Types and Objects

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Python Essential Reference, 3rd Edition

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This chapter is from the book

This chapter is from the book 

Python Essential Reference, 3rd Edition

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Built-in Types

Approximately two dozen types are built into the Python interpreter and grouped into a few major categories, as shown in Table 3.1. The Type Name column in the table lists the name that can be used to check for that type using isinstance() and other type-related functions. Types include familiar objects such as numbers and sequences. Others are used during program execution and are of little practical use to most programmers. The next few sections describe the most commonly used built-in types.

Table 3.1 Built-in Python Types

Note that object and type appear twice in Table 3.1 because classes and types are both callable. The types listed for "Classic Classes" refer to an obsolete, but still supported object-oriented interface. More details about this can be found later in this chapter and in Chapter 7, "Classes and Object-Oriented Programming."

The None Type

The None type denotes a null object (an object with no value). Python provides exactly one null object, which is written as None in a program. This object is returned by functions that don’t explicitly return a value. None is frequently used as the default value of optional arguments, so that the function can detect whether the caller has actually passed a value for that argument. None has no attributes and evaluates to False in Boolean expressions.

Numeric Types

Python uses five numeric types: Booleans, integers, long integers, floating-point numbers, and complex numbers. Except for Booleans, all numeric objects are signed. All numeric types are immutable.

Booleans are represented by two values: True and False. The names True and False are respectively mapped to the numerical values of 1 and 0.

Integers represent whole numbers in the range of –2147483648 to 2147483647 (the range may be larger on some machines). Internally, integers are stored as 2’s complement binary values, in 32 or more bits. Long integers represent whole numbers of unlimited range (limited only by available memory). Although there are two integer types, Python tries to make the distinction seamless. Most functions and operators that expect integers work with any integer type. Moreover, if the result of a numerical operation exceeds the allowed range of integer values, the result is transparently promoted to a long integer (although in certain cases, an OverflowError exception may be raised instead).

Floating-point numbers are represented using the native double-precision (64-bit) representation of floating-point numbers on the machine. Normally this is IEEE 754, which provides approximately 17 digits of precision and an exponent in the range of –308 to 308. This is the same as the double type in C. Python doesn’t support 32-bit single-precision floating-point numbers. If space and precision are an issue in your program, consider using Numerical Python (http://numpy.sourceforge.net).

Complex numbers are represented as a pair of floating-point numbers. The real and imaginary parts of a complex number z are available in z.real and z.imag.

Sequence Types

Sequences represent ordered sets of objects indexed by nonnegative integers and include strings, Unicode strings, lists, and tuples. Strings are sequences of characters, and lists and tuples are sequences of arbitrary Python objects. Strings and tuples are immutable; lists allow insertion, deletion, and substitution of elements. All sequences support iteration.

Table 3.2 shows the operators and methods that you can apply to all sequence types. Element i of sequence s is selected using the indexing operator s[i], and subsequences are selected using the slicing operator s[i:j] or extended slicing operator s[i:j:stride] (these operations are described in Chapter 4). The length of any sequence is returned using the built-in len(s) function. You can find the minimum and maximum values of a sequence by using the built-in min(s) and max(s) functions. However, these functions only work for sequences in which the elements can be ordered (typically numbers and strings).

Table 3.3 shows the additional operators that can be applied to mutable sequences such as lists.

Table 3.2 Operations and Methods Applicable to All Sequences

Table 3.3 Operations Applicable to Mutable Sequences

Additionally, lists support the methods shown in Table 3.4. The built-in function list(s) converts any iterable type to a list. If s is already a list, this function constructs a new list that’s a shallow copy of s. The s.append(x) method appends a new element, x, to the end of the list. The s.index(x) method searches the list for the first occurrence of x. If no such element is found, a ValueError exception is raised. Similarly, the s.remove(x) method removes the first occurrence of x from the list. The s.extend(t) method extends the list s by appending the elements in sequence t. The s.sort() method sorts the elements of a list and optionally accepts a comparison function, key function, and reverse flag. The comparison function should take two arguments and return negative, zero, or positive, depending on whether the first argument is smaller, equal to, or larger than the second argument, respectively. The key function is a function that is applied to each element prior to comparison during sorting. Specifying a key function is useful if you want to perform special kinds of sorting operations, such as sorting a list of strings, but with case insensitivity. The s.reverse() method reverses the order of the items in the list. Both the sort() and reverse() methods operate on the list elements in place and return None.

Table 3.4 List Methods

Python provides two string object types. Standard strings are sequences of bytes containing 8-bit data. They may contain binary data and embedded NULL bytes. Unicode strings are sequences of 16-bit characters encoded in a format known as UCS-2. This allows for 65,536 unique character values. Although the latest Unicode standard supports up to 1 million unique character values, these extra characters are not supported by Python by default. Instead, they must be encoded as a special two-character (4-byte) sequence known as a surrogate pair—the interpretation of which is up to the application. Python does not check data for Unicode compliance or the proper use of surrogates. As an optional feature, Python may be built to store Unicode strings using 32-bit integers (UCS-4). When enabled, this allows Python to represent the entire range of Unicode values from U+000000 to U+110000. All Unicode-related functions are adjusted accordingly.

Both standard and Unicode strings support the methods shown in Table 3.5. Although these methods operate on string instances, none of these methods actually modifies the underlying string data. Thus, methods such as s.capitalize(),s.center(), and s.expandtabs() always return a new string as opposed to modifying the string s. Character tests such as s.isalnum() and s.isupper() return True or False if all the characters in the string s satisfy the test. Furthermore, these tests always return False if the length of the string is zero. The s.find(), s.index(), s.rfind(), and s.rindex() methods are used to search s for a substring. All these functions return an integer index to the substring in s. In addition, the find() method returns -1 if the substring isn’t found, whereas the index() method raises a ValueError exception. Many of the string methods accept optional start and end parameters, which are integer values specifying the starting and ending indices in s. In most cases, these values may given negative values, in which case the index is taken from the end of the string. The s.translate() method is used to perform character substitutions. The s.encode() and s.decode() methods are used to transform the string data to and from a specified character encoding. As input it accepts an encoding name such as ‘ascii’, ‘utf-8’, or ‘utf-16’. This method is most commonly used to convert Unicode strings into a data encoding suitable for I/O operations and is described further in Chapter 9, "Input and Output." More details about string methods can be found in the documentation for the string module.

Table 3.5 String Methods

Because there are two different string types, Python provides an abstract type, basestring, that can be used to test if an object is any kind of string. Here’s an example:

if isinstance(s,basestring):
  print "is some kind of string"

The built-in function range([i,]j [,stride]) constructs a list and populates it with integers k such that i <= k < j. The first index, i, and the stride are optional and have default values of 0 and 1, respectively. The built-in xrange([i,] j [,stride]) function performs a similar operation, but returns an immutable sequence of type xrange. Rather than storing all the values in a list, this sequence calculates its values whenever it’s accessed. Consequently, it’s much more memory-efficient when working with large sequences of integers. However, the xrange type is much more limited than its list counterpart. For example, none of the standard slicing operations are supported. This limits the utility of xrange to only a few applications such as iterating in simple loops. The xrange type provides a single method, s.tolist(), that converts its values to a list.

Mapping Types

A mapping object represents an arbitrary collection of objects that are indexed by another collection of nearly arbitrary key values. Unlike a sequence, a mapping object is unordered and can be indexed by numbers, strings, and other objects. Mappings are mutable.

Dictionaries are the only built-in mapping type and are Python’s version of a hash table or associative array. You can use any immutable object as a dictionary key value (strings, numbers, tuples, and so on). Lists, dictionaries, and tuples containing mutable objects cannot be used as keys (the dictionary type requires key values to remain constant).

To select an item in a mapping object, use the key index operator m[k], where k is a key value. If the key is not found, a KeyError exception is raised. The len(m) function returns the number of items contained in a mapping object. Table 3.6 lists the methods and operations.

Table 3.6 Methods and Operations for Dictionaries

The m.clear() method removes all items. The m.copy() method makes a shallow copy of the items contained in a mapping object and places them in a new mapping object. The m.items() method returns a list containing (key,value) pairs. The m.keys() method returns a list with all the key values, and the m.values() method returns a list with all the objects. The m.update(b) method updates the current mapping object by inserting all the (key,value) pairs found in the mapping object b. The m.get(k [,v]) method retrieves an object, but allows for an optional default value, v, that’s returned if no such object exists. The m.setdefault(k [,v]) method is similar to m.get(), except that in addition to returning v if no object exists, it sets m[k] = v. If v is omitted, it defaults to None. The m.pop() method returns an item from a dictionary and removes it at the same time. The m.popitem() method is used to iteratively destroy the contents of a dictionary. The m.iteritems(), m.iterkeys(), and m.itervalues() methods return iterators that allow looping over all the dictionary items, keys, or values, respectively.

Set Types

A set is an unordered collection of unique items. Unlike sequences, sets provide no indexing or slicing operations. They are also unlike dictionaries in that there are no key values associated with the objects. In addition, the items placed into a set must be immutable. Two different set types are available: set is a mutable set, and frozenset is an immutable set. Both kinds of sets are created using a pair of built-in functions:

s = set([1,5,10,15])
f = frozenset([‘a’,37,’hello’])

Both set() and frozenset() populate the set by iterating over the supplied argument. Both kinds of sets provide the methods outlined in Table 3.7

Table 3.7 Methods and Operations for Set Types

The s.difference(t), s.intersection(t), s.symmetric_difference(t), and s.union(t) methods provide the standard mathematical operations on sets. The returned value has the same type as s (set or frozenset). The parameter t can be any Python object that supports iteration. This includes sets, lists, tuples, and strings. These set operations are also available as mathematical operators, as described further in Chapter 4.

Mutable sets (set) additionally provide the methods outlined in Table 3.8.

Table 3.8 Methods for Mutable Set Types

All these operations modify the set s in place. The parameter t can be any object that supports iteration.

Callable Types

Callable types represent objects that support the function call operation. There are several flavors of objects with this property, including user-defined functions, built-in functions, instance methods, and classes.

User-defined functions are callable objects created at the module level by using the def statement, at the class level by defining a static method, or with the lambda operator. Here’s an example:

def foo(x,y):
  return x+y


class A(object):
  @staticmethod
  def foo(x,y):
    return x+y


bar = lambda x,y: x + y

A user-defined function f has the following attributes:

Methods are functions that operate only on instances of an object. Two types of methods—instance methods and class methods—are defined inside a class definition, as shown here:

class Foo(object):
     def __init__(self):
      self.items = [ ]
     def update(self, x):
      self.items.append(x)
     @classmethod
     def whatami(cls):
      return cls

An instance method is a method that operates on an instance of an object. The instance is passed to the method as the first argument, which is called self by convention. Here’s an example:

f = Foo()
f.update(2)    # update() method is applied to the object f

A class method operates on the class itself. The class object is passed to a class method in the first argument, cls. Here’s an example:

Foo.whatami()  # Operates on the class Foo
f.whatami()    # Operates on the class of f (Foo)

A bound method object is a method that is associated with a specific object instance. Here’s an example:

a = f.update      # a is a method bound to f
b = Foo.whatami   # b is method bound to Foo (classmethod)

In this example, the objects a and b can be called just like a function. When invoked, they will automatically apply to the underlying object to which they were bound. Here’s an example:

a(4)    # Calls f.update(4)
b()     # Calls Foo.whatami()

Bound and unbound methods are no more than a thin wrapper around an ordinary function object. The following attributes are defined for method objects:

So far, this discussion has focused on functions and methods, but class objects (described shortly) are also callable. When a class is called, a new class instance is created. In addition, if the class defines an __init__() method, it’s called to initialize the newly created instance.

An object instance is also callable if it defines a special method, __call__(). If this method is defined for an instance, x, then x(args) invokes the method x.__call__(args).

The final types of callable objects are built-in functions and methods, which correspond to code written in extension modules and are usually written in C or C++. The following attributes are available for built-in methods:

For built-in functions such as len(), __self__ is set to None, indicating that the function isn’t bound to any specific object. For built-in methods such as x.append(), where x is a list object, __self__ is set to x.

Finally, it is important to note that all functions and methods are first-class objects in Python. That is, function and method objects can be freely used like any other type. For example, they can be passed as arguments, placed in lists and dictionaries, and so forth.

Classes and Types

When you define a class, the class definition normally produces an object of type type. Here’s an example:

>>> class Foo(object):
...  pass
...
>>> type(Foo)
<type ‘type’>

When an object instance is created, the type of the instance is the class that defined it. Here’s an example:

>>> f = Foo()
>>> type(f)
<class ‘__main__.Foo’>

More details about the object-oriented interface can be found in Chapter 7. However, there are a few attributes of types and instances that may be useful. If t is a type or class, then the attribute t.__name__ contains the name of the type. The attributes t.__bases__ contains a tuple of base classes. If o is an object instance, the attribute o.__class__ contains a reference to its corresponding class and the attribute o.__dict__ is a dictionary used to hold the object’s attributes.

Modules

The module type is a container that holds objects loaded with the import statement. When the statement import foo appears in a program, for example, the name foo is assigned to the corresponding module object. Modules define a namespace that’s implemented using a dictionary accessible in the attribute __dict__. Whenever an attribute of a module is referenced (using the dot operator), it’s translated into a dictionary lookup. For example, m.x is equivalent to m.__dict__["x"]. Likewise, assignment to an attribute such as m.x = y is equivalent to m.__dict__["x"] = y. The following attributes are available:

Files

The file object represents an open file and is returned by the built-in open() function (as well as a number of functions in the standard library). The methods on this type include common I/O operations such as read() and write(). However, because I/O is covered in detail in Chapter 9, readers should consult that chapter for more details.

Internal Types

A number of objects used by the interpreter are exposed to the user. These include traceback objects, code objects, frame objects, generator objects, slice objects, and the Ellipsis object. It is rarely necessary to manipulate these objects directly. However, their attributes are provided in the following sections for completeness.

Code Objects

Code objects represent raw byte-compiled executable code, or bytecode, and are typically returned by the built-in compile() function. Code objects are similar to functions except that they don’t contain any context related to the namespace in which the code was defined, nor do code objects store information about default argument values. A code object, c, has the following read-only attributes:

Frame Objects

Frame objects are used to represent execution frames and most frequently occur in traceback objects (described next). A frame object, f, has the following read-only attributes:

The following attributes can be modified (and are used by debuggers and other tools):

Traceback Objects

Traceback objects are created when an exception occurs and contains stack trace information. When an exception handler is entered, the stack trace can be retrieved using the sys.exc_info() function. The following read-only attributes are available in traceback objects:

Generator Objects

Generator objects are created when a generator function is invoked (see Chapter 6, "Functions and Functional Programming"). A generator function is defined whenever a function makes use of the special yield keyword. The generator object serves as both an iterator and a container for information about the generator function itself. The following attributes and methods are available:

Slice Objects

Slice objects are used to represent slices given in extended slice syntax, such as a[i:j:stride], a[i:j, n:m], or a[..., i:j]. Slice objects are also created using the built-in slice([i,] j [,stride]) function. The following read-only attributes are available:

Slice objects also provide a single method, s.indices(length). This function takes a length and returns a tuple (start,stop,stride) that indicates how the slice would be applied to a sequence of that length. For example:

s = slice(10,20)  # Slice object represents [10:20]
s.indices(100)    # Returns (10,20,1) --> [10:20]
s.indices(15)     # Returns (10,15,1) --> [10:15]

Ellipsis Object

The Ellipsis object is used to indicate the presence of an ellipsis (...) in a slice. There is a single object of this type, accessed through the built-in name Ellipsis. It has no attributes and evaluates as True. None of Python’s built-in types makes use of Ellipsis, but it may be used in third-party applications.

Classic Classes

In versions of Python prior to version 2.2, classes and objects were implemented using an entirely different mechanism that is now deprecated. For backward compatibility, however, these classes, called classic classes or old-style classes, are still supported.

The reason that classic classes are deprecated is due to their interaction with the Python type system. Classic classes do not define new data types, nor is it possible to specialize any of the built-in types such as lists or dictionaries. To overcome this limitation, Python 2.2 unified types and classes while introducing a different implementation of user-defined classes.

A classic class is created whenever an object does not inherit (directly or indirectly) from object. For example:

# A modern class
class Foo(object):
   pass


# A classic class. Note: Does not inherit from object
class Bar:
   pass

Classic classes are implemented using a dictionary that contains all the objects defined within the class and defines a namespace. References to class attributes such as c.x are translated into a dictionary lookup, c.__dict__["x"]. If an attribute isn’t found in this dictionary, the search continues in the list of base classes. This search is depth first in the order that base classes were specified in the class definition. An attribute assignment such as c.y = 5 always updates the __dict__ attribute of c, not the dictionaries of any base class.

The following attributes are defined by class objects:

A class instance is an object created by calling a class object. Each instance has its own local namespace that's implemented as a dictionary. This dictionary and the associated class object have the following attributes:

When the attribute of an object is referenced, such as in x.a, the interpreter first searches in the local dictionary for x.__dict__["a"]. If it doesn’t find the name locally, the search continues by performing a lookup on the class defined in the __class__ attribute. If no match is found, the search continues with base classes, as described earlier. If still no match is found and the object’s class defines a __getattr__() method, it’s used to perform the lookup. The assignment of attributes such as x.a = 4 always updates x.__dict__, not the dictionaries of classes or base classes.