Bikki Singh’s Post

👋 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

Here’s your Day 11 LinkedIn post continuing your Python journey 👇 --- 🚀 ✨ 𝐃𝐀𝐘 11: 𝐖𝐎𝐑𝐊𝐈𝐍𝐆 𝐖𝐈𝐓𝐇 𝐓𝐔𝐏𝐋𝐄𝐒 ✨ Today, I explored another useful data structure in Python — 💻 𝐓𝐮𝐩𝐥𝐞𝐬. 🔹 📘 𝐖𝐡𝐚𝐭 𝐀𝐫𝐞 𝐓𝐮𝐩𝐥𝐞𝐬? Tuples are similar to lists but are 𝐢𝐦𝐦𝐮𝐭𝐚𝐛𝐥𝐞 (𝐜𝐚𝐧𝐧𝐨𝐭 𝐛𝐞 𝐜𝐡𝐚𝐧𝐠𝐞𝐝) once created. 🔹 ⚙️ 𝐖𝐡𝐚𝐭 𝐈 𝐋𝐞𝐚𝐫𝐧𝐞𝐝 ✔️ Creating and accessing 𝐭𝐮𝐩𝐥𝐞𝐬 ✔️ 𝐢𝐧𝐝𝐞𝐱𝐢𝐧𝐠 & 𝐬𝐥𝐢𝐜𝐢𝐧𝐠 ✔️ Difference between 𝐥𝐢𝐬𝐭𝐬 vs 𝐭𝐮𝐩𝐥𝐞𝐬 🔹 🧠 𝐖𝐡𝐲 𝐈𝐭 𝐌𝐚𝐭𝐭𝐞𝐫𝐬 Tuples are useful when we need 𝐟𝐢𝐱𝐞𝐝 𝐝𝐚𝐭𝐚 and ensure data remains unchanged. 💡 𝐈𝐦𝐦𝐮𝐭𝐚𝐛𝐥𝐞 𝐝𝐚𝐭𝐚 = 𝐌𝐨𝐫𝐞 𝐫𝐞𝐥𝐢𝐚𝐛𝐥𝐞 𝐩𝐫𝐨𝐠𝐫𝐚𝐦𝐬! 💪 𝐆𝐚𝐢𝐧𝐢𝐧𝐠 𝐦𝐨𝐫𝐞 𝐜𝐥𝐚𝐫𝐢𝐭𝐲 on data structures! 🚀 𝐊𝐞𝐞𝐩 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠, 𝐤𝐞𝐞𝐩 𝐠𝐫𝐨𝐰𝐢𝐧𝐠! #Python #Day11 #CodingJourney #Tuples #DataStructures #LearningPython #Consistency🚀

👋 Welcome back! 📅 Python Learning – Day 60 Today we look at one of the simplest sorting techniques: Bubble Sort. Bubble sort works by repeatedly comparing adjacent elements and swapping them if they are in the wrong order. With each pass, the largest element “bubbles up” to its correct position. 📘 In this lesson, I’ve explained: 🔁 How bubble sort works step by step 🔄 How swapping helps arrange elements ⚠️ Common beginner mistakes like unnecessary iterations Bubble sort is not the fastest algorithm, but it is very useful for understanding how sorting actually works. Once this concept is clear, learning advanced sorting becomes easier. 🔗 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 Selection Sort #BubbleSort #SortingAlgorithms #LearnPythonDSA #CodingPractice #AlgorithmBasics #PythonForStudents #TechLearning #DeveloperJourney #python #learnpython #codepractice #softwaredevelopment #computerscience #pythondevelopment #pythonlearning

Day 2 of Learning Python Most people don’t fail in Python… They fail because they ignore the basics. Here are 4 things you MUST know 👇 1. Data Types Everything in Python has a type: int, float, str, bool 🎥 👉 https://lnkd.in/gDNAyz6E 2. Data Structures Store multiple values efficiently: ✔ List → ordered, changeable ✔ Tuple → ordered, fixed ✔ Set → unique values ✔ Dictionary → key-value pairs 🎥 👉 https://lnkd.in/gqWWihBJ 3. Indexing & Slicing Access data like a pro: list[0] → first element list[-1] → last element list[0:3] → slice 🎥 👉 https://lnkd.in/g7QVQFzK 4. Operators Perform actions: ➕ Addition ➖ Subtraction ✖ Multiplication ➗ Division 🤔 Logical , Comparison 🎥 👉https://lnkd.in/g_7gZcUZ 💡 Reality Check: You can’t become a Data Scientist just by watching tutorials… Just like you can’t become a cricketer 🏏 by watching IPL. 👉 You need practice. #Python #Coding #DataScience #MachineLearning #LearnToCode

🐍 Day 26 of My 30-Day Python Learning Challenge 🚀 Today I enhanced my Log File Analyzer Project by adding a new feature. 📌 New Feature: Top N Words (User Choice) Instead of showing only top 3 words, users can now choose how many top words they want. 📌 Code: top_n = int(input("Enter number of top words: ")) top_words = sorted(word_count.items(), key=lambda x: x[1], reverse=True)[:top_n] print(top_words) --- 📊 What Changed? • Before → Fixed output (Top 3 words) • Now → Dynamic output (User-defined) --- 💡 Why this matters? • Makes the project flexible • Improves user experience • Closer to real-world applications --- 📊 Quick Question What will happen if user enters a very large number? A) Error B) Full list is returned C) Empty output D) Program stops Answer tomorrow 👇 #Python #MiniProject #ProjectEnhancement #LearningInPublic #SoftwareDeveloper

👋 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

Let's Become ML Engineers Together — Phase 1: Foundation (Part-1) Python for Machine Learning There are many apps and environments where you can write and run Python code, But I would suggest Google Colab 1️⃣ Basics Variables & types— No type declarations needed. Python figures it out. Math & comparisons if / elif / else for loops 2️⃣ Data Structures List — ordered, changeable Dict — key → value pairs Tuple — immutable list Set — unique values List comprehension — compact loops 3️⃣ Functions & OOP 🌟 Defining functions Default arguments Lambda — one-line function Defining a class Inheritance 💭 You can learn from https://lnkd.in/gsYx6j8J #ML #Python #Basic

🚀 Day 6 of My Python Learning Journey Today, I focused on strengthening my understanding of Conditional Statements in Python by building a small but important logic-based program. 💡 What I learned: Taking user input using input() Type casting input into integers Applying conditional logic using if-else Using logical operators (and, or) 🧠 Mini Project: Leap Year Checker I built a program that determines whether a given year is a leap year using proper mathematical conditions: ✔ A year is divisible by 4 ✔ Not divisible by 100 unless also divisible by 400 🔍 This helped me understand how real-world logic is implemented in code and improved my problem-solving skills. 📌 Output Example: Input: 2000 → Output: Leap Year Input: 2023 → Output: Not a Leap Year 💪 Every small step is building a strong foundation toward my goal of becoming a Data Analyst. Next up: Loops & Functions 🔥 #Python #LearningJourney #DataAnalytics #Coding #BeginnerToPro #Consistency #100DaysOfCode

🔹 Python Learning – Working with Dictionaries Efficiently 🔹 Today I practiced how to access and filter data from Python dictionaries 🐍 Here’s what I explored: ✔️ Iterating through dictionary keys ✔️ Fetching specific key-value pairs ✔️ Writing cleaner and more efficient code 💡 Example: bdict={'a':'10','b':'40','c':'50','d':'praveen','e':'fun','f':'joy'} for key, value in bdict.items(): if key == 'd': print(key, value) 📌 Key takeaway: While loops help in understanding data flow, direct access (dict[key]) is often more efficient when you already know the key. 🚀 Improving my Python fundamentals step by step and focusing on writing cleaner code! #Python #Learning #Programming #DevOps #Automation #CodingJourney

🚀 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