What is Data Structure?

Data structures are a way to organize and manage data. They are used to store data in a format that will allow for easy access. There are many types of data structures. Trees, Linked lists, and Queues are just some of the many options. You can choose one that best fits your needs.

Hash tables

Hash tables are a data structure that allows storing and searching arbitrary values in a database. They are helpful for data science applications such as feature engineering, normalization, and simple histograms. In Python, hash tables are represented by dictionary data types. Dictionary keys are generated using a hash function, and the order of the dictionary elements is not fixed.

The key-value scheme of hash tables enables quick and efficient lookups of data. For example, if a table holds several employees, the hash function of each employee would return the value of the employee’s name, age, and position. In a database, there are several key-value pairs stored in each row. If a table contains many keys, this can cause collisions. However, collisions are minimized when the hash function is ‘good.’ Also, a prime table size reduces the chances of a hash collision. In addition, an efficient hash function distributes the index value evenly across the entire table.

Trees

Trees in data structure store data in a hierarchical manner. The root node is at the top of the tree. Nodes that come after this node are considered its children. The parent-child relationship is a special relationship between nodes. There are three types of nodes in a tree: the root node, the first node, and the last node.

The root node is the topmost node of a tree, while the child nodes follow the paths from that node. A leaf node does not have child nodes, while an internal node does. A tree contains a recursive set of nodes. Each subtree consists of node n and all its descendants.

Linked lists

A linked list is a collection of data elements in a sequence. The order is not determined by physical memory placement but rather by how the elements point to each other. A linked list is made of nodes, each of which points to the next element. These nodes represent a sequence of events.

There are several ways to add a node to a linked list. One way involves traversing the entire list. Another way is to add a new node at the end of the list. When doing this, you must find the last node of the list and set it as the next value.

Linked lists can be small or large. They are composed of nodes, which are the list elements. The first node in the list is called the head node. The last node is called the tail. Often, the end of the list is not a node but instead points to an empty value. On the other hand, single-linked lists contain nodes with data and a reference to the next node.

Queues

Queues in data structures are lists in which elements can be added to the front or removed from the back. This arrangement is similar to that found at a movie theater ticket counter. In a queue, elements are released from the front in the order in which they were added to the back. In the UK, queues are commonly referred to as “queues” and “queuing up.” Using queues for data management was a common practice for accountants and other professionals long before computers became household items.

Queues are helpful in data processing when ordering is essential. This is particularly useful when it comes to caching. By creating a queue, the program can avoid storing data in an unreliable way and still preserve the order in which elements are added and removed.

Stacks

Stacks are a simple type of data structure. They help store data in multiple locations. For example, your browser may use stacks to store URLs. Each time you type in a URL, it creates a new event stored in the stack.

Stacks are essentially lists of items. The topmost item is held at the top of the stack and points to the next top item. When you remove an item from a stack, the pointer on the top is reset to zero, and the following item is moved down.

Stacks can be implemented using arrays or linked lists. Stacks are more efficient than linked lists, but both require overhead to allocate and link data. And unlike linked lists, stacks have no limit on how large they can be.