Getting Started with Data

We stated above that Python supports the object-oriented programming paradigm. This means that Python considers data to be the focal point of the problem-solving process. In Python, as well as in any other object-oriented programming language, we define a class to be a description of what the data look like (the state) and what the data can do (the behavior). Classes are analogous to abstract data types because a user of a class only sees the state and behavior of a data item. Data items are called objects in the object-oriented paradigm. An object is an instance of a class.

What is OOP?

Built-in Atomic Data Types

We will begin our review by considering the atomic data types. Python has two main built-in numeric classes that implement the integer and floating point data types. These Python classes are called intand float. The standard arithmetic operations, +, -, *, /, and ** (exponentiation), can be used with parentheses forcing the order of operations away from normal operator precedence. Other very useful operations are the remainder (modulo) operator, %, and integer division, //. Note that when two integers are divided, the result is a floating point. The integer division operator returns the integer portion of the quotient by truncating any fractional part.

Figure 3: Variables Hold References to Data Objects

Figure 4: Assignment Changes the Reference

Built-in Collection Data Types

In addition to the numeric and boolean classes, Python has a number of very powerful built-in collection classes. Lists, strings, and tuples are ordered collections that are very similar in general structure but have specific differences that must be understood for them to be used properly. Sets and dictionaries are unordered collections.

A list is an ordered collection of zero or more references to Python data objects. Lists are written as comma-delimited values enclosed in square brackets. The empty list is simply [ ]. Lists are heterogeneous, meaning that the data objects need not all be from the same class and the collection can be assigned to a variable as below. The following fragment shows a variety of Python data objects in a list.

Note that when Python evaluates a list, the list itself is returned. However, in order to remember the list for later processing, its reference needs to be assigned to a variable.

Since lists are considered to be sequentially ordered, they support a number of operations that can be applied to any Python sequence. Table 2 reviews these operations and the following session gives examples of their use.

Repetition of References (intro_3)

The variable A holds a collection of three references to the original list called myList. Note that a change to one element of myList shows up in all three occurrences in A.

Lists support a number of methods that will be used to build data structures. Table 3 provides a summary. Examples of their use follow.

In this fragment we are asking the integer object 54 to execute its add method (called __add__ in Python) and passing it 21 as the value to add. The result is the sum, 75. Of course, we usually write this as 54+21. We will say much more about these methods later in this section.

One common Python function that is often discussed in conjunction with lists is the range function.range produces a range object that represents a sequence of values. By using the list function, it is possible to see the value of the range object as a list. This is illustrated below.

The range object represents a sequence of integers. By default, it will start with 0. If you provide more parameters, it will start and end at particular points and can even skip items. In our first example,range(10), the sequence starts with 0 and goes up to but does not include 10. In our second example,range(5,10) starts at 5 and goes up to but not including 10. range(5,10,2) performs similarly but skips by twos (again, 10 is not included).

Strings are sequential collections of zero or more letters, numbers and other symbols. We call these letters, numbers and other symbols characters. Literal string values are differentiated from identifiers by using quotation marks (either single or double).

Since strings are sequences, all of the sequence operations described above work as you would expect. In addition, strings have a number of methods, some of which are shown in Table 4. For example,

Tuples are very similar to lists in that they are heterogeneous sequences of data. The difference is that a tuple is immutable, like a string. A tuple cannot be changed. Tuples are written as comma-delimited values enclosed in parentheses. As sequences, they can use any operation described above. For example,

However, if you try to change an item in a tuple, you will get an error. Note that the error message provides location and reason for the problem.

A set is an unordered collection of zero or more immutable Python data objects. Sets do not allow duplicates and are written as comma-delimited values enclosed in curly braces. The empty set is represented by set(). Sets are heterogeneous, and the collection can be assigned to a variable as below.

Even though sets are not considered to be sequential, they do support a few of the familiar operations presented earlier. Table 5 reviews these operations and the following session gives examples of their use.

Sets support a number of methods that should be familiar to those who have worked with them in a mathematics setting. Table 6 provides a summary. Examples of their use follow. Note that union,intersection, issubset, and difference all have operators that can be used as well.

Our final Python collection is an unordered structure called a dictionary. Dictionaries are collections of associated pairs of items where each pair consists of a key and a value. This key-value pair is typically written as key:value. Dictionaries are written as comma-delimited key:value pairs enclosed in curly braces. For example,

We can manipulate a dictionary by accessing a value via its key or by adding another key-value pair. The syntax for access looks much like a sequence access except that instead of using the index of the item we use the key value. To add a new value is similar.

Using a Dictionary (intro_7)

It is important to note that the dictionary is maintained in no particular order with respect to the keys. The first pair added ('Utah': 'SaltLakeCity') was placed first in the dictionary and the second pair added ('California': 'Sacramento') was placed last. The placement of a key is dependent on the idea of “hashing,” which will be explained in more detail in Chapter 4. We also show the length function performing the same role as with previous collections.

Dictionaries have both methods and operators. Table 7 and Table 8 describe them, and the session shows them in action. The keys, values, and items methods all return objects that contain the values of interest. You can use the list function to convert them to lists. You will also see that there are two variations on the get method. If the key is not present in the dictionary, get will return None. However, a second, optional parameter can specify a return value instead.