A Complete Overview of Python Data Structures and Algorithms

Data structures and algorithms are essential topics in the field of computer science. Thousands of lines of code are written using a variety of algorithms to power our apps and software.

In this article, we will gain a clear understanding of data structures and algorithms and discuss the various types of data structures.

What is Data Structure?

Python data structure is a systematized arrangement of data that enables us to store and access it efficiently and effectively. The goal of using data structures is to reduce the time and space required to execute operations on the data. An efficient data structure will take up the least amount of memory and require the least time to process the data.

Types of Data Structure

Python includes built-in support for multiple data structures that allow you to store and access data in a variety of ways. These structures are called Lists, Dictionaries, Tuples, and Sets. 

Additionally, Python provides a way to create your own custom data structures, giving you the power to customise their functionality. Examples of such data structures include Stacks, Queues, Trees, Linked Lists, and more, also available in other programming languages. 

• Built-in data structures 

Built-in data structures are the ones that are already provided in the programming language you are using. They are already implemented, and you can use them directly.

• Tuple: Tuples in Python are fixed data structures and cannot have any items added or removed, nor can any of the data stored in the tuple be altered once it has been declared.

• List: A list is a sequential data structure that is modifiable.

• Dictionary: A dictionary data structure is based on the key-value pair notion. The dictionary keys are immutable, but the values can be changed. 

• Set: A set is a single variable that can store an unorganized grouping of items. 

• Data structures 

In addition to the default Data Structures mentioned, many custom data structures are commonly used in the creation of large-scale systems in many businesses. Some of them include the following:

• Arrays: Arrays are an efficient way of storing data in contiguous memory locations. Elements of the same data type are grouped, and their position can be calculated by adding an offset to a base value. Retrieving an element is incredibly fast since its index is known.

• Stack: A data structure with linear properties can be described as a stack, a collection of items placed on top of each other in a specific order.

• Linked Lists: A linear data structure in which a succession of nodes is connected using a reference pointer. The first node's reference is referred to as the Head, while the last node's reference is known as NULL. There are three types of linked lists: single, double, and circular. 

• Diagrams: In Graph Data Structures, all nodes contain data and are connected. For example, on Meta, the Graph Data Structure links together all elements, including when someone joins a group or likes a page. 

This creates a new edge in the graph data structure which symbolizes the relationship. Common terms used in this data structure are Adjacency, Path, and Directed Graph.

• Queues: The Python Queue data structure operates according to the First in First out (FIFO) principle. Elements added first to the queue are the first ones to be removed. The insertion of elements into the structure is referred to as enqueue, while the removal of elements is known as dequeue.

• Trees: A data structure that consists of nodes connected by edges in a non-linear fashion. It is commonly referred to as a tree data structure, and its key terms include node, edge, root, depth of a node, height of a node, height of a tree, and so on.

• Hash Maps: A hash map is similar to a dictionary, storing elements as key-value pairs. Hash maps have multiple uses, such as providing quick and efficient data search as well as being used in cryptography applications.

Now we will take a look at the basics of Python algorithms.

What is a Python Algorithm?

An algorithm can be thought of as a set of step-by-step instructions that are used to carry out a particular task or solve a group of problems. In simpler terms, it is a sequence of operations that are used to accomplish a specific goal.

What is the Step-by-Step Process for Writing Algorithms?

While developing any algorithm, certain steps must be followed.

• Identify the problem and understand what needs to be done 
• Figure out the goal to be accomplished and the starting point
• Set a finish line
• Outline the steps between the second and third steps 

• Review all of the preceding steps. 

What are the elements of a good algorithm?

When designing an algorithm, it is important to have a clearly defined objective or goal. Additionally, it is important to make sure that the assumptions used to create the algorithm are valid and support the end goal.

• Detailed Plan: Each step of the formulated algorithm should be finite, clear, and understandable. 
• Exact Inputs and Results: The input and output details should be explicit. 
• Explicitly Defined: Every step of the algorithm should produce an output determined by the input. 
• Durable: The algorithm should be multifaceted and modifiable to serve various purposes. 

• Language Neutral: The algorithm should be platform-independent and applicable to any programming language. 

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