Rishabh Gupta’s Post

🎉 Just crushed my Data Structures and Algorithms course in Python! 🔥 Started with the fundamentals, then tackled linear powerhouses like Stacks, Queues, and Lists—mastering inserts, updates, deletes, and beyond. Now unlocking the magic of non-linear structures for smarter, faster solutions. This has supercharged my problem-solving for data analytics! What's your go-to data structure for real-world projects? Stack or Queue fan? Drop your tips below—I'd love to hear! 👇 #DataStructures #Algorithms #Python #Coding #DataAnalytics #TechTips

Pandas Advanced – Part 8 is Live 🚀 In this video, we go beyond basics and explore: ✔ Rolling windows (rolling()) ✔ Expanding calculations (expanding()) ✔ Exponential weighting (ewm()) ✔ Targeted filtering with isin() This is real analytical thinking — not syntax memorization. These techniques help you understand trends, patterns, and behaviors in data. 📺 Watch here: https://lnkd.in/gB9tiaM2 📂 Code + dataset: https://lnkd.in/gdzNcMaT #PandasAdvanced #Python #DataAnalysis #Analytics #DataScience #PyAIHub

I just shipped a small but complete Python CLI project: Rock, Paper, Scissors. Beyond the game, the goal was to practice fundamentals that map directly to analytics and data work: Input validation (data quality mindset) Deterministic decision logic (rule-based classification) Modular functions + clean entry point (reusable, maintainable code) Reproducible execution from the command line Next iteration: I plan to log outcomes to CSV and run a quick analysis on win rates across multiple simulations. GitHub: https://lnkd.in/ea3fxBbi #Python #DataScience #Analytics #LearningInPublic #GitHub #ProblemSolving

While working with datasets in Pandas, one small thing that made a big difference for me was understanding vectorization. In the beginning, I used apply() for many transformations. It worked — but as datasets got bigger, I noticed things slowing down. Then I started using column-wise operations instead of row-wise logic, and my code became both simpler and faster. Now, apply() is something I use only when there’s no easier alternative. Still learning something new with every dataset I work on. What’s one Pandas habit or trick that improved your workflow? #Pandas #Python #DataEngineering #DataAnalysis

🚀 Day 9/30 | LeetCode Problem: Binary Search (704) Problem: Given a sorted array of integers nums and a target, return the index if the target exists. If not, return -1. 🧠 Approach: Binary Search Since the array is already sorted, we can apply Binary Search: Use two pointers: l (left) and r (right) Find the middle element Compare target with nums[mid] Reduce the search space by half each time This makes the solution very efficient. ⏱️ Complexity Time: O(log n) Space: O(1) 🧾 Python Code class Solution(object): def search(self, nums, target): l = 0 r = len(nums) - 1 while l <= r: mid = (l + r) // 2 if target == nums[mid]: return mid elif target < nums[mid]: r = mid - 1 else: l = mid + 1 return -1 ✅ Result Accepted ✅ Runtime: 0 ms (Beats 100%) Memory efficient 🎯 Key Learning Binary Search is a foundational algorithm used in: Search problems Optimization problems Interview questions Mastering it is essential for every developer. 🔖 Hashtags #LeetCode #30DaysOfLeetCode #Day9 #BinarySearch #Python #DSA #ProblemSolving #CodingJourney #SoftwareEngineering

Day 32 – Linked Lists: Why They Exist and When They Matter 🔗 Today I spent time understanding linked lists — why they exist in the first place. At a basic level, a linked list is a collection of elements (called nodes) where: Each node holds some data Each node points to the next one (and sometimes the previous one) Unlike regular Python lists: Linked lists don’t rely on indexes Elements don’t have to sit next to each other in memory So why does this matter? Linked lists are useful when: You need frequent insertions and deletions The order of items changes often You don’t care about fast random access by index Real-world use cases include: Undo/redo functionality in applications Browser navigation (back and forward) Music or image playlists Implementing caches (like LRU cache) Task scheduling and queues The key trade-off: Linked lists are slower to search But very efficient when modifying data in the middle Today reminded me that data structures aren’t just academic ideas — they’re design choices, each with strengths and weaknesses depending on the problem you’re solving. #Day32 #DataStructures #LinkedLists #Python #ComputerScience #LearningInPublic

🐍 Day 81 – From NumPy Mistakes to Pandas Confusion (They’re Connected) Many of the Pandas bugs I struggled with early on weren’t really Pandas problems. They were NumPy misunderstandings showing up later. Today, I connected a few dots that explained a lot of past confusion. What I noticed: ✅ Unexpected NaNs often came from shape misalignment ✅ Slow DataFrame operations traced back to inefficient NumPy arrays ✅ Confusing GroupBy results were usually axis or dtype issues ✅ “Pandas bugs” disappeared once the underlying arrays were fixed Pandas doesn’t replace NumPy — it builds on it. Mental shift that helped: Fix the arrays first. Then wrap them with labels. When NumPy is solid: • DataFrames behave predictably • Performance improves without touching Pandas syntax • Debugging becomes simpler • Your results are easier to trust Takeaway: Clean arrays lead to clean DataFrames. Python journey continues… onward and upward! #MyPythonJourney #NumPy #Python #DataAnalytics #LearningInPublic #AnalyticsJourney

Project:  Built and deployed an end-to-end Sentiment Analysis web application using modern ML and backend tools. Model: DistilBERT Dataset Source: Kaggle : 🔗 https://lnkd.in/g-4v3TUv TechStack: Python Hugging Face Transformers DistilBERT FastAPI uvicorn HTML, CSS, JavaScript Git & GitHub 🔗 GitHub Repository: https://lnkd.in/g9XymNHv #machinelearning #python #huggingface #sentimentanalysis #fastapi #uvicorn

One concept I understand better now is List Comprehensions. Sometimes I felt like my python scripts were getting cluttered with repetitive for loops and .append() calls. I used to think list comprehensions were just a shorthand trick, but they are actually a more efficient way to handle data transformation. It’s the difference between writing a paragraph and writing a perfectly concise sentence. The syntax used to look intimidating, but here is how it finally clicked for me: new_list = [expression for item in iterable if condition] The Expression: What do you want to keep? (e.g., x or x**2) The Loop: Where is it coming from? (e.g., for x in my_list) The Filter: Do you want all of them or just some? (e.g., if x > 10) Why I’m using it from now on: Readability: Once you know the syntax, you can read the logic in a single line. Speed: It’s technically faster than a standard loop. Less Room for Error: No need to initialize empty lists or manage counters. Small shifts in syntax lead to big jumps in code quality. NativesPlug @locus.ioe #LOCUS2026 #NativesPlug #LearningChallenge #15DayChallenge #NepalTech #LearnAndWin #PythonTips

Today I learned that small functions can answer big questions fast. A simple groupby() + mean() helped me quickly compare performance across categories instead of scanning rows manually. Lesson: you don’t need complex models to create value — just the right question and a clean dataset. Still building fundamentals, one function at a time. #Python #SQL #DataAnalytics #LearningInPublic #BeginnerDataAnalyst