LeetCode 347: Top K Frequent Elements in Linear Time with Python

LeetCode #128 – Longest Consecutive Sequence | Python Implementation I implemented a HashSet-based approach to find the longest consecutive sequence in O(n) time without sorting. The key insight is identifying sequence starts: if a number has no left neighbor (n-1 not in set), it marks the beginning of a potential sequence. From there, we count consecutive elements by checking n+1, n+2, and so on until the sequence breaks. This avoids redundant work by only initiating sequences from their true starting points. This pattern is essential in time-series analysis, event stream processing, and database query optimization for range detection. Key Takeaway: The "sequence start" check is what makes this O(n) instead of O(n²). Each element is visited at most twice: once in the outer loop and once during sequence counting. Recognizing when to skip unnecessary iterations is crucial for optimizing nested-loop patterns. Time: O(n) | Space: O(n) #LeetCode #DataStructures #Python #HashSet #Arrays #CodingInterview #ProblemSolving #SoftwareEngineering

LeetCode #21 – Merge Two Sorted Lists | Python Implementation I implemented an iterative two-pointer merge approach for combining two sorted linked lists. A dummy node serves as the anchor, and a tail pointer builds the merged list by always selecting the smaller current node from either list. After one list is exhausted, the tail directly attaches the remainder of the non-empty list since it's already sorted. This avoids unnecessary node-by-node traversal and keeps the solution clean. This pattern is core to merge sort implementations, database query result merging, and distributed log aggregation systems where sorted streams must be combined efficiently. Key Takeaway: Using a dummy node eliminates edge case handling for the first node insertion, simplifying the logic significantly. The direct attachment of remaining nodes after one list is exhausted is an optimization that leverages the sorted property, avoiding redundant comparisons. Time: O(n + m) where n, m are list lengths | Space: O(1) #LeetCode #DataStructures #Python #LinkedList #TwoPointers #CodingInterview #ProblemSolving #SoftwareEngineering

Discovered why Python lists kill performance (200ms vs 2ms for 1M elements!) while NumPy delivers 100x speedup + 8x less memory. Explained with a chef analogy: cache misses = running to the store, GIL = one chef rule. Read full blog on medium: https://lnkd.in/dWDGWtuw #NumPy hashtag #Python hashtag #DataScience hashtag #Performance hashtag #GenAi hashtag #DataScience

Python Tip: With Statement The 'with Statement' Is More Than Syntax Most developers see 'with' as a shortcut for opening files. It’s not. It’s Python’s way of guaranteeing clean resource management, automatically and safely. No forgotten .close() No hidden leaks No fragile cleanup logic That’s the real shift from old to modern Python: Old mindset -> “Remember to clean up.” Modern mindset -> “Design so cleanup is automatic.” 'with' isn’t just cleaner code. It’s safer architecture. FOLLOW FOR MORE PYHON TIPS & INSIGHTS #Python #CleanCode #SoftwareEngineering #ProgrammingTips

Hot take For analysts: Python > Excel Yes, I said it. Not because Excel is bad — but because automation changes everything. Ready for debate #AnalyticsTools #PythonVsExcel

LeetCode #33 – Search in Rotated Sorted Array | Python Implementation I implemented a modified binary search to handle rotated sorted arrays. At each step, we determine which half of the array is properly sorted by comparing nums[l] with nums[mid]. Once the sorted half is identified, we check if the target falls within that range — if yes, we search that half; otherwise, we search the other half. This preserves the O(log n) complexity despite the rotation. This pattern is crucial in distributed systems for searching circularly-buffered logs, time-series databases with wraparound indices, and cache eviction policies with rotational priority queues. Key Takeaway: The critical insight is determining which side is sorted at each iteration using nums[l] <= nums[mid]. Once identified, standard binary search range logic applies. This demonstrates how binary search can be adapted to non-standard sorted structures while maintaining logarithmic complexity. Time: O(log n) | Space: O(1) #LeetCode #DataStructures #Python #BinarySearch #Arrays #CodingInterview #ProblemSolving #SoftwareEngineering

Python Tip — Tuples: Small Feature, Big Signal Most developers see tuples as “lists you can’t modify.” That’s surface-level thinking. Tuples are about immutability and intent. When you use a tuple, you’re telling other developers: “This data should not change.” They’re: - Faster than lists - Hashable (usable as dictionary keys) - Safer for fixed data - Perfect for returning multiple values Use lists for collections that evolve. Use tuples for data that represents a fixed structure. In Python, the right data structure isn’t just technical, it communicates design. FOLOW FOR MORE PYTHON TIPS & INSIGHTS #Python #DataStructures #CleanCode #SoftwareEngineering #ProgrammingTips

Python Tip — Dictionaries Dictionaries are More Powerful Than You Think Most people use dictionaries just to store key–value pairs. Pros use them to design smarter systems. Dictionaries give you: - Instant lookups (O(1)) - Clean data modeling - Flexible, dynamic structures - Readable and expressive logic Need fast mapping? Use a dictionary. Replacing long `if-elif` chains? Use a dictionary. Modeling structured data? Use a dictionary. In Python, dictionaries aren’t just containers. They’re decision engines. FOLLOW FOR MORE PYTHON TIPS & INSIGHTS. #Python #DataStructures #CleanCode #SoftwareEngineering #ProgrammingTips

Built a YouTube Video Statistics Checker 📊 using Python and the YouTube Data API, which extracts video details like views, likes, and comments directly from a video URL. This project helped me gain hands-on experience with API integration, regex-based URL parsing, and building command-line applications in Python. GitHub: https://lnkd.in/gKetegTd #Python #APIs #YouTubeDataAPI #MiniProject #LearningByDoing

Your Python script isn’t slow. It’s just doing too much… the hard way. 🐍 Over the years, a few small automation habits saved me hours: • Use 𝐥𝐢𝐬𝐭/𝐝𝐢𝐜𝐭 𝐜𝐨𝐦𝐩𝐫𝐞𝐡𝐞𝐧𝐬𝐢𝐨𝐧𝐬 instead of nested loops where possible ⚡ • 𝐏𝐮𝐬𝐡 𝐡𝐞𝐚𝐯𝐲 𝐟𝐢𝐥𝐭𝐞𝐫𝐢𝐧𝐠 𝐭𝐨 𝐒𝐐𝐋 instead of cleaning everything in Python 🗄️ • 𝐂𝐚𝐜𝐡𝐞 𝐞𝐱𝐩𝐞𝐧𝐬𝐢𝐯𝐞 𝐀𝐏𝐈 𝐫𝐞𝐬𝐩𝐨𝐧𝐬𝐞𝐬 (even a simple in‑memory dict helps) 💾 • Log less, but 𝐥𝐨𝐠 𝐬𝐦𝐚𝐫𝐭𝐞𝐫 — timestamps + key IDs > dumping full payloads 📝 • Measure with 𝐭𝐢𝐦𝐞.𝐩𝐞𝐫𝐟_𝐜𝐨𝐮𝐧𝐭𝐞𝐫() before “optimizing” ⏱️ Most automation code grows quietly. Until one day it’s a 12‑minute job that used to take 90 seconds. Friday question: What’s one tiny refactor that made your script noticeably faster? 👇 #python #automation #productivity #backend #devlife