Note

In Python, a type and its constructor function are the same thing. So you can use the function, which we will discuss momentarily, to look up the type of an object, and then make instances of that same type.

bool([x])?

Convert a value to a Boolean, using the standard truth-testing procedure. If x is false or omitted, this returns False; otherwise it returns True. As noted at the start of this section, bool is also a class, which is a subclass of int. Class bool cannot be subclassed further. Its only instances are False and True. If no argument is given, this function returns False.

chr(i)?

Return a string of one character whose ASCII code is the integer i. For example, chr(97) returns the string ‘a(chǎn).’ This is the inverse of ord. The argument must be in the range [0..255], inclusive; ValueError will be raised if i is outside that range. See also unichr.

complex([real[, imag]])?

Create a complex number with the value real + imag*j or convert a string or number to a complex number. If the first parameter is a string, it will be interpreted as a complex number and the function must be called without a second parameter. The second parameter can never be a string. Each argument may be any numeric type (including complex). If imag is omitted, it defaults to zero and the function serves as a numeric conversion function like int, long and float. If both arguments are omitted, returns 0j.

dict([arg])?

Create a new data dictionary, optionally with items taken from arg. For other containers see the built-in list, set, and tuple classes, and the collections module. There is a convenient literal for creating dict objects:

a_dict = { 'alpha' : 1, 'beta' : 2, 'gamma' : 3 }

It can be more convenient to create dict objects using the dict function:

a_dict = dict(alpha=1, beta=2, gamma=3)

In this latter case, keyword arguments are passed where the argument names become the keys. Similarly, you can pass an iterator to the dict function which produces pairs.

file(filename[, mode[, bufsize]])?

Constructor function for the file type. The constructor’s arguments are the same as those of the open built-in function described in the following. When opening a file, it’s preferable to use open instead of invoking this constructor directly. file is more suited to type testing (for example, writing isinstance(f, file)). Version Added: 2.2.

float([x])?

Convert a string or a number to floating point. If the argument is a string, it must contain a possibly signed decimal or floating point number, possibly embedded in whitespace. The argument may also be [+|-]nan or [+|-]inf. Otherwise, the argument may be a plain or long integer or a floating point number, and a floating point number with the same value (within Python’s floating point precision) is returned. If no argument is given, returns 0.0.

frozenset([iterable])?

Return a new frozenset object whose elements are taken from iterable. The elements of a set must be hashable. To represent sets of sets, the inner sets must be frozenset objects. If iterable is not specified, a new empty frozenset is returned.

int([x[, radix]])?

Convert a string or number to a plain integer. If the argument is a string, it must contain a possibly signed decimal number representable as a Python integer, possibly embedded in whitespace. The radix parameter gives the base for the conversion (which is 10 by default) and may be any integer in the range [2, 36], or zero. If radix is zero, the proper radix is determined based on the contents of string; the interpretation is the same as for integer literals. (See numbers.) If radix is specified and x is not a string, TypeError is raised. Otherwise, the argument may be a plain or long integer or a floating point number. Conversion of floating point numbers to integers truncates (towards zero). If the argument is outside the integer range a long object will be returned instead. If no arguments are given, returns 0.

iter(o[, sentinel])?

Return an iterator object. The first argument is interpreted very differently depending on the presence of the second argument. Without a second argument, o must be a collection object, which supports the iteration protocol (the __iter__ method), or it must support the sequence protocol (the __getitem__ method with integer arguments starting at 0). If it does not support either of those protocols, TypeError is raised. If the second argument, sentinel, is given, then o must be a callable object. The iterator created in this case will call o with no arguments for each call to its next method; if the value returned is equal to sentinel, StopIteration will be raised, otherwise the value will be returned. Version Added: 2.2.

list([iterable])?

Return a list whose items are the same and in the same order as iterable’s items. Iterable may be either a sequence, a container that supports iteration, or an iterator object. If iterable is already a list, a copy is made and returned, similar to iterable[:]. For instance, list(‘a(chǎn)bc’) returns [‘a(chǎn)’, ‘b’, ‘c’] and list( (1, 2, 3) ) returns [1, 2, 3]. If no argument is given, returns a new empty list, [].

object()?

Return a new featureless object. Object is a base for all new style classes. It has the methods that are common to all instances of new style classes. Version Added: 2.2. Version Changed: CPython 2.3, Jython 2.5.0. This function does not accept any arguments. Formerly, it accepted arguments but ignored them.

open(filename[, mode[, bufsize]])?

Open a file, returning an object of the file type described previously. If the file cannot be opened, IOError is raised. When opening a file, it’s preferable to use open instead of invoking the file constructor directly. The first argument is the file name to be opened, and mode is a string indicating how the file is to be opened. The most commonly-used values of mode are ‘r’ for reading, ‘w’ for writing (truncating the file if it already exists), and ‘a(chǎn)’ for appending (which on some Unix systems means that all writes append to the end of the file regardless of the current seek position). If mode is omitted, it defaults to ‘r’. The default is to use text mode, which may convert ‘n’ characters to a platform-specific representation on writing and back on reading. Thus, when opening a binary file, you should append ‘b’ to the mode value to open the file in binary mode, which will improve portability. (Appending ‘b’ is useful even on systems that don’t treat binary and text files differently, where it serves as documentation.) The optional bufsize argument specifies the file’s desired buffer size: 0 means unbuffered, 1 means line buffered, any other positive value means use a buffer of (approximately) that size in bytes. A negative bufsize means to use the system default, which is usually line buffered for tty devices and fully buffered for other files. If omitted, the system default is used. Modes ‘r+’, ‘w+’ and ‘a(chǎn)+’ open the file for updating (note that ‘w+’ truncates the file). Append ‘b’ to the mode to open the file in binary mode, on systems that differentiate between binary and text files; on systems that don’t have this distinction, adding the ‘b’ has no effect. In addition to the standard fopen values mode may be ‘U’ or ‘rU’. Python is usually built with universal newline support; supplying ‘U’ opens the file as a text file, but lines may be terminated by any of the following: the Unix end-of-line convention ‘n’, the Macintosh convention ‘r’, or the Windows convention ‘rn’. All of these external representations are seen as ‘n’ by the Python program. If Python is built without universal newline support a mode with ‘U’ is the same as normal text mode. Note that open file objects also have an attribute called newlines which has a value of None (if no newlines have yet been seen), ‘n’, ‘r’, ‘rn’, or a tuple containing all the newline types seen. Python enforces that the mode, after stripping ‘U’, begins with ‘r’, ‘w’ or ‘a(chǎn)’. Python provides many file handling modules including fileinput, os, os.path, tempfile, and shutil.

range([start,] stop[, step])?

This is a versatile function to create lists containing arithmetic progressions. It is most often used in for loops. However, we recommend the use of xrange instead. The arguments must be plain integers. If the step argument is omitted, it defaults to 1. If the start argument is omitted, it defaults to 0. The full form returns a list of plain integers [start, start + step, start + 2 * step, ...]. If step is positive, the last element is the largest start + i * step less than stop; if step is negative, the last element is the smallest start + i * step greater than stop. step must not be zero (or else ValueError is raised). Some examples:

>>> range(10)
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> range(1, 11)
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
>>> range(0, 30, 5)
[0, 5, 10, 15, 20, 25]
>>> range(0, 10, 3)
[0, 3, 6, 9]
>>> range(0, -10, -1)
[0, -1, -2, -3, -4, -5, -6, -7, -8, -9]
>>> range(0)
[]
>>> range(1, 0)
[]

set([iterable])?

Return a new set, optionally with elements are taken from iterable. For other containers see the built-in dict, list, and tuple classes, and the collections module. Version Added: CPython 2.4, Jython 2.5.0.

slice([start,] stop[, step])?

Return a slice object representing the set of indices specified by range(start, stop, step). The start and step arguments default to None. Slice objects have read-only data attributes start, stop, and step, which merely return the argument values (or their default). They have no other explicit functionality; however they are used by Numerical Python and other third party extensions. Slice objects are also generated when extended indexing syntax is used. For example:

a[start:stop:step] or a[start:stop, i].

str([object])?

Return a string containing a nicely printable representation of an object. For strings, this returns the string itself. The difference with repr(object) is that str(object) does not always attempt to return a string that is acceptable to eval; its goal is to return a printable string. If no argument is given, returns the empty string, ‘’.

tuple([iterable])?

Return a tuple whose items are the same and in the same order as iterable’s items. Iterable may be a sequence, a container that supports iteration, or an iterator object. If iterable is already a tuple, it is returned unchanged. For instance, tuple(‘a(chǎn)bc’) returns (‘a(chǎn)’, ‘b’, ‘c’) and tuple([1, 2, 3]) returns (1, 2, 3). If no argument is given, returns a new empty tuple, ().

type(name, bases, dict)?

Return a new type object. This is essentially a dynamic form of the class statement. The name string is the class name and becomes the __name__ attribute; the bases tuple itemizes the base classes and becomes the __bases__ attribute; and the dict dictionary is the namespace containing definitions for class body and becomes the __dict__ attribute. For example, the following two statements create identical type objects:

>>> class X(object):
...     a = 1
...
>>> X = type('X', (object,), dict(a=1))

Version Added: Jython 2.2.

unichr(i)?

Return the Unicode string of one character whose Unicode code is the integer i. For example, unichr(97) returns the string u’a’. This is the inverse of ord for Unicode strings. The valid range for the argument depends how Python was configured—it may be either UCS2 [0..0xFFFF] or UCS4 [0..0x10FFFF]. ValueError is raised if i is outside this range. For ASCII and 8-bit strings see chr. Version Added: Jython 2.0.

unicode([object[, encoding [, errors]]])?

Return the Unicode string version of object using one of the following modes: If encoding and/or errors are given, unicode() will decode the object which can either be an 8-bit string or a character buffer using the codec for encoding. The encoding parameter is a string giving the name of an encoding; if the encoding is not known, LookupError is raised. Error handling is done according to errors; this specifies the treatment of characters which are invalid in the input encoding. If errors is ‘strict’ (the default), a ValueError is raised on errors, while a value of ‘ignore’ causes errors to be silently ignored, and a value of ‘replace’ causes the official Unicode replacement character, U+FFFD, to be used to replace input characters which cannot be decoded. See also the codecs module. If no optional parameters are given, unicode() will mimic the behavior of str() except that it returns Unicode strings instead of 8-bit strings. More precisely, if object is a Unicode string or subclass it will return that Unicode string without any additional decoding applied. For objects which provide a __unicode__ method, it will call this method without arguments to create a Unicode string. For all other objects, the 8-bit string version or representation is requested and then converted to a Unicode string using the codec for the default encoding in ‘strict’ mode.

xrange([start,] stop[, step])?

This function is very similar to range, but returns an “xrange object” instead of a list. This is an opaque sequence type which yields the same values as the corresponding list, without actually storing them all simultaneously. The advantage of xrange over range is minimal (since xrange still has to create the values when asked for them) except when a very large range is used on a memory-starved machine or when all of the range’s elements are never used (such as when the loop is usually terminated with break).

abs(x)?

Return the absolute value of a number. The argument may be a plain or long integer or a floating point number. If the argument is a complex number, its magnitude is returned.

cmp(x, y)?

Compare the two objects x and y and return an integer according to the outcome. The return value is negative if x < y, zero if x == y and strictly positive if x > y.

divmod(a, b)?

Take two (noncomplex) numbers as arguments and return a pair of numbers consisting of their quotient and remainder when using long division. With mixed operand types, the rules for binary arithmetic operators apply. For plain and long integers, the result is the same as (a // b, a % b). For floating point numbers the result is (q, a% b), where q is usually math.floor(a / b) but may be 1 less than that. In any case q * b + a % b is very close to a, if a % b is non-zero it has the same sign as b, and 0 <= abs(a % b) < abs(b). Changed in Jython 2.5.0: Using divmod() with complex numbers is deprecated.

pow(x, y[, z])?

Return x to the power y; if z is present, return x to the power y, modulo z (computed more efficiently than pow(x, y) % z). The two-argument form pow(x, y) is equivalent to using the power operator: x**y. The arguments must have numeric types. With mixed operand types, the coercion rules for binary arithmetic operators apply. For int and long int operands, the result has the same type as the operands (after coercion) unless the second argument is negative; in that case, all arguments are converted to float and a float result is delivered. For example, 10**2 returns 100, but 10**-2 returns 0.01. If the second argument is negative, the third argument must be omitted. If z is present, x and y must be of integer types, and y must be non-negative.

round(x[, n])?

Return the floating point value x rounded to n digits after the decimal point. If n is omitted, it defaults to zero. The result is a floating point number. Values are rounded to the closest multiple of 10 to the power minus n; if two multiples are equally close, rounding is done away from 0 (for example, round(0.5) is 1.0 and round(-0.5) is -1.0).

all(iterable)?

The all() function returns True if all elements of the iterable are true (or if the iterable is empty). It is equivalent to the following:

def all(iterable):
    for element in iterable:
        if not element:
            return False
    return True

Version Added: 2.5.

any(iterable)?

The any() function returns True if any of the elements of the iterable are true (or False if iterable is empty). It is equivalent to the following:

def any(iterable):
    for element in iterable:
        if element:
            return True
    return False

Version Added: 2.5.

enumerate(sequence[, start=0])?

Return an enumerate object. sequence must be a sequence, an iterator, or some other object which supports iteration. The next() method of the iterator returned by enumerate() returns a tuple containing a count (from start which defaults to 0) and the corresponding value obtained from iterating over iterable. enumerate() is useful for obtaining an indexed series: (0, seq[0]), (1, seq[1]), (2, seq[2]), ....

filter(function, iterable)?

Construct a list from those elements of iterable for which function returns true. Iterable may be either a sequence, a container which supports iteration, or an iterator. If iterable is a string or a tuple, the result also has that type; otherwise it is always a list. If function is None, the identity function is assumed, that is, all elements of iterable that are false are removed. Note that filter(function, iterable) is equivalent to [item for item in iterable if function(item)] if function is not None and [item for item in iterable if item] if function is  None.

map(function, iterable, ...)?

Apply function to every item of iterable and return a list of the results. If additional iterable arguments are passed, function must take that many arguments and is applied to the items from all iterables in parallel. If one iterable is shorter than another it is assumed to be extended with None items. If function is None, the identity function is assumed; if there are multiple arguments, map() returns a list consisting of tuples containing the corresponding items from all iterables (a kind of transpose operation). The iterable arguments may be a sequence or any iterable object; the result is always a list.

max(iterable[, key])or max([, arg, ...][, key])?

With a single argument iterable, return the largest item of a non-empty iterable (such as a string, tuple or list). With more than one argument, return the largest of the arguments. The optional key argument specifies a one-argument ordering function like that used for list.sort(). The key argument, if supplied, must be in keyword form (for example, max(a,b,c,key=func)). Changed in version 2.5: Added support for the optional key argument.

min(iterable[, key]) or min([, arg, ...][, key])?

With a single argument iterable, return the smallest item of a non-empty iterable (such as a string, tuple or list). With more than one argument, return the smallest of the arguments. The optional key argument specifies a one-argument ordering function like that used for list.sort(). The key argument, if supplied, must be in keyword form (for example, min(a,b,c,key=func)). Changed in version 2.5: Added support for the optional key argument.

reduce(function, iterable[, initializer])?

Apply function of two arguments cumulatively to the items of iterable, from left to right, so as to reduce the iterable to a single value. For example, reduce(lambda x,y: x+y, [1, 2, 3, 4, 5]) calculates ((((1+2)+3)+4)+5). The left argument, x, is the accumulated value and the right argument, y, is the update value from the iterable. If the optional initializer is present, it is placed before the items of the iterable in the calculation, and serves as a default when the iterable is empty. If initializer is not given and iterable contains only one item, the first item is returned.

reversed(seq)?

Return a reverse iterator (An iterator which gives you the elements of seq in reverse order). The argument seq must be an object which has a __reversed__ method or supports the sequence protocol (the __len__ method and the __getitem__ method with integer arguments starting at 0). Version Added: CPython 2.4, Jython 2.5.

sorted(iterable[, cmp[, key[, reverse]]])?

The sorted function returns a sorted list. Use the optional key argument to specify a key function to control how it’s sorted. So for example, this will sort the list by the length of the elements in it:

>>> sorted(['Massachusetts', 'Colorado', 'New York', 'California', 'Utah'], key=len)
['Utah', 'Colorado', 'New York', 'California', 'Massachusetts']

And this one will sort a list of Unicode strings without regard to it whether the characters are upper or lowercase:

>>> sorted(['apple', 'Cherry', 'banana'], key=str.upper)
['apple', 'banana', 'Cherry']

Although using a key function requires building a decorated version of the list to be sorted, in practice this uses substantially less overhead than calling a cmp function on every comparison. We recommend you take advantage of a keyed sort.

sum(iterable[, start=0])?

Sums start and the items of an iterable from left to right and returns the total. start defaults to 0. The iterable‘s items are normally numbers, and are not allowed to be strings. The fast, correct way to concatenate a sequence of strings is by calling ’‘.join(sequence). Note that sum(range(n), m) is equivalent to reduce(operator.add, range(n), m) To add floating point values with extended precision, see math.fsum().

zip([iterable, ...])?

This function returns a list of tuples, where the i-th tuple contains the i-th element from each of the argument sequences or iterables. The returned list is truncated in length to the length of the shortest argument sequence. When there are multiple arguments which are all of the same length, zip is similar to map with an initial argument of None. With a single sequence argument, it returns a list of 1-tuples. With no arguments, it returns an empty list. The left-to-right evaluation order of the iterables is guaranteed. This makes possible an idiom for clustering a data series into n-length groups using zip(*[iter(s)]*n). zip in conjunction with the * operator can be used to unzip a list:

>>> x = [1, 2, 3]
>>> y = [4, 5, 6]
>>> zipped = zip(x, y)
>>> zipped
[(1, 4), (2, 5), (3, 6)]
>>> x2, y2 = zip(*zipped)
>>> x == x2, y == y2
True

Version Added: 2.0. Version Changed: CPython 2.4, Jython 2.5. Formerly, zip required at least one argument and zip() raised a TypeError instead of returning an empty list.

..note

Although filter, map, and reduce are still useful, their use is largely superseded by using other functions, in conjunction with generator expressions. The range function is still useful for creating a list of a given sequence, but for portability eventually to Python 3.x, using list(xrange()) instead is better.

Some advice:
Generator expressions (or list comprehensions) are easier to use than filter.
Most interesting but simple uses of reduce can be implemented through sum. And anything more complex should likely be written as a generator.

hex(x)?

Convert an integer number (of any size) to a hexadecimal string. The result is a valid Python expression. Version Changed: CPython 2.4, Jython 2.5. Formerly only returned an unsigned literal.

long([x[, radix]])?

Convert a string or number to a long integer. If the argument is a string, it must contain a possibly signed number of arbitrary size, possibly embedded in whitespace. The radix argument is interpreted in the same way as for int, and may only be given when x is a string. Otherwise, the argument may be a plain or long integer or a floating point number, and a long integer with the same value is returned. Conversion of floating point numbers to integers truncates (towards zero). If no arguments are given, returns 0L.

oct(x)?

Convert an integer number (of any size) to an octal string. The result is a valid Python expression. Version Changed: CPython 2.4, Jython 2.5. Formerly only returned an unsigned literal.

ord(c)?

Given a string of length one, return an integer representing the Unicode code point of the character when the argument is a unicode object, or the value of the byte when the argument is an 8-bit string. For example, ord(‘a(chǎn)’) returns the integer 97, ord(u’u2020’) returns 8224. This is the inverse of chr for 8-bit strings and of unichr for unicode objects. If a unicode argument is given and Python was built with UCS2 Unicode, then the character’s code point must be in the range [0..65535] inclusive; otherwise the string length is two, and a TypeError will be raised.

classmethod(function)?

Return a class method for function. A class method receives the class as implicit first argument, just like an instance method receives the instance. To declare a class method, use this idiom:

class C:
    @classmethod
    def f(cls, arg1, arg2, ...): ...

The @classmethod form is a function decorator. See the description of function decorators in Chapter 4 for details. It can be called either on the class (such as C.f()) or on an instance (such as C().f()). The instance is ignored except for its class. If a class method is called for a derived class, the derived class object is passed as the implied first argument. Class methods are different than C++ or Java static methods. If you want those, see staticmethod in this section. Version Added: 2.2. Version Changed: CPython 2.4, Jython 2.5. Function decorator syntax added.

compile(source, filename, mode[, flags[, dont_inherit]])?

Compile the source into a code or AST object. Code objects can be executed by an exec statement or evaluated by a call to eval. source can either be a string or an AST object. Refer to the ast module documentation for information on how to work with AST objects. The filename argument should give the file from which the code was read; pass some recognizable value if it wasn’t read from a file (‘<string>’ is commonly used). The mode argument specifies what kind of code must be compiled; it can be ‘exec’ if source consists of a sequence of statements, ‘eval’ if it consists of a single expression, or ‘single’ if it consists of a single interactive statement (in the latter case, expression statements that evaluate to something other than None will be printed). The optional arguments flags and dont_inherit control which future statements (see PEP 236) affect the compilation of source. If neither is present (or both are zero) the code is compiled with those future statements that are in effect in the code that is calling compile. If the flags argument is given and dont_inherit is not (or is zero) then the future statements specified by the flags argument are used in addition to those that would be used anyway. If dont_inherit is a non-zero integer then the flags argument is it. The future statements in effect around the call to compile are ignored. Future statements are specified by bits which can be bitwise ORed together to specify multiple statements. The bitfield required to specify a given feature can be found as the compiler_flag attribute on the _Feature instance in the __future__ module. This function raises SyntaxError if the compiled source is invalid, and TypeError if the source contains null bytes.

Version Changed: CPython 2.3, Jython 2.5. The flags and dont_inherit arguments were added.

eval(expression[, globals[, locals]])?

The arguments are a string and optional globals and locals. If provided, globals must be a dictionary. If provided, locals can be any mapping object. The expression argument is parsed and evaluated as a Python expression (technically speaking, a condition list) using the globals and locals dictionaries as global and local namespace. If the globals dictionary is present and lacks ‘__builtins__’, the current globals are copied into globals before expression is parsed. This means that expression normally has full access to the standard __builtin__ module and restricted environments are propagated. If the locals dictionary is omitted it defaults to the globals dictionary. If both dictionaries are omitted, the expression is executed in the environment where eval is called. The return value is the result of the evaluated expression. Syntax errors are reported as exceptions. Example:

>>> x = 1
>>> print eval('x+1')
2

This function can also be used to execute arbitrary code objects (such as those created by compile). In this case pass a code object instead of a string. If the code object has been compiled with ‘exec’ as the kind argument, eval’s return value will be None.

Version Changed: CPython 2.4, Jython 2.5. Formerly, locals was required to be a dictionary.

execfile(filename[, globals[, locals]])?

This function is similar to the exec statement, but parses a file instead of a string. It is different from the import statement in that it does not use the module administration: it reads the file unconditionally and does not create a new module. The arguments are a file name and two optional dictionaries. The file is parsed and evaluated as a sequence of Python statements (similarly to a module) using the globals and locals dictionaries as global and local namespace. If provided, locals can be any mapping object. If the locals dictionary is omitted it defaults to the globals dictionary. If both dictionaries are omitted, the expression is executed in the environment where execfile is called. The return value is None. Version Changed: CPython 2.4, Jython 2.5. Formerly, locals was required to be a dictionary.

property([fget[, fset[, fdel[, doc]]]])?

Return a property attribute for new-style classes (classes that derive from object). fget is a function for getting an attribute value, likewise fset is a function for setting, and fdel a function for del’ing, an attribute. Typical use is to define a managed attribute x:

class C(object):
    def __init__(self):
        self._x = None

    def getx(self):
        return self._x
    def setx(self, value):
        self._x = value
    def delx(self):
        del self._x
    x = property(getx, setx, delx, "I'm the 'x' property.")

If given, doc will be the docstring of the property attribute. Otherwise, the property will copy fget’s docstring (if it exists). This makes it possible to create read-only properties easily using property as a decorator:

class Parrot(object):
    def __init__(self):
        self._voltage = 100000

    @property
    def voltage(self):
        """Get the current voltage."""
        return self._voltage

turns the voltage method into a “getter” for a read-only attribute with the same name. A property object has getter, setter, and deleter methods usable as decorators that create a copy of the property with the corresponding accessor function set to the decorated function. This is best explained with an example:

class C(object):
    def __init__(self):
        self._x = None

    @property
    def x(self):
        """I'm the 'x' property."""
        return self._x

    @x.setter
    def x(self, value):
        self._x = value

    @x.deleter
    def x(self):
        del self._x

This code is exactly equivalent to the first example. Be sure to give the additional functions the same name as the original property (x in this case.) The returned property also has the attributes fget, fset, and fdel corresponding to the constructor arguments. Version Added: 2.2. Version Changed: 2.5.

staticmethod(function)?

Return a static method for function. A static method does not receive an implicit first argument. To declare a static method, use this idiom:

class C:
    @staticmethod
    def f(arg1, arg2, ...): ...

The @staticmethod form is a function decorator. See the description of function definitions in Chapter 4 for details. It can be called either on the class (such as C.f()) or on an instance (such as C().f()). The instance is ignored except for its class. Static methods in Python are similar to those found in Java or C++. For a more advanced concept, see classmethod in this section. Version Added: 2.2. Version Changed: CPython 2.4, Jython 2.5. Function decorator syntax added.

super(type[, object-or-type])?

Return a proxy object that delegates method calls to a parent or sibling class of type. This is useful for accessing inherited methods that have been overridden in a class. The search order is same as that used by getattr except that the type itself is skipped. The __mro__ attribute of the type lists the method resolution search order used by both getattr and super. The attribute is dynamic and can change whenever the inheritance hierarchy is updated. If the second argument is omitted, the super object returned is unbound. If the second argument is an object, isinstance(obj, type) must be true. If the second argument is a type, issubclass(type2, type) must be true (this is useful for classmethods).

There are two typical use cases for super. In a class hierarchy with single inheritance, super can be used to refer to parent classes without naming them explicitly, thus making the code more maintainable. This use closely parallels the use of super in other programming languages. The second use case is to support cooperative multiple inheritance in a dynamic execution environment. This use case is unique to Python and is not found in statically compiled languages or languages that only support single inheritance. This makes it possible to implement “diamond diagrams” where multiple base classes implement the same method. Good design dictates that this method have the same calling signature in every case (because the order of calls is determined at runtime, because that order adapts to changes in the class hierarchy, and because that order can include sibling classes that are unknown prior to runtime). For both use cases, a typical superclass call looks like this:

class C(B):
    def method(self, arg):
        super(C, self).method(arg)

Note that super is implemented as part of the binding process for explicit dotted attribute lookups such as super().__getitem__(name). It does so by implementing its own __getattribute__ method for searching classes in a predictable order that supports cooperative multiple inheritance. Accordingly, super is undefined for implicit lookups using statements or operators such as super()[name]. Also note that super is not limited to use inside methods. The two argument form specifies the arguments exactly and makes the appropriate references.

Version Added: 2.2.

input([prompt])?

Equivalent to eval(raw_input(prompt)).

If the readline module was loaded, then input will use it to provide elaborate line editing and history features. Consider using the raw_input function for general input from users.

raw_input([prompt])?

If the prompt argument is present, it is written to standard output without a trailing newline. The function then reads a line from input, converts it to a string (stripping a trailing newline), and returns that. When EOF is read, EOFError is raised. Here’s an example:

>>> s = raw_input('--> ')
--> Monty Python's Flying Circus
>>> s
"Monty Python's Flying Circus"

If the readline module was loaded, then raw_input will use it to provide elaborate line editing and history features.

callable(object)?

Return True if the object argument appears callable, False if not. If this returns true, it is still possible that a call fails, but if it is false, calling object will never succeed. Note that classes are callable (calling a class returns a new instance); class instances are callable if they have a __call__ method.

delattr(object, name)?

This is a relative of setattr. The arguments are an object and a string. The string must be the name of one of the object’s attributes. The function deletes the named attribute, provided the object allows it. For example, delattr(x, ‘foobar’) is equivalent to del x.foobar.

dir([object])?

Without arguments, return the list of names in the current local scope. With an argument, attempt to return a list of valid attributes for that object.

If the object has a method named __dir__, this method will be called and must return the list of attributes. This allows objects that implement a custom __getattr__ or __getattribute__ function to customize the way dir reports their attributes.

If the object does not provide __dir__, the function tries its best to gather information from the object’s __dict__ attribute, if defined, and from its type object. The resulting list is not necessarily complete, and may be inaccurate when the object has a custom __getattr__.

The default dir mechanism behaves differently with different types of objects, as it attempts to produce the most relevant, rather than complete, information:

• If the object is a module object, the list contains the names of the module’s attributes.
• If the object is a type or class object, the list contains the names of its attributes, and recursively of the attributes of its bases.
• Otherwise, the list contains the object’s attributes’ names, the names of its class’ attributes, and recursively of the attributes of its class’ base classes.

The resulting list is sorted alphabetically. For example:

>>> import struct
>>> dir()   # doctest: +SKIP
['__builtins__', '__doc__', '__name__', 'struct']
>>> dir(struct)   # doctest: +NORMALIZE_WHITESPACE
['Struct', '__builtins__', '__doc__', '__file__', '__name__',
 '__package__', '_clearcache', 'calcsize', 'error', 'pack', 'pack_into',
 'unpack', 'unpack_from']
>>> class Foo(object):
...     def __dir__(self):
...         return ["kan", "ga", "roo"]
...
>>> f = Foo()
>>> dir(f)
['ga', 'kan', 'roo']

getattr(object, name[, default])?

Return the value of the named attribute of object. name must be a string. If the string is the name of one of the object’s attributes, the result is the value of that attribute. For example, getattr(x, ‘foobar’) is equivalent to x.foobar. If the named attribute does not exist, default is returned if provided, otherwise AttributeError is raised.

globals()?

Return a dictionary representing the current global symbol table. This is always the dictionary of the current module (inside a function or method, this is the module where it is defined, not the module from which it is called).

hasattr(object, name)?

The arguments are an object and a string. The result is True if the string is the name of one of the object’s attributes, False if not. (This is implemented by calling getattr(object, name) and seeing whether it raises an exception or not.)

hash(object)?

Return the hash value of the object (if it has one). Hash values are integers. They are used to quickly compare dictionary keys during a dictionary lookup. Numeric values that compare equal have the same hash value (even if they are of different types, as is the case for 1 and 1.0).

help([object])?

Invoke the built-in help system. (This function is intended for interactive use.) If no argument is given, the interactive help system starts on the interpreter console. If the argument is a string, then the string is looked up as the name of a module, function, class, method, keyword, or documentation topic, and a help page is printed on the console. If the argument is any other kind of object, a help page on the object is generated. This function is added to the built-in namespace by the site module. For more information on the site module, take a look at the Python documentation http://docs./library/site.html#module-site.

Version Added: 2.2.

id(object)?

Return the “identity” of an object. This is an integer (or long integer) which is guaranteed to be unique and constant for this object during its lifetime. Two objects with non-overlapping lifetimes may have the same id value. (Implementation note: this is the address of the object.)

isinstance(object, classinfo)?

Return true if the object argument is an instance of the classinfo argument, or of a (direct or indirect) subclass thereof. Also return true if classinfo is a type object (new-style class) and object is an object of that type or of a (direct or indirect) subclass thereof. If object is not a class instance or an object of the given type, the function always returns false. If classinfo is neither a class object nor a type object, it may be a tuple of class or type objects, or may recursively contain other such tuples (other sequence types are not accepted). If classinfo is not a class, type, or tuple of classes, types, and such tuples, a TypeError exception is raised.

Version Changed: 2.2.

Support for a tuple of type information was added.

issubclass(class, classinfo)?

Return true if class is a subclass (direct or indirect) of classinfo. A class is considered a subclass of itself. classinfo may be a tuple of class objects, in which case every entry in classinfo will be checked. In any other case, a TypeError exception is raised.

Version Changed: CPython 2.3, Jython 2.5.

Support for a tuple of type information was added.

len(s)?

Return the length (the number of items) of an object. The argument may be a sequence (string, tuple or list) or a mapping (dictionary).

locals()?

Update and return a dictionary representing the current local symbol table.

Free variables are returned by locals when it is called in a function block. Modifications of free variables may not affect the values used by the interpreter. Free variables are not returned in class blocks.

reload(module)?

Reload a previously imported module. The argument must be a module object, so it must have been successfully imported before. This is useful if you have edited the module source file using an external editor and want to try out the new version without leaving the Python interpreter. The return value is the module object (the same as the module argument).

When reload(module) is executed:

• Python modules’ code is recompiled and the module-level code reexecuted, defining a new set of objects which are bound to names in the module’s dictionary. The init function of extension modules is not called a second time.
• As with all other objects in Python the old objects are only reclaimed after their reference counts drop to zero in CPython. However, in Jython once the object is no longer in use then it becomes garbage collected.
• The names in the module namespace are updated to point to any new or changed objects.
• Other references to the old objects (such as names external to the module) are not rebound to refer to the new objects and must be updated in each namespace where they occur if that is desired.

There are a number of other caveats: if a module is syntactically correct but its initialization fails, the first import statement for it does not bind its name locally, but does store a (partially initialized) module object in sys.modules. To reload the module you must first import it again (this will bind the name to the partially initialized module object) before you can reload it.

When a module is reloaded, its dictionary (containing the module’s global variables) is retained. Redefinitions of names will override the old definitions, so this is generally not a problem. If the new version of a module does not define a name that was defined by the old version, the old definition remains. This feature can be used to the module’s advantage if it maintains a global table or cache of objects. With a try statement it can test for the table’s presence and skip its initialization if desired:

try:
    cache
except NameError:
    cache = {}

It is legal though generally not very useful to reload built-in or dynamically loaded modules, except for sys, __main__ and __builtin__. In many cases, however, extension modules are not designed to be initialized more than once, and may fail in arbitrary ways when reloaded. If a module imports objects from another module using from ... import ..., calling reload for the other module does not redefine the objects imported from it. One way around this is to re-execute the from statement, another is to use import and qualified names (module.*name*) instead. If a module instantiates instances of a class, reloading the module that defines the class does not affect the method definitions of the instances: they continue to use the old class definition. The same is true for derived classes.

repr(object)?

Return a string containing a printable representation of an object. This is the same value yielded by conversions (reverse quotes). It is sometimes useful to be able to access this operation as an ordinary function. For many types, this function makes an attempt to return a string that would yield an object with the same value when passed to eval, otherwise the representation is a string enclosed in angle brackets that contains the name of the type of the object together with additional information often including the name and address of the object. A class can control what this function returns for its instances by defining a __repr__ method.

setattr(object, name, value)?

This is the counterpart of getattr. The arguments are an object, a string and an arbitrary value. The string may name an existing attribute or a new attribute. The function assigns the value to the attribute, provided the object allows it. For example, setattr(x, ‘foobar’, 123) is equivalent to x.foobar = 123.

type(object)?

Return the type of an object. The return value is a type object. The isinstance built-in function is recommended for testing the type of an object.

With three arguments, type functions as a constructor as detailed earlier in this appendix.

vars([object])?

Without arguments, return a dictionary corresponding to the current local symbol table. With a module, class or class instance object as argument (or anything else that has a __dict__ attribute), returns a dictionary corresponding to the object’s symbol table.

__import__(name[, globals[, locals[, fromlist[, level]]]])?

This function is invoked by the import statement. It can be replaced (by importing the builtins module and assigning to builtins.__import__) in order to change semantics of the import statement, but nowadays it is usually simpler to use import hooks (see PEP 302). Direct use of __import__ is rare, except in cases where you want to import a module whose name is only known at runtime. The function imports the module name, potentially using the given globals and locals to determine how to interpret the name in a package context. The fromlist gives the names of objects or submodules that should be imported from the module given by name. The standard implementation does not use its locals argument at all, and uses its globals only to determine the package context of the import statement. level specifies whether to use absolute or relative imports. The default is -1 which indicates both absolute and relative imports will be attempted. 0 means only perform absolute imports. Positive values for level indicate the number of parent directories to search relative to the directory of the module calling __import__. When the name variable is of the form package.module, normally, the top-level package (the name up till the first dot) is returned, not the module named by name. However, when a non-empty fromlist argument is given, the module named by name is returned.

For example, the statement import spam results in bytecode resembling the following code:

spam = __import__('spam', globals(), locals(), [], -1)

The statement import spam.ham results in this call:

spam = __import__('spam.ham', globals(), locals(), [], -1)

Note how __import__ returns the toplevel module here because this is the object that is bound to a name by the import statement.

On the other hand, the statement from spam.ham import eggs, sausage as saus results in:

_temp = __import__('spam.ham', globals(), locals(), ['eggs', 'sausage'], -1)
eggs = _temp.eggs
saus = _temp.sausage

Here, the spam.ham module is returned from __import__. From this object, the names to import are retrieved and assigned to their respective names. If you simply want to import a module (potentially within a package) by name, you can get it from sys.modules:

>>> import sys
>>> name = 'foo.bar.baz'
>>> __import__(name)
<module 'foo' from ...>
>>> baz = sys.modules[name]
>>> baz
<module 'foo.bar.baz' from ...>

Version Changed: 2.5.

The level parameter was added.

Version Changed: 2.5.

Keyword support for parameters was added.