Python Language Reference Manual by Guido van Rossum and Fred L. Drake, Jr. Paperback (6"x9"), 120 pages ISBN 0954161785 RRP £12.95 ($19.95) Sales of this book support the Python Software Foundation! Get a printed copy>>>

Python Language Reference Manual Buy the book here! >>> support free software

3.2 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’

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:

‘Integers’

These represent elements from the mathematical set of integers (positive and negative). There are three types of integers:

‘Plain integers’

These represent whole 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 except left shift, if it yields a result in the plain integer domain without causing overflow, will yield the same result in the long integer domain or when using mixed operands.

‘Floating point numbers’

These represent machine-level double-precision floating point numbers. You are at the mercy of the underlying machine architecture (and C or Java implementation) for the accepted range and handling of overflow. Python does not support single-precision floating point numbers; the savings in processor and memory usage that are usually the reason for using these is dwarfed by the overhead of using objects in Python, so there is no reason to complicate the language with two kinds of floating point numbers.

‘Complex numbers’

These represent complex numbers as a pair of machine-level double-precision floating point numbers. The same caveats apply as for floating point numbers. The real and imaginary parts of a complex number z can be retrieved through the read-only attributes z.real and z.imag.

‘Sequences’

These represent finite ordered sets indexed by non-negative numbers. The built-in function len() returns the number of items of a sequence. When the length of a sequence is n, the index set contains the numbers 0, 1, ..., $n-1$. Item i of sequence a is selected by a[i]. Sequences also support slicing: a[i:j] selects all items with index k such that i <= k < j. When used as an expression, a slice is a sequence of the same type. This implies that the index set is renumbered so that it starts at 0. Some sequences also support "extended slicing" with a third "step" parameter: a[i:j:k] selects all items of a with index x where x = i + n*k, n >= 0 and i <= x < j. Sequences are distinguished according to their mutability:

‘Immutable sequences’

An object of an immutable sequence type cannot change once it is created. (If the object contains references to other objects, these other objects may be mutable and may be changed; however, the collection of objects directly referenced by an immutable object cannot change.) The following types are immutable sequences:

‘Strings’

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 {\sc ebcdic} in their internal representation, provided the functions chr() and ord() implement a mapping between ASCII and {\sc 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 is currently a single intrinsic mutable sequence type:

‘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.)

The extension module ‘array’ provides an additional example of a mutable sequence type.

‘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 5.2.6, "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 5.3.4, "Calls") can be applied:

‘User-defined functions’

A user-defined function object is created by a function definition (see section 7.6, "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
-------	------	------
func_doc	The function's documentation string, or None if unavailable	Writable
__doc__	Another way of spelling func_doc	Writable
func_name	The function's name	Writable
__name__	Another way of spelling func_name	Writable
__module__	The name of the module the function was defined in, or None if unavailable.	Writable
func_defaults	A tuple containing default argument values for those arguments that have defaults, or None if no arguments have a default value	Writable
func_code	The code object representing the compiled function body.	Writable
func_globals	A reference to the dictionary that holds the function's global variables--the global namespace of the module in which the function was defined.	Read-only
func_dict	The namespace supporting arbitrary function attributes.	Writable
func_closure	None or a tuple of cells that contain bindings for the function's free variables.	Read-only

Most of the attributes labelled "Writable" check the type of the assigned value. (Changed in Python version 2.4) 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 Python version 2.2) 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 (the same as the im_class attribute), 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 6.8, "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 list.

‘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(args) is a shorthand for x.__call__(args).

‘Modules’

Modules are imported by the import statement (see section 6.12, "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. 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’

Class objects are created by class definitions (see section 7.7, "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"]’. When the attribute name is not found there, the attribute search continues in the base classes. The search is depth-first, left-to-right in the order of occurrence in the base class list. 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_class and im_self attributes are both C. When it would yield a static method object, it is transformed into the object wrapped by the static method object. See section 3.4.2.2 for another way in which attributes retrieved from a class may differ from those actually contained in its __dict__. Class attribute assignments update the class's dictionary, never the dictionary of a base class. A class object can be called (see above) to yield a class instance (see below). Special attributes: __name__ is the class name; __module__ is the module name in which the class was defined; __dict__ is the dictionary containing the class's namespace; __bases__ is a tuple (possibly empty or a singleton) containing the base classes, in the order of their occurrence in the base class list; __doc__ is the class's documentation string, or None if undefined.

‘Class instances’

A class instance is created by calling a class object (see above). A class instance has a namespace implemented as a dictionary which is the first place in which attribute references are searched. When an attribute is not found there, and the instance's class has an attribute by that name, the search continues with the class attributes. If a class attribute is found that is a user-defined function object 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 3.4.2.2 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 3.4, "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 the Python Library Reference Manual 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 byte code 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 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 7.4, "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:

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. (Added in Python 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.