What are Data Structures in Python: From Coding to Execution

Introduction

Data structures are a way to arrange data so one can access them more efficiently. Thus, data structures are a basic building block of any programming language and serve as the basis for all programs. Python is more user-friendly than other programming languages when it comes to learning the principles of these data structures.

A complete selection of data structures is present in the standard library of Python. This blog will guide you on data structures in Python including its types.

Data Structure Definition

It is crucial to organize, manage, and store data to make it easier to access and more effective to modify. You can arrange your data using data structures so that you can store groups of data. It also allows you to relate data groups, and carry out actions on them as necessary. 

Data structures and algorithms in Python are some of the most basic concepts for beginners to learn. While you already know about data structure, what is the use of algorithms? An algorithm is a complex set of instructions for processing data in a particular way. Thus, an algorithm makes logical use of several data structures to solve a particular computing issue.

Types of Data Structures in Python

Users of Python have complete control over the functionality of Data Structures due to the ability to design their own. Below are the main types and subtypes of Python Data structures:

1. Built-in Data Structures

There are multiple in-built data structures in Python, which makes programming easier. Additionally, it enables programmers to use them to find solutions more quickly. Let's know more details about each of them.
 

• Lists

Lists can sequentially store several types of data. There is an address allocated to each item in the list, also known as the Index.
 

• Dictionary 

Dictionaries keep key-value pairs. The dictionary data structures using Python works like a phone book that has thousands of names. A phone book also has matching numbers added to names for better understanding. So, here the Name and the Phone Numbers are constant values or the keys.
 

• Tuple

The only difference between tuples and lists regarding the modification of data. So once you enter the data, you cannot modify it under any circumstances in tuple. But, the only exception is you can alter the data if it is mutable. 
 

• Sets

Sets are collections of different, unordered elements. You can enter the data into the set once, even if it is repeated more than once. So, it is similar to the sets you have studied in mathematics. 
 

2. User-defined Data Structures

A user-defined structure of a Python program is a data structure that is not supported by Python. But, you can design them to reflect the same functionality using concepts supported by Python. 
 

• Stack

They are linear Data Structures working on the principle of Last-In-First-Out (LIFO). Here, the data that you enter last will be the first to get accessed.
 

• Queue

The data accessible first will be that which you enter first in a queue. Hence, it is like a linear data structure working on the First-In-First-Out (FIFO) principle.
 

• Tree

Trees are non-linear data structures in Python having roots and nodes. The additional data points that are available to us are the nodes. Whereas, the root is the node from where the data originates. The parent node comes before, while the child node comes after.
 

• Linked Lists

Linear data structures known as linked lists connect via pointers. They are not stored sequentially. A linked list's node consists of data and a pointer named next.
 

• Graph

You cannot ignore Graph while discussing DSA in Python. They help to store data collection of points or vertices (nodes) and edges (edges). Graphs are also the most accurate representation of a real-world map. They also help find the various cost-distance between the various data points or the nodes.
 

• HashMaps

HashMaps are the same as what dictionaries are in Python. They are similar to Python's dictionaries. Furthermore, for many other things, they can construct apps like phone books and populate data with lists.

Why Should You Learn Data Structures and Algorithms Using Python? 

Once you are aware of DSA, you know the core algorithmic techniques and concepts of Python. You also know about sorting and searching, divide and conquer, greedy algorithms, and dynamic programming. Additionally, you will discover how data sorting facilitates searches.

Every programmer must master the idea of data structures and algorithms or DSA. Moreover, it helps to write code that effectively uses the resources at hand. DSA is more of a basic idea regardless of the programming language.

Advantages of Data Structure in Python
 

1. Data structures have the advantage of allowing for the storage of information on hard drives.
2. Large data sets are simpler to manipulate.
3. The program is more effective when you choose the suitable ADT (Abstract Data Type).
4. Data structures create effective algorithms.
5. It offers abstraction and reusability.
6. The right data structures can help programmers do the job faster.
7. It assists when executing tasks like data processing, retrieval, or storage.
    

Data Structure Application
 

1. Helps Organise data in a computer’s memory
2. Information representation in databases
3. Data-searching algorithms 
4. Data-manipulation algorithms 
5. Graphics-rendering software, such as a web browser or 3D modeling software
6. Data analysis algorithms 
7. Data-generating algorithms 
8. An application that controls directories and files 
9. Data compression and decompression methods
10. Data encryption and decryption methods 

Conclusion

The use of Python is widespread, including in the creation of websites, AI, and more. But data plays a crucial role in all of this to be possible. Thus, data must be stored effectively and accessed promptly. Data structures in Python are very crucial to accomplish this.

A data structure is a fast way to logically arrange data in computer memory so that you can use it further. It also enables the organized addition, deletion, storing, and maintenance of data. We hope you now understand the significance of both the built-in and user-defined data structures in Python.