Python Performance: Blame Skills, Not Language

One line of bit manipulation that changed how I think about numbers 👇 Number of 1 Bits - LeetCode 191 - Easy Also known as the Hamming Weight problem. Given an integer, count the number of 1 bits in its binary representation. Simple to understand, but the optimal solution is a beautiful bit manipulation trick. The naive approach loops through all 32 bits and checks each one. But there is a smarter way. The trick is n & (n-1) which always flips the rightmost set bit of n to 0. So instead of checking every bit, I only iterate as many times as there are 1 bits. Each iteration removes exactly one 1 bit from n until n becomes 0. The count of iterations is the answer. Key Learnings 1) n & (n-1) Trick: This is one of the most powerful and commonly asked bit manipulation patterns in interviews. It eliminates the lowest set bit in a single operation. 2) Efficiency Over Naive: Instead of always looping 32 times, this approach loops only as many times as there are 1 bits — making it faster for sparse binary numbers. 3) Thinking in Bits: Bit manipulation forces you to think at the binary level which builds a deeper understanding of how computers actually process numbers. Time and Space Complexity Time Complexity: O(K) — Where K is the number of 1 bits in n. We loop exactly K times. Space Complexity: O(1) — Only a single counter variable is used. Did you know this exact trick is used in low level systems and competitive programming to count set bits efficiently? Drop a 🔥 if this clicked for you! #LeetCode #BitManipulation #Blind75 #SDEPrep #DataStructures #Python #ProblemSolving #CodingJourney #Freshers #AmazonSDE

🚀 Stop killing your CPU with Python loops. I recently refactored a data transformation pipeline that was crawling because it processed 5 million rows using a standard row-by-row iteration. Moving from native loops to vectorized operations changed everything. Before optimisation: results = [] for i in range(len(df)): val = df.iloc[i]['price'] * df.iloc[i]['tax_rate'] results.append(val) df['total'] = results After optimisation: df['total'] = df['price'] * df['tax_rate'] Performance gain: 45x faster execution time. Vectorization offloads the heavy lifting to highly optimised C code under the hood. When you use Pandas or NumPy native methods, you stop fighting the interpreter and start leveraging memory alignment. If you are still writing loops for data manipulation, you are leaving massive amounts of compute time on the table. It is the easiest performance win you can claim this week. What is the biggest speed boost you have ever achieved by swapping a loop for a built-in vectorised function? #DataEngineering #Python #Pandas #Performance #Optimization

You write for loops every day. Do you know what actually runs underneath them? Day 03 of 30 -- Generators and Iterators Deep Dive Advanced Python + Real Projects Series Python calls iter() to get the iterator, then next() repeatedly until StopIteration is raised. That is every for loop you have ever written. And yield pauses the function, hands the value out, and resumes from the exact same line next time. Today's topic covers: The lazy vs eager evaluation problem -- why loading 10GB into a list crashes servers The full iterator protocol -- what powers every for loop 3 types -- generator function, expression, async generator Annotated syntax -- basic, yield from, and the send() two-way pattern Real fintech pipeline -- 52GB log file, 4.2MB memory used 5 production mistakes including exhausting a generator twice Generator pipeline architecture -- identical to Unix pipes Key insight: Don't store what you can stream. #Python #PythonProgramming #DataEngineering #BackendDevelopment #LearnPython #100DaysOfCode #PythonDeveloper #SoftwareEngineering #TechContent #BuildInPublic #TechIndia #CleanCode #CodingTips #CodeNewbie #LinkedInCreator #PythonTutorial

Do you actually understand what Python is… or do you just know its definition?🐍 Most people say: “Python is a high-level, interpreted language created by Guido van Rossum in 1991.” That’s not understanding. That’s memorization. Python is not just a language. Python is a layer of abstraction. ⚙️ When early languages like C were designed, they stayed very close to the machine. 💻 You had to think about memory, pointers, and low-level details. That’s why C is fast—because it sits close to hardware. But here’s the trade-off: Closer to hardware → more control, more complexity Higher abstraction → less control, more productivity Python was built to move you away from the machine and toward problem-solving. Someone already did the hard work: Memory management? Handled. Complex system interactions? Hidden. Syntax complexity? Reduced. So instead of thinking: “How does the computer execute this?” You think: “What logic solves this problem?” 🚀 That’s why Python is widely used in: Machine Learning Web Development Automation Data Analysis Not because it’s the fastest — it’s not. But, because it allows you to build faster and think more clearly. Final point: 🎯 Python didn’t become popular by accident. It became popular because it removes friction between your idea and implementation. #python #pythonprogramming #learnpython #coding #programming #machinelearning #deeplearning #datascience #artificialintelligence #ai #ml #softwareengineering #systemdesign #computerscience #codinglife #programminglogic

Some problems are less about brute force and more about understanding patterns in bits. Day 9/100 — Data Structures & Algorithms Journey Today’s Problem: Gray Code Approach: The task was to generate a sequence where each number differs from the previous one by only a single bit. Instead of manually constructing sequences, I used a bit manipulation technique. For each number from 0 to 2ⁿ − 1, I applied the transformation: gray = i ^ (i >> 1) This ensures that consecutive numbers differ by exactly one bit, which satisfies the Gray Code property. Key Takeaways: - Bit manipulation can simplify complex problems - XOR operations are powerful for pattern-based transformations - Understanding binary representation opens new ways to solve problems efficiently This problem improved my confidence in handling bit-level logic and pattern recognition. #DSA #LeetCode #ProblemSolving #SoftwareEngineering #CodingJourney #100DaysOfCode #TechLearning #DeveloperJourney #Programming #Python #InterviewPreparation #CodingSkills #ComputerScience #JobReady #FutureEngineer #TechCareers #SoftwareDeveloper #LearnInPublic #OpenToWork

🚀 ✨ 𝐃𝐀𝐘 12: 𝐔𝐍𝐃𝐄𝐑𝐒𝐓𝐀𝐍𝐃𝐈𝐍𝐆 𝐒𝐄𝐓𝐒 ✨ Today, I explored another important data structure in Python — 💻 𝐒𝐞𝐭𝐬. 🔹 📘 𝐖𝐡𝐚𝐭 𝐀𝐫𝐞 𝐒𝐞𝐭𝐬? Sets are 𝐮𝐧𝐨𝐫𝐝𝐞𝐫𝐞𝐝 collections of unique elements, meaning they automatically remove duplicates. 🔹 ⚙️ 𝐖𝐡𝐚𝐭 𝐈 𝐋𝐞𝐚𝐫𝐧𝐞𝐝 ✔️ Creating and using 𝐬𝐞𝐭𝐬 ✔️ Performing operations like 𝐮𝐧𝐢𝐨𝐧, 𝐢𝐧𝐭𝐞𝐫𝐬𝐞𝐜𝐭𝐢𝐨𝐧, 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞 ✔️ Understanding how sets handle 𝐮𝐧𝐢𝐪𝐮𝐞 𝐯𝐚𝐥𝐮𝐞𝐬 🔹 🧠 𝐖𝐡𝐲 𝐈𝐭 𝐌𝐚𝐭𝐭𝐞𝐫𝐬 Sets are very useful for 𝐫𝐞𝐦𝐨𝐯𝐢𝐧𝐠 𝐝𝐮𝐩𝐥𝐢𝐜𝐚𝐭𝐞𝐬 and performing fast mathematical operations. 💡 𝐔𝐧𝐢𝐪𝐮𝐞 𝐝𝐚𝐭𝐚 = 𝐂𝐥𝐞𝐚𝐧 𝐚𝐧𝐝 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭 𝐜𝐨𝐝𝐞! 💪 𝐂𝐨𝐧𝐭𝐢𝐧𝐮𝐢𝐧𝐠 𝐭𝐨 𝐛𝐮𝐢𝐥𝐝 𝐚 𝐬𝐭𝐫𝐨𝐧𝐠 𝐟𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧! 🚀 𝐎𝐧𝐞 𝐬𝐭𝐞𝐩 𝐜𝐥𝐨𝐬𝐞𝐫 𝐭𝐨 𝐛𝐞𝐜𝐨𝐦𝐢𝐧𝐠 𝐚 𝐛𝐞𝐭𝐭𝐞𝐫 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫! #Python #Day12 #CodingJourney #Sets #DataStructures #LearningPython #Consistency 🚀

💡 A small mistake that taught me a big lesson in Python… Early in my career, I wrote a data pipeline that worked perfectly. No errors. No crashes. Everything looked clean. But the output? ❌ Completely wrong. The issue wasn’t syntax. It wasn’t performance. It was logic. Since then, I’ve learned: ✔ A system failing fast is easy to fix ❌ A system silently giving wrong results is dangerous Now whenever I build APIs, pipelines, or ML workflows, I focus on: 🔹 Data validation at every step 🔹 Clear logging (not just success logs) 🔹 Edge case testing, not just happy paths 🔹 Verifying outputs - not assuming correctness Because in real systems, 👉 “It runs fine” doesn’t always mean “It’s correct” Curious - What’s a bug that taught you the most? #Python #DataEngineering #SoftwareEngineering #Debugging #BackendDevelopment #TechLessons #Developers #Learning

A.I. without knowing Python = DANGER I built a Valuation Dashboard. It took me a few hours to build a client-ready valuation dashboard. Under the hood: - 4,100 lines of Python code - yet to be debugged - So many libraries used and functions defined - Not written by me - So many functionalities which should be dynamic but are static - Dashboard working fine right now but may break big time when used for practical purposes Leaders to take note: Can someone impress us showing this? - Yes Is it deployable - Not until it is debugged and tested thoroughly But can't another A.I. debug these 4,100 lines of code? Yes, but who validates the validator? A year back I thought what's the point to knowing/teaching Python when A.I. is here doing all the coding. Now I feel knowing a programming language (specially Python) is more important than ever before if you want to reap the benefits of A.I. for complex tasks. Have you experienced a similar situation before? #Python #AI #Valuation #Claude #Anthropic

"𝐀𝐬𝐲𝐧𝐜 𝐦𝐚𝐝𝐞 𝐦𝐲 𝐀𝐏𝐈 𝐟𝐚𝐬𝐭𝐞𝐫… 𝐮𝐧𝐭𝐢𝐥 𝐢𝐭 𝐝𝐢𝐝𝐧’𝐭.” This week, while working with FastAPI, I went deep into Python concurrency—and realized something important: 👉 𝐀𝐬𝐲𝐧𝐜 ≠ 𝐏𝐚𝐫𝐚𝐥𝐥𝐞𝐥𝐢𝐬𝐦 ⚡ Here’s the simple breakdown: 🔹 𝐀𝐬𝐲𝐧𝐜𝐈𝐎 Best for I/O-bound work (APIs, DB calls) Runs on a single thread → event loop magic ✔ Scales insanely well ❌ CPU tasks block everything (As shown in my AsyncIO notes, even with thousands of requests, it’s still one thread running the loop ) 🔹 𝐓𝐡𝐫𝐞𝐚𝐝𝐢𝐧𝐠 Good for blocking libraries (like requests) ✔ Works because I/O releases the GIL ❌ Still no real CPU parallelism (Only one thread executes Python code at a time due to GIL ) 🔹 𝐌𝐮𝐥𝐭𝐢𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 Where real performance kicks in ✔ True parallelism (multi-core) ❌ Higher memory + overhead (Each process has its own GIL → actual parallel execution ) 🧠 My biggest takeaway: 𝐀𝐬𝐲𝐧𝐜𝐈𝐎 → 𝐇𝐚𝐧𝐝𝐥𝐞 𝐦𝐚𝐧𝐲 𝐭𝐚𝐬𝐤𝐬 𝐓𝐡𝐫𝐞𝐚𝐝𝐢𝐧𝐠 → 𝐇𝐚𝐧𝐝𝐥𝐞 𝐛𝐥𝐨𝐜𝐤𝐢𝐧𝐠 𝐭𝐚𝐬𝐤𝐬 𝐌𝐮𝐥𝐭𝐢𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 → 𝐇𝐚𝐧𝐝𝐥𝐞 𝐡𝐞𝐚𝐯𝐲 𝐭𝐚𝐬𝐤𝐬 ⚠️ Mistakes I was making: ❌ Using async for CPU-heavy work ❌ Blocking code inside async routes ❌ Assuming threads = speed 🎯 Final thought: Choosing the wrong concurrency model doesn’t throw errors… It silently kills performance. Curious — what’s your go-to approach for scaling Python backends? 👇 #Python #FastAPI #AsyncIO #BackendDevelopment #SystemDesign #SoftwareEngineering

Power up your data capabilities by learning how modern data pipelines are designed, built and managed. Gain practical experience using Python to ingest and connect data through APIs, strengthen your database expertise with advanced SQL and understand the full data lifecycle from source to insight. Walk away with the ability to build end-to-end pipelines that deliver real-world data impact. To find out more, visit Data Engineering Fundamentals: https://lnkd.in/gNtG3S7z Python for Data Engineering: https://lnkd.in/gbXRa9Hs Databases for Data Engineering: https://lnkd.in/gR3qiQHv NUS Computing #dataengineering #python #database