Shivam Saxena’s Post

💾 The Day I Taught My Code to Remember... You know that moment when you finally build something cool… then realize it forgets everything once you close it? 😅 That was me, until today. Every small program I’ve built so far (from trackers to dashboards) vanished the moment I hit “stop.” It worked fine… but it had no memory. So today, I decided to fix that. I learned how to make my code remember, using File Handling in Python. Now, my program can save data to a file and retrieve it later, even after it closes. Simple? Yes. But this small lesson taught me something much bigger, In the real world, data doesn’t live in code… it lives in systems. This is the beginning of data persistence, the foundation of databases, logs, data lakes, and pipelines. The stuff that makes data real. And for me, it’s another reminder that learning Data Engineering isn’t just about code, it’s about building systems that remember, scale, and last. Next, I’ll be learning how to move data between files and databases, the first step toward data pipelines. 🚀 What’s one small concept that completely changed how you see coding or data? 💭 #DataEngineering #Python #LearningJourney #TechkyAcademy #DataPersistence #CareerGrowth #CodingJourney #ContinuousLearning

Day 24 of my #100DaysOfCode challenge 🚀 Today I worked on a Python program to find duplicate elements in a list. The goal was to detect repeated values efficiently without using nested loops. What the program does: • Takes a list of numbers • Uses a set to track seen elements • Identifies elements that appear more than once • Stores duplicate values in a separate list • Prints the duplicate elements How the logic works: 1)An empty list duplicates is created to store repeated elements 2)A set seen is initialized to track elements already encountered 3)The program iterates through each element in the list 4)If the element is already in seen, it is added to duplicates 5)Otherwise, it is added to the seen set 6)Finally, duplicates are printed (converted to set to avoid repetition in output) Example: Input: Input list: [1, 2, 2, 3, 4, 4, 5, 6, 6, 7] Output: Duplicate elements: [2, 4, 6] Why this approach is efficient: – Uses a set for O(1) average lookup time – Avoids nested loops – Time Complexity: O(n) Key learnings from Day 24: – Using sets for fast membership checking – Detecting duplicates efficiently – Writing optimized list-processing logic – Strengthening data structure fundamentals #100DaysOfCode #Day24 #Python #PythonProgramming #DataStructures #SetOperations #Algorithms #ProblemSolving #CodingPractice #LearnByDoing #ComputerScience #InterviewPrep #BTech #CSE #AIandML #VITBhopal #TechJourney

Python in 2026: More than just syntax. I’ve reorganized the standard Python roadmap into a Radial Ecosystem to better visualize how core concepts branch into specialized career paths. Whether you are diving into Data Science, mastering DSA, or building Web Frameworks, the center remains the same: solid foundations. What's inside: 🔹 Core Logic: From Basics to Advanced (Decorators, Generators). 🔹 Engineering: DSA & Object-Oriented patterns. 🔹 Specializations: Data Science, Automation, and Web. 🔹 Quality: Integrated Testing & QA. Evolution is constant—keep your roadmap updated. 📈 #Python #DataScience #CodingRoadmap #SoftwareEngineering #PythonProgramming #DataAnalytics #CareerGrowth #Programming2026

🚀 Day 9 | Python Functions – Writing Reusable and Structured Code Every programmer eventually realizes that writing the same logic again and again is inefficient. That’s where functions become powerful—they help us organize, reuse, and structure code effectively. In today’s notebook / carousel, I explored: ✔ Purpose and definition of functions ✔ Parts of a function and execution phases (Input → Process → Output) ✔ Different approaches to defining functions ✔ Arguments vs Parameters (formal and actual) ✔ Types of arguments: Positional arguments Default arguments Keyword arguments Variable-length arguments (*args) Keyword variable-length arguments (**kwargs) What stood out to me while learning this is how functions are not just about syntax—they’re about thinking in modular, reusable blocks, which is a core mindset in software engineering, data science, and machine learning workflows. Day_09_Python_Functions 📌 Part of my learning-in-public journey, building Python step by step with strong fundamentals. 🙏 Grateful to my mentor, Nallagoni Omkar Sir, for guiding me through these concepts clearly. 👉 Next up: Global and Local variables, lambda functions, and special functions (map, filter, reduce) #Python #DataScience #CorePython #Functions #LearningInPublic #ProgrammingFundamentals #StudentOfDataScience #MachineLearning #NeverStopLearning

🚀 Day 5/100 — Working with Persistent Storage 🧠 “Persistence transforms execution into continuity.” Systems become meaningful when they retain and retrieve information reliably. Today, I learned how Python interacts with files to store and retrieve persistent data. ⚙️ 🔧 Today’s focus areas: 📂 File Reading — Accessing stored data 📝 File Writing — Persisting new information 🔄 File Modes — Managing read and write operations 🎯 Data Persistence — Ensuring continuity across executions 🎯 The objective was to enable programs to maintain state beyond runtime. ✅ Day 5 complete: Persistent data handling established. ▶️ Day 6: Strengthening reliability through exception handling. Step by step. The system evolves. 🏗️ #Python #BackendDevelopment #100DaysOfCode #SoftwareEngineering

🚀 Just Published: Pandas Learning Notes Excited to share my latest GitHub repository where I’ve documented my phase-wise learning of Pandas through handwritten notes + practical Python implementation. 📚 What this repo includes: ✅ Handwritten Pandas concept notes ✅ Structured .py implementation examples ✅ File handling (CSV, Excel, JSON) ✅ Data cleaning & missing value handling ✅ Data filtering & transformation ✅ Merging & joining datasets ✅ Real-world data analysis workflows This repository is designed to help beginners and intermediate learners build strong fundamentals and practical understanding of Pandas. 🔗 GitHub Repository: https://lnkd.in/gPJbnDEa I’ll keep updating this with more advanced concepts, real datasets, and data preprocessing workflows. Would love feedback and suggestions from the community! ⭐ #Python #Pandas #DataScience #MachineLearning #LearningInPublic #GitHub #Developers #AI #DataAnalysis #PythonDeveloper #DataEngineer #Analytics #CodingJourney #OpenSource #TechCommunity #SoftwareDevelopment #Programming #DataAnalytics #100DaysOfCode #AIEngineering #BackendDevelopment #PythonProjects

Day 6/30: Beyond the Basics Engineering Efficiency in Python 🏗️ I’m officially 5 days into my #30DaysOfDataChallenge, and the transition from "writing code" to "engineering solutions" has begun. Today was a deep dive into Python’s most powerful modular tools: Functions, Methods, and Packages. Key Technical Milestones: ↔️ Modular Programming with Packages: Successfully implemented the math package using selective imports (from math import pi) to optimize memory and simplify complex calculations like circumference and area. ↔️ Object-Oriented Logic (Methods): Moved beyond general functions to specific object methods. I leveraged .count() for data frequency analysis and explored how methods allow for cleaner, more readable code. ↔️ Data Manipulation & Type Casting: Mastered list concatenation and type conversion (Casting Booleans to Integers), ensuring data integrity across different variable types. ↔️ Advanced Data Organization: Used sorted() with optional arguments (reverse=True) to handle data ranking, a fundamental skill for any data-driven role. As I progress, I’m focusing not just on how to code, but on how to write professional code that is modular, readable, and efficient. Building a strong foundation one day at a time. On to Day 7! 🚀 #Python #DatafestAfrica #DCDonates #30DaysOfDataChallenge #DCA #LearningInPublic

🚀 Strengthening My Core DSA Skills – Hands-on Practice in Python Today, I focused on building strong fundamentals by implementing some important Data Structures & Algorithms concepts from scratch (without using built-in shortcuts). 🔹 Quick Sort (In-Place Implementation) Implemented Quick Sort using the partition logic and recursion. Worked deeply on understanding: Pivot selection Partitioning mechanism Role of low, high, and pivot index Time Complexity: O(n log n) average, O(n²) worst case This helped me clearly understand how divide-and-conquer works internally. 🔹 Palindrome Check (Logic-Based Approach) Built a string palindrome checker without using slicing shortcuts. Focused on: String traversal Reversing logic manually Comparing original and reversed string Improved clarity on string manipulation fundamentals. 🔹 Array Rotation (Right Rotation by K Steps) Solved array rotation using the reverse algorithm approach. Key takeaways: Handling edge cases (k > n) Using modulo for optimization In-place reversal for O(1) space complexity 💡 Key Learning: Understanding the logic behind algorithms is more important than just writing working code. Debugging partition logic in Quick Sort gave me deeper insight into how memory and indexes actually work. Practicing these core problems is strengthening my problem-solving foundation step by step. #DataStructures #Algorithms #Python #CodingPractice #DSA #ProblemSolving #LearningJourney 🚀

🚀 New milestone in my Python journey (42 / Growing Code series) After sharing my C projects, I’m starting a new chapter: a structured path through Python modules—each one built around a different “world” and a different set of skills. Here’s the roadmap I’m working through: 🌱 Module 00 — Growing Code: Python fundamentals (syntax, variables, functions, control flow) 🌿 Module 01 — CodeCultivation: OOP foundations (classes, systems thinking) 🛡️ Module 02 — Garden Guardian: exceptions & robust error handling 🎮 Module 03 — Data Quest: collections + data structures (lists/tuples/sets/dicts, comprehensions, generators) 📚 Module 04 — Data Archivist: files, streams, and resilient I/O patterns 🕸️ Module 05 — Code Nexus: inheritance, overriding, and polymorphism 📦 Module 06 — The Codex: packages, imports, relative/absolute paths, circular dependencies 🃏 Module 07 — DataDeck: abstract classes, interfaces, and modular architecture 💊 Module 08 — The Matrix: virtual envs, dependency management, environment config 🪐 Module 09 — Cosmic Data: Pydantic models, validation, nested structures 🧙 Module 10 — FuncMage: functional programming (lambda, HOFs, decorators, scope) What I like about this path: it’s not just “learning Python”—it’s building cleaner design habits, more reliable code, and stronger engineering thinking module by module. #Python #42Network #42Firenze #SoftwareEngineering #OOP #DataEngineering #FunctionalProgramming #Pydantic #LearningInPublic #CodingJourney

🚀 Day 11/30 | LeetCode Problem: Merge Two Sorted Lists (21) Problem: You are given the heads of two sorted linked lists list1 and list2. Merge the two lists into one sorted linked list and return its head. 💡 Approach (Recursive) Since both lists are already sorted: If one list is empty → return the other. Compare the current values of both lists. Attach the smaller node to the result. Recursively merge the remaining nodes. This keeps the final list sorted automatically. ⏱ Complexity Time Complexity: O(n + m) Space Complexity: O(n + m) (due to recursion stack) 🧠 Python Code class Solution: def mergeTwoLists(self, list1, list2): if not list1: return list2 if not list2: return list1 if list1.val < list2.val: list1.next = self.mergeTwoLists(list1.next, list2) return list1 else: list2.next = self.mergeTwoLists(list1, list2.next) return list2 📌 Example Input: list1 = [1,2,4] list2 = [1,3,4] Output: [1,1,2,3,4,4] 🎯 Key Takeaway When working with sorted data structures, compare and attach is a powerful pattern. Also, recursion makes linked list problems elegant and clean. ✅ Accepted 🔖 Hashtags #LeetCode #30DaysOfLeetCode #Day11 #Python #LinkedList #Recursion #DataStructures #Algorithms #ProblemSolving #CodingJourney