Problem Link :- leetcode-28
Problem statement
Given two strings needle and haystack, return the index of the first occurrence of needle in haystack, or -1 if needle is not part of haystack.
More Than Code: What I Discovered by Solving Problems
The "strStr" problem, often associated with the task of implementing a substring search function, is a classic and fundamental problem in computer science and programming. It has multiple intentions behind its creation:
- Algorithmic Understanding : The problem serves as an introduction to basic string manipulation and searching algorithms. It encourages programmers to think about how to efficiently search for a smaller sequence of characters within a larger sequence.
- Real-world Relevance : The problem reflects a common real-world scenario: finding a specific pattern or substring within a larger text or dataset. Similar tasks are encountered in various applications, such as text processing, data analysis, and bioinformatics.
- Algorithmic Complexity : Solving this problem provides an opportunity to discuss and understand time complexity. Different approaches to solving substring search problems can have different time complexities, and the "strStr" problem allows for exploration of these concepts.
- Implementation Challenges : The problem introduces challenges related to implementing algorithms with correct logic and handling edge cases. It requires attention to detail and debugging skills, which are crucial in software development.
- Educational Purpose : For educators and learning platforms, the "strStr" problem serves as a pedagogical tool to teach key concepts to learners, including string manipulation, loops, conditional statements, and algorithmic thinking.
- Historical Context : This problem has a historical significance as it has been part of coding interviews for software engineering positions. Understanding how to efficiently implement substring search algorithms is a valuable skill in technical interviews.
- Building Blocks for Advanced Topics : Solving this problem lays the foundation for more advanced topics in algorithms and data structures. It is a stepping stone for understanding more sophisticated string matching algorithms, like the Knuth-Morris-Pratt algorithm or the Boyer-Moore algorithm.
- Problem-Solving Skills : By grappling with this problem, programmers enhance their problem-solving skills. It requires breaking down a complex task into manageable steps and implementing a solution using the principles of computer science.
- Coding Standards and Readability: The problem encourages adherence to coding standards and practices, promoting clean and readable code. This is crucial for collaboration in a team and for maintaining code in the long run.
In summary, the "strStr" problem is designed to offer a multifaceted learning experience, covering foundational programming concepts, algorithmic thinking, and practical application in real-world scenarios. It caters to a wide range of skill levels, making it a valuable exercise for beginners and a basis for more advanced exploration in computer science and software development.
Easiest Explanation
Let's say you have two pieces of paper - one piece with a long word or sentence written on it, and another piece with a shorter word written on it. We will call the first piece of paper "haystack" and the second piece of paper "needle."
We want to write a program that will tell us whether the shorter word on the "needle" paper is hidden somewhere in the longer words on the "haystack" paper. If it is, the program will tell us where it starts.
The code we have written does this by looking at each letter in the "haystack" paper one by one, starting from the beginning, and checking if the letters match the letters on the "needle" paper.
If the letters match, the program checks the next letters to see if they also match. It keeps doing this until it has checked all the letters on the "needle" paper. If all the letters match, the program knows it has found the shorter word in the longer words, and it tells us where it starts.
If the program reaches the end of the "haystack" paper without finding a match, it means the shorter word is not in the longer words, and the program tells us that it couldn't find it.
I hope that explanation helps! If you find any difficulty in solving, feel free to comment with your doubts.
We want to write a program that will tell us whether the shorter word on the "needle" paper is hidden somewhere in the longer words on the "haystack" paper. If it is, the program will tell us where it starts.
The code we have written does this by looking at each letter in the "haystack" paper one by one, starting from the beginning, and checking if the letters match the letters on the "needle" paper.
If the letters match, the program checks the next letters to see if they also match. It keeps doing this until it has checked all the letters on the "needle" paper. If all the letters match, the program knows it has found the shorter word in the longer words, and it tells us where it starts.
If the program reaches the end of the "haystack" paper without finding a match, it means the shorter word is not in the longer words, and the program tells us that it couldn't find it.
I hope that explanation helps! If you find any difficulty in solving, feel free to comment with your doubts.
class Solution { public: int strStr(string h, string n) { if(n.size()> h.size()) return -1; int j=0; for(int i=0;i<h.size()-n.size()+1;i++){ if(h[i]==n[j]){ int l=i; while(j<n.size() && h[l]==n[j]){ l++; j++; } if(j==n.size()) return i; } j=0; } return -1; } };
class Solution:
def strStr(self, haystack: str, needle: str) -> int:
if not needle:
return 0
if len(needle) > len(haystack):
return -1
for i in range(len(haystack) - len(needle) + 1):
if haystack[i:i+len(needle)] == needle:
return i
return -1
Here's how the function works:
1. If the needle is an empty string, return 0 because it's always present in the haystack.
2. If the length of the needle is greater than the length of the haystack, return -1 because it can't be present in the haystack.
3. Iterate over each character of the haystack from 0 to len(haystack) - len(needle) + 1.
4. Check if the substring of haystack starting from the current index and having the same length as needle is equal to needle. If it is, return the current index.
5. If the function reaches this point, it means the needle is not present in the haystack, so return -1.
2. If the length of the needle is greater than the length of the haystack, return -1 because it can't be present in the haystack.
3. Iterate over each character of the haystack from 0 to len(haystack) - len(needle) + 1.
4. Check if the substring of haystack starting from the current index and having the same length as needle is equal to needle. If it is, return the current index.
5. If the function reaches this point, it means the needle is not present in the haystack, so return -1.
Building Fundamental Programming Competencies with the "strStr" Problem
the "strStr" problem and understanding the provided code can help you grasp several fundamental concepts in programming and problem-solving. Here are some key learnings:
- String Manipulation : TYou'll learn how to access individual characters in a string using array notation (h[i] and n[j]) and Understand how to get the length of a string using size() method (n.size() and h.size()).
- Looping and Iteration : The code uses a for loop to iterate through the characters of the haystack. It's a fundamental construct for repetitive tasks.
- Conditional Statements : Checking conditions using if statements (if (n.size() > h.size())). Nested if Statements. The main logic involves nested if statements for character matching.
- Indexing and Positioning : Understanding and manipulating indices (i and j) to track positions in strings. The function returns the index where the substring is found or -1 if not found.
- Algorithmic Thinking : The code performs a linear search, checking each position in the haystack for the presence of the needle. Understanding basic search algorithms is crucial.
- Problem-Solving Strategies : The code uses two pointers (i and j) to compare characters, a common technique in string matching problems.
Conclusion
In conclusion, unraveling the intricacies of the "strStr" problem offers a vibrant journey through the foundational elements of programming. From wielding the power of string manipulation, where characters are accessed with precision, to orchestrating loops in a rhythmic dance, this challenge immerses learners in the art of crafting elegant solutions. The conditional wizardry, showcased through if statements and nested logic, adds a touch of magic to character matching endeavors.
The mastery of indexing and positioning emerges as a key skill, allowing programmers to navigate strings with finesse and accurately return indices. Algorithmic thinking takes center stage, introducing the concept of linear search as a fundamental technique in the problem-solving repertoire. Debugging skills become a symphony, with each error identified and resolved contributing to a harmonious code.
The problem-solving strategies, such as the two-pointer approach, present dynamic choreography for character comparison. Understanding time complexity as O(m * n) becomes a pivotal lesson, emphasizing the significance of efficient algorithms. Real-world application scenarios bring the skills to life, demonstrating the practical relevance of substring searches in text processing.
In this coding odyssey, best practices and coding style serve as guiding principles, ensuring not only functionality but also readability. As the final note, the "strStr" problem encapsulates the essence of foundational programming skills, setting the stage for an exciting journey into more complex algorithms and real-world applications.
The mastery of indexing and positioning emerges as a key skill, allowing programmers to navigate strings with finesse and accurately return indices. Algorithmic thinking takes center stage, introducing the concept of linear search as a fundamental technique in the problem-solving repertoire. Debugging skills become a symphony, with each error identified and resolved contributing to a harmonious code.
The problem-solving strategies, such as the two-pointer approach, present dynamic choreography for character comparison. Understanding time complexity as O(m * n) becomes a pivotal lesson, emphasizing the significance of efficient algorithms. Real-world application scenarios bring the skills to life, demonstrating the practical relevance of substring searches in text processing.
In this coding odyssey, best practices and coding style serve as guiding principles, ensuring not only functionality but also readability. As the final note, the "strStr" problem encapsulates the essence of foundational programming skills, setting the stage for an exciting journey into more complex algorithms and real-world applications.
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