200 LeetCode Problems Solved in Python

💭 I still remember my first Python program… It was just one line: print("Hello, World!") Nothing fancy. No big achievement. But somehow… it felt powerful. Like I had just unlocked a new language—one that computers understand. At first, Python looked too simple. No complex syntax, no overwhelming rules… just clean, readable code. But that simplicity? That’s where the real magic was hiding. Slowly, “Hello World” turned into small scripts… Scripts turned into projects… And projects turned into confidence. 🐍 Python isn’t just a programming language. It’s a starting point. A gateway into AI, Data Science, Automation, Web Development, and so much more. And the best part? You don’t need to be a genius to start. Just curious enough to try. ✨ Every expert was once a beginner staring at a blinking cursor. So if you’ve been thinking about starting… This is your sign. #Python #CodingJourney #LearnToCode #Programming #TechStory #DeveloperLife #AI #DataScience #CareerGrowth🚀

🚀 Day 9 of #100DaysOfCode — Finding the Sum of the Smallest Numbers in Python Today I explored two different approaches to solve a simple but important problem: 👉 Find the sum of the smallest N numbers in a list ✅ Approach 1: Pythonic & Efficient numbers = [5, 2, 9, 1, 7] n = 2 result = sum(sorted(numbers)[:n]) print(result)🔹 How it works: sorted(numbers) → sorts the list[:n] → picks the smallest n elementssum() → adds them up💡 Clean, readable, and perfect for most use cases. ✅ Approach 2: Manual Logic (Without Built-ins) arr = [5, 2, 9, 4, 3, 5] N = 2 total = 0 data = len(arr) for k in range(N): min_index = 0 for i in range(1, data): if arr[i] < arr[min_index]: min_index = i total += arr[min_index] arr[min_index] = float('inf') print("Sum of smallest", N, "numbers:", total)🔹 How it works: Repeatedly finds the smallest elementAdds it to totalMarks it as used (by setting to infinity)💡 Great for understanding core logic and algorithm design 🔍 Key Takeaways ✔️ Built-in functions save time and reduce complexity ✔️ Manual approach helps strengthen problem-solving skills ✔️ Always balance readability vs control 💬 Best Comment Insight “Don’t just learn shortcuts — understand what’s happening under the hood. That’s where real growth happens.” #Python #CodingJourney #30DaysOfCode #LearnToCode #Programming #Developers #ProblemSolving #PythonBasics

🚀 Python Series – Day 19: Polymorphism (One Name, Many Forms!) Yesterday, we learned Inheritance 🔁 Today, let’s understand another powerful OOP concept — 👉 Polymorphism 🧠 What is Polymorphism? 👉 The word Polymorphism means: 📌 Poly = Many 📌 Morph = Forms So, One method / function behaves differently in different situations 🔹 Real-Life Example Think of the word Run 🏃 Human runs 🚗 Car runs 💻 Software runs 👉 Same word run, different meanings. That is Polymorphism 🔥 💻 Example 1: Same Method, Different Classes class Dog: def sound(self): print("Dog barks") class Cat: def sound(self): print("Cat meows") for animal in (Dog(), Cat()): animal.sound() Output: Dog barks Cat meows 🔹 Example 2: Built-in Polymorphism print(len("Python")) print(len([1,2,3,4])) Output: 6 4 👉 Same len() function works for string and list. 🎯 Why Polymorphism is Important? ✔️ Cleaner code ✔️ Flexible programs ✔️ Easy to extend features ✔️ Used in real-world software development Pro Tip 👉 Write generic code that works with many object types. 🔥 One-Line Summary 👉 Polymorphism = Same method name, different behavior 📌 Tomorrow: Encapsulation (Protect Your Data Like a Pro!) Follow me to master Python step-by-step 🚀 #Python #Coding #Programming #OOP #DataScience #LearnPython #100DaysOfCode #Tech #MustaqeemSiddiqui

If you're still juggling pip, venv, and poetry in your Python projects , there's a better way. uv is the Rust-powered package manager from Astral that replaces your entire Python tooling stack with a single, fast, reproducible workflow. And it's quietly become the standard for MCP server development too. I published a comprehensive guide covering everything from day-to-day Python development to building AI-native tooling: →Why uv is 10–100x faster than pip → Core commands: init, add, sync, run, lock → Full FastAPI project walkthrough → Head-to-head comparison with pip, poetry, and pip-tools → Best practices for teams, CI/CD, and lock file reproducibility → How uv powers Python MCP servers and why Claude uses uv run to launch AI tooling Whether you're a backend engineer cleaning up dependency chaos, a data scientist tired of broken environments, or building LLM-integrated tools and MCP integrations , this is the workflow worth standardizing on. 📖 Full article on Medium: https://lnkd.in/gZDQZNyg #Python #uv #PackageManagement #MCP #ModelContextProtocol #AIEngineering #DevTools #BackendDevelopment #Claude #LLM

🚀 Built a Random Story Generator using Python Excited to share my latest mini-project where I combined creativity with coding! 🔹 The program generates random stories based on different genres like Horror 👻, Sci-Fi 🚀, and Comedy 😂 🔹 It uses Python concepts like lists, random module, loops, and file handling 🔹 Each story includes a unique twist ending to make it more interesting 🔹 Stories are automatically saved into a file for future use 💡 This project helped me strengthen my logic building and understand how to structure real-world programs Sample Output: "One day a robot in the future city discovered a portal. Suddenly, it hacked someone. But it was a trap!" I’m currently working on improving this project by adding: ➡️ GUI interface (Tkinter) ➡️ More advanced story logic ➡️ User customization GitHub Repo Link :- https://lnkd.in/gmPkM3gk Would love your feedback and suggestions! 🙌 #Python #Coding #Projects #BeginnerProjects #Learning #AI #DeveloperJourney Python #Automation #BeginnerProject #CodingJourney #LearningByDoing

📅 Day 5 of Python — and today was all about putting knowledge to the test! 💪 Instead of learning something new, I took on a full practice session covering everything I've studied so far on Python's core data structures. 🧪 Here's what I worked through: ✅ Lists — creating, slicing, methods like append(), extend(), insert(), pop(), remove(), and sort() ✅ List Comprehensions — squares, filters, tuple pairs, and more ✅ Tuples — declaration, immutability (yes, I triggered the TypeError 😅), unpacking, and zip() ✅ Sets — deduplication, membership checks, add/remove/discard, and set operations like union, intersection, difference & symmetric difference ✅ Dictionaries — key-value access, get(), items(), keys(), values(), nested dicts, and updating/deleting entries ✅ Dictionary Comprehensions — building mappings with filters ✅ Applied Problems — frequency maps, common elements using sets, zip() with conditional logic The practice set had 30+ exercises and solving each one back-to-back really helped solidify the concepts rather than just reading about them. Key takeaway from today: You don't truly understand a concept until you've broken it, debugged it, and fixed it yourself. 🔧 On to Day 6! 🚀 #Python #100DaysOfCode #DataStructures #LearningInPublic #CodingJourney #PythonProgramming

People ask "how can 750 lines do what LangChain does in 60,000?" The answer: it can't do everything LangChain does. But it does everything you actually need. And you understand every line.

🚀 Day 82 – Strengthening Python Foundations 🐍 Today’s focus was on revisiting and revising the basics of Python, right up to comprehensions. Reinforcement of fundamentals is not just repetition — it’s about building clarity, confidence, and precision for advanced problem‑solving. 🔹 Core Syntax Refreshed – Variables, operators, and expressions, ensuring fluency in the language’s building blocks. 🔹 Control Flow Mastery – Conditionals and loops revisited, sharpening logical thinking and structured problem‑solving. 🔹 Functions & Scope – Re‑examined how modular code works, reinforcing the importance of reusability and clarity. 🔹 Data Structures – Lists, tuples, sets, and dictionaries revised with practical examples, strengthening understanding of storage and retrieval. 🔹 Comprehensions – Explored list, set, and dictionary comprehensions, appreciating how they transform verbose loops into elegant, Pythonic one‑liners. 🌱 Reflection – Revisiting basics is like polishing the foundation stones of a building. Each concept feels sharper, cleaner, and more intuitive, preparing me for deeper explorations in algorithms, problem‑solving, and real‑world applications. ⚡ Day 82 was about consolidation — turning knowledge into confidence, and confidence into readiness for the next leap forward. #Day82 #PythonLearning #CodingJourney #100DaysOfCode #LearnInPublic #10000Coders

🚀 Solved: Group Anagrams Problem (LeetCode) Today I worked on an interesting problem — grouping anagrams efficiently using Python. 💡 Approach: I used a hashmap (dictionary) where: The key is the sorted version of each word The value is a list of words (anagrams) matching that key Example: "eat", "tea", and "ate" → all become "aet" after sorting → grouped together 🧠 Key Insight: Sorting each string gives a unique identifier for all its anagrams. ⚙️ Time Complexity: Sorting each word takes O(k log k) For n words → O(n * k log k) 📦 Space Complexity: O(n * k) for storing grouped anagrams ✅ Result: Accepted ✔️ Runtime: 5 ms (faster than ~99% submissions) 📈 Growth & Consistency: Improving step by step by solving problems daily and focusing on writing clean and optimized code. Small consistent efforts are helping me build stronger problem-solving skills and deeper understanding of DSA. 🔁 Staying consistent is the real game changer! #Python #DSA #LeetCode #Coding #ProblemSolving #Consistency #Growth #LearningJourney

Master the Pythonic Way: DSA Basics Ready to level up your Data Structures and Algorithms (DSA) game? Doing DSA in Python isn’t just about getting the right output; it’s about writing clean, efficient, and Pythonic code. 🚀 If you’re still using 5 lines of code for a simple filter or a basic if-else block, it’s time for an upgrade. Today, I’m diving into two essential tools that make your algorithms sleeker: List Comprehensions and Ternary Operators. 1. List Comprehensions: The One-Liner Powerhouse Why write a for loop when you can generate a list in a single, readable line? It’s faster and keeps your workspace clutter-free. 2. Ternary Operators: Logic at a Glance When your algorithm needs a quick decision, ternary operators (conditional expressions) are your best friend. They are perfect for assigning values based on a condition without breaking the flow. The Syntax: value_if_true if condition else value_if_false 💡 Why this matters for DSA: Readability: Interviewers love code that is easy to follow. Efficiency: List comprehensions are often slightly faster than manual append() calls. Focus: It allows you to focus on the logic of the algorithm rather than the boilerplate of the syntax. What’s your favorite Python trick for competitive programming? Let’s discuss in the comments! 👇 #Python #DataStructures #Algorithms #Coding #SoftwareEngineering #Pythonic #ProgrammingTips