Removing Duplicates with Python Sets

Stop choosing between [] and () at random. It’s the difference between a high-performance script and a MemoryError. 🐍 In Python, List Comprehensions and Generator Expressions might look similar, but under the hood, they handle your system's RAM very differently. I’ve put together a slide deck to break down: ✅ The 'Eager' vs. 'Lazy' evaluation model. ✅ Real-world memory benchmarks. ✅ When to prioritize Speed over Memory. Swipe through to see which one you should be using for your next data pipeline! 👇 What’s your go-to? Do you default to Lists for simplicity, or Generators for efficiency? Let’s discuss in the comments! #Python #Coding #SoftwareEngineering #DataScience #Backend #Performance

The 5 Legacy Python Habits You Need to Break Today 🚫 1️⃣ Using os.path instead of pathlib. 2️⃣ Ignoring the "Walrus Operator" (:=) in loops. 3️⃣ Writing "Naked" Python without Type Hints. 4️⃣ Avoiding threads due to old GIL fears. 5️⃣ Long if-elif chains instead of Pattern Matching. Why? Because modern Python (3.12+) is faster, cleaner, and built for the AI era. Check out my new article on the shifting Python landscape. 🔗 [https://lnkd.in/gSWqqz59] #Python313 #SoftwareEngineering #CodingHabits #BackendDevelopment #TechTrends2026

Day 302: Python time for Measuring Performance ⏱ Because Performance Actually Matters We often assume code is “fast enough” — until it isn’t. The time module helps answer one simple question: “How long did this actually take?” 👉 Basic timing example: import time start = time.time() time.sleep(2) end = time.time() print(end - start) Simple, but powerful. 💡 Personal Tip: Always measure before optimizing. Assumptions are usually wrong. 🔹 Challenge: Build a simple stopwatch or countdown timer using time. #Python #Performance #Optimization #Time

A "merge()" can explode your dataset if both tables contain multiple rows for each key (this refers to the many-to-many join situation). Result: millions of rows + memory error must aggregate first, then merge Great reminder on the importance of understanding data relationships versus actually writing code. #Python #Pandas #DataScience

🚀 Consistent DSA Practice – Learning One Problem at a Time I’ve been regularly practicing Data Structures & Algorithms using Python, focusing on understanding the logic behind problems, not just writing code. Recently worked on problems like: Good Pairs Majority Element (Boyer–Moore Voting Algorithm) Jewels and Stones Decode the Message Isomorphic Strings Key learnings from my practice: ✅ How to optimize solutions from brute force to efficient approaches ✅ Writing clean and readable code ✅ Understanding time & space complexity ✅ Solving problems step-by-step with proper dry runs Consistency is helping me build confidence and problem-solving skills every day. Looking forward to learning more and improving continuously 🚀 #Python #DSA #ProblemSolving #CodingPractice #LearningJourney #LeetCode #Consistency

Today I faced a small but real problem: filtering SLA data with multiple conditions started getting messy with plain Python. Instead of loops, I used NumPy logical operations — AND, OR, NOT, XOR. Example: finding tickets that are SLA breached AND high priority took one clean line, no complex if blocks. This matters because real datasets have thousands of rows, and speed + readability are critical in projects. NumPy keeps the logic clear, fast, and production-ready 📊 Small habits like this separate “working code” from “analyst-grade code”. Do you still rely on loops for filtering, or have you switched to NumPy? 🤔 #DataAnalytics #NumPy #Python #DataAnalyst #LearningByDoing #CareerGrowth

Day 3 — Back to Python Numerics. Today I built a Tip Calculator as a way to revisit Python number handling. This project helped me refresh: • Converting user input into the right data types • Working with floats and integers • Manipulating values for accurate calculations • Rounding results to two decimal places • Structuring simple logic into a working tool It’s a small program, but it reinforced how important the basics are — especially when accuracy matters. 🔗 GitHub: https://lnkd.in/dVNi2MJP #LearningInPublic #Python #DataScience

Problem Statement (leetcode 58): Given a string s consisting of words and spaces, return the length of the last word in the string. A word is a maximal substring consisting of non-space characters only. Time complexity: O(n) Space complexity: O(n) as we use split() method to create a list of words #dsa #strings #python #competitiveprogramming #leetcode #algorithms #datastructures

After going through multiple senior Python interviews and discussions, one question kept coming up implicitly: “Does this person understand Python’s runtime or just the syntax?” That gap shows up in: - mutability bugs - default argument traps - GC and memory leaks - confusing behavior with is vs == I’ve started a Python-first series focused entirely on that gap. Hour 1 is live: The Python Object Lifecycle identity, references, memory management, GC, and argument passing. It’s dense by design. Because shallow understanding is what fails senior interviews. 👉 Read Hour 1: https://lnkd.in/g-3S7Qi9 If you use Python professionally, this is worth your time. I would love to work on suggestions to improve the article. Thank You #interview #pythoninterviewpreparation #pythondeveloper