In this chapter, we are going to examine the Python data types in detail. We have already been introduced to two data types, the string,str(), and list(). It is often the case where we want more specialized objects to represent our data. In addition to the built-in types, there are several internal modules that allow us to address common issues when working with data structures. First, we are going to review some operations and expressions that are common to all data types.

There are a number of operations that are common to all data types. For example, all data types, and generally all objects, can be tested for a truth value in some way. The following are values that Python considersFalse:

• TheNone type
• False
• An integer, float, or complex zero
• An empty sequence or mapping
• An instance of a user-defined class that defines a__len__() or__bool__() method that returns zero orFalse

All other values are consideredTrue.

A Boolean operation returns a value of either True orFalse. Boolean operations are ordered in priority, so if more than one Boolean operation occurs in an expression, the operation with the highest priority will occur first...

Python data types can be divided into three categories: numeric, sequence, and mapping. There is also theNone object that represents aNull, or absence of a value. It should not be forgotten either that other objects such as classes, files, and exceptions can also properly be consideredtypes; however, they will not be considered here.

Every value in Python has a data type. Unlike many programming languages, in Python you do not need to explicitly declare the type of a variable. Python keeps track of object types internally.

Python built-in data types are outlined in the following table:

Sets are unordered collections of unique items. Sets are themselves mutable, we can add and remove items from them; however, the items themselves must be immutable. An important distinction with sets is that they cannot contain duplicate items. Sets are typically used to perform mathematical operations such as intersection, union, difference, and complement.

Unlike sequence types, set types do not provide any indexing or slicing operations. There are also no keys associated with values, as is the case with dictionaries. There are two types of set objects in Python, the mutableset object and the immutablefrozenset object. Sets are created using comma-separated values within curly braces. By the way, we cannot create an empty set usinga={}, because this will create a dictionary. To create an empty set, we write eithera=set() ora=frozenset().

Methods and operations of sets...

In addition to the built-in types, there are several Python modules that we can use to extend these built-in types and functions. In many cases, these Python modules may offer efficiency and programming advantages that allow us to simplify our code.

So far, we have looked at the built-in datatypes of strings, lists, sets, and dictionaries as well as thedecimal andfractions modules. They are often described by the termabstract data types (ADTs). ADTs can be considered as mathematical specifications for the set of operations that can be performed on data. They are defined by their behavior rather than their implementation. In addition to the ADTs that we have looked at, there are several Python libraries that provide extensions to the built-in datatypes. These will be discussed in the following section.