Mastering Python Logic and Decision-Making with Tech4Dev

Stop learning Python like it’s 2015. 🛑 If I were starting from absolute zero today, I would not follow the outdated advice floating around online. The landscape of programming has shifted, and your learning strategy needs to shift with it. Here is a modern, step-by-step roadmap to mastering Python fast: 1. Start with the "Why," not the "How" 🎯 Before touching a single line of code, research what Python is actually used for in the current market—think AI, automation, data engineering, and backend systems. Set a concrete goal, like building an API or automating a task at work; without direction, most people simply quit. 2. Focus on Logic over Syntax 🧠 The fundamentals (variables, loops, functions, and dictionaries) represent the majority of programming logic you will use for years. Don't just memorize the "grammar"; understand how and when to use these tools to solve problems. 3. Move from Passive to Active Learning 💻 Research shows that watching a tutorial only gives you about 20% retention, but writing code yourself jumps that to 90%. If a 15-minute video takes you an hour because you are constantly pausing to type and experiment, you are learning effectively. 4. Use AI as a Tutor, Not a Crutch 🤖 In the modern era, AI is your personal assistant. Instead of asking it to write code for you, prompt it to generate practice problems based on your specific weaknesses. Drill these concepts daily until the theory is "knocked into your brain" through repetition. 5. Embrace the "Pain" of Messy Code 🏗️ Don't rush into Object-Oriented Programming (OOP) on day one. Wait until you’ve built enough small programs to feel the frustration of messy code that is when classes and objects will finally make sense as real solutions rather than abstract concepts. 6. Specialization is the End Game 🚀 Python is a tool, not the destination. Once you know the basics, you must niche down into a field like AI, Data Science, or DevOps. You can't be an expert in everything, so pick the area that aligns with your original goal and build depth there. Learning Python isn't about finishing a tutorial series; it's about building things and solving problems. Are you still stuck in "tutorial hell," or are you building something real today? 👇 #Python #Coding #CareerAdvice #SoftwareEngineering #TechTrends #LearningToCode

🚀 Milestone #1 4 Weeks. 200+ Python Scripts. First Major Bootcamp Milestone Unlocked. 🚀 I switched my life to Sleep → Eat → Code → Repeat – and it’s officially paying off. 😅💻 I’m in the middle of an intensive Python, ML, DS, NLP Bootcamp, and today I’ve hit my first major milestone: I can look at a business problem and instantly visualize the code that could solve it. 🧠🐍 What I’ve Actually Built So Far: ******************************* This phase was not about watching videos. It was about writing 200+ real-world Python scripts in VS Code, breaking problems down. Here are the core areas I’ve covered in this milestone: Python basics and control flow (conditions, loops, inputs, real decisions) Lists, tuples, sets, and dictionaries with real-world use cases and assignments Functions and advanced functions to write clean, reusable logic Importing modules and working with packages for better project structure File handling in depth: text, logs, binary files, CSV, JSON Exception handling and custom exception handling so my scripts fail safely, not silently OOP foundations: classes and objects to model real entities Inheritance to reuse and extend behavior Polymorphism, encapsulation, and abstraction for clean, scalable design Magic methods and operator overloading to make classes feel “native” in Python Every notebook, every .py/.ipynb file, every log and data file you see in my VS Code explorer is a tiny brick in this new foundation I’m building. What I Gave Up To Get Here: ******************************* For the last 4 weeks: ❌ Social plans: on hold ❌ Aimless scrolling: deleted ❌ “I’ll start tomorrow”: replaced with “I’m coding now” Instead, it was: Late nights debugging in VS Code Early mornings refactoring yesterday’s code Meals with Python videos and docs on the side Falling asleep thinking about classes, functions, and edge cases It’s intense. But this is what it takes when you’re not just trying to finish a course – you’re trying to rebuild your brain around a new skill. The Mindset Upgrade: ******************************* Something has flipped. Now when I hear a business problem, I don’t think: “That’s complex.” I think: What’s the input data? How do I clean and transform it? Which functions and classes do I need? How can I make this reusable for the next project? I’m not just writing scripts anymore. I’m starting to think in systems, in data flows, in architecture – in code. That’s the real win of this first milestone. The Bootcamp is ongoing, but my mindset is already changing from “student” to builder. #Python #PythonProgramming #MachineLearning #DataScience #NLP #AI #BootcampJourney #OngoingLearning #FirstMilestone #CodingLife #DeepWork #CareerTransition #LifelongLearning #DeveloperJourney #TechCareer #Programming #Coding

🚀 Mastering Arrays (Lists) in Python – Complete Guide Arrays (Lists) are one of the most important and powerful data structures in Python. Whether you're preparing for coding interviews, improving your problem-solving skills, or building real-world applications, strong knowledge of lists is essential. 🔹 Creating Lists Lists can be created in multiple ways — directly with values, using repetition, generating sequences with range, using list comprehension, creating 2D lists (matrices), or even converting strings into lists. Python gives flexible and simple ways to initialize data. 🔹 Accessing Elements You can access elements using positive indexing (from the start) or negative indexing (from the end). You can also determine the size of the list using length functions. Understanding indexing is the foundation of list operations. 🔹 Modifying Elements Lists are mutable, meaning you can change their values after creation. You can update a single element or multiple elements at once using slicing techniques. 🔹 Slicing Techniques Slicing allows you to extract portions of a list. You can define start, stop, and step values. It also enables advanced operations like skipping elements or reversing a list efficiently. 🔹 Adding Elements You can add elements at the end, at specific positions, or merge multiple lists together. Python provides built-in methods that make list expansion simple and efficient. 🔹 Removing Elements Elements can be removed by value, by index, or completely clearing the list. Understanding the difference between these removal methods is important for avoiding errors. 🔹 Searching Elements Lists allow you to find the index of an element, count occurrences, or simply check whether an element exists. These operations are widely used in problem-solving scenarios. 🔹 Linear Search Concept Linear search scans each element one by one until the target is found. Its time complexity is O(n), which means performance depends on the size of the list. This concept builds the base for understanding more advanced search algorithms. 🔹 Sorting & Reversing Lists can be sorted in ascending or descending order. Python also allows custom sorting based on conditions like length or absolute value. Reversing a list is another fundamental operation often used in algorithms. 🔹 Traversal Techniques Lists can be traversed using for loops, while loops, backward iteration, or enumeration with index tracking. Choosing the right traversal method improves readability and efficiency. 🎯 Why Learning Lists is Important? Lists are the backbone of data handling in Python. Most advanced topics like stacks, queues, dynamic programming, and even frameworks rely on strong list fundamentals. Master the basics. Practice consistently. Strong foundations create strong programmers. #Python #DataStructures #Programming #InterviewPreparation #CodingJourney

As I go deeper into Python, I’m realizing that learning to code isn’t just about writing lines of syntax - it’s about learning how to organize information, ask better questions, and think in structures. This phase of my journey introduced me to Data Structures & Functions, and one concept stood out immediately: 𝐁𝐞𝐲𝐨𝐧𝐝 𝐕𝐚𝐫𝐢𝐚𝐛𝐥𝐞𝐬: 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐢𝐧𝐠 𝐋𝐢𝐬𝐭𝐬. I like to think of a variable as a single grocery bag - it can only hold one item at a time. A list, on the other hand, is a shopping trolley. It holds multiple items, keeps them organized, and lets you access any item whenever you need it. In Python, lists are written using 𝒔𝒒𝒖𝒂𝒓𝒆 𝒃𝒓𝒂𝒄𝒌𝒆𝒕𝒔 [], with items separated by commas. Simple syntax, powerful idea. Suddenly, I wasn’t just working with single values - I was managing collections of data. 𝐈𝐧𝐝𝐞𝐱𝐢𝐧𝐠: 𝐓𝐡𝐞 “𝐙𝐞𝐫𝐨 𝐑𝐮𝐥𝐞” (𝐓𝐡𝐞 𝐏𝐚𝐫𝐭 𝐓𝐡𝐚𝐭 𝐓𝐫𝐢𝐩𝐬 𝐄𝐯𝐞𝐫𝐲𝐨𝐧𝐞 𝐔𝐩) One of the first mindset shifts was understanding indexing. In Python, counting doesn’t start at 1 - it starts at 0. That means: The first item in a list is at index 0 The second item is at index 1 The third is at index 2, and so on It feels strange at first, but it’s non-negotiable in most programming languages. To access any item, you simply place the index inside square brackets after the list name. Once this clicks, lists suddenly feel predictable and logical. 𝐒𝐥𝐢𝐜𝐢𝐧𝐠: 𝐆𝐞𝐭𝐭𝐢𝐧𝐠 𝐚 𝐂𝐡𝐮𝐧𝐤 𝐨𝐟 𝐃𝐚𝐭𝐚. Indexing lets you grab one item, but slicing is where things get really interesting. Slicing allows you to extract a range of items from a list. I imagine it like cutting a sandwich — you decide where to start cutting and where to stop, and Python hands you that section neatly. Even better, Python offers slicing shortcuts. These shorthand patterns make your code cleaner, faster to write, and easier to read. It’s one of those moments where you realize programmers value efficiency just as much as correctness. 𝐌𝐨𝐝𝐢𝐟𝐲𝐢𝐧𝐠 𝐋𝐢𝐬𝐭𝐬: 𝐁𝐞𝐜𝐚𝐮𝐬𝐞 𝐃𝐚𝐭𝐚 𝐂𝐡𝐚𝐧𝐠𝐞𝐬 Unlike some data structures, lists are mutable — meaning they can change after creation. You can add items, remove them, rearrange them, or update values entirely. Python makes this easy with built-in list methods and functions that handle common operations. Instead of reinventing the wheel, you focus on the logic and let Python do the heavy lifting. 𝐌𝐨𝐝𝐢𝐟𝐲𝐢𝐧𝐠 𝐋𝐢𝐬𝐭𝐬: 𝐁𝐞𝐜𝐚𝐮𝐬𝐞 𝐃𝐚𝐭𝐚 𝐂𝐡𝐚𝐧𝐠𝐞𝐬 This was a big “𝒂𝒉𝒂” moment. List comprehension provides a concise and elegant way to create new lists from existing ones. Instead of writing multiple lines with loops and conditionals, you can express your intent in one clean, readable line. It shifts your thinking from “how do I do this step by step?” to “what do I want to create?”. What I’m learning is that Python isn’t just teaching me syntax - it’s teaching me how to structure data, reason logically, and write code that scales.

I finally understood why Python works — not just how to write it. Today I stopped memorizing syntax and started learning programming from first principles. Here’s what clicked 👇 --- 1️⃣ Functions are not code blocks A function is a mapping: Input → Transformation → Output The real difference I learned: - "print()" → produces a side effect - "return" → produces a value That single distinction explains why many beginner programs “run” but cannot be reused. --- 2️⃣ Scope (this confused me for weeks) Variables don’t belong to a file. They belong to a namespace. Python searches variables in a fixed order: Local → Enclosing → Global → Built-in (LEGB rule) The most interesting part: "nonlocal" lets a child function modify its parent function’s variable without turning it into a global variable. Now closures finally make sense. --- 3️⃣ Recursion (the big breakthrough) Recursion is NOT a loop alternative. It is: «solving a big problem by delegating a smaller version of the same problem to yourself.» Two rules: • Base case (stop condition) • Recursive case (smaller problem) When printing numbers 10 → 1, the secret is not repetition. It is the call stack. Python stores unfinished function calls and resolves them in reverse order. That is why recursion prints backwards when unwinding. This was my “aha moment”. --- 4️⃣ Control statements - "break" → terminate loop - "continue" → skip iteration - "pass" → structural placeholder Simple — but critical for writing predictable programs. --- 5️⃣ Yield (Generators) The most powerful concept today. "return" → ends a function "yield" → pauses a function A generator does lazy evaluation: it creates values only when requested. Meaning: a list stores all values in memory a generator streams values one-by-one. This is why large-data pipelines in data science prefer generators. --- Today I realized: Coding is not typing instructions. Programming is controlling state and execution flow. Tomorrow I’m starting recursion problems: factorial, Fibonacci, and tracing the call stack manually. --- If you struggled with recursion or "yield" before, what was the exact point where it didn’t click? I’d genuinely like to know 👇 #Python #LearnInPublic #CodingJourney #ComputerScience #100DaysOfCode #DataScienceJourney #ProgrammingBeginners #Developers #TechLearning

Python Learning Series 🐍 PYTHON MEMORY MANAGEMENT 📌 TOPIC 1: What is Memory Management? Memory management is the process of allocating and deallocating memory for data in a program. It's crucial for application performance. In Python, this is handled automatically by the Python Memory Manager (PMM), which is part of the Python Virtual Machine (PVM). 💼 Interview Q: "What is memory management, and who handles it in Python?" ✅ Answer: Memory management is about controlling how memory is used—when to grab it and when to free it. In Python, we don't do this manually. Python has its own built-in Python Memory Manager (PMM) inside the Python Virtual Machine (PVM) that handles all of this automatically behind the scenes. 📌 TOPIC 2: Types of Memory Allocation 🔹 Static Allocation → Happens at compile time, uses the STACK. Python does NOT use this. 🔹 Dynamic Allocation → Happens at runtime, uses the HEAP. This is what Python uses. 💼 Interview Q: "What are the types of memory allocation? Which one does Python use?" ✅ Answer: There are two types—static (at compile time, using the stack) and dynamic (at runtime, using the heap). Python exclusively uses dynamic memory allocation. Every time you create a variable or object in Python, memory is allocated at runtime on the heap. ━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📌 TOPIC 3: Stack vs Heap Memory 🔹 Stack: - Sequential (ordered) memory - Stores function calls & local variables - Fast but limited 🔹 Heap: - Random order memory - Stores all Python objects & data structures - Larger and flexible 💼 Interview Q: "What is the difference between stack and heap memory in Python?" ✅ Answer: The stack is used for function calls and local variables—it's sequential and fast. When a function is called, it gets a slot on the stack; when it returns, that slot is freed. The heap is where all actual Python objects live—integers, lists, classes, everything. It's randomly ordered but much more flexible. In Python, variables don't store values directly; they store references (like arrows) pointing to objects sitting in the heap. Example: x = 10 Here, 'x' is stored in the variable area, and the integer object '10' lives in the heap. 'x' just holds the memory address of that object. 📌 TOPIC 4: Python Memory Areas & Execution Model Python's memory is divided into 5 areas: 1️⃣ Code Area – Stores the entire program/code 2️⃣ Variable Area – Stores variable names with references to heap objects 3️⃣ Heap Area – Stores all Python objects dynamically 4️⃣ Free Area – Unused memory, available to stack/heap as needed 5️⃣ Stack Area – Stores functions and their local variables 🔑 Key point: Python's heap is PRIVATE—only the Python interpreter can access it directly. Both the stack and the heap grow/shrink dynamically, borrowing from or returning memory to the free area. Each object in the heap has a unique memory address, which you can check using: 👉 id(variable_name)

🚀 From Loops to Lambda — My Python Learning Journey (with Real Examples) Over the past few weeks I decided to stop “just watching tutorials” and instead train programming from first principles. I focused on one question: How does Python actually execute logic? Instead of jumping into frameworks, I built foundations — functions, return values, parameters, variable arguments, and lambda expressions. Here are some key things I learned 👇 1️⃣ Functions are not syntax — they are reusable logic A function is simply a named process that converts input → output. def add(a, b): return a + b print(add(3,4)) # 7 The important part was understanding: print() shows a value return gives value back to the program def test(): print(5) x = test() print(x) Output: 5 None This single example clarified more than hours of videos. 2️⃣ Return values enable composition Once functions return values, they behave like mathematics: def square(n): return n*n print(square(3) + square(4)) Output: 25 Now functions become building blocks. 3️⃣ Parameters make functions flexible def interest(p, r=5, t=1): return (p*r*t)/100 print(interest(1000)) Understanding default parameters taught me that: default values are decided during function definition not during function call 4️⃣ Mutable vs Immutable (critical concept) Numbers: def change(x): x = x + 5 a = 10 change(a) print(a) Output: 10 Lists: def add_item(lst): lst.append(100) a = [1,2,3] add_item(a) print(a) Output: [1, 2, 3, 100] This was the moment Python memory model started making sense. 5️⃣ Variable Arguments — *args and **kwargs Accept unlimited inputs: def total(*nums): s = 0 for x in nums: s += x return s print(total(1,2,3,4)) And keyword data: def info(**data): for key in data: print(key, ":", data[key]) info(name="Sara", age=22) This is exactly how real APIs pass data. 6️⃣ Lambda Functions — anonymous behavior Instead of defining a function only used once: print(list(map(lambda x: x*x, [1,2,3,4]))) Output: [1, 4, 9, 16] I finally understood: Lambda lets you pass behavior as data. What changed for me Before: I memorized code. Now: I understand execution flow. Programming became easier because I stopped treating Python as commands and started treating it as a logical system. My current focus problem solving data structures writing clean reusable functions If you are also learning programming, my biggest advice: 👉 Don’t rush to frameworks. Master functions + loops + data structures first. Everything else becomes easier. I’d love feedback from experienced developers — what concepts should I learn next to become industry-ready? #Python #Programming #LearningInPublic #CodingJourney #SoftwareDevelopment #ComputerScience #BeginnerToPro #DataStructures #100DaysOfCode

Is learning Python still optional in 2026? ✅ Let’s talk in numbers Over 10 million developers worldwide actively use Python today. That’s not a trend. That’s a shift. Python is used across 5+ high growth domains including Data Analytics, AI and ML, Automation, Web Development, and even Supply Chain Analytics. One language. Multiple career doors. More than 70% of AI and data driven companies prefer Python as their core language. ▶️ So the question is simple. Are our skills aligned with future roles or past experience? Python also has one of the lowest learning curves, which is why platforms like Coursera have made Python courses accessible to everyone, from beginners to working professionals. I’ve started exploring this Python course and sharing it here in case it helps someone who wants to upgrade their skills https://lnkd.in/gEqv7gXC If you are from the corporate world looking to stay relevant, or a student preparing to enter the corporate world, this could be a strong skill to add to your profile. Technology is evolving fast. Skills are becoming the real differentiator. ✅ So tell me. Are you planning to learn Python in 2026? 🚀

𝗧𝗵𝗲 𝗣𝘆𝘁𝗵𝗼𝗻 𝗦𝗸𝗶𝗹𝗹 𝗧𝗿𝗲𝗲 𝗬𝗼𝘂 𝗡𝗲𝗲𝗱 𝗶𝗻 𝟮𝟬𝟮𝟲 🌳🐍 Python in 2026 is no longer “just a programming language.” It’s an ecosystem that spans 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴, 𝗱𝗮𝘁𝗮, 𝗔𝗜, 𝗰𝗹𝗼𝘂𝗱, 𝗮𝗻𝗱 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝘀𝘆𝘀𝘁𝗲𝗺𝘀. This skill tree captures what industry-ready Python expertise really looks like today. Here’s how experienced Python professionals think about it 👇 🌱 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 (𝗥𝗼𝗼𝘁𝘀 𝗠𝗮𝘁𝘁𝗲𝗿) Strong Python careers are built on fundamentals: • Core Python (data types, OOP, error handling) • Algorithms & problem-solving • Math & data fundamentals (statistics, linear algebra) • Development tools (Git, Jupyter, IDEs) Without strong roots, advanced skills won’t scale. 🌿 𝗖𝗼𝗿𝗲 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 (𝗧𝗵𝗲 𝗧𝗿𝘂𝗻𝗸) This is where Python becomes production-grade: • 𝐃𝐚𝐭𝐚 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭: Pandas, NumPy, data pipelines • 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 & 𝐌𝐋: scikit-learn, model evaluation, experimentation • 𝐀𝐏𝐈𝐬 & 𝐁𝐚𝐜𝐤𝐞𝐧𝐝𝐬: FastAPI, Flask, async Python • 𝐂𝐥𝐨𝐮𝐝 & 𝐃𝐞𝐯𝐎𝐩𝐬: Docker, CI/CD, cloud-native Python This layer separates coders from engineers. 🌳 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗚𝗿𝗼𝘄𝘁𝗵 (𝗕𝗿𝗮𝗻𝗰𝗵𝗲𝘀) As systems grow, specialization matters: • Deployment & scaling (containers, orchestration) • Performance optimization • Backend data processing • Domain-driven design & architecture This is where Python powers real-world platforms. 🤖 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗠𝗼𝗱𝗲𝗹𝘀 & 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 The fastest-growing branch: • LLM integration & orchestration • Prompt engineering & evaluation • Vector databases & embeddings • AI agents and workflow orchestration Python is the default language for GenAI systems. 🎯 𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆 You don’t need to learn everything at once. You need to 𝗴𝗿𝗼𝘄 𝗹𝗮𝘆𝗲𝗿 𝗯𝘆 𝗹𝗮𝘆𝗲𝗿: 𝟭 Strong foundations 𝟮 Solid core techniques 𝟯 One or two deep specializations 𝟰 Awareness of GenAI & agentic systems That’s how Python careers stay relevant in 2026 and beyond. If you’re building or mentoring Python talent, this tree is a great roadmap. What branch are you currently growing into? 🌱➡️🌳 📘 𝙇𝙚𝙖𝙧𝙣 𝙋𝙮𝙩𝙝𝙤𝙣 𝙩𝙝𝙚 𝙎𝙩𝙧𝙪𝙘𝙩𝙪𝙧𝙚𝙙 𝙒𝙖𝙮 🔗 𝗣𝘆𝘁𝗵𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀:-https://lnkd.in/drnrg2uQ 💬 𝙅𝙤𝙞𝙣 𝙩𝙝𝙚 𝙇𝙚𝙖𝙧𝙣𝙞𝙣𝙜 𝘾𝙤𝙢𝙢𝙪𝙣𝙞𝙩𝙮 📲 𝗪𝗵𝗮𝘁𝘀𝗔𝗽𝗽 𝗖𝗵𝗮𝗻𝗻𝗲𝗹:-https://lnkd.in/dTy7S9AS 👉𝗧𝗲𝗹𝗲𝗴𝗿𝗮𝗺:- https://t.me/pythonpundit

🚀 DSA Problem Solved: Longest Balanced Subarray in Python 💡 Intuition A subarray is called balanced when the number of distinct even numbers equals the number of distinct odd numbers. We convert this into a balance score: Distinct even → +1 Distinct odd → −1 If the score becomes 0, the subarray is balanced. To maintain this efficiently while handling duplicates, we use a Segment Tree with Lazy Propagation. ⚙️ Approach • Use a segment tree to maintain balance values over index ranges • Track last occurrence of each number to ensure only distinct contributions count • Apply range updates when a number appears • Query the leftmost index where balance becomes zero • Update maximum subarray length ⏱️ Complexity Time Complexity: O(n log n) Space Complexity: O(n) 💻 Code PYTHON class Solution(object): def longestBalanced(self, nums): n = len(nums) tree_min = [0] * (4 * n) tree_max = [0] * (4 * n) lazy = [0] * (4 * n) def push(v): if lazy[v] != 0: for u in (2*v, 2*v+1): lazy[u] += lazy[v] tree_min[u] += lazy[v] tree_max[u] += lazy[v] lazy[v] = 0 def update(v, tl, tr, l, r, add): if l > r: return if l == tl and r == tr: tree_min[v] += add tree_max[v] += add lazy[v] += add return push(v) tm = (tl + tr) // 2 update(2*v, tl, tm, l, min(r, tm), add) update(2*v+1, tm+1, tr, max(l, tm+1), r, add) tree_min[v] = min(tree_min[2*v], tree_min[2*v+1]) tree_max[v] = max(tree_max[2*v], tree_max[2*v+1]) def find_first_zero(v, tl, tr, limit): if tl > limit: return -1 if tree_min[v] > 0 or tree_max[v] < 0: return -1 if tl == tr: return tl if tree_min[v] == 0 else -1 push(v) tm = (tl + tr) // 2 res = find_first_zero(2*v, tl, tm, limit) if res != -1: return res return find_first_zero(2*v+1, tm+1, tr, limit) last_pos = {} max_len = 0 for i, x in enumerate(nums): val = 1 if x % 2 == 0 else -1 prev = last_pos.get(x, -1) last_pos[x] = i update(1, 0, n-1, prev + 1, i, val) left_idx = find_first_zero(1, 0, n-1, i) if left_idx != -1: max_len = max(max_len, i - left_idx + 1) return max_len #DSA #PythonDeveloper #CodingPractice #InterviewPreparation #ComputerScience #Developers #TechCareers