For the optimum utilisation of the following data structure, the popular Python language must be learned. Get the best Python training in Chennai from the best institute.
Around the world, Python is utilised in a variety of disciplines, including developing websites and AI systems. But in order for all of this to be possible, data must play a crucial role. As a result, data must be stored efficiently and accessed promptly. Depending on the circumstance, using data structures in Python allows you to organise data so that it can be retrieved more quickly. Following a general discussion of data structures in Python, we will go more into the list, dictionary, and tuple data structures found in Python.
A Data Structure: What Is It?
Data organisation, management, and storage are crucial since they make it possible for quicker access and more effective alterations. A data structure in Python is a method of organising data in computer memory that is implemented in a programming language. In order to store, retrieve, and modify data effectively, this organisation is necessary. Any programming language must include data structures as a foundation upon which to build a programme. Compared to other programming languages, Python makes it easier to master the fundamentals of the data structures in Python.
Data Structures in Python and Their Types
The data structures in Python differ in terms of mutability and order. The term “mutability” describes an object’s capacity for modification after creation. Immutable objects cannot be changed once they are formed, whereas mutable objects can be changed, added to, or removed after they have been created. The ability to access an element depends on its position, which is determined by order. The three mutable common Python data structures are lists, dictionaries, and sets. And tuple is the only fundamentally built-in immutable data structures in Python.
Let’s get into more detail about Python’s mutable and immutable data structure types.
Lists:
One of the fundamental data structures in Python when building a project in Python is a list, which is defined as an ordered collection of items. Given the definition of “ordered collections,” each item in a list has a unique order that serves as their identification. An essential quality of the list that endures the life of the list is the element order.
Because everything in Python is considered an object, making a list is essentially generating a Python object of a specified type. Items must be placed between [] to be declared as a list. Let’s look at a few different approaches to declare a list.
## Create a list of items
list_1 = [8, ‘Anurag’, ‘Caleb’, ‘Faariq’, 98]
## Create Empty List
list_2 = list()
## Create a list from existing tuple
list_3 = list(<tuple>)
We can employ the type()method as shown below to verify the type:
type(list_1)
Output:
<type ‘list’>
Since the list’s elements are organized in an ordered list, we can access list elements by using their index values, which range from 0 to count-1. We can modify these values by changing their index value. Negative indexing, which provides the count from last to first starting at -1, is another method that Python utilises for indexing. The indexing for each value will be done as shown in the following table.
| Elements: | 8 | ‘Anurag’ | ‘Caleb’ | ‘Faariq’ | 24 |
| Positive Index: | 0 | 1 | 2 | 3 | 4 |
| Negative Index: | -5 | -4 | -3 | -2 | -1 |
Tuples:
A tuple is a built-in data structure in Python that represents an ordered collection of things. Tuples provide less functionality than lists do.
Mutability serves as the main point of distinction between lists and tuples. Tuples cannot be changed, although lists may. Once a triple is formed, it cannot be changed, added to, or removed. The elements of lists are denoted by enclosing them in parentheses and separating them with commas.
Although parentheses are not required for creating tuples, it is advised that they be used to indicate the beginning and end of the tuple. As an instance, consider the following tuple:
tuple_A = (item 1, item 2, item 3,…, item n)
We must insert elements between ( ) for the declaration of a tuple. Let’s have a look at some other approaches to declare a tuple.
## Create a tuple with defined items
tuple_1 = (8, ‘Anurag’, ‘Caleb’, ‘Faariq’, 98)
## Create an empty tuple
tuple_2 = ()
## Create a tuple without the use of parathesis (they are optional)
tuple_3 = 1, ‘Yatharth’, ‘Sharma’, ‘Developer’, ‘Knoldus’
## Single item tuple
tuple_4 = ‘knoldus’,
## Tuple from existing list
tuple_5 = tuple(<list>)
We can employ the type()method as shown below to verify the type:
type(tuple_1)
Output:
<type ‘tuple’>
The members of the Tuple are accessed by their index values, just like the elements of a List are arranged in an ordered sequence. The concept of negative indexing applies here as well.
Dictionaries:
Python dictionaries are extremely similar to dictionaries in the real world. These are mutable data structures in Python that have a set of keys and the associated values for those keys. Because of their structure, they are quite similar to word-definition dictionaries. For instance in the case, the word “dictionary” (our key) is connected to its definition (value) in the Oxford online dictionary which is a book or electronic resource that provides an alphabetical list of words from a language and their definitions, or a word for those words in a different language.
For easy access to specific data linked to a specific key, dictionaries are utilised. As we need to access only specific information and avoid confusing it with other entries, uniqueness is crucial.
We utilise dictionaries when we can associate (in technical terms, map) a unique key to specific data and want to retrieve that data rapidly and in real time, regardless of dictionary size.
The keys and values must be inserted between { } and separated by :. in order to declare a dictionary. Hence, the syntax basically reads {“key”:”value.”}. Let’s look at a few various approaches to declare a dictionary.
## Create a dictionary with defined values
dict_1 = {‘Yatharth’:24, ‘Akash’:23, ‘Jatin’:19, ‘Divyanshu’:24}
## Creates an empty dictionary
dict_2 = {}
## Creates a dictionary from provided variables
dict_3 = dict (‘Yatharth’=24, ‘Akash’=23, ‘Jatin’=25, ‘Divyanshu’=27)
We can employ the type()method as shown below to verify the type:
type(dict_1)
Output:
<type ‘dict’>
Because the Dictionary stores the key: value pair, we can not utilise the index value to access its elements; rather, we provide the key to retrieve the value mapped to that key as variable[‘key’].
Sets:
Another sort of data structures in Python is Sets, which consists of a group of distinct, unordered elements. This means that even if the data is repeated multiple times, it will only be entered into the set once. That is comparable to the sets you have learned in mathematics. The operations are similar to how they are with arithmetic sets. An example program of this common Python data structure would assist you in better understanding.
We must insert elements between { } in order to declare a set.
Let’s look at a few different ways to declare a set.
## Create a set of unique items
set_1 = {1, ‘Yatharth’, ‘Delhi’, ‘Knoldus’, 24}
## Creates and empty set
set_2 = set()
## Create a set from existing list
set_3 = set(<list>)
The type()method can be used as follows to verify the type:
type(set_1)
Output:
<type ‘set’>
Also, you can read a more comprehensive tutorial on this common Python data structure, sets.
Wrapping up!
That concludes the discussion of all the important basic data structures in Python that includes list, set, tuples, and dictionary. I hope this article has provided some insight into the built-in data structures using Python and their significance. In addition, to the built-in data structure in Python, there are also user-defined data structures in Python.
