Understanding Conditional Statements in Python

🚀 Big News in Python 3.14 – You Can Finally Disable the GIL! For years, Python developers have faced a major bottleneck — the Global Interpreter Lock (GIL) — which restricted Python to running only one thread at a time, even in multi-threaded programs. Now, that’s changing. Python 3.14 introduces the option to disable the GIL, unlocking true parallel execution for CPU-bound workloads. ⚙️🔥 And guess what? uv already fully supports it! 💪 🎥 The video below shows a clear run-time difference — multi-threaded Python code finally running in parallel. Let’s recap what this means: The GIL limits one thread per process — hurting performance in CPU-heavy tasks. I/O-bound tasks (like network requests) were mostly unaffected. Multi-processing was the old workaround, but it introduced complexity — since processes don’t share memory directly and require IPC (pipes, queues, shared memory, etc.). With this update, Python can scale multi-threaded workloads like never before — opening new possibilities in AI, scientific computing, and parallel data processing. 👉 Question for you: What are some good reasons Python originally enforced the GIL — and should it still exist as an option? 🤔 #Python #Multithreading #GIL #Python314 #ParallelComputing #AI #uv #Developers #Performance #Programming

🐍 Exploring Python Data Types — The Foundation of Every Program! 💡 Understanding data types is one of the most important parts of learning Python. Each data type helps define how information is stored, accessed, and manipulated in a program. In Python, data types are categorized into several groups: 🔹 Numeric – Integer, Float, Complex Number 🔹 Sequence Type – String, List, Tuple 🔹 Dictionary – Stores data in key–value pairs 🔹 Set – Stores unique, unordered items 🔹 Boolean – Represents True or False values Every type serves a different purpose — whether it’s performing calculations, storing collections, or managing logical operations. Learning about Python data types gave me a deeper understanding of how Python handles data so efficiently and flexibly. A big thanks to Talal Ahmed for explaining these core concepts so clearly and helping us build a strong foundation in Python programming. 🙌 #Python #Programming #Coding #DataTypes #LearningJourney #SoftwareDevelopment #TechEducation #SMIT #AI

🐍 Understanding Python Operators — The Building Blocks of Every Program! In yesterday’s class, I explored one of the most important fundamentals in Python: Operators. These are the tools that enable us to perform actions such as calculations, comparisons, logic building, assignments, and much more. Without operators, writing any meaningful program would be impossible. Here’s what I learned today: 🔹 Arithmetic Operators – perform math operations like +, -, *, / 🔹 Comparison Operators – compare values using ==, !=, >, < 🔹 Logical Operators – combine conditions using and, or, not 🔹 Assignment Operators – update variable values using +=, -=, *= 🔹 Membership Operators – check if a value exists using in, not in 🔹 Identity Operators – check whether two variables reference the same object (is, is not) Learning these concepts provided me with a deeper understanding of how Python processes data and how logic flows within a program. It’s amazing how these small symbols play such a big role in programming! A big thanks to Talal Ahmed for explaining these concepts in such a simple and practical way. 🙌 #Python #Programming #LearningJourney #PythonOperators #CodingFundamentals #SMIT #TechSkills #AI

Today I learned something that genuinely changed how I look at data processing in Python — Generators. Instead of creating and storing an entire list in memory, generators produce items one at a time, only when needed. And that simple idea makes them incredibly efficient. Here’s a simple example that explains the difference: What surprised me is this: yield doesn’t return all results at once. It pauses the function, remembers its state, and continues from where it left off the next time it’s called. This makes generators perfect for: 🔹 Large datasets 🔹 Streaming data 🔹 Memory-efficient pipelines 🔹 Infinite sequences Instead of thinking in terms of “lists”, generators helped me start thinking in terms of flows — generating data only when the program actually needs it. Learning Python is slowly shifting from “how to write code” to “how to write efficient code.” 👉 This code prints the square of every number from 0 to 4, but it does NOT create any list in memory. It only generates the next value when needed. #Python #LearningInPublic #Generators #DeveloperJourney #ProgrammingConcepts #Efficiency #100DaysOfCode

Day 4 – Starting with Python Python is the base language for Gen AI because it is simple, readable, and powerful. Today was about building the foundation before moving into complex logic. Step 1: Installed Python and set up VS Code as the workspace. Step 2: Learned to use the terminal to run programs with confidence. Step 3: Understood the core data types: Numbers → calculations Text → names & labels True/False → decisions Lists → storing multiple related values These concepts appear everywhere in the real world: UPI → amount, status, transaction history E-commerce → product name, price, recommendation lists Chatbots → conversation context and responses The focus today was clarity, not speed. When the fundamentals are clear, the next steps become natural. Python basics are not “the beginning” — they are the foundation that holds everything else up. Foundation first. Growth next. 🌱✨ Additional Note 📝 No matter which tool, framework, or library we learn in AI: -Always refer to the official documentation. -Check what has changed from older versions to the latest release. -Understand new features, improvements, and deprecations. -Learn how and why a feature is used — not just how to copy and paste it. -This approach builds long-term capability, not temporary shortcuts. -Tools will change. Versions will change. -But your ability to learn with clarity will stay. 💡 #Day4 #PythonBasics #GenAIJourney #LearningByDoing #DocumentationFirst #StrongFoundation #AICommunity #GrowthMindset #BuildWithClarity

🚀 Set in Python - A Set in Python is a collection data type that is unordered, unindexed, and contains unique elements. It is mainly used when you want to store non-duplicate items and perform mathematical set operations like union, intersection, and difference. 🧩 Key Features: ▪️ Unordered: Elements have no defined order. ▪️ Mutable: You can add or remove items after creation. ▪️ No duplicates: Automatically removes repeated elements. ▪️ Supports set operations like union(), intersection(), difference(), etc. 💡 When to Use: 🔸 You need unique values. 🔸 You want to perform fast membership testing. 🔸 You need set-based operations (like finding common elements). #Python #PythonLearning #PythonBasics #DataStructures #Coding #LearnPython #SetInPython

Day 53 of Python Problem-Solving – “Missing Number” Challenge! Today’s problem was about finding the missing number from a list of n distinct integers within the range [0, n]. Only one number is missing — and the task is to identify it efficiently. ✅ Approach & Learning I solved this in two ways: 🔹 1. Optimized Approach (O(n) Time | O(1) Space) Used the mathematical formula: Sum of first n natural numbers=n(n+1)2\text{Sum of first n natural numbers} = \frac{n(n+1)}{2}Sum of first n natural numbers=2n(n+1)Simply subtracting the actual sum of the list from the expected sum gives the missing number. 🔹 2. Brute Force Approach Sort the list Compare index with value to find mismatch This helped reinforce the contrast between brute force and optimized logic. 🧠 Key Takeaways ✔ Mathematical formulas can eliminate loops & improve performance ✔ Always think about time and space efficiency ✔ Sometimes the smartest solution is also the simplest one 📍 Code Available On GitHub: 🔗 https://lnkd.in/g2HA9WKb #Day53 #100DaysOfCode #Python #CodingJourney #ProblemSolving #LearningDaily #DataStructures #Algorithms #LeetCode #KeepCoding

⚙️ Deep Dive into Python Operators 🐍 Today’s Python class focused on one of the most essential topics — Operators, the symbols that tell Python what actions to perform! 🔹 Arithmetic Operators – Used for mathematical operations: + (Addition), - (Subtraction), * (Multiplication), / (Division), // (Floor Division), % (Modulus), ** (Exponentiation). 📘 Example: a + b adds two numbers. 🔹 Comparison (Relational) Operators – Compare two values and return True or False: ==, !=, >, <, >=, <=. 📘 Example: a > b checks if a is greater than b. 🔹 Assignment Operators – Used to assign or modify variable values: =, +=, -=, *=, /=, %=, **=, //=. 📘 Example: x += 5 is the same as x = x + 5. 🔹 Logical Operators – Combine multiple conditions: and, or, not. 📘 Example: (a > 5 and b < 10) returns True only if both conditions are true. Each operator helps in building expressive, flexible, and efficient code that drives decision-making and computation! 💡 #Python #Programming #Operators #CodingBasics #LearningJourney #TechSkills #PythonProgramming #StudentLife Codegnan

Day:80/100 Recursion of Python Section-3✅️ #100daysofcodingchallenge Recursion: ✔️Function calls itself: A function invokes itself repeatedly until a base case is reached. ✔️Base case: A condition that stops the recursion. ✔️Recursive case: The function calls itself with a smaller input or a modified version of the original input. ✔️Stack overflow: A potential risk if recursion is too deep. ✔️Memoization: Caching results to avoid redundant calculations. ✔️Tail recursion: A special case where the recursive call is the last operation. Benefits: 🔸️Tree traversals: Recursion is natural for traversing tree-like data structures. 🔸️Dynamic programming: Recursion with memoization can solve complex optimization problems. 🔸️Mathematical computations: Recursion can be used to calculate factorials, Fibonacci numbers, and more. #Nxtwave #Intensive #100dayscoding #Python #Tech #coding #Programming #TechSkills #CareerDevelopment #DataLiteracy #BusinessIntelligence