How We Approach Our Daily LeetCode Python Challenge: A Step-By-Step Guide

Guidelines to follow when working on every leetcode question yourself and during interview

Today, I wish to share something foundational. This blog will outline the standard structure I use to tackle our daily LeetCode problems in Python.

Not only is this approach beneficial for our journey here, but I genuinely believe that adopting this structure will serve you immensely, whether you're diving into problems on your own or navigating the intense waters of coding interviews. It's a tried and tested blueprint that has often been my guiding light.

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Step by step :

1. Understand the problem

2. example go thru

3. Identify Patterns & Choose the Right Data Structure

4. Brute Force solution

5. Optimization

6. Write the Code

7. Test the Code

8. Debug

9. Analyze Time and Space Complexity

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1. Understand the Problem:

When faced with a coding problem, especially during an interview, understanding the problem is of paramount importance. But how do we ensure we've truly grasped it?

• Rephrase in Simpler Terms: After hearing or reading the problem, take a moment to rephrase it in your own words. For instance, if given a problem statement like, "Find the longest palindrome that can be read both ways in a string," you might simplify it as, "I need to find the longest word in the text that reads the same backward as forward."

• Ask Clarifying Questions: Don't hesitate to ask your interviewer for clarification. Questions like, "Can I assume the input will always be non-empty?" or "What should be returned if no palindrome is found?" can shed light on edge cases and expected behavior.

Taking the time to truly understand the problem often unravels half its complexity. It also demonstrates to the interviewer that you approach problems methodically and don't rush into coding without a clear plan.

2. Example Go Through:

Using concrete examples to navigate a problem often illuminates the path to its solution. Here's how I like to approach it, especially during interviews:

• Start with Provided Examples: If the interviewer gives an example, start there. Let's say you're tasked with reversing words in a sentence. Given "Hello World", the output should be "World Hello". Walk the interviewer through your thought process: "For 'Hello World', I notice the words are 'Hello' and 'World'. When reversed, they become 'World Hello'."

• Craft Your Own Examples: This can showcase your thoroughness. With the same problem, you might say, "If I had the string 'I love coding', reversing the words would make it 'coding love I'."

• Include Edge Cases: Think of scenarios that might be outliers. For instance, "What happens if there's just one word? Like 'Hello'. Reversing it would still be 'Hello', right?"

Walking through examples not only clarifies the problem's requirements but also demonstrates to the interviewer that you're considering a range of possible inputs, ensuring a comprehensive solution.

3. Identify Patterns & Choose the Right Data Structure:

Every coding problem hides certain patterns within it, and recognizing them can point us towards the right data structures. Let me break down how this can be done:

• Recognizing Patterns: Let's consider a problem where you're asked to find the first non-repeated character in a string. As you think about the problem, you realize you need a way to keep track of each character's frequency. This observation is a crucial pattern.

• Choosing Data Structures: Given the pattern you've recognized, a data structure that supports quick look-up times would be ideal. For our character frequency problem, a dictionary (or hashmap) immediately comes to mind. Here, characters can be keys and their counts as values.

• Consider the Trade-offs: It's also essential to think about the costs and benefits of your chosen data structure. For instance, while a dictionary provides quick look-ups, it might use more memory than a simple list. Is this trade-off acceptable for the problem at hand?

Identifying the underlying patterns and aligning them with suitable data structures is pivotal. This alignment can streamline your approach and lead you closer to an optimal solution while also showcasing your deep understanding to interviewers..

4. Brute Force:

The brute force approach is all about getting to a solution, even if it's not the most efficient one. It often provides a clearer understanding of the problem and lays the groundwork for optimization. Here's how it typically plays out:

• Think Simple: What's the most straightforward way to solve the problem, without initially worrying about efficiency?

• Example: Imagine you're asked to find the shortest distance between two words in a large book. A brute force approach? Read the book word by word, noting the positions of these two words each time you encounter them, and then calculate the distances.

Using a brute force approach serves as a starting point and offers a reference solution. By addressing its shortcomings, you're also setting the stage for a more refined and optimized solution, demonstrating to the interviewer your analytical thinking and problem-solving progression.

5. Optimization:

Optimization is the art of refining our solution to make it more efficient, whether in terms of speed, memory usage, or both. It's about building upon our initial brute force method and finding smarter, more streamlined pathways. Let’s walk through this:

• Revisit the Brute Force: Reflect on the brute force approach's weaknesses. Where did it feel slow? Where did it feel redundant?

• Example: Going back to our book problem, repeatedly reading the entire book is redundant. Instead, maybe we could store the positions of the two words as we read and then only calculate the shortest distance once at the end.

Optimization demonstrates deeper problem understanding and showcases one's ability to innovate and improve. In interviews, it highlights your forward-thinking approach and your commitment to delivering the best solution possible.

6. Write the Code:

Transforming our solution strategy into actual code is a delicate process. Here, clarity and correctness are king. But how do we best approach this task, especially with potential trade-offs in mind? Let’s break it down:

• Define Input/Output: Start by clearly defining what the function or method will accept as input and what it will return as output. For instance, if we're writing a function to reverse words, it might look like:

  def reverse_words(s: str) -> str:

• Write Pseudo-Code: Before diving into the actual coding, draft a pseudo-code. This step helps in visualizing the flow of the solution. Using our reversing words example:

1. Split the string into words 
2. Reverse the list of words 
3. Join the words back into a string 
4. Return the resulting string

• Translate to Code: Now, convert your pseudo-code into actual Python code. This translation should be straightforward if your pseudo-code was clear.

def reverse_words(s: str) -> str: 
    words = s.split() 
    reversed_words = words[::-1] 
    return ' '.join(reversed_words)

Coding isn't just about getting the right output. It's a blend of efficiency, clarity, and understanding the trade-offs you're making. In interviews, this meticulous approach not only offers a functional solution but also showcases your depth of understanding and thoughtful decision-making.

7. Test the Code:

Once the code is penned down, it’s essential to put it to the test. Ensuring our solution works across a variety of scenarios is the hallmark of robust code. Here’s how I approach this vital step:

• Begin with Provided Examples: If the problem or the interviewer has provided certain test cases, start there. It's a basic validation of the correctness of your solution.

• Introduce Your Own Test Cases: Think of potential edge cases or unique scenarios that might challenge your solution. For the reverse words problem, you could test:

  • A single word string: "Hello"

  • A string with multiple spaces: "Hello World"

  • An empty string: ""

• Look for Potential Edge Cases: These are scenarios that are at the boundaries of expected input or that might cause your code to behave unexpectedly. For instance, very long strings or strings with special characters.

• Verbally Walk Through Your Tests: As you run each test, explain to the interviewer (or to yourself, if you’re practicing) what you expect the output to be and why. This way, if there's an unexpected result, you can identify the reasoning gap.

• Iteratively Debug: If a test case doesn’t produce the expected result, don’t panic. Dive back into your code, understand the discrepancy, adjust, and then test again.

Testing isn't just about ensuring your code works; it's about demonstrating thoroughness, anticipation of diverse scenarios, and a commitment to quality. Remember, in interviews, how you handle and rectify mistakes can be as telling as the mistakes themselves.

8. Debug:

Let's be honest, things don’t always go right the first time. And that's okay. Debugging is an essential part of the process, helping us refine and perfect our solution.

9. Analyze Time and Space Complexity:

After crafting our solution and ensuring its correctness, it's crucial to analyze its efficiency. This understanding helps us gauge how well our code will perform, especially with larger inputs. Let’s delve into this step:

• Break Down Operations: Start by assessing the main operations in your code. For the reverse words example, splitting, reversing, and joining are significant steps.

• Estimate Time Complexity: Consider the number of operations as your input grows. For our reversing example, each of the steps mentioned roughly takes O(n) time, where n is the number of characters in the string.

• Assess Memory Usage: Evaluate the additional space or memory your solution utilizes. In the reverse words problem, the new list of words we create while splitting adds to the space complexity, making it O(n) as well.

• Discuss Trade-offs: Just as with writing code, understanding time and space complexity often involves trade-offs. A faster solution might use more memory, and vice versa. Highlight these trade-offs and justify your choices. For instance, while our solution to reverse words uses additional memory for clarity and simplicity, is there a way to optimize it further without this overhead?

Evaluating time and space complexity not only provides insight into your solution's scalability but also demonstrates your depth of understanding and foresight. During interviews, this analysis reveals a deeper grasp of algorithms and data structures, elevating the quality of your solution.

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Each step, while defined, is fluid. Sometimes we might bounce back and forth between them, and that's natural. The journey of solving a problem is as educational as the solution itself.

I hope this provides a clearer picture of our daily exploration. Do reach out with any further queries or insights. Your thoughts truly light up my day.