Introduction of Data Structures in Python:

Not only are these Data Structures in Python helpful in organizing the data, but they also help to manage and store it. Apart from easier access and allowance to make modifications efficiently, data structures allow the management of your data in a connected way. They enable you to store multiple data collections, relate them with each other and perform multiple operations per your requirement.

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What are the Various Types of Data Structures in Python?

Embedded support for multiple Data Structures in Python enables a programmer to store data and access it whenever required. Some of these data structures are:

• List
• Dictionary
• Tuple
• Set

Python gives a lot of flexibility to programmers, as they can create their own Data Structures in Python by giving them complete functionality control. Some examples of these types of data structures are Linked List, Stack, Queue and Tree. You will not enjoy this sort of flexibility in any other programming language. Now, we will advance to the types of data structures in Python and understand what they are and how they can be implemented in full detail.  There are two types of data structures for Python Data Types. They are:

• Built-in data structures
• User-defined data structures

What is Built-in Data Structures?

The name makes the definition obvious. These types of Data Structures in Python have been constructed. These data structures make programming easy and help programmers get a quick solution. We shall discuss each of these data structures by studying them in detail.

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• List:

Lists are built-in Data Structures in Python that are used for storing data of several types in a single variable. These values are used in the way of a sequence. Every list element is assigned an address or value, also known as an index. The value of the index begins from 0, and the addressing continues until the last element. This type of indexing is called positive indexing. There is also negative indexing where the value begins from -1. The negative index helps you access elements in reverse order. This Data Structure in Python also allows storing the same value as a different piece of data. A list is created by adding square brackets to the program. Elements can be added using append, extend and insert. 

• Dictionary:

Dictionaries (or Python dictionaries) are utilized for storing multiple pairs of key values. Every piece of stored data is assigned a key. For better comprehension, you can take the example of a telephone directory where millions of names and telephone numbers correspond to it. Name and phone numbers are the constant values assigned to the keys. These keys help access the name and phone number data types. That is why names and phone numbers are referred to as keys. By accessing these keys, the names and phone numbers can be retrieved. These key-value pairs are stored in Data Structures in Python called the dictionary, which has various pairs of this type. Dictionaries are lookup tables, maps, hashmaps or associative arrays. Dictionaries make the lookup and deletion quite efficient.

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• Tuples:

Tuples are nothing but lists, but there is a catch to them. The only difference between the lists and tuples is that the data that is once entered inside the tuple cannot be modified in any way, no matter what. Only the data in the mutable tuples can be changed. Otherwise, there can be no alteration of any type. In Python, the tuple object collection is separated by commas. In terms of indexing, tuples and lists are quite similar. In terms of nested objects also, both data structures are similar, and the difference is only in mutability.

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• Sets:

Sets are unordered element data structures that are unique in multiple aspects. Even if the data has been repeated multiple times, it would be entered only once in the set. It is very similar to mathematical sets and even resembles them closely. The operations performed on sets are the same as the mathematical operators used in sets. Sets are more useful when compared to a list. The method of looking up a particular element is quite specialized. A data structure called a hash table is used. However, sets being unordered cannot be accessed by using indexes.

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What are User-defined Data Structures in Python?

A user-defined Data Structure in Python is a data type collection defined by the user and is used to create a data structure for a specific portion of the system, which can be used throughout a project. It is also a logical way to organize data in the computer memory to use it effectively. It allows the addition, removal, storage and maintenance of data in a highly structured manner. Python’s core data structure does not support user-defined data structures, but several modifications can be programmed to display similar or functionality with Python-supported concepts. The data structures that can be employed with the help of user-defined data structures are:

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• Linked List:

A linked list is a user-defined Data Structure in Python with a linear layout. The contents of a linked list are not deposited at neighboring locations in the memory and are linked with the help of pointers and nodes. The data is stored in a chain format, and they are very effective in inserting and deleting data. They can also be used to implement queues, stacks and many other data types that are abstract.  

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• Stack:

Stack is another linear Data Structure in Python. It follows a specific sequence for the performance of operations. This sequence could either be the Last In First Out (LIFO) order or the First In Last Out (FILO) sequence. Stacks are used to program many real-life examples of problems. An example of the usage of stacks is the design of a program for an arrangement of stacking plates in a canteen. Since stacking is a sequential process, the topmost plate is the first removed one. Similarly, the bottommost is the last one to be removed. So, stacking is helpful for programs where some sequential actions are necessary.

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• Queue:

Another linear data structure in user-defined data structures is Queues. They are helpful from the point of view of insertion and deletion. From one end, insertion can be performed, and from the other end, deletion can be performed, and it follows a First In, First Out (FIFO) process.

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• Tree:

The tree Data Structure in Python is non-linear, but it is hierarchical. The element at the top is the root and the tree’s starting point, and the end elements are called the leaves. Trees are more useful for storing data types that form a hierarchy. 

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• Graph:

Again, the graph is a non-linear Data Structure in Python with edges and nodes. The nodes are the points where elements are stored, and the edges are the connection of two nodes in a graph. A graph has a finite number of nodes.

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• Hashmap:

The only Data Structures in Python that can be indexed are hashmaps. Hashmaps use hash functions for the computation of slotted arrays. After computation, the value is mapped to the bucket with an index corresponding to it. The value is called a key, and it is immutable and unique. In Python, an example of a hashmap would be a dictionary.

Wrapping up:

These are some of the Data Structures in Python that you will need to learn and become Job Ready. There are multiple programs in which these data structures are incorporated and used to create both essential apps and advanced and complex programs. For more information and Python learning-based content, keep a tab on this section of Training Basket.

Important Note:

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