In the previous chapter, you learned the basics of the Python programming language and essential elements such as string, int, and the use of conditionals and loops that control the flow of a Python program. You should now be familiar with writing programs in Python by utilizing these elements.

In this chapter, you are going to look at how to use data structures to store more complex types of data that help to model the actual data and represent it in the real world.

In programming languages, data structures refer to objects that can hold some data together, which means they are used to store a collection of related data.

For instance, you can use a list to store our to-do items for the day. The following is an example to show you how lists are coded:

todo = ["pick up laundry", "buy Groceries", "pay electric bills"]

We can also use a dictionary object to store more complex information such as subscribers' details...

You will now look at the first type of data structure in Python: lists.

A list is a type of container in Python that is used to store multiple data sets at the same time. Python lists are often compared to arrays in other programming languages, but they do a lot more.

Figure 2.2: A Python list with a positive index

A list in Python is written within square brackets, [ ]. Each element in the list has its own distinct position and index. The elements in a list have a finite sequence. Like other programming languages, the index of the first item of a list is 0, and the second item has an index of 1, and so on. This has to do with how lists are implemented at a lower programming level, so do take note of this when you are writing index-based operations for lists and other iterable objects.

You will now look at the different ways that lists can be useful by completing Exercise 21, Working with Python Lists.

Exercise 21: Working with...

You will continue to look at how to use nested lists for some basic matrix operations. First, you look at how to add two matrices in Python. Matrix addition requires both matrices to have the same dimensions; the results will also be of the same dimensions.

In Exercise 23, Implementing Matrix Operations (Addition and Subtraction), you will be using the following matrix data, X and Y, in figures 2.7 and 2.8:

Figure 2.7: Matrix data for matrix X

Figure 2.8: Matrix data for matrix Y

Exercise 23: Implementing Matrix Operations (Addition and Subtraction)

In this exercise you will add and subtract the X and Y matrixes using Python.

The following steps will enable you to complete the exercise:

1. Open a new Jupyter Notebook.
2. Create two nested lists, X and Y, to store the values:

   X = [[1,2,3],[4,5,6],[7,8,9]]
   Y = [[10,11,12],[13,14,15],[16,17,18]]

3. Initialize a 3 x 3 zero matrix called result as a placeholder...

As discussed before, since a list is a type of sequence, it supports all sequence operations and methods.

Lists are one of the best data structures to use. Python provides a set of list methods that makes it easy for us to store and retrieve values in order to maintain, update, and extract data. These common operations are what Python programmers perform, including slicing, sorting, appending, searching, inserting, and removing data.

The best way to understand this is to see them at work. You will learn about these handy list methods in the following exercises.

Exercise 25: Basic List Operations

In this exercise, you are going to use the basic functions of lists to check the size of a list, combining lists and duplicating lists as well. Follow these steps:

1. Open a new Jupyter notebook.
2. Type the following code

   shopping = ["bread","milk", "eggs"]

3. The length of a list is found using the len function.

   print(len(shopping...

A Python dictionary is an unordered collection. Dictionaries are written with curly brackets, and they have keys and values.

For instance, have a look at the following example, where you store the details of an employee:

employee = {
  'name': "Jack Nelson",
  'age': 32,
  'department': "sales"
}

You might have noticed a certain resemblance between Python dictionaries and JSON. Although you can load JSON directly into Python, a Python dictionary is a complete data structure that implements its own algorithms, and JSON is just a pure string written in a similar format..

Python dictionaries are something similar to key-value pairs. They simply map keys to associated values, as shown in Figure 2.12:

Figure 2.12: Mapping keys and values in Python dictionaries

Dictionaries are like lists. They both share the following properties:

• Both can...

Now that you have learned about dictionaries and when you should use a dictionary. You will now look at a few other dictionary methods. To start with, you should follow the exercises from here onward to learn how to access the values and other related operations of a dictionary in Python.

Exercise 30: Accessing a Dictionary Using Dictionary Methods

In this exercise, we will learn how to access a dictionary using dictionary methods. The goal of the exercise is to print the order values against the item while accessing the dictionary by using dictionary methods:

1. Open a new Jupyter Notebook.
2. Enter the following code in a new cell:

   orders = {'apple':5, 'orange':3, 'banana':2}
   print(orders.values())
   print(list(orders.values()))

   You should get the following output:

   dict_values([5, 3, 2])
   [5, 3, 2]

   The values() method in this code returns an iterable object. In order to use the values straight away, you can wrap them in a list...

A tuple object is similar to a list, but it cannot be changed. Tuples are immutable sequences, which means their values cannot be changed after initialization. You use a tuple to represent fixed collections of items:

Figure 2.17: A representation of a Python tuple with a positive index

For instance, you can define the weekdays using a list, as follows:

weekdays_list = ['Monday', 'Tuesday', 'Wednesday','Thursday','Friday','Saturday', 'Sunday']

However, this does not guarantee that the values will remain unchanged throughout its lifetime because a list is mutable. What we can do is to define it using a tuple, as shown in the following code:

weekdays_tuple = ('Monday', 'Tuesday', 'Wednesday','Thursday','Friday','Saturday', 'Sunday')

As tuples are immutable you can be certain that the values are consistent...

So far, in this chapter, you have covered lists, dictionaries, and tuples. You can now have a look at sets, which are another type of Python data structure.

Sets are a relatively new addition to the Python collection type. They are unordered collections of unique and immutable objects that support operations mimicking mathematical set theory. As sets do not allow multiple occurrences of the same element, they can be used to effectively prevent duplicate values.

A set is a collection of objects (called members or elements). For instance, you can define set A as even numbers between 1 to 10, and it will contain {2,4,6,8,10}, and set B can be odd numbers between 1 to 10, and it will contain {1,3,5,7,9}. In the following exercise, you will get our hands on sets in Python:

Figure 2.21: Set A and Set B – each set contains a unique, distinct value

Exercise 32: Using Sets in Python

In this exercise, you will gain an understanding...

By now, you have learned about most of the common data structures in Python. One of the challenges you might face is knowing when to use the various data types.

When choosing a collection type, it is useful to understand the unique properties of that type. For example, a list is for you to store multiple objects and to retain a sequence, a dictionary is for us to store unique key-value pair mappings, tuples are immutable, and sets only store unique elements. Choosing the right type for a particular dataset could mean an increase in efficiency or security.

Choosing an incorrect type for your data will lead to data loss, in most cases it leads to low efficiency while running our code, and in the worst case, we might lose our data.

To summarize, you need to remember that Python data structures include lists, tuples, dictionaries, and sets. Python provides these structures to enable you to code better as a developer. In this chapter, you have covered lists, which are one of the important data types in Python that store multiple objects, and also other data types, such as dictionaries, tuples, and sets. Each of these data types helps us to store and retrieve data effectively.

Data structures are an essential part of all programming languages. Most programming languages only provide basic data types to store different types of numbers, strings, and Booleans, as you learned in Chapter 1, Vital Python - Math, Strings, Conditionals, and Loops. They are an essential part of any program. In this chapter, you learned how to utilize advanced data structures such as nested lists and mixed data types, and lists with dictionaries — structures that you can use to store complex data.

Next up, we are going...