Implementing common data structures and algorithms in python

 

What are Data Structures?

A data structure is a way to store data.

We structure data in different ways depending on what data we have, and what we want to do with it.

Family tree

First, let's consider an example without computers in mind, just to get the idea.

If we want to store data about people we are related to, we use a family tree as the data structure. We choose a family tree as the data structure because we have information about people we are related to and how they are related, and we want an overview so that we can easily find a specific family member, several generations back.

With such a family tree data structure visually in front of you, it is easy to see, for example, who my mother's mother is—it is 'Emma,' right? But without the links from child to parents that this data structure provides, it would be difficult to determine how the individuals are related.

Data structures give us the possibility to manage large amounts of data efficiently for uses such as large databases and internet indexing services.

Data structures are essential ingredients in creating fast and powerful algorithms. They help in managing and organizing data, reduce complexity, and increase efficiency.

In Computer Science there are two different kinds of data structures.

Primitive Data Structures are basic data structures provided by programming languages to represent single values, such as integers, floating-point numbers, characters, and booleans.

Abstract Data Structures are higher-level data structures that are built using primitive data types and provide more complex and specialized operations. Some common examples of abstract data structures include arrays, linked lists, stacks, queues, trees, and graphs.

This tutorial is a beginner-friendly guide for learning data structures and algorithms using Python. In this article, we will discuss the in-built data structures such as lists, tuples, dictionaries, etc, and some user-defined data structures such as linked lists, trees, graphs, etc, and traversal as well as searching and sorting algorithms with the help of good and well-explained examples and practice questions.

Lists

Python Lists are ordered collections of data just like arrays in other programming languages. It allows different types of elements in the list. The implementation of Python List is similar to Vectors in C++ or ArrayList in JAVA. The costly operation is inserting or deleting the element from the beginning of the List as all the elements are needed to be shifted. Insertion and deletion at the end of the list can also become costly in the case where the preallocated memory becomes full.

Why Every Developer Should Learn Data Structures and Algorithms?

Last Updated : 19 Apr, 2024

Software developers are regarded as the unknown heroes who design, execute, deploy and manage software programs. It is indeed a lucrative career option that promises insanely high salaries, amazing career growth, and global opportunities. As per the survey software development will witness an amazing growth rate of 19% which is far more than the average.  

Having proper knowledge of Data Structure and Algorithms makes a good identity for a Software Developer. The main reason behind this is that it improves developers’ problem-solving ability to a great extent. So tech giants like Google, Meta, Adobe, Amazon, Netflix, etc. hire software developers with in-depth knowledge of Data Structure and Algorithms as they are more capable of making the right decisions when it comes to problem-solving.

In this blog, we will answer the question of Why Every Developer Should Learn Data Structures and Algorithms. Further, we will provide you with a foundational understanding and a complete roadmap of how to learn Data Structures and Algorithms. So let’s get started with the basic understanding of data structure and algorithms

Data Structures and Algorithms (Python) on ResearchGate covers topics such as abstract data types, object-oriented programming, algorithm analysis, and more. It includes examples and hands-on experience with Python to help readers understand the following concepts

In the realm of programming, data structures and algorithms are the foundational building blocks that guide the organization, storage, and manipulation of data. These entities play a pivotal role in determining the efficiency and effectiveness of software solutions.

Conclusion

We commenced with the basic concepts where we delineated the intricacies of algorithms and data structures, drawing attention to their properties and classifications. We then delved deep into linear data structures like arrays, linked lists, stacks, and queues, understanding their operations, applications, and time complexities through Python examples.

Further Resources and Learning Paths

For readers eager to further deepen their understanding and hone their skills, here are some resources and learning paths you might find beneficial:

1. Online Courses and Tutorials: Platforms like Coursera, Udacity, and Udemy offer comprehensive courses on data structures and algorithms.
2. Books: Numerous books cover this topic in detail. Some popular choices are “Introduction to Algorithms” by Cormen et al. and “Algorithms” by Robert Sedgewick and Kevin Wayne.
3. Websites and Blogs: Websites like GeeksforGeeks, Stack Overflow, and Khan Academy provide an abundance of information and examples on this topic.
4. Practice Platforms: Engage in hands-on practice through platforms like LeetCode, HackerRank which offer problems ranging from beginner to advanced levels