Suman Saha’s Post

Practiced Python functions today — *args, **kwargs, return types, and function types. The one thing that clicked — *args is for unknown number of values (stores as tuple), **kwargs is when you don't know the keys either (stores as dict). Simple but I was mixing them up before. Also learned there are actual types of functions — action, transformation, validation. https://lnkd.in/ducSzXzK #Python #LearningInPublic #DataAnalysis

Just practiced Pandas and data cleaning hits different when you're working with real messy data. Covered data types, type conversion, handling missing values, replacing inconsistent entries, and using category dtype to save memory — FuelType column went from 11488 bytes to 1460 bytes just by changing the dtype. Notebook here 👉 https://lnkd.in/d3djYPvp #Python #Pandas #DataAnalysis #LearningInPublic

🧠 Python Concept: Mutable vs Immutable Why your data changes… or doesn’t 😳 ❌ Confusing Behavior x = [1, 2, 3] y = x y.append(4) print(x) 👉 Output: [1, 2, 3, 4] 😵💫 🧒 Why? 👉 Lists are mutable (can change) 👉 Both x and y point to same object ✅ Immutable Example x = (1, 2, 3) y = x y = y + (4,) print(x) 👉 Output: (1, 2, 3) ✅ 🧒 Simple Explanation 👉 Mutable = can change 🧱 👉 Immutable = cannot change 🔒 💡 Why This Matters ✔ Avoid unexpected bugs ✔ Important for memory understanding ✔ Used in real-world debugging ✔ Frequently asked in interviews ⚡ Bonus Tip x = [1, 2, 3] y = x.copy() 👉 Now changes in y won’t affect x 🐍 Know your data types 🐍 Small concept, big impact #Python #PythonTips #CleanCode #LearnPython #Programming #DeveloperLife #100DaysOfCode

🚀 𝗗𝗮𝘆 𝟮𝟭/𝟯𝟬 – 𝟯𝟬 𝗗𝗮𝘆𝘀 𝗼𝗳 𝗣𝘆𝘁𝗵𝗼𝗻 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 Continuing my journey of building one Python project every day to improve consistency and real-world problem-solving. Today’s focus: **Core Python Concepts** 🧠 𝗣𝗿𝗼𝗷𝗲𝗰𝘁: 𝗧𝘆𝗽𝗶𝗻𝗴 𝗦𝗽𝗲𝗲𝗱 𝗧𝗲𝘀𝘁 Build a Python-based typing speed test that measures how fast and accurately a user can type a given sentence. A simple yet powerful project to understand timing, user input handling, and performance metrics. ✨ 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀: • Random sentences loaded from external file 📄 • Real-time typing speed calculation (WPM) ⚡ • Error detection (word-based comparison) ❌ • Accuracy calculation (%) 🎯 • Continuous testing loop for practice 🔁 💡 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀 𝗨𝘀𝗲𝗱: • File handling in Python (`.txt` file) • Time module for performance tracking • Random module for sentence selection • String manipulation & comparison • Looping and control flow 🔗 𝗚𝗶𝘁𝗛𝘂𝗯: https://lnkd.in/d3MUYR2T A simple idea, but very useful and practical — and a great way to improve both coding and typing skills together. Building discipline through code — one project at a time. Follow along as I complete 30 Python projects in 30 days 🚀 #Python #BuildInPublic #DeveloperJourney #30DaysOfCode #PythonProjects #Coding #Automation #Learning

I built a Python script that organized my entire Downloads folder in under 3 seconds. Here's the embarrassing truth : my Downloads folder had 100+ files. PDFs, random images, zip files, old code. All just sitting there. A complete mess. So instead of cleaning it manually (again), I wrote a Python script to do it for me. What it does: → You point it at any folder → It scans every file → Sorts them into Images, Videos, Documents, Code, Archives → Shows a preview before touching ANYTHING → Logs every move with a timestamp What I learnt building this: → pathlib makes file/folder handling so much cleaner than I expected → sys.exit() is how you kill a program gracefully instead of just crashing → mkdir() creates folders on the fly if they don't exist in a single line The part I'm most proud of is that it asks for confirmation before moving anything, so you never accidentally mess something up. Full code on GitHub: https://lnkd.in/gCwyzJAv 🤘 #Python #Automation #7DaysOfPython #100DaysOfCode

🚀 Day 3/30 of My LeetCode Journey (Python + SQL) Showing up daily and building consistency, one problem at a time! 💻🔥 🔹 **Python Problems of the Day** 👉 *1. Move Zeroes* Given an integer array, move all 0’s to the end while maintaining the relative order of non-zero elements. Do it in-place without making a copy. 💡 *Key Concept:* Two-pointer technique for efficient in-place rearrangement. 👉 *2. Remove Element* Given an array and a value, remove all occurrences of that value in-place and return the number of remaining elements. 💡 *Key Concept:* In-place filtering using pointer overwrite approach. 🔹 **SQL Problem of the Day** 👉 *Find Duplicate Emails* Given a `Person` table with an email column, write a query to report all duplicate emails. 💡 *Key Concept:* GROUP BY with HAVING COUNT > 1. Small steps daily = Big progress over time 📈 Staying consistent and enjoying the process! #LeetCode #30DaysChallenge #Python #SQL #CodingJourney #Consistency #ProblemSolving #LearnInPublic

🧠 Python Concept: lambda functions Write quick functions in one line 😎 ❌ Traditional Way def square(x): return x * x print(square(5)) ❌ Problem 👉 Extra lines 👉 Not always needed ✅ Pythonic Way square = lambda x: x * x print(square(5)) 🧒 Simple Explanation Think of lambda like a mini function ⚡ ➡️ No name needed ➡️ One-line function ➡️ Quick & simple 💡 Why This Matters ✔ Less code ✔ Useful for short operations ✔ Works great with map(), filter() ✔ Cleaner for small tasks ⚡ Bonus Example nums = [1, 2, 3, 4] even = list(filter(lambda x: x % 2 == 0, nums)) print(even) 🐍 Small functions, big impact 🐍 Keep it simple & Pythonic #Python #PythonTips #CleanCode #LearnPython #Programming #DeveloperLife #100DaysOfCode

Manually scanning logs during incidents can be slow and inefficient — especially when trying to quickly identify errors. To understand how SREs handle this, I built a Log Analyzer in Python. 🔍 What it does: • Parses log files and classifies logs (INFO, WARNING, ERROR) • Counts occurrences of each log level • Identifies the most frequent error messages • Generates structured output for easier debugging 💡 Why this matters: In real-world systems, logs grow rapidly and manual analysis doesn't scale. This project helped me understand the basics of log analysis used in observability and incident response. 🚀 GitHub Repo: https://lnkd.in/dEZyK7qH 🔧 Next step: Planning to enhance this with CLI support, pattern detection, and real-time monitoring. Would love to hear your feedback! #SRE #Python #DevOps #Observability #LogAnalysis #LearningInPublic #GitHub

Writing clean, predictable code is just as important as the analysis itself. In Python, understanding memory references is the "hidden" skill that separates scripts that work from scripts that scale. I see many developers struggle with unexpected mutations when handling nested data structures. A simple new_list = old_list doesn't just copy the data; it copies the problem. I just published a deep dive into "Why Your Python List Copies Keep Betraying You." It’s a guide to mastering the copy module so you can stop debugging "impossible" errors and start building more resilient data pipelines. #PythonProgramming #DataAnalytics #TechWriting #CleanCode #MachineLearnin

🚀 Solved another problem on LeetCode: Concatenation of Array Today I worked on a simple yet important array problem that focuses on understanding how data structures behave in Python. Problem Summary: Given an integer array "nums", create a new array "ans" such that: 👉 "ans = nums + nums" (i.e., the array is repeated twice) 💡 My Approach: I used Python’s built-in list concatenation: "return nums + nums" ⚡ Why this approach is best? - No loops required - Clean and readable code - Uses optimized internal operations 📊 Complexity Analysis: • Time Complexity: O(n) • Space Complexity: O(n) 🔁 Comparison with other approaches: 🔸 Using loops → More lines of code, less readable 🔸 Using append() → Slightly slower due to repeated operations 🔸 Using list comprehension → Still less direct My approach is the most efficient and pythonic way to solve this problem. 🧠Key Learning: Sometimes the simplest solution is the most powerful. Knowing built-in operations can save time and improve code quality. Consistency in DSA is the key 🔥 #LeetCode #DSA #Python #CodingJourney #ProblemSolving #Code