What Is A Linked List Data Structure What Are The Applications For The Linked List
A linked list can be thought of as a series of linked nodes that are connected by links . Each link represents an entry into the linked list, and each entry points to the next node in the sequence. The order in which nodes are added to the list is determined by the order in which they are created.
Following are some applications of linked list data structure:
- Stack, Queue, binary trees, and graphs are implemented using linked lists.
- Dynamic management for Operating System memory.
- Round robin scheduling for operating system tasks.
- Forward and backward operation in the browser.
What Is The Difference Between The Breadth First Search And Depth First Search
| Breadth First Search | |
|---|---|
| DFS necessitates less memory. | |
| Nodes that have been traversed multiple times are removed from the queue. | When there are no more nodes to visit, the visited nodes are added to the stack and then removed. |
| Backtracking is not an option in BFS. | The DFS algorithm is a recursive algorithm that employs the concept of backtracking. |
| It is based on the FIFO principle . | It is based on the LIFO principle . |
How To Implement A Queue Using A Stack
You can implement a queue using a stack by making either the enqueue or dequeue operation costly. In each example below, we will assume two stacks, s1 and s2. The s1 stack holds the data that we are working with while s2 is for temporary data storage.
Enqueue Method
Step 1: If s1 is empty, then push the first element of the stack into s1.
Step 2: If s1 is not empty, then push elements one by one into s2. Now take the first element and place it in s1, then take all the elements in s2 and place it back in s1.
Weve thus ensured that the first element is always on top and all of the other elements follow it.
Dequeue Method
You can implement a queue using stacks by making the dequeue operation costly in the following way:
Step 1: If s1 is empty, then dont execute any operation on the data. Simply return an error message saying that the stack is empty.
Step 2: If s1 is not empty, then take all the elements in that stack and place them in s2 one by one. Remove the first element in s2 and move all the elements in s2 into s1.
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Question : Briefly Explain Your Understanding Of Push And Pop
Answer: PUSH and POP operations in Data Structure define how data is inserted and retrieved from the Stack. PUSH specifies that a particular data is inserted into the Stack and PULL denotes its retrieval or deletion from the Stack. The same terminology can also be observed in GitHub, a cloud-based Git repository to assist in coding collaboration.
What Is The Process Behind Storing Variables In Memory
The simplest way to store anything in the computers memory is using a variable. A variable represents one piece of data, such as a character or a number. Variables make it easier to write programmes because you can refer to values by their names and write generic programs or functions that work with any value.
The way variables are stored depends on the programming language being used. Some programming languages require declaring variables and others dont. There are certain programming languages in which variables can only be of a certain type while others are more flexible.
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List Of Frequently Asked Binary Tree And Bst
Hello folks, I have been sharing a lot of resources about programming job interviews like the books, courses, and some interview questions on the software design and data structures like an array, string, and linked list.
So far, we have looked at only the linear data structures, like an array and linked list, but all information in the real world cannot be represented in a linear fashion, and that’s where tree data structure helps.
A tree data structure is a hierarchical data structure that allows you to store hierarchical data like a family tree or office hierarchy. Depending on how you store data, there are different types of trees, such as a binary tree, where each node has, at most, two child nodes.
Along with its close cousin binary search tree, it’s also one of the most popular tree data structures. Therefore, you will find a lot of questions based on them, such as how to traverse them, count nodes, find depth, and check if they are balanced or not.
A key point to solving binary tree questions is a strong knowledge of theory, like what is the size or depth of the binary tree, what is a leaf, and what is a node, as well as an understanding of the popular traversing algorithms, like pre-order, post-order, and in-order traversal.
If you are not familiar with these concepts then I strongly suggest you first go through a comprehensive data structure and algorithm course which explains essential data structure in detail.
What Are The Differences Between The B Tree And The B+ Tree
The B tree is a self-balancing m-way tree, with m defining the tree’s order. Depending on the number of m, Btree is an extension of the Binary Search tree in which a node can have more than one key and more than two children. The data is provided in the B tree in a sorted manner, with lower values on the left subtree and higher values on the right subtree.
The B+ tree is an advanced self-balanced tree since every path from the tree’s root to its leaf is the same length. The fact that all leaf nodes are the same length indicates that they all occur at the same level. Specific leaf nodes cant appear at the third level, while others appear at the second level.
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Top Data Structures And Algorithms Interview Q& as
Data Structures can be defined as techniques used to define, store, and access data systematically. They form the most important component of any algorithm. Depending on the type of Data Structures, they store different kinds of data and are accessible in different ways. For an algorithm to return a result, it needs to operate on and manipulate a set of data structures in an organised and efficient manner to come to the final result.
In File Structures, the data is stored on disks following standard file storage policies and is not compatible with external, third-party applications. In Data Structures, on the other hand, the data is stored both on the disk as well as RAM in customised storage policies, and these are highly compatible with external apps.
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Data Structures can be broadly divided into two categories:
4. What are some key usage areas of Data Structures?
Data Structures are pretty much required in all the fields of computing that you can think of, especially Algorithms and Algorithm Optimization. Here are some other areas where Data Structures are extensively used:
- Operating system design
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How Do You Implement Stack Using Queues
- A stack can be implemented using two queues. We know that a queue supports enqueue and dequeue operations. Using these operations, we need to develop push, pop operations.
- Let stack be s and queues used to implement be q1 and q2. Then, stack s can be implemented in two ways:
1. By making push operation costly:
- This method ensures that the newly entered element is always at the front of q1 so that pop operation just dequeues from q1.
- q2 is used as auxillary queue to put every new element in front of q1 while ensuring pop happens in O complexity.
- Push element to stack s: Here push takes O time complexity.
push: Enqueue data to q2 Dequeue elements one by one from q1 and enqueue to q2. Swap the names of q1 and q2
- Pop element from stack s: Takes O time complexity.
pop:dequeue from q1 and return it.
2. By making pop operation costly:
- In push operation, the element is enqueued to q1.
- In pop operation, all the elements from q1 except the last remaining element, are pushed to q2 if it is empty. That last element remaining of q1 is dequeued and returned.
- Push element to stack s: Here push takes O time complexity.
push:Enqueue data to q1
- Pop element from stack s: Takes O time complexity.
pop: Step1: Dequeue every elements except the last element from q1 and enqueue to q2.Step2: Dequeue the last item of q1, the dequeued item is stored in result variable. Step3: Swap the names of q1 and q2 Step4: Return the result.
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Data Structures And Algorithms Interview Questions
In this article
Algorithms and data structures are foundational to computer science. As the scaffolding for programming languages, tech recruiters place an emphasis on algorithms and data structures in interviews.
If youre looking for help with interview questions in those areas, youve come to the right place. Were going to cover all the data structure and algorithms interview questions that you should prepare for in 2022.
Breadth First Traversal In A Given Graph
If you are familiar with the concept of Breadth First Traversal in a Tree then this concept is closely related to that.
The only difference is that unlike the structure for the trees, the graphs may consist of cycles within the data structure.
Refer to the following problem statement and answer key to better understand this concept:
Problem Statement:
Q. Traverse the given Graph and figure out the position of the two adjacent vertices.
Answer Key: Follow the methods below to implement BFS traversal:
- Initialize the visited array and mark the starting node as visited.
- Follow the process below until the queue is empty.
- In the queue, remove the first vertex.
- Queue all unvisited neighbors of the node.
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Data Structures And Algorithms Problems Asked During Coding Interviews
I am Amit Shekhar, a mentor helping developers learn to code and get high-paying jobs.
Here, I have listed the 100 Data Structures and Algorithms Problems asked during the coding interviews in companies like Amazon, Microsoft, Facebook, LinkedIn, Adobe, Uber, Yahoo, eBay, etc.
Practice problems and never give up!
Lets become friends on,,Github,Quora, and .
Lets get started with the questions.
Asked in: Amazon, Microsoft, Facebook
2. Reverse Bits
3. Square Root of Integer
Asked in: Amazon, Microsoft, Facebook
4. Calculate power function
Asked in: Google, LinkedIn, Amazon
5. Greatest Common Divisor
6. Find the Closest Palindrome
Asked in: Microsoft, Amazon
Asked in: Amazon, Google, Adobe
10. Set Matrix Zeroes
11. maximum j i such that A > A
Asked in: Google, Amazon, Adobe
12. Move zeroes to an end
Asked in: Facebook, Uber
13. Merge two sorted arrays
Asked in: Microsoft, Adobe
14. Container with Most Water
Asked in: Amazon, Google, Facebook, Adobe
15. Remove duplicates from sorted array
Asked in: Amazon, Microsoft, Google
16. Find an element in Bitonic array
Asked in: Amazon
17. Find minimum element in sorted and rotated array
Asked in: Facebook
18. Median of two sorted array of same size
Asked in: Amazon, Microsoft, Google
19. Inversion count in an array
Asked in: Amazon, Google
20. Search for a Range in a sorted array
Asked in: Microsoft, Google
22. Median in row wise sorted matrix
Asked in: Amazon
23. Swap List Nodes in pairs
Asked in: Amazon, Microsoft
Write A Function For Zigzag Traversal In A Binary Tree
- Explanation: Zigzag Traversal first iterates the given level of the tree from left to right and then the next level as the right to the level.
// Tree Nodestruct Node //Function to store the zigzag order traversal of a tree in a list. vector < int> zigZagTraversal //Iterate until the second stack is not empty while) } return result }
- Time Complexity: O
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The Shortest Path Between Two Nodes In A Graph
The shortest path between two nodes in a graph can be found by traversing the graph from one node to the other and then taking the shortest distance.
It is important to know the difference between a path and a route:
- A path is a series of nodes that are connected.
- A route is a path that has been followed.
Lets have a look at the following question based on the shortest path between two nodes in a graph.
Problem Statement:
Q. Given a graph, figure out the shortest paths between two given nodes where the graph is undirected and unweighted.
Answer Key: Lets say you want to find the shortest path from Node A to Node B. You could do a breadth-first search that starts at Node A and ends at Node B. Or you could do a depth-first search that starts at Node B and ends at Node A.
The difference between these two algorithms is that the breadth-first algorithm starts at the first node and then goes to the next node, while the depth-first algorithm starts at the last node and goes to the first node.
Explain The Implementation Of Lru Cache Using Data Structures
Two data structures are used to implement an LRU cache. The first is a queue, which is implemented using a linked list. The other is a hash, which holds the page number as a key and the value of the hash is the address of the corresponding queue node.
If a particular page is referenced and is available in the memory, then the node of the list is detached and is placed at the head of the queue. If a particular page is requested and is not in the memory, then it is brought into the memory.
There are situations in which the queue becomes full, in which case we remove a node from the back of the queue and add a new one in front.
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Find The Subsequence Of Length 3 With The Highest Product From A Sequence Of Non
- Input: n = 8 arr =
The three increasing elements of the given arrays are 10, 11, and 12, which form a three-size subsequence with the highest product.
vector< int> maxProductSubsequence it-- largestOnLeft=*it } int m=0 long long p=INT_MIN vector< int> result result=-1 for else } } } return v }
- Time Complexity: O)
Write A Function To Convert An Infix Expression To Postfix Expression
- Output: abcd^*+
int prec public: // Function to convert an infix expression to a postfix expression. string infixToPostfix st.pop } // If an operator is scanned else } st.push } } // Pop all the remaining elements from the stack while ) return result }
- Time Complexity: O
- Space Complexity: O
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Write A C Function To Find The Maximum Sum Of K Consecutive Elements In The Given Array
Solution:
We’re given an integer array and an integer k. We need to find the maximum sum possible in the array when elements in k consecutive indexes are added. Using nested loops, we can use the brute-force approach to check all the k-sized possible sub-arrays in the array. It could be more efficient. Another notable technique we can use is the “Sliding window technique“.
Sliding Window technique:
We take the value of k from the user, and the concept here is that we create a window of size k, and we’ll keep sliding it by a unit index.
For example:
Suppose we need the maximum sum of 2 consecutive indexes, create a 2-sized window, and keep sliding it throughout the array. We’ll find the sum of elements of each window and return the maximum sum:
Code:
These Are The Best Online Courses To Learn Data Structure And Algorithms In Python Algorithm Code Examples Are Given In The Python Programming Language
Hello Python Programmers, if you want to learn Data structure and Algorithms in 2022 and looking for the best online courses where you can find common data structure examples in Python then you have come to the right place.
In the past, I have shared a lot of useful resources like best data structure courses, books, and tutorials to learn Data Structure and Algorithms for programmers.
I have also shared a lot of Algorithmic interview questions and their solutions in Java, but I have been constantly getting queries about good courses to learn Data Structure and Algorithms in Python.
Even though the topics are completely independent of the programming language, Python developers definitely like the courses and books which teach Data Structure and Algorithms in Python.
This time, I have focused more on coverage of essential data structure in a fun and interesting way, rather than picking the course which covers a huge number of data structure and algorithms but didnt do justice with that.
Another reason I have included more than a few courses is because not everybody connects to the instructor I like. Everybody is different and they should only join the course where they can connect to the instructor, I mean they like his voice, the style of explanation, and the content.
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