Checking if Array is Sorted in Python

DSA practice: Handling the "messy" side of production. In my CV pipeline, data doesn't always arrive in a clean JSON format. Sometimes you’re stuck with raw batch logs using different separators. Today’s warm-up was about building a robust parser to turn those messy strings into structured dictionaries for the next stage of the model. It’s a simple script, but in a production environment, if your parsing logic fails, your whole defect detection pipeline fails. One step closer to the final thesis. Check the logic: https://lnkd.in/gGXZgvSv #Python #DataEngineering #ProductionAI #BuildInPublic

Tree Depth: Recursive Decomposition Mirrors Tree Structure Depth calculation decomposes naturally — tree depth = 1 + max(left depth, right depth). The recursive structure matches the data structure, making recursion the clean solution. Problem structure alignment is when recursion shines. When Recursion Wins: Problems where solution composes directly from subproblem solutions (divide-and-conquer, optimal substructure). Implementation mirrors mathematical definition. Time: O(n) | Space: O(h) recursion #Recursion #TreeDepth #DivideAndConquer #AlgorithmSimplicity #Python #SoftwareEngineering

Are Matplotlib abstractions helping—or getting in the way? Let’s ask Cameron Riddell! In this week’s Cameron’s Corner, Cameron looks at the layers of abstraction in Matplotlib and how they shape the way we write plotting code. While higher-level interfaces can make things faster to write, they can also obscure what’s actually happening underneath. Learn: ✅ How Matplotlib’s abstraction layers are structured ✅ When higher-level APIs simplify your workflow ✅ Why dropping down a level can sometimes give you more control Read here: https://lnkd.in/gVJKvErq Do you prefer high-level plotting tools or working closer to Matplotlib’s core? Let us know how you approach it 👇 #Python #Matplotlib #DataViz #CameronsCorner

Day 7 - Hash Table Deep Dive The answer is O(1) AMORTIZED - and the 'amortized' part is what trips people up. In the best case, hash lookups are O(1). But with hash collisions, worst case is O(n). The key insight: with a good hash function and load factor below 0.75, the AVERAGE case stays O(1). Python dicts use open addressing with random probing, keeping collisions rare. This is why interviewers ask 'average' vs 'worst case' - they want to see if you understand the nuance. Drop your answer! Heart for correct ones. Follow DatascienceBro for Week 2! #datastructures #hashtable #timecomplexity #python #codinginterview #algorithms #bigO #programming #techinterview #softwareengineering

🚀 Solved: Find a String (Substring Count) Challenge Just solved another problem on HackerRank under the Python Strings section! ✅ 🧠 Problem Overview: Count how many times a substring appears in a string — including overlapping occurrences. 🔍 Key Learnings: Practiced string traversal techniques Understood why built-in methods like count() may not always work (no overlapping support) Strengthened concepts of slicing and iteration in Python 💡 Example Insight: For string "ABCDCDC" and substring "CDC", the answer is 2 (overlapping counts matter!). ⚡ Approach Used: Iterated through the string Compared substrings using slicing Counted valid matches efficiently 📈 Problems like this help build strong fundamentals in string manipulation, which is crucial for coding interviews and real-world applications. #Python #HackerRank #Coding #Strings #ProblemSolving #DSA #LearningJourney #AI link of #Solution :- https://lnkd.in/gtqcy8fX

🎬 Building my first Movie Recommendation System. Explored a dataset with 1.4M+ rows and found heavy missing values in key features like genres, keywords, and overview. My approach: dropping missing data to keep recommendations meaningful. What would you do? Drop or fill? Let me know your thoughts in the comments 👇 #MachineLearning #Python #DataScience

I was cleaning a dataset — filtering rows, transforming values, the usual. My 5-line for loop worked fine. But I wanted to be "Pythonic." So I compressed it into a one-liner. Then I added another layer. The next morning I stared at it for two full minutes trying to decode my own logic. If I couldn't read it, my future teammates had no chance. This carousel breaks down: → The mental model that makes list comprehensions click instantly → The reading order most beginners get backwards → The exact rule for when to stop using them and write a real loop What's the longest you've stared at your own code before realizing you had no idea what it does? #Python #DataAnalytics #DataAnalyst #PythonTips #LearnInPublic #AHAMoments #DataAnalystJourney

🚀 Day 72 | Array Problem Solving Today I focused on solving array-based problems in Python 💻 🔹 What I Worked On: • Found largest and smallest elements in an array • Found second maximum element without sorting • Reversed an array without using extra space • Checked if an array is sorted • Counted even and odd numbers in an array • Removed duplicates from a sorted array • Found the missing number in range 1 to n • Calculated frequency of elements • Found pairs with a given sum 💡 Key Learning: • Arrays are fundamental for problem solving • Learned different approaches without using built-in shortcuts • Improved understanding of traversal, conditions, and edge cases 🔥 Takeaway: 👉 Strong array concepts are the base for DSA and coding interviews Consistency is improving problem-solving skills day by day 🚀 #Day72 #Python #Arrays #ProblemSolving #DSA #CodingJourney #10000Coders #PythonDeveloper #SravanKumarSir

💡 Solved Group Anagrams today! 🧠 Key idea: Instead of comparing strings, I used a frequency array (size 26) to create a unique key for each word. ⚠️ Catch in constraints: All characters are lowercase English letters, which allows us to use a fixed-size array of 26 for efficient hashing. 🚀 Result: Achieved O(n · k) time complexity. Great example of converting a comparison problem into a hashing problem! #LeetCode #DSA #Algorithms #Python #CodingInterview

I got tired of writing Cypher by hand so I built something to do it for me. You paste in raw text → it builds a knowledge graph. You ask a question in plain English → it writes the Cypher, runs it, and gives you the answer. The part that surprised me most was how well schema inference works. After the text-to-graph step, the schema is detected automatically and fed into the query pipeline — so both sides stay in sync without any hardcoding. Also added a Cypher validation layer because watching bad queries crash the DB got old fast. Stack: Python, Memgraph #python #memgraph #graphdb #LLM #mcp Short demo in the video 👇