Binary Search Solves First Bad Version Problem in Python

How to annotate *args in #Python? Just set the elements' type; we know it's a tuple: def mysum(*args:int) -> int: total = 0 for n in args: total += n return total Normally, tuples contain different types. In args, we assume all values have the same type.

Python does not always automatically convert data types during operations. When we try to combine incompatible types like string and boolean, Python raises an error instead of guessing the conversion. Understanding type compatibility is important to avoid runtime errors. Answer: C) TypeError #Python #TypeConversion #PythonBasics #BeginnerFriendly #CodingJourney #LearningInPublic #ProblemSolving

Sorting is the pre-step that makes fast searches possible. In Python, use sorted() when you want a new list back, and .sort() when you want to sort the original list in place. Bonus: reverse=True flips to descending order—perfect prep for binary search and many other algorithms.#Python #Sorting #Algorithms #CodingTips

Friday brings #Python It's time to post about ELSE for loops. Yeah, that's right. Python allows you to pair ELSE with FOR or WHILE in a way that the ELSE block executes only if the loop was finished naturally without interruption by BREAK.

Python’s Modern Runtime: JIT. Free-threading. Subinterpreters. Short notes — what’s stable, what’s not, what actually changes in your code. #Python #PythonJIT #FreethreadedPython #Subinterpreters #Python314

LeetCode #23 – Merge K Sorted Lists | Python Implementation I implemented a divide-and-conquer approach that merges lists in pairs iteratively. Core Insight: Pairing reduces the problem exponentially instead of linearly. Each merge round halves the list count, achieving O(N log k) instead of O(kN). Time: O(N log k) where N = total nodes, k = number of lists | Space: O(1) #LeetCode #DataStructures #Python #LinkedList #DivideAndConquer #CodingInterview #SoftwareEngineering

Benchmarking Python vs Rust across real use cases shows a clear pattern: performance depends on workload, not language hype. Rust excels in CPU-heavy tasks, while Python can outperform with optimized libraries. The real decision is aligning performance gains with business impact. #Blog #FAMRO #Python #RUST #Benchmarking https://lnkd.in/dDgvQ9mC

Day 31/100 – #100DaysOfCode 🚀 Solved LeetCode #1346 – Check If N and Its Double Exist (Python). Today I practiced brute-force comparison to check whether there exist two indices i and j such that arr[i] = 2 * arr[j]. Approach: 1) Use two nested loops to check all possible pairs. 2) Ensure that i ≠ j. 3) For each pair, check if arr[i] == 2 * arr[j]. 4) If condition is satisfied, return True. 5) If no such pair is found, return False. Time Complexity: O(n²) Space Complexity: O(1) Starting with brute force helps build understanding before optimization 💪 #LeetCode #Python #DSA #Arrays #BruteForce #ProblemSolving #100DaysOfCode

Day 19 of my 20 Day Linkedin Challenge I recently learned about loops in Python. Here’s the simplest way to understand them: A loop tells the computer to repeat something. Instead of writing the same instruction multiple times you write it once and let the loop handle the repetition. For example: If you want to print numbers from 1 to 10, You don’t write 10 separate lines. You use a loop. This matters because computers are great at repetition. Loops allow you to: - save time - reduce errors - handle large tasks efficiently It’s one of those concepts that seems small but it’s actually very powerful. #AfricaAgility #ArtificialIntelligence #Python #MachineLearning #GIT20DayChallenge