Bikki Singh’s Post

👋 Welcome back! 📅 Python Learning – Day 63 Today we move to a faster and more efficient sorting method: Quick Sort. Quick sort works by selecting a pivot element and dividing the list into two parts — smaller values on one side and larger on the other. This divide-and-conquer approach makes it much faster than basic sorting methods. 📘 In this lesson, I’ve explained: ⚡ How quick sort works using partitioning 🔀 How recursion helps sort the divided parts ⚠️ Common beginner mistakes when choosing pivot and handling recursion Quick sort is widely used because of its speed and efficiency. Once you understand how it breaks problems into smaller parts, your approach to problem-solving improves. 🔗 Tutorial link is in the comments. 💬 If you're following this learning journey and want to stay connected with more tutorials and discussions, you can join our LinkedIn community here: 👉👉 CodePractice Group: (https://lnkd.in/g3xbN4GJ) ⏭️ Tomorrow: Python Counting Sort #QuickSort #DivideAndConquer #LearnPythonDSA #SortingAlgorithms #CodingPractice #PythonProgramming #TechStudents #DeveloperSkills #python #learnpython #codepractice #softwaredevelopment #computerscience #pythondevelopment #pythonlearning

👋 Welcome back! 📅 Python Learning – Day 65 Today we explore another non-comparison sorting technique: Radix Sort. Radix sort works by sorting numbers digit by digit, starting from the least significant digit to the most significant. Instead of comparing values directly, it groups and orders them step by step. 📘 In this lesson, I’ve explained: 🔢 How radix sort processes numbers digit by digit 🧩 How it uses counting logic internally ⚠️ Common beginner mistakes when handling digits and place values Radix sort can be very efficient for large datasets with numbers. Understanding it helps you see that sorting is not always about comparisons. 🔗 Tutorial link is in the comments. 💬 If you're following this learning journey and want to stay connected with more tutorials and discussions, you can join our LinkedIn community here: 👉👉CodePractice Group: (https://lnkd.in/g3xbN4GJ) ⏭️ Tomorrow: Python Merge Sort #RadixSort #SortingAlgorithms #LearnPythonDSA #AlgorithmConcepts #CodingPractice #PythonProgramming #TechStudents #DeveloperJourney #python #learnpython #codepractice #softwaredevelopment #computerscience #pythondevelopment #pythonlearning

👋 Welcome back! 📅 Python Learning – Day 64 Today we explore a different kind of sorting technique: Counting Sort. Unlike comparison-based algorithms, counting sort works by counting how many times each value appears and then placing them in order. This makes it very fast for specific types of data. 📘 In this lesson, I’ve explained: 🔢 How counting sort works step by step ⚡ Why it can be faster than other sorting algorithms ⚠️ Common beginner mistakes when handling ranges and counts Counting sort is powerful, but it works best when the range of values is limited. Understanding when to use it is just as important as knowing how it works. 🔗 Tutorial link is in the comments. 💬 If you're following this learning journey and want to stay connected with more tutorials and discussions, you can join our LinkedIn community here: 👉👉CodePractice Group: (https://lnkd.in/g3xbN4GJ) ⏭️ Tomorrow: Python Radix Sort #CountingSort #NonComparisonSort #LearnPythonDSA #AlgorithmLearning #CodingPractice #PythonForStudents #TechLearning #DeveloperGrowth #python #learnpython #codepractice #softwaredevelopment #computerscience #pythondevelopment #pythonlearning

🚀 Learning Something New Every Day! Today I learned an important Python concept — *args and **kwargs. 🔹 *args allows a function to take multiple positional arguments (stored as a tuple) 🔹 **kwargs allows a function to take multiple keyword arguments (stored as a dictionary) 💡 This makes functions more flexible and reusable in real-world scenarios. Here’s a simple example: def demo(*args, **kwargs): print(args) print(kwargs) Step by step, I’m strengthening my Python fundamentals and building a strong base for data analytics. #Python #LearningJourney #Coding #DataAnalytics

🚀 Python Practice – Function Examples Taking my Python learning a step further by practicing real-world function-based problems 🐍 In this session, I worked on: ✔️ Temperature Conversion (Celsius ↔ Fahrenheit) ✔️ Password Strength Checker ✔️ Shopping Cart Total Cost Calculator ✔️ Palindrome Checker ✔️ Factorial using Recursion These examples helped me understand how functions can be used to solve practical problems and write reusable, structured code. A big thanks to Krish Naik Sir for his amazing teaching and clear explanations 🙌 Documented all my practice in a Jupyter Notebook and shared it as a PDF to track my progress. Learning by building real logic step by step 📊 #Python #Functions #Practice #LearningJourney #DataAnalytics #Coding

Most people learn Python by staring at output. I tried something different. 👇 This is what actually happens when Python executes a basic function — step by step, visually. No theory. No slides. Just execution in real time. Still early in my journey — but this is how I'm learning. If you're learning Python too, drop a 👋 below. 🔧 Tool: pythontutor / staying.fun 🐍 Concept: Functions — how they're called, executed & returned #Python #LearningInPublic #DataAnalytics #BBA #100DaysOfCode #PythonBeginners

📘 Learning Update – Keeping the Momentum Going Today was a good learning day. I spent some time practicing a few important Python concepts that really helped me understand how things work behind the scenes. Here’s what I covered: 🔹 Inheritance 🔹 Multiple Inheritance 🔹 Raising Exceptions 🔹 Iterators 🔹 Generators Some of these concepts were a bit tricky at first, especially iterators and generators, but practicing them made things much clearer. Slowly starting to see how powerful Python can be when writing clean and efficient code. Just trying to stay consistent and improve a little every day 💪 🔗 GitHub Progress: https://lnkd.in/dH9c5ExV #Python #LearningJourney #Consistency #AIJourney #MachineLearning #100DaysOfCode

#Day12 of learning Python 🐍 Today I learned about file handling in Python and how to work with files using different modes like read (r), write (w), and append (a). Also explored using the with statement (context manager) to handle files more safely and efficiently. Practiced tasks like counting words in a file, checking whether a specific word exists, and building a small function to verify if a password exists inside a file. Also revised how try–except–finally helps handle file-related errors like missing files. Working with files made me realize how Python programs can store and retrieve real data, which is an important step toward building practical applications. Day 12 complete — 88 days to go! 🚀 #Day12 #PythonLearning #FileHandling #PythonFiles #100DaysOfLearning #CodingJourney #SkillShikshya

Day 18: Today i explored how Python handles memory efficiently using Generators 🔹 What I learned and practiced: ✔️ Generators Functions that return an iterator and produce values one at a time. Great for saving memory when working with large datasets. ✔️ yield Keyword Used to produce a value and pause the function execution. It resumes from the same point when called again, unlike a normal return. ✔️ next() Function Used to retrieve the next value from the generator. Automatically stops when no more values are left to produce. ✔️ Created a square_gen() function to generate squares of numbers one by one. Key takeaway: Generators and yield are powerful tools for writing smarter, more efficient code by only processing what we need, when we need it.and also push in github #Python #codegnan #LearningPython

🚀 Day 10/30 – Python Challenge Learning about tuples in Python today! 🐍 🔹 Key Concepts Covered: * Creating tuples * Accessing elements using index * Iterating through tuple elements using loops * Understanding immutability (tuples cannot be changed) 💻 Mini Task: Created a tuple of numbers, accessed the first element, and used a loop to display all the values. 🎯 Learning Outcome: Understood how tuples are used to store fixed collections of data and how they differ from lists. They are especially useful when data should not be modified. Building a strong foundation in data structures step by step 💪 #Python #CodingChallenge #LearningJourney #DataStructures #StudentDeveloper #Day10