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3.2 The standard type hierarchy

Below is a list of the types that are built into Python. Extension modules written in C 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. There are also some `generic' special attributes, not listed with the individual objects: `__methods__` is a list of the method names of a built-in object, if it has any; `__members__` is a list of the data attribute names of a built-in object, if it has any.

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.

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 and floating point numbers:

  Integers
    These represent elements from the mathematical set of whole numbers.

    There are two 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 falls outside this range, the exception `OverflowError` is raised. 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.

    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. For any operation except left shift, if it yields a result in the plain integer domain without causing overflow, it 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 implementation for the accepted range and handling of overflow. Python does not support single-precision floating point numbers; the savings in CPU 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 value of a complex number `z` can be retrieved through the attributes `z.real`and `z.imag`.

Sequences
  These represent finite ordered sets indexed by natural numbers. The built-in function `len()` returns the number of items of a sequence. When the lenth 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.

  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?)

    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 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 are created by the `{...}` notation (see section 5.2.5, ``Dictionary Displays'').

    The extension modules `dbm`, `gdbm`, `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 7.5, ``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: `func_doc` or `__doc__` is the function's documentation string, or None if unavailable; `func_name` or `__name__` is the function's name; `func_defaults` is a tuple containing default argument values for those arguments that have defaults, or `None` if no arguments have a default value; `func_code` is the code object representing the compiled function body; `func_globals` is (a reference to) the dictionary that holds the function's global variables -- it defines the global namespace of the module in which the function was defined. Of these, `func_code`, `func_defaults` and `func_doc` (and this `__doc__`) may be writable; the others can never be changed. 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 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 that defined the method (which may be a base class of the class of which `im_self` is an instance); `__doc__` is the method's documentation (same as `im_func.__doc__`); `__name__` is the method name (same as `im_func.__name__`).

    User-defined method objects are created in two ways: when getting an attribute of a class that is a user-defined function object, or when getting an attributes of a class instance that is a user-defined function object. In the former case (class attribute), the `im_self` attribute is `None`, and the method object is said to be unbound; in the latter case (instance attribute), `im_self` is the instance, and the method object is said to be bound. For instance, when `C` is a class which contains a definition for a function `f()`, `C.f` does not yield the function object `f`; rather, it yields an unbound method object `m` where `m.im_class` is `C`, `m.im_func` is `f()`, and `m.im_self` is `None`. When `x` is a `C` instance, `x.f` yields a bound method object `m` where `m.im_class` is `C`, `m.im_func` is `f()`, and `m.im_self` is `x`.

    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)`.

    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.

  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).

  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 `list.append()`, assuming list is a list object. In this case, the special read-only attribute `__self__` is set to the object denoted by `list`.

  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 6.11, ``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 7.6, ``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 would yield a user-defined function object, it is transformed into an unbound user-defined method object (see above). The `im_class` attribute of this method object is the class in which the function object was found, not necessarily the class for which the attribute reference was initiated.

  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 (and in no other case), it is transformed into an unbound user-defined method object (see above). The `im_class` attribute of this method object is the class in which the function object was found, not necessarily the class of the instance for which the attribute reference was initiated. 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 3.3, ``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 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_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 detais 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 2 is set if the function uses the "`*arguments`" syntax to accept an arbitrary number of positional arguments; bit 3 is set if the function uses the "`**keywords`" syntax to accept arbitrary keyword arguments; other bits are used internally or reserved for future 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_lineno` gives the line number and `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 most recent exception caught in this frame.

  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 lowerbound; `stop` is the upperbound; `step` is the step value; each is `None` if omitted. These attributes can have any type.