Sachin Nomula’s Post

A manager once told me her team spent 3 hours every Monday compiling a report that nobody read until Wednesday. 3 hours. Every single week. Just moving numbers from one spreadsheet to another. That's 150+ hours a year on a task that a Python script can do in 30 seconds. The problem wasn't the people. The problem was the process. If your team has a Monday morning task like this — let's talk. 📩 DM me. #ProcessAutomation #Python #OperixSolutions

🐍 Python interview prep made easier I came across a list of 100 Python interview questions (with answers) covering everything—from basics to Pandas, NumPy, and ML concepts. 💡 The key? Practice real problems, not just theory. What’s your go-to Python question for interviews? 👇 #Python #DataScience #InterviewPrep #Coding

You spent 45 minutes manually deleting duplicates in Excel last week. I wrote a Python script that does it in 3 seconds. It removes duplicates → clears empty rows → auto-saves the clean file. No manual work. Ever again. #Python #DataAnalytics #Excel #DataCleaning #DataScience

🐍 Day 116 — Cross Validation Day 116 of #python365ai 🔁 Cross-validation splits data multiple times. Example: from sklearn.model_selection import cross_val_score 📌 Why this matters: Provides more reliable performance estimates. 📘 Practice task: Run cross-validation on a model. #python365ai #CrossValidation #MachineLearning #Python

🐍 Day 103 — Decision Trees (Implementation) Day 103 of #python365ai 🧑💻 Example: from sklearn.tree import DecisionTreeClassifier model = DecisionTreeClassifier() model.fit(X, y) 📌 Why this matters: Decision Trees handle both classification and regression tasks. 📘 Practice task: Train a simple decision tree model. #python365ai #DecisionTree #MachineLearning #Python

🚀 Master Python Interviews – One Problem at a Time! Just uploaded my Python Interview Practical Q&A notebook on GitHub! 🎯 This notebook covers 10 essential Python practice problems including: - ✅ Basic operations (add, subtract, multiply, divide) - ✅ String manipulation - ✅ List & dictionary problems - ✅ Functions and more Perfect for beginners prepping for Python interviews! 💻 🔗 Check it out: https://lnkd.in/gCdBn8V3 #Python #Coding #InterviewPrep #Programming #LearningPython #TechJobs #LinkedIn #Developer

Copying vs Reference 🐍 I thought this would create a copy: b = a It didn’t. a = [1, 2, 3] b = a b.append(4) print(a) 👉 [1, 2, 3, 4] Both variables point to the same list. No copy was made. In Python, variables are just labels, not containers. When you use =, you aren’t duplicating data, you’re just giving the same memory address a second name. To actually copy: b = a.copy() Looks the same. Behaves completely differently. 💡 And also: .copy() only goes one layer deep and that's why we need deep copy. ➡️ Assignment ≠ Copy Day 17/30 #Python #30DaysOfCode #SoftwareEngineering

Python Interview Questions ✅ Core Python concepts: memoization, generators vs iterators, *args/**kwargs, decorators ✅ Data handling: pandas groupby, apply, transform, query, MultiIndex, pipe ✅ Performance: NumPy broadcasting, vectorization vs loops ✅ Visualization: Matplotlib dual axes, Seaborn vs Matplotlib ✅ Error handling: custom exceptions, logging ✅ Python fundamentals: is vs ==, dictionary key checks, list comprehension, duplicate removal ✅ Scenario questions: login duplicates, log parsing, data cleaning #Python #InterviewPrep #Pandas #NumPy #Decorators #Generators #DataEngineering

🚫 𝗦𝘁𝗼𝗽 𝗺𝗮𝗻𝘂𝗮𝗹 𝘀𝗰𝗿𝗮𝗽𝗶𝗻𝗴. 𝗨𝘀𝗲 𝘁𝗵𝗲 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗔𝗣𝗜. I just published a simple guide on Medium about fetching and visualizing YouTube data using Python. 𝗪𝗵𝗮𝘁'𝘀 𝗶𝗻𝘀𝗶𝗱𝗲: - Getting your API key. - Fetching channel stats. - Visualizing data with Python. - Exporting to Excel. Read the full guide here: https://lnkd.in/gkRijvnS #Python #YouTubeAPI #DataScience #Automation LinkedIn YouTube

NaNs ruining your analysis? Here’s the quick Pandas trio: use isna() to detect missing values, dropna() to remove incomplete rows, and fillna() to replace gaps with defaults. This tiny example shows all three so you can clean data in seconds.#pandas #python #datascience #dataengineering