Python Loop Error: The Loophole

🚀 Day 4/30 | LeetCode Problem: Running Sum of 1D Array (1480) Problem: Given an array nums, return the running sum, where each element at index i is the sum of elements from index 0 to i. Approach: Initialize a variable to store the cumulative sum Traverse the array one by one Add each element to the cumulative sum Store the result in a new list This is a simple single-pass solution. Time Complexity: O(n) Space Complexity: O(n) Python Code: class Solution: def runningSum(self, nums): a = 0 b = [] for i in nums: a = a + i b.append(a) return b Example: Input: [1, 2, 3, 4] Output: [1, 3, 6, 10] Takeaway: Running sum is a basic form of prefix sum, which is very useful in many array problems. 📌 Accepted ✅ | All test cases passed 🔖 Hashtags #LeetCode #30DaysOfLeetCode #Day4 #Python #DSA #Algorithms #ProblemSolving #CodingJourney #SoftwareEngineering #TechCommunity

hi connections I just moved from Maximum Subarrays to LeetCode 167: Two Sum II. The challenge? Find two numbers that hit a target sum. The "cheat code"? The input array is already sorted. When you see "Sorted Array," your mind should immediately go to Two Pointers. The Strategy: Left Pointer: Starts at the beginning (smallest values). Right Pointer: Starts at the end (largest values). The Logic: If the sum is too low, nudge the left pointer up. If it’s too high, slide the right pointer down. Why I love this approach: Zero Extra Space: Unlike the original Two Sum, you don’t need a Hash Map. O(1) space. Speed: You only pass through the data once. O(n) time. Simplicity: It’s clean, readable, and highly optimized. Programming isn't just about solving the problem; it's about finding the most elegant way to do it by leveraging the constraints you're given. #Coding #DataStructures #Algorithms #TwoSum #Python #LeetCode #SoftwareEngineering #TwoPointers

I just shipped a small but complete Python CLI project: Rock, Paper, Scissors. Beyond the game, the goal was to practice fundamentals that map directly to analytics and data work: Input validation (data quality mindset) Deterministic decision logic (rule-based classification) Modular functions + clean entry point (reusable, maintainable code) Reproducible execution from the command line Next iteration: I plan to log outcomes to CSV and run a quick analysis on win rates across multiple simulations. GitHub: https://lnkd.in/ea3fxBbi #Python #DataScience #Analytics #LearningInPublic #GitHub #ProblemSolving

Day 63/100: Literals in Programming A literal is data whose value is defined by the data itself. When you see 123, there’s no ambiguity, it’s the number one hundred and twenty-three. The meaning is self-contained. Contrast that with something like c: its value depends entirely on context. Without extra information, it could mean anything. This distinction matters more than it first appears. In Python, for example, "123" and 123 may look identical when printed, but internally they’re worlds apart. One is a string (stored as a sequence of characters), the other an integer (stored in binary form). The print() function smooths over that difference for human readability, but under the hood, the computer treats them very differently. It’s a simple concept, yet it highlights a broader point: what we see isn’t always what the system processes.

🧠 LeetCode Insight — Total Fruit (Sliding Window with Constraints) I’ve been working on problems that involve maintaining constraints over a moving window — a pattern that shows up often in real systems. Problem: Given a row of fruit trees, collect the maximum number of fruits using only two baskets, where each basket can hold only one type of fruit. 🌍 Real-World Analogy Think of this like a service processing pipeline: You can handle only two types of requests at a time Requests keep coming in continuously Your goal is to process the longest uninterrupted sequence without exceeding capacity Once a third type appears, you must: release older requests adjust your window continue efficiently That’s exactly what this problem models. 💡 Core Logic Use a sliding window to track the current range Maintain a frequency map of fruit types in the window Expand the window while the constraint (≤ 2 types) holds Shrink from the left as soon as the constraint breaks ✅ Python Implementation: https://lnkd.in/gFPwnh9Y 🧩 Why This Works The window always represents a valid state Each element enters and exits the window once Constraints are enforced immediately when violated No unnecessary recomputation 🎯 Takeaway This problem reinforced an important idea for me: Sliding window problems are about enforcing constraints dynamically, not just expanding ranges. Once the constraint logic is clear, the solution becomes both clean and efficient. 👉 Where have you seen similar “limited capacity” constraints in real systems? #Python #SlidingWindow #ProblemSolving #DataStructures #LeetCode #SoftwareEngineering #LearningInPublic

hi connections I just tackled LeetCode 70: Climbing Stairs. It’s a classic Dynamic Programming (DP) problem that teaches you how to break a big challenge into smaller, manageable steps. The Problem: You can climb 1 or 2 steps at a time. How many ways can you reach the top? The Logic: To get to step n, you must have come from either step n-1 or n-2. So: totalWays(n) = ways(n-1) + ways(n-2). The Optimization: Instead of a recursive approach that repeats work, or a DP array that eats up memory, I used two variables to track only the previous two steps. ✅ Time: O(n) ✅ Space: O(1) — Maximum efficiency! Sometimes the most complex-looking problems have the simplest mathematical patterns. 💡 #DynamicProgramming #LeetCode #SoftwareEngineering #Python #Algorithms #Optimization

Day 32 – Linked Lists: Why They Exist and When They Matter 🔗 Today I spent time understanding linked lists — why they exist in the first place. At a basic level, a linked list is a collection of elements (called nodes) where: Each node holds some data Each node points to the next one (and sometimes the previous one) Unlike regular Python lists: Linked lists don’t rely on indexes Elements don’t have to sit next to each other in memory So why does this matter? Linked lists are useful when: You need frequent insertions and deletions The order of items changes often You don’t care about fast random access by index Real-world use cases include: Undo/redo functionality in applications Browser navigation (back and forward) Music or image playlists Implementing caches (like LRU cache) Task scheduling and queues The key trade-off: Linked lists are slower to search But very efficient when modifying data in the middle Today reminded me that data structures aren’t just academic ideas — they’re design choices, each with strengths and weaknesses depending on the problem you’re solving. #Day32 #DataStructures #LinkedLists #Python #ComputerScience #LearningInPublic

Python with DSA — Day 33 What I worked on: Prime checks: moved from naive divisibility to the √n optimization and the 6k±1 rule to cut unnecessary iterations. Time complexity intuition: compared O(n) vs O(√n) for primality tests; saw how early exits change best/worst cases. Clean loops & edge cases: handled n <= 1, negative inputs, and printed primes from 1–100 with a tight loop. Key snippet (conceptual): Idea: Only test divisors up to √n; if none divide, the number is prime. #Day33 #PythonWithDSA #DataStructures #DSAJourney #ProblemSolving #PythonProgramming #SoftwareEngineer

There is a better way to deal with averages and uncertainty: Simulations With a few lines of Python, you can run 10,000 "what-if" scenarios in seconds. It’s the ultimate tool to: ✅ Predict project deadlines with 90% confidence. ✅ Model profit margins using real historical data. ✅ Stress-test your budget against "worst-case" scenarios. Whether you're using Normal, Uniform, or empirical distributions, it’s time to move past the "Flaw of Averages" and embrace the math of uncertainty. Check out my latest post to see how to build your own simulation script in under 50 lines! 👉 https://lnkd.in/ekKMTQdm #DataScience #Python #RiskManagement #Coding #Innovation

There is a better way to deal with averages and uncertainty: Simulations With a few lines of Python, you can run 10,000 "what-if" scenarios in seconds. It’s the ultimate tool to: ✅ Predict project deadlines with 90% confidence. ✅ Model profit margins using real historical data. ✅ Stress-test your budget against "worst-case" scenarios. Whether you're using Normal, Uniform, or empirical distributions, it’s time to move past the "Flaw of Averages" and embrace the math of uncertainty. Check out my latest post to see how to build your own simulation script in under 50 lines! 👉 https://lnkd.in/e8q3eptU #DataScience #Python #RiskManagement #Coding #Innovation