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Grokking the Coding Interview Patterns in Python

The ultimate guide to coding interviews, developed by FAANG engineers. Learn patterns to tackle problems from top companies and get interview-ready in just a few hours.
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With thousands of potential questions to account for, preparing for the coding interview can feel like an impossible challenge. Yet with a strategic approach, coding interview prep doesn’t have to take more than a few weeks. Stop drilling endless sets of practice problems, and prepare more efficiently by learning coding interview patterns. This course teaches you the underlying patterns behind common coding interview questions. By learning these essential patterns, you will be able to unpack and answer any problem the right way — just by assessing the problem statement. This approach was created by FAANG hiring managers to help you prepare for the typical rounds of interviews at major tech companies like Apple, Google, Meta, Microsoft, and Amazon. Before long, you will have the skills you need to unlock even the most challenging questions, grok the coding interview, and level up your career with confidence. This course is also available in JavaScript, C++, Java, and Go — with more coming soon!
With thousands of potential questions to account for, preparing for the coding interview can feel like an impossible challenge. ...Show More

WHAT YOU'LL LEARN

Understand the essential patterns behind common coding interview questions — without having to drill endless problem sets
Identify the underlying pattern of each interview question by assessing the problem statement (and learn the tricks required to solve it)
Practice your skills in a hands-on, setup-free coding environment
Learn to efficiently evaluate the tradeoffs between time and space complexity in different solutions
Develop a flexible conceptual framework for solving any question, by connecting problem characteristics, solution techniques, and coding design patterns
Understand the essential patterns behind common coding interview questions — without having to drill endless problem sets

Show more

TAKEAWAY SKILLS

Python

Programming Language

Coding Interview Patterns

Data Structures

Content

749 Lessons391 Quizzes390 Challenges

1.

Getting Started

2 Lessons

Get familiar with coding design patterns to enhance your problem-solving skills for interviews.
Course Overview
Who Should Take This Course

2.

Two Pointers

24 Lessons

Get started with two-pointers to solve linear data structure problems efficiently.
Introduction to Two Pointers
Valid Palindrome
Solution: Valid Palindrome
3Sum
Solution: 3Sum
Remove Nth Node from End of List
Solution: Remove Nth Node from End of List
Sort Colors
Solution: Sort Colors
Reverse Words in a String
Solution: Reverse Words in a String
Valid Word Abbreviation
Solution: Valid Word Abbreviation
Strobogrammatic Number
Solution: Strobogrammatic Number
Minimum Number of Moves to Make Palindrome
Solution: Minimum Number of Moves to Make Palindrome
Next Palindrome Using Same Digits
Solution: Next Palindrome Using Same Digits
Lowest Common Ancestor of a Binary Tree III
Solution: Lowest Common Ancestor of a Binary Tree III
Count Pairs Whose Sum is Less than Target
Solution: Count Pairs Whose Sum is Less than Target
Valid Palindrome II

4.

Sliding Window

27 Lessons

Break down the sliding window technique to solve computational problems with optimized efficiency.
Introduction to Sliding Window
Repeated DNA Sequences
Solution: Repeated DNA Sequences
Sliding Window Maximum
Solution: Sliding Window Maximum
Minimum Window Subsequence
Solution: Minimum Window Subsequence
Longest Repeating Character Replacement
Solution: Longest Repeating Character Replacement
Minimum Window Substring
Solution: Minimum Window Substring
Longest Substring without Repeating Characters
Solution: Longest Substring without Repeating Characters
Minimum Size Subarray Sum
Solution: Minimum Size Subarray Sum
Maximum Average Subarray I
Solution: Maximum Average Subarray I
Diet Plan Performance
Solution: Diet Plan Performance
Fruit Into Baskets
Solution: Fruit Into Baskets
Contains Duplicate II
Solution: Contains Duplicate II
Frequency of the Most Frequent Element
Solution: Frequency of the Most Frequent Element
Subarrays with K Different Integers
Solution: Subarrays with K Different Integers

6.

In-Place Manipulation of a Linked List

24 Lessons

Tackle in-place linked list manipulations for efficient and memory-optimized operations.
Introduction to In-Place Manipulation of a Linked List
Reverse Linked List
Solution: Reverse Linked List
Reverse Nodes in k-Group
Solution: Reverse Nodes in k-Group
Reverse Linked List II
Solution: Reverse Linked List II
Reorder List
Solution: Reorder List
Swapping Nodes in a Linked List
Solution: Swapping Nodes in a Linked List
Reverse Nodes in Even Length Groups
Solution: Reverse Nodes in Even Length Groups
Remove Duplicates from Sorted List
Solution: Remove Duplicates from Sorted List
Remove Linked List Elements
Solution: Remove Linked List Elements
Split Linked List in Parts
Solution: Split Linked List in Parts
Delete N Nodes After M Nodes of a Linked List
Solution: Delete N Nodes After M Nodes of a Linked List
Insert into a Sorted Circular Linked List
Solution: Insert into a Sorted Circular Linked List
Swap Nodes in Pairs

7.

Heaps

23 Lessons

Practice using heaps to solve dynamic data processing and optimization problems efficiently.
Introduction to Heaps
IPO
Solution: IPO
Find Median from Data Stream
Solution: Find Median from Data Stream
Sliding Window Median
Solution: Sliding Window Median
Schedule Tasks on Minimum Machines
Solution: Schedule Tasks on Minimum Machines
Meeting Rooms III
Solution: Meeting Rooms III
Largest Number After Digit Swaps by Parity
Solution: Largest Number After Digit Swaps by Parity
Find Right Interval
Solution: Find Right Interval
Minimum Cost to Connect Sticks
Solution: Minimum Cost to Connect Sticks
Longest Happy String
Solution: Longest Happy String
Maximum Average Pass Ratio
Solution: Maximum Average Pass Ratio
The Number of the Smallest Unoccupied Chair
Solution: The Number of the Smallest Unoccupied Chair

9.

Top K Elements

29 Lessons

Walk through the top k elements pattern and learn efficient selection using heaps.
Introduction to Top K Elements
Kth Largest Element in a Stream
Solution: Kth Largest Element in a Stream
Reorganize String
Solution: Reorganize String
K Closest Points to Origin
Solution: K Closest Points to Origin
Top K Frequent Elements
Solution: Top K Frequent Elements
Kth Largest Element in an Array
Solution: Kth Largest Element in an Array
Third Maximum Number
Solution: Third Maximum Number
Find Subsequence of Length K with the Largest Sum
Solution: Find Subsequence of Length K with the Largest Sum
Minimum Cost to Hire K Workers
Solution: Minimum Cost to Hire K Workers
Maximal Score After Applying K Operations
Solution: Maximal Score After Applying K Operations
Find the Kth Largest Integer in the Array
Solution: Find the Kth Largest Integer in the Array
Maximum Performance of a Team
Solution: Maximum Performance of a Team
Smallest Range Covering Elements from K Lists
Solution: Smallest Range Covering Elements from K Lists
K Maximum Sum Combinations From Two Arrays
Solution: K Maximum Sum Combinations From Two Arrays
K Empty Slots
Solution: K Empty Slots

10.

Modified Binary Search

28 Lessons

Break apart modified binary search adaptations and solutions for diverse search problems.
Introduction to Modified Binary Search
Binary Search
Solution: Binary Search
Search in Rotated Sorted Array
Solution: Search in Rotated Sorted Array
First Bad Version
Solution: First Bad Version
Random Pick with Weight
Solution: Random Pick with Weight
Find K Closest Elements
Solution: Find K Closest Elements
Single Element in a Sorted Array
Solution: Single Element in a Sorted Array
Maximum Value at a Given Index in a Bounded Array
Solution: Maximum Value at a Given Index in a Bounded Array
The K Weakest Rows in a Matrix
Solution: The K Weakest Rows in a Matrix
Split Array Largest Sum
Solution: Split Array Largest Sum
Find Minimum in Rotated Sorted Array II
Solution: Find Minimum in Rotated Sorted Array II
Maximum Running Time of N Computers
Solution: Maximum Running Time of N Computers
Minimize Max Distance to Gas Station
Solution: Minimize Max Distance to Gas Station
Divide Chocolate
Solution: Divide Chocolate
Search in Rotated Sorted Array II

12.

Greedy Techniques

31 Lessons

Dig into optimization problems with greedy algorithms through practical scenarios and solutions.
Introduction to Greedy Techniques
Jump Game
Solution: Jump Game
Boats to Save People
Solution: Boats to Save People
Gas Station
Solution: Gas Station
Two City Scheduling
Solution: Two City Scheduling
Minimum Number of Refueling Stops
Solution: Minimum Number of Refueling Stops
Largest Palindromic Number
Solution: Largest Palindromic Number
Assign Cookies
Solution: Assign Cookies
Rearranging Fruits
Solution: Rearranging Fruits
Number of Steps to Reduce a Binary Number to One
Solution: Number of Steps to Reduce a Binary Number to One
Maximum Swap
Solution: Maximum Swap
Can Place Flowers
Solution: Can Place Flowers
Largest Odd Number in String
Solution: Largest Odd Number in String
Candy
Solution: Candy
Minimum Replacements to Sort the Array
Solution: Minimum Replacements to Sort the Array
Jump Game II
Best Time to Buy and Sell Stock

13.

Backtracking

25 Lessons

Explore backtracking algorithm for solving complex problems like N-Queens, word search, binary tree paths, and Sudoku.
Introduction to Backtracking
N-Queens
Solution: N-Queens
Word Search
Solution: Word Search
House Robber III
Solution: House Robber III
Restore IP Addresses
Solution: Restore IP Addresses
Flood Fill
Solution: Flood Fill
Minimum Moves to Spread Stones Over Grid
Solution: Minimum Moves to Spread Stones Over Grid
Binary Tree Paths
Solution: Binary Tree Paths
Binary Watch
Solution: Binary Watch
Optimal Account Balancing
Solution: Optimal Account Balancing
Split a String Into the Max Number of Unique Substrings
Solution: Split a String Into the Max Number of Unique Substrings
All Paths From Source to Target
Solution: All Paths From Source to Target
Sudoku Solver
Matchsticks to Square

14.

Dynamic Programming

36 Lessons

Build on dynamic programming concepts to efficiently solve complex optimization problems.
Introduction to Dynamic Programming
0/1 Knapsack
Solution: 0/1 Knapsack
Coin Change
Solution: Coin Change
N-th Tribonacci Number
Solution: N-th Tribonacci Number
Partition Equal Subset Sum
Solution: Partition Equal Subset Sum
Counting Bits
Solution: Counting Bits
01 Matrix
Solution: 01 Matrix
House Robber II
Solution: House Robber II
Maximum Product Subarray
Solution: Maximum Product Subarray
Combination Sum
Solution: Combination Sum
Word Break
Solution: Word Break
Palindromic Substrings
Solution: Palindromic Substrings
Longest Common Subsequence
Solution: Longest Common Subsequence
Word Break II
Solution: Word Break II
Decode Ways
Solution: Decode Ways
Count the Number of Good Subsequences
Solution: Count the Number of Good Subsequences
Binary Tree Cameras
Solution: Binary Tree Cameras
Number of Ways to Form Target String Given a Dictionary
Solution: Number of Ways to Form Target String Given a Dictionary
Climbing Stairs

17.

Sort and Search

31 Lessons

Solve problems by organizing data through sorting and applying binary search, two-pointer, and sliding window for efficient solutions.
Introduction to Sort and Search
Find the Distance Value Between Two Arrays
Solution: Find the Distance Value Between Two Arrays
Longest Subsequence With Limited Sum
Solution: Longest Subsequence With Limited Sum
Find Target Indices After Sorting Array
Solution: Find Target Indices After Sorting Array
Count Pairs in Two Arrays
Solution: Count Pairs in Two Arrays
Valid Triangle Number
Solution: Valid Triangle Number
Minimum Operations to Make All Array Elements Equal
Solution: Minimum Operations to Make All Array Elements Equal
Sum of Mutated Array Closest to Target
Solution: Sum of Mutated Array Closest to Target
Range Sum of Sorted Subarray Sums
Solution: Range Sum of Sorted Subarray Sums
Magnetic Force Between Two Balls
Solution: Magnetic Force Between Two Balls
Find K-th Smallest Pair Distance
Solution: Find K-th Smallest Pair Distance
Minimum Space Wasted from Packaging
Solution: Minimum Space Wasted from Packaging
Russian Doll Envelopes
Solution: Russian Doll Envelopes
Put Marbles in Bags
Solution: Put Marbles in Bags
H-Index
Solution: H-Index
Two Sum Less Than K
Maximum Number of Integers to Choose from a Range I

18.

Matrices

31 Lessons

Go hands-on with matrix operations, transformations, and efficient traversal techniques in coding.
Introduction to Matrices
Set Matrix Zeroes
Solution: Set Matrix Zeroes
Rotate Image
Solution: Rotate Image
Spiral Matrix
Solution: Spiral Matrix
Where Will the Ball Fall
Solution: Where Will the Ball Fall
Transpose Matrix
Solution: Transpose Matrix
Count Negative Numbers in a Sorted Matrix
Solution: Count Negative Numbers in a Sorted Matrix
Minimum Time Takes to Reach Destination Without Drowning
Solution:Minimum Time Takes to Reach Destination Without Drowning
Smallest Rectangle Enclosing Black Pixels
Solution: Smallest Rectangle Enclosing Black Pixels
Island Perimeter
Solution: Island Perimeter
Convert 1D Array Into 2D Array
Solution: Convert 1D Array Into 2D Array
Spiral Matrix II
Solution: Spiral Matrix II
Flip Columns For Maximum Number of Equal Rows
Solution: Flip Columns For Maximum Number of Equal Rows
Number of Spaces Cleaning Robot Cleaned
Solution: Number of Spaces Cleaning Robot Cleaned
Minimize Maximum Value in a Grid
Solution: Minimize Maximum Value in a Grid
Kth Smallest Number in Multiplication Table
Solution: Kth Smallest Number in Multiplication Table

19.

Stacks

24 Lessons

Apply your skills to use stacks for various tasks, including calculators, string manipulation, and queuing.
Introduction to Stacks
Basic Calculator
Solution: Basic Calculator
Remove All Adjacent Duplicates In String
Solution: Remove All Adjacent Duplicates In String
Minimum Remove to Make Valid Parentheses
Solution: Minimum Remove to Make Valid Parentheses
Exclusive Time of Functions
Solution: Exclusive Time of Functions
Flatten Nested List Iterator
Solution: Flatten Nested List Iterator
Implement Queue Using Stacks
Solution: Implement Queue Using Stacks
Daily Temperatures
Solution: Daily Temperatures
Decode String
Solution: Decode String
Minimum String Length After Removing Substrings
Solution: Minimum String Length After Removing Substrings
Number of Valid Subarrays
Solution: Number of Valid Subarrays
Number of Visible People in a Queue
Solution: Number of Visible People in a Queue
Valid Parentheses

20.

Graphs

31 Lessons

Deepen your knowledge of graph theory, algorithms, practical solutions, and real-world applications.
Introduction to Graphs
Network Delay Time
Solution: Network Delay Time
Paths in Maze That Lead to Same Room
Solution: Paths in Maze That Lead to Same Room
Clone Graph
Solution: Clone Graph
Graph Valid Tree
Solution: Graph Valid Tree
Bus Routes
Solution: Bus Routes
Reconstruct Itinerary
Solution: Reconstruct Itinerary
Find the Town Judge
Solution: Find the Town Judge
Find Center of Star Graph
Solution: Find Center of Star Graph
Lucky Numbers in a Matrix
Solution: Lucky Numbers in a Matrix
Path with Maximum Probability
Solution: Path with Maximum Probability
Tree Diameter
Solution: Tree Diameter
Reorder Routes to Make All Paths Lead to the City Zero
Solution: Reorder Routes to Make All Paths Lead to the City Zero
Minimum Cost to Make at Least One Valid Path in a Grid
Solution: Minimum Cost to Make at Least One Valid Path in a Grid
Longest Cycle in a Graph
Solution: Longest Cycle in a Graph
Shortest Cycle in a Graph
Solution: Shortest Cycle in a Graph

21.

Tree Depth-First Search

31 Lessons

Follow the process of exploring tree structures and implementing various binary tree problems.
Introduction to Tree Depth-First Search
Flatten Binary Tree to Linked List
Solution: Flatten Binary Tree to Linked List
Diameter of Binary Tree
Solution: Diameter of Binary Tree
Serialize and Deserialize Binary Tree
Solution: Serialize and Deserialize Binary Tree
Invert Binary Tree
Solution: Invert Binary Tree
Binary Tree Maximum Path Sum
Solution: Binary Tree Maximum Path Sum
Convert Sorted Array to Binary Search Tree
Solution: Convert Sorted Array to Binary Search Tree
Build Binary Tree from Preorder and Inorder Traversal
Solution: Build Binary Tree from Preorder and Inorder Traversal
Binary Tree Right Side View
Solution: Binary Tree Right Side View
Lowest Common Ancestor of a Binary Tree
Solution: Lowest Common Ancestor of a Binary Tree
Validate Binary Search Tree
Solution: Validate Binary Search Tree
Nested List Weight Sum II
Solution: Nested List Weight Sum II
Inorder Successor in BST
Solution: Inorder Successor in BST
Height of Binary Tree After Subtree Removal Queries
Solution: Height of Binary Tree After Subtree Removal Queries
Delete Nodes And Return Forest
Solution: Delete Nodes And Return Forest
Maximum Depth of Binary Tree
Kth Smallest Element in a BST

24.

Hash Maps

36 Lessons

Discover the logic behind implementing efficient hash maps and their applications in problem-solving.
Introduction to Hash Maps
Design HashMap
Solution: Design HashMap
Fraction to Recurring Decimal
Solution: Fraction to Recurring Decimal
Logger Rate Limiter
Solution: Logger Rate Limiter
Next Greater Element I
Solution: Next Greater Element I
Isomorphic Strings
Solution: Isomorphic Strings
Find Duplicate File in System
Solution: Find Duplicate File in System
Dot Product of Two Sparse Vectors
Solution: Dot Product of Two Sparse Vectors
High Five
Solution: High Five
Bulls and Cows
Solution: Bulls and Cows
Custom Sort String
Solution: Custom Sort String
Number of Distinct Islands
Solution: Number of Distinct Islands
Number of Wonderful Substrings
Solution: Number of Wonderful Substrings
Total Appeal of a String
Solution: Total Appeal of a String
Continuous Subarray Sum
Solution: Continuous Subarray Sum
Unique Number of Occurrences
Solution: Unique Number of Occurrences
Longest Happy Prefix
Solution: Longest Happy Prefix
Find Longest Self-Contained Substring
Solution: Find Longest Self-Contained Substring
Longest Palindrome

25.

Knowing What to Track

30 Lessons

Master the steps to efficiently track data, solve permutations, anagrams, and game design problems.
Introduction to Knowing What to Track
Palindrome Permutation
Solution: Palindrome Permutation
Valid Anagram
Solution: Valid Anagram
Design Tic-Tac-Toe
Solution: Design Tic-Tac-Toe
Group Anagrams
Solution: Group Anagrams
Maximum Frequency Stack
Solution: Maximum Frequency Stack
First Unique Character in a String
Solution: First Unique Character in a String
Find All Anagrams in a String
Solution: Find All Anagrams in a String
Longest Palindrome by Concatenating Two-Letter Words
Solution: Longest Palindrome by Concatenating Two-Letter Words
Rank Teams by Votes
Solution: Rank Teams by Votes
Pairs of Songs With Total Durations Divisible by 60
Solution: Pairs of Songs With Total Durations Divisible by 60
Minimum Number of Pushes to Type Word II
Solution: Minimum Number of Pushes to Type Word II
Task Scheduler
Solution: Task Scheduler
Count Anagrams
Solution: Count Anagrams
Divide Array Into Increasing Sequences
Solution: Divide Array Into Increasing Sequences
Ransom Note

28.

Bitwise Manipulation

30 Lessons

Explore bitwise operations for problem-solving and algorithm optimization in Python.
Introduction to Bitwise Manipulation
Find the Difference
Solution: Find the Difference
Complement of Base 10 Integer
Solution: Complement of Base 10 Integer
Flipping an Image
Solution: Flipping an Image
Single Number
Solution: Single Number
Single Number II
Solution: Single Number II
Encode and Decode Strings
Solution: Encode and Decode Strings
Sum of All Subset XOR Totals
Solution: Sum of All Subset XOR Totals
Find The K-th Lucky Number
Solution: Find The K-th Lucky Number
Minimum Number of K Consecutive Bit Flips
Solution: Minimum Number of K Consecutive Bit Flips
Find the Longest Substring Having Vowels in Even Counts
Solution: Find the Longest Substring Having Vowels in Even Counts
Count Triplets That Can Form Two Arrays of Equal XOR
Solution: Count Triplets That Can Form Two Arrays of Equal XOR
Longest Subarray With Maximum Bitwise AND
Solution: Longest Subarray With Maximum Bitwise AND
Minimum One Bit Operations to Make Integers Zero
Solution: Minimum One Bit Operations to Make Integers Zero
Triples with Bitwise AND Equal To Zero
Solution: Triples with Bitwise AND Equal To Zero
Reverse Bits

29.

Math and Geometry

35 Lessons

Explore mathematical and geometric coding challenges, focusing on efficient problem-solving techniques.
Introduction to Math and Geometry
Check If It Is a Straight Line
Solution: Check If It Is a Straight Line
Minimum Cuts to Divide a Circle
Solution: Minimum Cuts to Divide a Circle
Rectangle Overlap
Solution: Rectangle Overlap
Minimum Time Visiting All Points
Solution: Minimum Time Visiting All Points
Reverse Integer
Solution: Reverse Integer
Valid Square
Solution: Valid Square
Rectangle Area
Solution: Rectangle Area
Minimum Area Rectangle
Solution: Minimum Area Rectangle
Maximum Area Rectangle With Point Constraints I
Solution: Maximum Area Rectangle With Point Constraints I
Convex Polygon
Solution: Convex Polygon
Queries on Number of Points Inside a Circle
Solution: Queries on Number of Points Inside a Circle
Max Points on a Line
Solution: Max Points on a Line
Maximum Number of Visible Points
Solution: Maximum Number of Visible Points
Minimize Manhattan Distances
Solution: Minimize Manhattan Distances
Self Crossing
Solution: Self Crossing
Erect the Fence
Solution: Erect the Fence
Minimum Number of Lines to Cover Points
Detonate the Maximum Bombs

30.

Challenge Yourself

40 Lessons

Test your understanding of various coding challenges focusing on algorithm efficiency.
Challenge Yourself: Introduction
Shortest Bridge
Number of Connected Components in an Undirected Graph
Median of Two Sorted Arrays
Pacific Atlantic Water Flow
Contains Duplicate
Maximum Subarray
Two Sum
Find Minimum in Rotated Sorted Array
Non-overlapping Intervals
Meeting Rooms
Largest Rectangle in Histogram
Subtree of Another Tree
Sort List
Number of 1 Bits
Container with Most Water
Evaluate Reverse Polish Notation
4Sum
Loud and Rich
Product of Array Except Self
Longest Increasing Subsequence
Sum of Two Integers
Majority Element
Unique Paths
Longest Palindromic Substring
Permutations II
Number of Provinces
Linked List Cycle II
Minimum Flips to Make the Binary String Alternate
Lemonade Change
House Robber
Find All Numbers Disappeared in an Array
Find All Duplicates in an Array
Same Tree
Design In-Memory File System
Design File System
Asteroid Collision
Rotting Oranges
Add Binary
Multiply Strings

31.

Conclusion

1 Lessons

Learn how to use coding interview skills and engage in continuous learning.
Where to Go from Here?
Certificate of Completion
Showcase your accomplishment by sharing your certificate of completion.
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Free Resources

Frequently Asked Questions

Is Python good for coding interviews?

Python is a great choice for coding interviews because it is a popular programming language with all the core data structures and algorithms. Its clean syntax allows you to write code efficiently without worrying about time constraints during the interview.

How to crack Python coding interviews?

To crack Python coding interviews, focus on practicing and mastering core data structures (like lists, dictionaries, and sets), algorithms (sorting, searching, and dynamic programming), and problem-solving patterns. You can use platforms like Educative, LeetCode, or HackerRank to practice coding problems regularly. Additionally, learn to write clean, optimized code and explain your thought process during interviews.

Which is better for Python, HackerRank, or LeetCode?

LeetCode offers diverse problems for interview prep, and HackerRank is great for learning Python basics. However, Educative stands out by combining interactive lessons to learn Python deeply and coding challenges to practice, all in one platform, making it the ideal choice for comprehensive interview preparation.

Does Google allow Python in coding interviews?

Yes, Google allows Python in coding interviews, and it’s a popular choice due to its simplicity and readability.

How many problems are in grokking the coding interview?

Our Grokking the Coding Interview course at Educative currently offers 228 LeetCode coding problems categorized under key coding patterns, and we’re continuously updating this number. Some of the patterns include:

  • Cyclic sort: It sorts elements in a range by placing them correctly.
  • Two pointers: It moves pointers from both ends to solve pair or rearrangement problems.
  • Sliding window: It adjusts a window over data to solve subarray or substring problems.
  • Merge intervals: It combines overlapping intervals into single ranges.
  • Fast and slow pointers: It detects cycles or finds elements using two different speed pointers.

Practicing these ensures comprehensive preparation for coding interviews at top tech companies like FAANG.

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