HIMANSHU MAHESHWARI’s Post

⏳ What used to take hours now takes one click. 🐍 I recently replaced a manual process of merging 15+ data files with a simple Python workflow. What was once time-intensive and error-prone now runs in minutes—consistently and reliably. 💰 The real impact isn’t just efficiency. It’s shifting time and attention away from data preparation and toward analysis, insights, and decision-making. This is where small, practical uses of technology create meaningful leverage.

🧠 Python Concept: set() Operations (Union, Intersection, Difference) Work with collections like a pro 😎 ❌ Traditional Way a = [1, 2, 3, 4] b = [3, 4, 5, 6] common = [] for i in a: if i in b: common.append(i) print(common) ✅ Pythonic Way a = {1, 2, 3, 4} b = {3, 4, 5, 6} print(a & b) # Intersection print(a | b) # Union print(a - b) # Difference 🧒 Simple Explanation Think of sets like circles 🔵 ➡️ & → Common items ➡️ | → All items combined ➡️ - → Items only in one 💡 Why This Matters ✔ Super fast operations ✔ Cleaner than loops ✔ Useful in real-world data comparison ✔ Great for removing duplicates ⚡ Bonus Example a = {1, 2, 3} b = {3, 4, 5} print(a ^ b) # Symmetric difference 👉 Output: {1, 2, 4, 5} 🐍 Stop writing long loops 🐍 Use set power instead #Python #PythonTips #CleanCode #LearnPython #Programming #Set #DeveloperLife #100DaysOfCode

New blog post! Live Life on the Edge: A Layered Strategy for Testing Data Models This post is about a three-layer testing pattern for complex software systems I've landed on in python: structural coverage with Polyfactory, value-level probing with Hypothesis, cross-field invariants with icontract. Includes a practical example, an honest tradeoffs section, and a note on what schema-first design and consumer-driven contract testing solve instead. Link in comments. #Python #SoftwareTesting #SoftwareArchitecture #Pydantic #PropertyBasedTesting

Day 43 of LeetCoding everyday until I get a J-O-B: Check If a String Contains All Binary Codes of Size K. If K = 2, the possible codes are 00, 01, 10, 11. We need to prove every combination exists inside our string. The Noob Trap: Generating all $2^k$ combinations upfront to cross them off. If K = 20, you need over a million strings. You just blew up your RAM. The Senior Fix: Math & Sliding Windows 1. The Math Filter: For a string to even physically fit all combinations, its length must be at least 2^k + K - 1. If it's shorter? Instant return False. Just like my job applications. 2. The Set: We slide a window of size K across the string, dumping every slice into a Python Set (which automatically vaporizes duplicates). 3. The Check: At the end, we check if len(seen) == (1 << k). (We use a Bitwise Left Shift because we are elite). See more: https://lnkd.in/gUki2imQ #LeetCode #Python #DataStructures #Engineering #TechHumor

🚀 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

Day 4/30 🔹 Problem: Find the second largest number in a list 🔹 What I focused on today: Thinking beyond the obvious solution and handling edge cases 🔹 My Thinking Process: Take a list of numbers from the user Remove duplicate values Sort the list Pick the second last element 👉 Simple idea, but requires careful steps 🔹 Inputs I used: List of numbers 🔹 Code: numbers = list(map(int, input("Enter numbers separated by space: ").split())) # Remove duplicates numbers = list(set(numbers)) # Sort the list numbers.sort() # Find second largest if len(numbers) < 2: print("Not enough elements") else: print("Second Largest Number:", numbers[-2]) 🔹 Example: Input: 10 20 30 40 Output → 30 🔹 Key Takeaway: Breaking a problem into steps like cleaning data, sorting, and selecting values makes it easier to solve #Day4 #Python #30DaysOfCode #LearningInPublic #DataAnalytics #ProblemSolving

Didn't know you could extract tables from a Word doc using Python until today. python-docx lets you loop through tables, pull cell data, and load it straight into a DataFrame. Spent some time cleaning it up — splitting on ':', transposing, fixing headers — but it worked. Also practiced groupby() and lambda functions inline. Small things but they make the code so much cleaner. Notebook here 👉 https://lnkd.in/dfTwrvqT #Python #Pandas #DataAnalysis #LearningInPublic

Day 5/10 🚀 This is where you stop copying code and start writing it. Functions — the core of every real Python project. Without them? Repetition, messy code, hard to scale. With them? Write once, reuse everywhere. 📋 What I covered today: 01 → Functions & definitions 02 → Positional, keyword & default arguments 03 → *args & **kwargs 04 → Scope — local & global 05 → map, filter, reduce 06 → Lambda functions 07 → Closures & recursion 08 → Decorators & generators 09 → Mini Project — Salary Pipeline Build a small pipeline using map, filter & reduce to find top earners above average salary — a real-world data engineering pattern. Day 1 ✅ Day 2 ✅ Day 3 ✅ Day 4 ✅ Day 5 ✅ 5 more to go. Drop a 🐍 if *args and **kwargs ever confused you 😄 #Python #Functions #DataEngineering #LearningInPublic #CleanCode #10DaysOfPython

📁 Day 24: Mastering File Systems & Mail Merging I just automated a tedious administrative task by building a Mail Merge program that handles file I/O and string manipulation with ease! 💡 Fun Fact: The concept of "Mail Merge" dates back to the 1970s with early word processors like WordStar, saving humans millions of hours by separating the content from the contact list. 🚀 Key Features: • File Automation: Reads template files and recipient lists dynamically. • String Cleaning: Uses .strip() and .replace() to personalize content. • Scalable Output: Automatically generates and saves unique letters for every name. Explore the automation logic here: 🔗https://lnkd.in/gwdWmATu #Python #Automation #FileHandling #PythonProjects #Coding #100DaysOfCode

Day 26 of 50 days of LeetCode The Problem: Best Time to Buy and Sell Stock. The Mistake: My initial logic used global max() and min() functions. This failed because it didn't account for time—you can't sell a stock before you've actually bought it! The Correction: I refactored the code to a single-pass approach. By tracking the min_price seen so far and updating the max_profit dynamically, I ensured the "buy" always happens before the "sell" while keeping the efficiency at O(n). #LeetCode #Python #ProblemSolving #CodingJourney #CSDesign