Amin Razzouki’s Post

🚨 Stop killing your Python performance with string concatenation! I see this mistake everywhere: ❌ The slow way (O(n²)) result = "" for word in words: result += word # Creates a NEW string every time! Here's what's actually happening: Every += creates an entirely new string in memory, copying all previous characters. Process 1000 strings? That's ~500,000 character copies. Ouch. The fix is beautifully simple: # ✅ The fast way (O(n)) result = [] for word in words: result.append(word) # Just adds to list return ''.join(result) # Single concatenation at the end Why it matters: → Strings are immutable in Python → Lists are mutable and efficient for building → One final join operation vs thousands of copies → Can be 100x+ faster on large datasets Real impact: I've seen this single change cut processing time from minutes to seconds in production code. Pro tip: For simple cases, list comprehensions + join are even cleaner: result = ''.join([word for word in words]) What's your favorite Python performance trick? #Python #Programming #SoftwareEngineering #CodingTips #PerformanceOptimization #CleanCode #TechTips #PythonProgramming #SoftwareDevelopment #CodeEfficiency

✨ Small projects can spark big learning! It was a 3-day personal challenge: building an automated test suite for a portfolio website using Playwright + Python + SQLite 💻🧪 🎯 The goal was to explore Playwright with Python and track test results in SQLite, instead of relying solely on built-in reports. Designing a simple tracker that logs each test with a timestamp made the project feel structured and data-driven 🗂️⏱️ Along the way, I practiced Python , explored data storage 📊, and learned how small experiments add up. With AI accelerating learning , exploring and building feels more exciting than ever 🤖✨ 🔧Next step: Improve the tests, apply similar setup to a medium-sized project to tackle more complex scenarios and continue to explore 💡 #softwaretesting #playwright #python #automation #qualityassurance #testautomation

🐍Python Day 3 Each day, the logic gets tougher, but so does my clarity. 🔹 What I Tried: I wanted to take yesterday’s logic and make it work across multiple numbers instead of just one. 🔹 What I Built: A Python program that: ✅ Generates numbers within a range ✅ Checks if they’re even, odd, positive, or negative ✅ Detects prime numbers efficiently using the √n trick 🔹 What I Learned: Even a small piece of code can teach how to think systematically — to break a big problem into smaller parts and optimize as you go. Coding truly shapes how you feel, not just what you type. 🔹 What Was Challenging: Getting the prime number logic right without unnecessary loops. It took a few test runs (and print statements), but it finally clicked. Each day, a new script. Each script is a new insight. On to the next challenge! #PythonJourney #100DaysOfCode #LearnToCode #WomenWhoCode #CodingCommunity #PythonForBeginners #ProblemSolving #TechLearning #CodeNewbie #LogicBuilding #KeepLearning 

Had one of those classic "bug" moments that turned out to be a fundamental Python feature: lexical closures. It was bugging me, so I had to go deep. In short, a closure is when a nested function remembers the variables from its "enclosing" scope, even after that scope has finished. Why it's powerful: It's perfect when you need multiple functions to react to a single, changing piece of state without using global variables. Why it's a headache (the 'gotcha'): It can be a real trip-up if you're not expecting it, especially in loops where all your functions might end up using the last value of the loop variable. (Ask me how I know 😂) It's a classic feature that, when used right, is super clean. When used by accident, it's a real head-scratcher. What's a "simple" Python feature that's given you a headache before? #Python #SoftwareDevelopment #Programming #PythonDeveloper #DevCommunity

Day 17/120 of my Python Full Stack Journey Topic: Loop Control Statements – break, continue, pass Today I learned how to control the flow of loops using special keywords. #Python #BreakContinuePass #ProgrammingBasics #DailyLearning #FullStackDeveloperJourney #KeepGoing Pooja Chinthakayala mam Saketh Kallepu sir Uppugundla Sairam sir Codegnan #break #break statement is used to terminate the entire loop a=10 while a>1:   print(a)   a=a-1 a=10 while a>1:   a=a-1   print(a)     a=10 while a>1:   print(a)   a=a-1   if a==4:     break for i in range(10):   print(i) for i in range(10):   if i==8:     break   print(i) a="python" for i in a:   if i=="h":     break   print(i) #continue #the continue statement is used to skip the current iteration and rest of the code will continue a=20 while a>2:   print(a)   a=a-1 a=20 while a>2:   a=a-1   print(a) a=20 while a>2:   print(a)   a=a-1   if a==12:     continue a=20 while a>2:   a=a-1   if a==12:     continue   print(a) for i in range(15):   if i==9:     continue   print(i) a="code" for i in a:   if i=="o":     continue   print(i) #pass #pass statement is a null statement it does nothing but syntactically we need. a=30 while a>5:   print(a)   a=a-1   if a==15:     pass for i in range(14):   if i==10:     pass   print(i)

🐍 Python isn’t hard — you just haven’t learned it the right way yet. Everyone says “learn Python,” but few explain how to build a solid foundation. If you want to grow from beginner ➜ advanced, here’s your blueprint 🔥 🚀 Master These 10 Python Concepts First: 1️⃣ Variables & Data Types → the building blocks. 2️⃣ Functions → reusable, clean, and modular code. 3️⃣ Libraries & Modules → stop reinventing the wheel. 4️⃣ Classes & Objects → think OOP, not just code. 5️⃣ Error Handling → your code shouldn’t crash. 6️⃣ Iterators & Generators → memory-efficient loops. 7️⃣ Map, Filter, Reduce → cleaner functional code. 8️⃣ Decorators → modify behavior without rewriting. 9️⃣ Regex → string superpowers. 🔟 Serialization (JSON) → move data between systems. 💡 Pro tip: You don’t need 100 tutorials. You need 20 concepts done deeply — and one real project to connect them all. #Python #Programming #DataEngineering #LearningInPublic #CareerGrowth #CodeNewbie

🐍 Python isn’t hard — you just haven’t learned it the right way yet. Everyone says “learn Python,” but few explain how to build a solid foundation. If you want to grow from beginner ➜ advanced, here’s your blueprint 🔥 🚀 Master These 10 Python Concepts First: 1️⃣ Variables & Data Types → the building blocks. 2️⃣ Functions → reusable, clean, and modular code. 3️⃣ Libraries & Modules → stop reinventing the wheel. 4️⃣ Classes & Objects → think OOP, not just code. 5️⃣ Error Handling → your code shouldn’t crash. 6️⃣ Iterators & Generators → memory-efficient loops. 7️⃣ Map, Filter, Reduce → cleaner functional code. 8️⃣ Decorators → modify behavior without rewriting. 9️⃣ Regex → string superpowers. 🔟 Serialization (JSON) → move data between systems. 💡 Pro tip: You don’t need 100 tutorials. You need 20 concepts done deeply — and one real project to connect them all. #Python #Programming #DataEngineering #LearningInPublic #CareerGrowth #CodeNewbie

🐍 Python isn’t hard — you just haven’t learned it the right way yet. Everyone says “learn Python,” but few explain how to build a solid foundation. If you want to grow from beginner ➜ advanced, here’s your blueprint 🔥 🚀 Master These 10 Python Concepts First: 1️⃣ Variables & Data Types → the building blocks. 2️⃣ Functions → reusable, clean, and modular code. 3️⃣ Libraries & Modules → stop reinventing the wheel. 4️⃣ Classes & Objects → think OOP, not just code. 5️⃣ Error Handling → your code shouldn’t crash. 6️⃣ Iterators & Generators → memory-efficient loops. 7️⃣ Map, Filter, Reduce → cleaner functional code. 8️⃣ Decorators → modify behavior without rewriting. 9️⃣ Regex → string superpowers. 🔟 Serialization (JSON) → move data between systems. 💡 Pro tip: You don’t need 100 tutorials. You need 20 concepts done deeply — and one real project to connect them all. #Python #Programming #DataEngineering #LearningInPublic #CareerGrowth #CodeNewbie

A small-but-mighty Python tip! I was finding the last-but-one value in an array, the runner-up score. My quick approach was : ➡️ Convert to a set to remove duplicates ➡️ Sort to get ascending order ➡️ Pick the second-to-last It worked. But the real win wasn’t just getting the answer. It was understanding why this approach works and when it doesn’t. Knowing the difference between simple data structures like sets and tuples pays dividends every day in Python. ➡️ Sets vs Tuples in the wild : 1. Set ↪️ Unordered, unique elements ↪️ Great for de-duplication and fast membership checks ↪️ Supports set algebra (union, intersection) 2. Tuple ↪️Ordered and immutable ↪️Stable position/indexing ↪️Can be used as dict keys if elements are hashable ➡️ Why that mattered here? ↪️I used a set to remove duplicates so the highest score doesn’t block the runner-up. ↪️Then I sorted the unique values to reliably grab the second-highest. This tiny choice embodies a bigger lesson : pick the structure that matches the job. #datastructures #python #datascience #coding #hackerrank #debug