Python Structure | One Video to Explain It All!! Part #4

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By the end of this chapter, you will be able to explain the different types of Python data structures; create lists, dictionaries, and sets and describe the differences between them; create matrices and manipulate both a matrix as a whole and its individual cells; call the zip() function to create different Python structures; find what methods are available for lists, dictionaries, and sets; write a program using the most popular methods for lists, dictionaries, and sets and convert between different Python structures.

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By the end of this chapter, you will be able to explain the different types of Python data structures; create lists, dictionaries, and sets and describe the differences between them; create matrices and manipulate both a matrix as a whole and its individual cells; call the zip() function to create different Python structures; find what methods are available for lists, dictionaries, and sets; write a program using the most popular methods for lists, dictionaries, and sets and convert between different Python structures.

There are four types of data structures in Python: list, tuple, dictionary, and set.

A list is a type of container in Python that is used to store multiple data sets at the same time.

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

In this exercise, you will learn how to work with a Python list by coding and creating a list and adding items.

Since a list is a type of iterable in Python, you can use a for loop to iterate over all of the elements inside a list.

But you might be wondering, in that case, shouldn't we be allowed to store a list of lists inside a list? In Exercise 21, Working with Python Lists, you were introduced to the basics of Python lists.

What you can do is to present the table structure shown in Figure 2.3 using a list of lists; for example, if you want to store the following fruit orders using a list: We can store the matrix as a series of lists inside a list which is called as a nested list.

In Activity 6, Using a Nested List to Store Employee Data, you will implement the concepts you have learned about lists and nested lists to store employee data.

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.

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.

As you can see in the output, lists also support many string operations as well, one of which is concatenation, which is joining two or more lists together.

In the previous section and Exercise 26, Accessing an Item from Shopping List Data, you learned how to access items from a list.

• Both can be
You can also store a list inside a dictionary and store a dictionary within that dictionary: Now that you have learned about lists and dictionaries, you will see how you can store and access our data more effectively using these two data types — lists and dictionaries: As you can't directly iterate a dictionary, you first convert it to a list of tuples using the items() method, then iterate the resulting list and access it.

In addition to this, you were able to list the keys mentioned in the dictionary, and later, in step 4, you were able to iterate the dictionary after converting the list to a tuple.

A union operation returns a single set that contains all the unique elements in both set A and B; an intersect operation returns a single set that contains unique elements that belong to set A and also belongs to set B at the same time: 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.

To sum
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.