8 patterns to solve 80% Leetcode problems

Summary

In this insightful video, the presenter shares eight essential coding patterns to master for solving problems efficiently. Starting with the sliding window technique for processing data subsets, the video explores subset patterns for generating all possible arrangements, modified binary search for complex queries, and top-k elements using heaps. It also delves into binary tree traversals with DFS and BFS, topological sorting for dependency management, and the two-pointer technique for sorted arrays. The speaker emphasizes the importance of understanding data structures and algorithms, offering a free email crash course to enhance these skills.

Takeaways

• 🔍 The importance of recognizing patterns in coding problems to improve efficiency.
• 🪟 The sliding window pattern is useful for processing subsets of data in lists or strings to find elements that satisfy certain conditions.
• 🍽️ The subset pattern helps in finding all combinations of elements from a set, with or without repetitions, akin to a breath-first search approach.
• 🔍 Modified binary search involves adjusting the standard binary search algorithm to fit specific problem requirements, such as searching in a rotated sorted array.
• 📈 The top K elements pattern is for selecting the top K elements from a data set based on a given condition, often using a heap for efficient tracking.
• 🌳 Binary tree DFS (Depth-First Search) involves exploring each node in a tree by focusing on one branch at a time, typically using recursion.
• 🔄 Topological sort is essential for arranging elements with dependencies, especially in directed acyclic graphs, to determine a sequence of processing.
• 🌐 Binary tree BFS (Breadth-First Search) explores all nodes at the same level before moving to the next level, using a queue to maintain order.
• 👀 The two-pointer pattern is effective for iterating through sorted arrays to solve problems like finding pairs or triplets that meet specific sum conditions.
• 📚 A strong foundation in data structures and algorithms is crucial for effectively applying these patterns to solve coding problems.
• 📧 The speaker offers a free 5-day email crash course on 'interview.io' to master data structures and algorithms.

Q & A

What is the sliding window pattern used for in coding problems?

The sliding window pattern is used for processing a series of data elements like a list or string by examining a smaller part of the list at a time, known as the window, which then slides one step at a time until the entire list is scanned. It's particularly useful when you need to find a subset of elements that satisfy a given condition.

Can you provide an example of a problem that would use the sliding window pattern?

An example of a problem that uses the sliding window pattern is finding the longest substring with K unique characters in a given string. You need to find the longest substring that contains no more than K unique characters.

What is the subset pattern in coding and when should it be used?

The subset pattern is used when you need to find all possible combinations of elements from a given set, with or without repetitions. It should be considered when a problem requires exploring all possible arrangements of elements from a set, such as in permutation problems.

How is the modified binary search pattern different from the standard binary search?

The modified binary search pattern retains the core idea of dividing the search space in half repeatedly but requires adjustments to the logic to solve specific problems, such as searching in a rotated sorted array where the pivot point is unknown.

Why is it important to understand the core binary search algorithm well when dealing with modified binary search problems?

Understanding the core binary search algorithm is crucial because it provides the foundation for adapting the logic to solve modified binary search problems. It helps in visualizing where the left and right pointers end up in various scenarios, such as when the array contains duplicates or does not contain the target.

What is the purpose of the top k elements pattern in coding problems?

The top k elements pattern is used to select k elements from a larger data set based on a particular condition. It is useful when a problem asks to find the top ranking elements from a data set, such as finding the kth largest number in an array.

How does the depth-first search (DFS) pattern differ from the breadth-first search (BFS) pattern in a binary tree?

The DFS pattern explores the tree by going deep into a branch and fully exploring it before moving on to other branches, typically using recursion. In contrast, the BFS pattern explores all nodes at the same level across different branches first, using a queue data structure to process nodes level by level.

What is the topological sort pattern used for in graph problems?

The topological sort pattern is used to arrange elements in a specific order when they have dependencies on each other, particularly in directed acyclic graphs. It helps in determining the order of processing elements based on their prerequisites.

Can you give an example of a problem that would use the two-pointer pattern?

An example of a problem that uses the two-pointer pattern is the two-sum problem, where you are given a sorted array and need to find the indices of two numbers that add up to a specific target sum. By moving two pointers smartly, one starting from the beginning and the other from the end of the array, the problem can be solved efficiently in a single pass.

What is the significance of understanding data structures and algorithms in solving coding problems discussed in the script?

Understanding data structures and algorithms is fundamental to solving the coding problems discussed in the script. It provides the necessary knowledge to recognize patterns, apply appropriate algorithms efficiently, and develop solutions that can handle various problem scenarios.

How can one improve their understanding of binary search, as mentioned in the script?

One can improve their understanding of binary search by implementing the 'bisect_left' and 'bisect_right' functions from Python's 'bisect' module in the language of their choice. This helps in visualizing how the binary search algorithm works and enhances the ability to adapt it to different scenarios.

Outlines

00:00

🔍 Mastering Coding Patterns for Problem-Solving

This paragraph introduces the concept of recognizing patterns in coding problems as a strategy to improve efficiency and skill. The speaker shares their journey of solving over 500 problems and identifies eight key patterns. The first pattern discussed is the 'sliding window', which is ideal for processing subsets of data within linear structures like arrays or strings. The example given is finding the longest substring with K unique characters. The 'subset pattern' follows, useful for generating all combinations of elements from a set, with an example of permutations. The 'modified binary search' pattern is then introduced, which involves adjusting binary search logic for specific problems, such as searching in a rotated sorted array. The speaker emphasizes the importance of understanding core algorithms and suggests implementing Python's 'bisect' module to enhance binary search comprehension. Lastly, the paragraph concludes with a call to action to join a free email course for mastering Data Structures and Algorithms (DSA).

05:02

📚 Advanced Coding Patterns and Their Applications

The second paragraph delves into more advanced coding patterns, starting with the 'top k elements' pattern, which is used for selecting the top k elements from a data set based on a condition, with an example of finding the kth largest number using a heap. The 'depth-first search (DFS)' pattern for binary trees is next, where the focus is on exploring each branch completely before moving to the next, illustrated with the problem of finding the maximum depth of a binary tree. 'Topological sort' is then discussed, a pattern for arranging elements with dependencies, particularly useful in directed acyclic graphs, with an example of a course schedule problem. The paragraph continues with the 'breadth-first search (BFS)' pattern for binary trees, which explores all nodes at the same level before moving deeper, using a queue data structure. The 'two-pointer' pattern concludes the discussion, which is used for iterating through sorted arrays to solve problems like the two-sum problem or finding triplets that sum to zero. The speaker, Sahil, ends with an invitation to sign up for a free email course on interview.io to further master these patterns.

Keywords

💡Sliding Window Pattern

The Sliding Window Pattern is a concept used in coding problems where a subset of elements within a linear data structure, such as an array or string, must be processed to satisfy a condition. In the video, it is mentioned as a method for finding the longest substring with K unique characters, which is a classic example of this pattern. The window 'slides' over the data, examining smaller segments one at a time until the entire structure is scanned.

💡Subset Pattern

The Subset Pattern involves finding all possible combinations of elements from a given set, with or without repetitions, depending on the problem's constraints. This pattern is crucial when a problem requires exploring every possible arrangement of elements, akin to generating permutations. In the script, it is used to solve problems like generating all permutations of a set, which is similar to a breadth-first search approach.

💡Modified Binary Search Pattern

The Modified Binary Search Pattern is an adaptation of the traditional binary search algorithm, where the search space is repeatedly divided in half. In the context of the video, it is used to solve problems like finding an element in a rotated sorted array, which requires adjusting the binary search logic to account for the unknown pivot point. Understanding the core binary search algorithm is emphasized as essential for effectively modifying it.

💡Top K Elements Pattern

The Top K Elements Pattern is utilized when a problem requires selecting the K highest or lowest elements from a dataset based on a specific condition. The video script mentions using a heap data structure to efficiently track and manage the K most important elements encountered so far, which is particularly useful for finding the Kth largest element in an array.

💡Depth-First Search (DFS)

Depth-First Search, or DFS, is an algorithm for traversing or searching tree or graph data structures. It explores as far as possible along each branch before backtracking. In the video, DFS is related to binary tree problems, such as finding the maximum depth of a binary tree, where the depth is tracked as the algorithm progresses down each branch.

💡Topological Sort

Topological Sort is a linear ordering of vertices in a directed acyclic graph (DAG) such that for every directed edge from vertex U to vertex V, U comes before V in the ordering. The video script describes it as useful for arranging elements with dependencies, like modules in a complex program or courses with prerequisites, ensuring that each element is processed after all its prerequisites.

💡Breadth-First Search (BFS)

Breadth-First Search, or BFS, is an algorithm that explores all the neighbors of a node before moving on to the next level of nodes. In the context of binary trees, as mentioned in the video, BFS is used to explore all nodes at the same level before moving on to the next level, which is useful for problems like level order traversal or reversal of a binary tree.

💡Two-Pointer Pattern

The Two-Pointer Pattern is a technique used to solve problems involving sorted arrays, where two pointers, each representing an index in the array, are moved intelligently to find a solution. The video script provides the example of the Two Sum problem, where the pointers start at opposite ends of the array and move towards each other based on the sum of the values they point to, in relation to a target sum.

💡Data Structures and Algorithms

Data Structures and Algorithms are foundational to computer science and are essential for solving complex coding problems. The video emphasizes the importance of a good understanding of these concepts to effectively apply the discussed patterns. Examples from the script include using heaps for the Top K Elements Pattern and understanding binary search for the Modified Binary Search Pattern.

💡Bisect Module

The Bisect Module in Python contains functions like 'bisect_left' and 'bisect_right' which are used to maintain a list in sorted order without having to sort the list after each insertion. The video script suggests implementing similar functions to improve one's understanding of binary search, as they help visualize where the left and right pointers would end up in various scenarios.

Highlights

The speaker emphasizes the importance of recognizing patterns in coding problems to improve efficiency.

Eight key coding patterns are introduced to help solve problems more effectively.

The sliding window pattern is explained for processing subsets of data in lists or strings.

The subset pattern is used to explore all possible combinations of elements from a given set.

Modified binary search pattern is discussed for solving problems with sorted arrays that have been rotated.

Understanding the core binary search algorithm is crucial for modified binary search problems.

The topk elements pattern is introduced for selecting the top K elements from a data set based on a condition.

Depth-first search (DFS) in binary trees is explained, focusing on exploring one branch at a time.

Topological sort is described for arranging elements with dependencies in directed acyclic graphs.

Breadth-first search (BFS) in binary trees is detailed, exploring nodes level by level.

The two-pointer pattern is introduced for solving problems in sorted arrays with two indices.

The two-sum problem is an example of using the two-pointer pattern to find indices that sum to a target.

The importance of mastering data structures and algorithms for solving coding problems is stressed.

A free 5-day email crash course on interview.io is mentioned for mastering DSA.

The speaker, Sahil, offers a personal touch by inviting viewers to his next video.

The transcript provides a comprehensive guide to common coding patterns for problem-solving.

The bisect module in Python is recommended for improving binary search visualization.

The course schedule problem on LeetCode is cited as an example of topological sort application.

Level order reversal of a binary tree is an example of a problem solved using binary tree BFS pattern.

Transcripts

I have solved 554 lead code problems but

you don't have to it took me 500 plus

problems to realize that there is an

easier way to become better at coding

problems and that is just focus on the

patterns that repeat over and over again

in this video I'll show you eight such

patterns let's start with sliding window

pattern the sliding window pattern is

used when you need to process a series

of data elements like a list or string

in the sliding window pattern you find

specific things in a list by looking at

a smaller part of the list at a time the

part of the list that you are looking at

is called your window this window then

slides one step at a time until the

entire list is scanned when do we use

the sliding window pattern if a problem

asks you to find a subset of elements

that satisfies a given condition think

about sliding window pattern your input

would be a linear data structure like an

array string or a link list and you

would have to find the longest or a

shortest substring or subarray that

satisfies a particular condition for

example look at this longest substring

with K unique characters problem your

input is a string and you need to find

the longest substring that satisfies

unique characters condition it's a

classic sliding window problem next we

have the subset pattern the subset

pattern is used when you need to find

all the possible combinations of

elements from a given set repetitions

may or may not be allowed depending on

the problem in the subset pattern we

need to explore all the possible

Arrangements of elements from the given

set take for example this permutations

problem from lead code to solve this

problem you can iteratively build all

the subsets level by level start with an

empty set at each level consider all the

ways to add the next element to the

existing subset and create create a new

one this approach is very similar to

breath first search or BFS next we have

the modified binary search pattern the

core idea of binary search is to divide

the search space in half again and again

in the modified binary search pattern

the core idea Remains the Same but we

need to adjust the logic a little bit to

solve the given problem let's take the

example of search and rotated sorted

array problem here the array is sorted

but rotated at an unknown Pivot Point

you'll need to modify the standard

binary search to figure out which half

of the array to search in one thing that

helped me a lot to solve modified binary

search problems is understanding the

core binary search algorithm really well

for the core binary search algorithm you

need to be able to visualize where left

and right pointer end up if the array

contains duplicates or if the array does

not contain the target let me give you a

good way to improve your visualization

of binary search bisect module in Python

contains two functions bisect left and

bisect right Implement these two

functions in the language of your choice

and your understanding of binary search

will improve a lot before we talk about

the next pattern I want to make it very

clear that you need to have a good

understanding of data structures and

algorithms to solve the problems that we

are discussing today if you want to know

how to master DSA and how much DSA you

need to learn before you can solve these

problems you can join my free 5-day

email crash course on

interview.io moving on the next pattern

we have is topk elements pattern the

topk elements pattern is used to select

K elements from a larger data set given

a particular condition think about this

pattern whenever the problem asks you to

find top ranking elements from a data

set the input would usually be a linear

data structure like like an array or a

list for example given an array of

numbers you need to find the kth largest

number efficiently to solve this problem

you need to keep track of the K most

important numbers that you have seen so

far since you care about the kth largest

element the largest K elements you have

seen so far are most important so you

would store them using a data structure

called Heap we'll come back to why we

use a heap in a moment anyway the

smallest number on the Heap would be the

K largest number we have seen so far if

the new number is larger than the K

largest number so far you will remove

the K largest number so far and add the

new number to the Heap we use a heap

here because it makes finding and

removing the smallest number very

efficient next we have depth for search

of a binary tree or binary tree DFS

pattern binary tree DFS helps you visit

every node on the tree focusing on one

branch at a time generally you would use

recursion to do this here is how the

flow looks like you will start at the

root node after that you apply DFS

recursively to the left node this

process continues going deeper and

deeper into the left subtree until it

reaches a node with no children once the

DFS reaches a dead end on the left side

it backtracks now it focuses on the

right node if it exists and applies TFS

recursively Again by doing this you

explore the entire right subtree for

example in this very popular problem

called maximum depth of binary tree you

need to find the length of the longest

path from the root node to a leaf node

for this you simply keep track of the

depth as you do a binary treat DFS and

whenever you reach a dead end you update

the maximum depth if the current depth

is more than the maximum depth it's that

simple next pattern we have on the list

is topological sort the topological sort

is used to arrange elements in a

specific order when they have

dependencies on each other it's

particularly useful for directed a

cyclic graphs whenever the nodes of a

graph have one-way connection between

them and there is no cycle it's called a

directed a cyclic graph think of

topological sort whenever you have a

prerequisite chain let me explain what I

mean by this imagine that you're

building a complex program some parts of

the code might rely on some other

modules being written and tested and

these modules can in turn depend on some

other modules topological sort can help

you figure out the order in which you

should write your modules by analyzing

the dependencies between different

modules it creates a sequence where each

module is processed only after all its

prerequisites have been completed try

this course schedule problem on lead

code where instead of modules we have

courses as prerequisites for other

courses next we have breath for search

of binary tree or binary tree BFS

pattern so we already covered binary

tree DF BS in binary tree DFS we go deep

into a branch and explore it completely

then we move on to the other branches in

binary tree BFS we take a different

approach BFS explores all the nodes at

same level in different branches first

to do this you'll need to use Q data

structure in the beginning the queue

would only contain the root node after

that you're going to repeat the process

I'm going to show you again and again

you'll remove a node from the front of

the queue and do any operation that you

might want to do with it then you add

both its children on the Queue you keep

keep doing this until the queue is empty

by doing this the elements at the same

level of the tree will always remain

next to each other on the Queue this way

you can process them one after the other

a problem that is a direct application

of this pattern is called level ordit

reversal of a binary tree it's exactly

the same what I just explained lastly we

have the two-pointer pattern the

two-pointer pattern is used to solve

problems when you need to iterate

through a sorted array taking a hint

from the name itself we'll be using two

pointers in this pattern each pointer

will keep track of an index in the array

by moving these pointers smartly we can

often solve the problem in a single pass

making the algorithm more efficient

let's take the example of the two sum

problem you're given a sorted array and

you need to return the index of two

numbers that add up to a Target sum to

solve this problem you can use two

pointers the first pointer starts at the

beginning and the second one starts at

the end of the array depending on

whether the sum of the numbers at the

pointers is less than or greater than

the target sum you will either move the

left pointer to the right or the right

pointer to the left this works because

the input array is sorted another

variation of the same problem is when

you need to find triplets that add up to

zero in a sorted array so far we only

covered some popular lead code patterns

but your job is not done yet you need to

find some problems that fall in each of

these patterns and solve them one by one

but before you do that learn how to

master data structures and algorithms by

signing up for my free email crash

course on

interview.io my name is sahil and I'll

see you in the next one