Coding Interview

Tips to solve any DSA question by understanding patterns⚡ If input array is sorted then - Binary search - Two pointers If asked for all permutations/subsets then - Backtracking If given a tree then - DFS - BFS If given a graph then - DFS - BFS If given a linked list then - Two pointers If recursion is banned then - Stack If must solve in-place then - Swap corresponding values - Store one or more different values in the same pointer If asked for maximum/minimum subarray/ subset/options then - Dynamic programming If asked for top/least K items then - Heap - QuickSelect If asked for common strings then - Map - Trie Else - Map/Set for O(1) time & O(n) space - Sort input for O(nlogn) time and O(1) space Best DSA RESOURCES: https://topmate.io/coding/886874 All the best 👍👍

DSA is really tough, but you don't seem to struggle?🤨 I get this question a lot. Here are some of the hardest questions you might face in an interview. Practice these using the 𝟯-𝟳-𝟭𝟱 𝗿𝘂𝗹𝗲: First solve the question, then note down the answer. After three days, try to remember the question from the answer and solve it again. Repeat the same after 7 and 15 days. This way, you'll solve the same question 4 times in 15 days, making it easier if you encounter it again. 𝟭. 𝗔𝗿𝗿𝗮𝘆𝘀 & 𝗦𝘁𝗿𝗶𝗻𝗴𝘀 - Minimum Window Substring - Trapping Rain Water - Largest Rectangle in Histogram 𝟮. 𝗟𝗶𝗻𝗸𝗲𝗱 𝗟𝗶𝘀𝘁𝘀 - Merge k Sorted Lists - Reverse Nodes in k-Group - LFU Cache 𝟯. 𝗧𝗿𝗲𝗲𝘀 - Binary Tree Maximum Path Sum - Serialize and Deserialize Binary Tree - Vertical Order Traversal of a Binary Tree 𝟰. 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 - Edit Distance - Burst Balloons - Shortest Common Supersequence 𝟱. 𝗚𝗿𝗮𝗽𝗵𝘀 - Alien Dictionary - Minimum Cost to Make at Least One Valid Path in a Grid - Swim in Rising Water 𝟲. 𝗥𝗲𝗰𝘂𝗿𝘀𝗶𝗼𝗻 & 𝗕𝗮𝗰𝗸𝘁𝗿𝗮𝗰𝗸𝗶𝗻𝗴 - N-Queens II - Sudoku Solver - Word Search II 𝟳. 𝗦𝗼𝗿𝘁𝗶𝗻𝗴 & 𝗦𝗲𝗮𝗿𝗰𝗵𝗶𝗻𝗴 - Count of Smaller Numbers After Self - Median of Two Sorted Arrays - Split Array Largest Sum 𝟴. 𝗗𝗲𝘀𝗶𝗴𝗻 - Design Search Autocomplete System - Design In-Memory File System - Design Excel Sum Formula 𝟵. 𝗚𝗿𝗲𝗲𝗱𝘆 - Minimum Number of Arrows to Burst Balloons - Candy - Patching Array 𝟭𝟬. 𝗕𝗶𝘁 𝗠𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻 - Maximum Product of Word Lengths - Smallest Sufficient Team - Minimum Cost to Connect Two Groups of Points 𝟭𝟭. 𝗧𝘄𝗼 𝗣𝗼𝗶𝗻𝘁𝗲𝗿𝘀 - Minimum Window Subsequence - Minimum Operations to Make a Subsequence - Minimum Adjacent Swaps to Reach the Kth Smallest Number 𝟭𝟮. 𝗛𝗲𝗮𝗽 - Minimum Number of Refueling Stops - Sliding Window Median - Minimum Number of K Consecutive Bit Flips By following the 3-7-15 rule and practicing these tough questions regularly, you'll build strong problem-solving skills and be well-prepared for your interviews. Keep pushing yourself, and remember, consistency is key. Best DSA RESOURCES: https://topmate.io/coding/886874 All the best 👍👍

Complete DSA Roadmap |-- Basic_Data_Structures | |-- Arrays | |-- Strings | |-- Linked_Lists | |-- Stacks | └─ Queues | |-- Advanced_Data_Structures | |-- Trees | | |-- Binary_Trees | | |-- Binary_Search_Trees | | |-- AVL_Trees | | └─ B-Trees | | | |-- Graphs | | |-- Graph_Representation | | | |- Adjacency_Matrix | | | └ Adjacency_List | | | | | |-- Depth-First_Search | | |-- Breadth-First_Search | | |-- Shortest_Path_Algorithms | | | |- Dijkstra's_Algorithm | | | └ Bellman-Ford_Algorithm | | | | | └─ Minimum_Spanning_Tree | | |- Prim's_Algorithm | | └ Kruskal's_Algorithm | | | |-- Heaps | | |-- Min_Heap | | |-- Max_Heap | | └─ Heap_Sort | | | |-- Hash_Tables | |-- Disjoint_Set_Union | |-- Trie | |-- Segment_Tree | └─ Fenwick_Tree | |-- Algorithmic_Paradigms | |-- Brute_Force | |-- Divide_and_Conquer | |-- Greedy_Algorithms | |-- Dynamic_Programming | |-- Backtracking | |-- Sliding_Window_Technique | |-- Two_Pointer_Technique | └─ Divide_and_Conquer_Optimization | |-- Merge_Sort_Tree | └─ Persistent_Segment_Tree | |-- Searching_Algorithms | |-- Linear_Search | |-- Binary_Search | |-- Depth-First_Search | └─ Breadth-First_Search | |-- Sorting_Algorithms | |-- Bubble_Sort | |-- Selection_Sort | |-- Insertion_Sort | |-- Merge_Sort | |-- Quick_Sort | └─ Heap_Sort | |-- Graph_Algorithms | |-- Depth-First_Search | |-- Breadth-First_Search | |-- Topological_Sort | |-- Strongly_Connected_Components | └─ Articulation_Points_and_Bridges | |-- Dynamic_Programming | |-- Introduction_to_DP | |-- Fibonacci_Series_using_DP | |-- Longest_Common_Subsequence | |-- Longest_Increasing_Subsequence | |-- Knapsack_Problem | |-- Matrix_Chain_Multiplication | └─ Dynamic_Programming_on_Trees | |-- Mathematical_and_Bit_Manipulation_Algorithms | |-- Prime_Numbers_and_Sieve_of_Eratosthenes | |-- Greatest_Common_Divisor | |-- Least_Common_Multiple | |-- Modular_Arithmetic | └─ Bit_Manipulation_Tricks | |-- Advanced_Topics | |-- Trie-based_Algorithms | | |-- Auto-completion | | └─ Spell_Checker | | | |-- Suffix_Trees_and_Arrays | |-- Computational_Geometry | |-- Number_Theory | | |-- Euler's_Totient_Function | | └─ Mobius_Function | | | └─ String_Algorithms | |-- KMP_Algorithm | └─ Rabin-Karp_Algorithm | |-- OnlinePlatforms | |-- LeetCode | |-- HackerRank Best DSA RESOURCES: https://topmate.io/coding/886874 Credits: https://t.me/free4unow_backup All the best 👍👍

A four step method to attempt coding problems. https://www.freecodecamp.org/news/how-to-solve-coding-problems/

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Python Interview Q&A: https://topmate.io/coding/898340 ENJOY LEARNING 👍👍

Top 10 Python Interview Questions 1. What is Python and what are its key features? Python is a high-level, interpreted programming language known for its simplicity and readability. Its key features include dynamic typing, automatic memory management, a large standard library, and support for multiple programming paradigms (such as procedural, object-oriented, and functional programming). 2. What are the differences between Python 2 and Python 3? Python 2 and Python 3 are two major versions of the Python programming language. Some key differences include: - Python 3 has stricter syntax rules and is not backward compatible with Python 2. - Python 3 has improved Unicode support and better handling of byte strings. - Python 3 has some new features and improvements over Python 2, such as the print function being replaced by the print() function. 3. Explain the difference between list and tuple in Python. - Lists are mutable, meaning their elements can be changed after creation, while tuples are immutable and their elements cannot be changed. - Lists are defined using square brackets [], while tuples are defined using parentheses (). - Lists are typically used for collections of items that may need to be modified, while tuples are used for fixed collections of items that should not change. 4. What is PEP 8 and why is it important in Python programming? PEP 8 is the official style guide for Python code, outlining best practices for writing clean, readable, and maintainable code. Following PEP 8 helps ensure consistency across projects, makes code easier to understand and maintain, and promotes good coding habits within the Python community. 5. How do you handle exceptions in Python? Exceptions in Python can be handled using try-except blocks. The code that may raise an exception is placed within the try block, and any potential exceptions are caught and handled in the except block. Additionally, you can use the finally block to execute cleanup code regardless of whether an exception occurs. 6. What is a decorator in Python and how do you use it? A decorator in Python is a function that takes another function as input and extends or modifies its behavior without changing its source code. Decorators are typically used to add functionality to functions or methods, such as logging, authentication, or performance monitoring. To use a decorator, you simply place the "@decorator_name" above the function definition. 7. Explain the difference between '==' and 'is' in Python. The '==' operator checks for equality of values between two objects, while the 'is' operator checks for identity, meaning it compares whether two objects refer to the same memory location. In other words, '==' checks if two objects have the same value, while 'is' checks if they are the same object. 8. How do you create a virtual environment in Python? You can create a virtual environment in Python using the venv module, which is included in the standard library. To create a virtual environment, you run the command "python -m venv myenv" in your terminal or command prompt, where "myenv" is the name of your virtual environment. You can then activate the virtual environment using the appropriate command for your operating system. 9. What is the difference between a shallow copy and a deep copy in Python? A shallow copy creates a new object but does not recursively copy nested objects within it, meaning changes to nested objects will affect both the original and copied objects. A deep copy creates a new object and recursively copies all nested objects within it, ensuring that changes to nested objects do not affect the original object. 10. How do you handle file I/O operations in Python? File I/O operations in Python can be performed using built-in functions such as open(), read(), write(), close(), and more. To read from a file, you open it in read mode ('r') and use functions like read() or readline(). To write to a file, you open it in write mode ('w') or append mode ('a') and use functions like write() or writelines().

Pattern 3: Fast and Slow pointers The Fast and Slow pointer approach, also known as the Hare & Tortoise algorithm, is a pointer algorithm that uses two pointers which move through the array (or sequence/linked list) at different speeds. This approach is quite useful when dealing with cyclic linked lists or arrays. By moving at different speeds (say, in a cyclic linked list), the algorithm proves that the two pointers are bound to meet. The fast pointer should catch the slow pointer once both the pointers are in a cyclic loop. How To Identify - The problem will deal with a loop in a linked list or array - When you need to know the position of a certain element or the overall length of the linked list Questions - Linked List Cycle (easy) - Palindrome Linked List (medium) - Cycle in a Circular Array (hard) Best DSA RESOURCES: https://topmate.io/coding/886874 ENJOY LEARNING 👍👍

Pattern 2: Two Pointers Pattern Two Pointers is a pattern where two pointers iterate through the data structure in tandem until one or both of the pointers hit a certain condition. Two Pointers is often useful when searching pairs in a sorted array or linked list; for example, when you have to compare each element of an array to its other elements. How To Identify - It will feature problems where you deal with sorted arrays (or Linked Lists) and need to find a set of elements that fulfill certain constraints - The set of elements in the array is a pair, a triplet, or even a subarray Questions - Squaring a sorted array (easy) - Triplets that sum to zero (medium) - Comparing strings that contain backspaces (medium) You can check these resources for Coding interview Preparation All the best 👍👍

Pattern 1: Sliding Window Pattern The Sliding Window pattern is used to perform a required operation on a specific window size of a given array or linked list, such as finding the longest subarray containing all 1s. Sliding Windows start from the 1st element and keep shifting right by one element and adjust the length of the window according to the problem that you are solving. In some cases, the window size remains constant and in other cases the sizes grows or shrinks. How To Identify - The problem input is a linear data structure such as a linked list, array, or string - You’re asked to find the longest/shortest substring, subarray, or a desired value Questions - Maximum sum subarray of size ‘K’ (easy) - Longest substring with ‘K’ distinct characters (medium) - String anagrams (hard) Best DSA RESOURCES: https://topmate.io/coding/886874 ENJOY LEARNING 👍👍