Alex C.’s Post

Headline: Stop writing loops to clean your data. 🛑 One of the most common tasks in Python is handling duplicate entries. While you could write a for-loop with a conditional check, there’s a much faster, more "Pythonic" way to do it: Sets. Sets are unordered collections of unique elements. By casting your list to a set, Python handles the heavy lifting of deduplication instantly. Why use this? ✅ Cleaner, more readable code. ✅ Better performance for large datasets. ✅ Built-in membership testing (O(1) complexity). How are you using Sets in your current workflow? Let’s discuss below! 👇 #PythonProgramming #Pyspiders #CodingTips #SoftwareDevelopment

Day 12 of #50DaysOfPython Today’s concept: Finding missing numbers in a sequence. A practical problem to understand list operations, iteration, and logical comparison in Python. 🔎 Code Explanation: 1. Store the array arr = [1,2,3,5,6] This list contains numbers where one value is missing. 2. Find the total count including the missing number n = len(arr) + 1 len(arr) gives the count of numbers present (5). Since one number is missing, we add 1 → n = 6 3. Calculate the expected sum expected_sum = n * (n + 1) // 2 This formula calculates the sum of numbers from 1 to n. For n = 6 → 6 × 7 / 2 = 21 4. Calculate the actual sum actual_sum = sum(arr) Add the numbers in the list → 1 + 2 + 3 + 5 + 6 = 17 5. Find the missing number missing = expected_sum - actual_sum 21 − 17 = 4 6. Print the result print("Missing number:", missing) 👉This approach shows how mathematical logic can simplify problems instead of using loops. Swipe through the slides to see the explanation, code, and output 👇 #50DaysOfPython #PythonLearning #CodingLife #LearnToCode

🚀 Day 2/30 – Understanding Variables & Data Types in Python Today was all about building the foundation. After learning basic syntax on Day 1, I moved to something very important — Variables and Data Types. At first, it sounded simple. But when I started practicing, I realized how powerful these basics really are. 📌 What I learned today: • What a variable is (a container that stores data) • How to declare variables in Python • Different data types: – int (numbers) – float (decimal numbers) – str (text) – bool (True/False) • How Python automatically detects data types • Using type() to check the data type The biggest realization today: Programming is not about memorizing syntax — it’s about understanding how data flows and how logic works. Small concepts, but they build big systems. Day 2 complete ✅ Learning one concept at a time, consistently. #Python #30DaysChallenge #LearningInPublic #ProgrammingBasics #TechJourney Aditya Chaturvedi

 Why Python remembers things after a function is done (code screenshot below) db_connector has finished execution. Its stack frame is gone. Yet connect still remembers host and port. That preserved state is a closure — created automatically when an inner function captures outer scope. You don’t “use closures” explicitly. You design around them. Why this matters: • avoids globals   • keeps config scoped   • cleaner APIs   • safer state Closures aren’t a trick. They’re how Python naturally models state + behavior. Once you notice this, patterns such as DB clients, API wrappers, and rate limiters become obvious. #Python   #SoftwareEngineering   #BackendDevelopment  

Weekly Challenge 4: Binary Search (Iterative). Why search harder when you can search smarter? Use Binary Search. Imagine looking for a word in a dictionary. Do you read every page from the beginning? No. You open it in the middle and decide: "Left or Right?". That is the essence of Binary Search, and it's the focus of my coding challenge for Week 4. The Implementation: I wrote a Python script (using NumPy) that generates a random environment to test the algorithm. Key Takeaway: While a simple loop (Linear Search) checks elements one by one ($O(n)$), Binary Search cuts the problem in half with every step ($O(\log n)$). Check out the trace in the console output below to see how few attempts it takes to find the target! 👇 📂 Full code on GitHub: https://lnkd.in/ezPaaiDM #Python #BinarySearch #Algorithms #CodingChallenge #DataStructures #Engineering

How async/await turns 30 seconds into 3 seconds (without changing the server)? TL;DR: By letting the program handle multiple requests while waiting using async and await! What does AWAIT do? When code hits await, it tells Python: "This will take time, go do something else meanwhile." Python's event loop then switches to another task, keeping the CPU busy instead of idle. What's Actually Happening? -> async def marks a function as asynchronous -> await marks points where the program can pause and switch tasks Note that this works for I/O bound tasks only, CPU bound tasks need multiprocessing. Takeaway: -> async/await is perfect for I/O bound concurrent operations -> 10x performance improvements are common -> The learning curve is worth it for any I/O-heavy application I'm diving deep into Python concurrency. Follow along and share your bottleneck stories in comments! #Python #Concurrency #SoftwareEngineering #BackendDevelopment #Performance

Day 6 of my #100DaysOfCode challenge 🚀 Today I worked on a Python program to find the second largest number in a list without using sorting. Finding the second largest element means identifying the number that is greater than all others except the maximum value. What the program does: • Takes a list of numbers as input • Handles edge cases like lists with fewer than two elements • Traverses the list only once for efficiency • Finds the second largest value without using built-in sorting How the logic works: If the list has fewer than two elements, the function returns None Two variables (first and second) are initialized to negative infinity The list is traversed element by element If the current number is greater than first, – second is updated with the previous first – first is updated with the current number If the number lies between first and second, second is updated Example: Original list: [3, 1, 4, 1, 5, 9, 2, 6, 5, 3, 5] Output: Second largest number = 6 Key learnings from Day 6: – Tracking multiple values in a single loop – Writing optimized logic without sorting – Understanding time complexity (O(n)) – Improving problem-solving skills in Python #100DaysOfCode #Day6 #Python #PythonProgramming #ProblemSolving #DSA #LearningInPublic #BTech #CSE #AIandML #DataStructures #CodingChallenge #CodeNewbie #TechJourney

Implemented Depth-First Search (DFS) from Scratch in Python! DFS is one of the most fundamental graph traversal algorithms — think of it like navigating a maze: you pick a path and follow it all the way until you hit a dead end, then backtrack and try the next unexplored route. I implemented the iterative version using an explicit stack (preferred in production since it avoids Python's recursion depth limit). Here's how it works: 1. Push the start node onto a stack 2. Pop the top node (LIFO — last in, first out) 3. Visit all its unvisited neighbours and push them onto the stack 4. Repeat until the stack is empty Key takeaways: Time Complexity: O(V + E) — visits every vertex and edge once Space Complexity: O(V) — for the visited set + stack Used reversed() on neighbours so the traversal visits them in natural left-to-right order (since stack is LIFO, reversing ensures the first neighbour gets popped first) The stack is what makes this DFS — swap it with a queue and you get BFS! DFS vs BFS in one line: DFS goes deep before going wide (Stack / LIFO) BFS goes wide before going deep (Queue / FIFO) #Python #DataStructures #Algorithms #DFS #DepthFirstSearch #GraphAlgorithms #CodingJourney #DSA #Programming #SoftwareEngineering #LearningInPublic