns to be encapsulated for convenient reuse. with_stmt ::= "with" with_item ("," with_item)* ":" suite with_item ::= expression ["as" target] The execution of the "with" statement with one "item" proceeds as follows: 1. The context expression (the expression given in the "with_item") is evaluated to obtain a context manager. 2. The context manager's "__exit__()" is loaded for later use. 3. The context manager's "__enter__()" method is invoked. 4. If a target was included in the "with" statement, the return value from "__enter__()" is assigned to it. Note: The "with" statement guarantees that if the "__enter__()" method returns without an error, then "__exit__()" will always be called. Thus, if an error occurs during the assignment to the target list, it will be treated the same as an error occurring within the suite would be. See step 6 below. 5. The suite is executed. 6. The context manager's "__exit__()" method is invoked. If an exception caused the suite to be exited, its type, value, and traceback are passed as arguments to "__exit__()". Otherwise, three "None" arguments are supplied. If the suite was exited due to an exception, and the return value from the "__exit__()" method was false, the exception is reraised. If the return value was true, the exception is suppressed, and execution continues with the statement following the "with" statement. If the suite was exited for any reason other than an exception, the return value from "__exit__()" is ignored, and execution proceeds at the normal location for the kind of exit that was taken. With more than one item, the context managers are processed as if multiple "with" statements were nested: with A() as a, B() as b: suite is equivalent to with A() as a: with B() as b: suite Note: In Python 2.5, the "with" statement is only allowed when the "with_statement" feature has been enabled. It is always enabled in Python 2.6. Changed in version 2.7: Support for multiple context expressions. See also: PEP 343 - The "with" statement The specification, background, and examples for the Python "with" statement. t��withs�� The "yield" statement ******************* yield_stmt ::= yield_expression The "yield" statement is only used when defining a generator function, and is only used in the body of the generator function. Using a "yield" statement in a function definition is sufficient to cause that definition to create a generator function instead of a normal function. When a generator function is called, it returns an iterator known as a generator iterator, or more commonly, a generator. The body of the generator function is executed by calling the generator's "next()" method repeatedly until it raises an exception. When a "yield" statement is executed, the state of the generator is frozen and the value of "expression_list" is returned to "next()"'s caller. By "frozen" we mean that all local state is retained, including the current bindings of local variables, the instruction pointer, and the internal evaluation stack: enough information is saved so that the next time "next()" is invoked, the function can proceed exactly as if the "yield" statement were just another external call. As of Python version 2.5, the "yield" statement is now allowed in the "try" clause of a "try" ... "finally" construct. If the generator is not resumed before it is finalized (by reaching a zero reference count or by being garbage collected), the generator-iterator's "close()" method will be called, allowing any pending "finally" clauses to execute. For full details of "yield" semantics, refer to the Yield expressions section. Note: In Python 2.2, the "yield" statement was only allowed when the "generators" feature has been enabled. This "__future__" import statement was used to enable the feature: from __future__ import generators See also: PEP 255 - Simple Generators The proposal for adding generators and the "yield" statement to Python. PEP 342** - Coroutines via Enhanced Generators The proposal that, among other generator enhancements, proposed allowing "yield" to appear inside a "try" ... "finally" block. t��yieldN(��t��topics(��(��(��s6��/opt/alt/python27/lib64/python2.7/pydoc_data/topics.pyt��<module>��s�� '��)��9U �� 2 �K��M��A#��%-UJ��K<��,1c��U%+&��{�*;;�Mm��D��&"�� B&��\�Q