Recursion in Python: Elegant but Challenging

🚀 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

Day 7 Today’s focus was on mastering the "Big Three" of Python collections: Tuples Sets Dictionaries. Here’s a breakdown of the key takeaways from my latest session on Sololearn: 1️⃣ Tuples (Immutability is Key! 🔒) Unlike lists, tuples are immutable, meaning once they are created, they cannot be changed. This makes them perfect for: Storing data that should remain constant (like coordinates or dates). Tuple Unpacking: Using the * operator to flexibly assign multiple elements to a single variable as a list (Game changer for clean code!). 2️⃣ Sets 💎 When you need to ensure every item in your collection is unique, Sets are the go-to. They are perfect for filtering out duplicates and performing mathematical set operations like unions and intersections. 3️⃣ Dictionaries 📖 I explored the fundamental concept of key-value pairs. Dictionaries are: Mutable: You can add or update items using the .update() method. Highly Searchable: Using the .get() function allows for safe data retrieval without crashing the program if a key is missing. Iterative: Easily loop through keys, values, or items to process complex data sets. 4️⃣ Next Stop: List Comprehensions ⚡ I’m just starting to scratch the surface of List Comprehensions—a much more "Pythonic" and efficient way to create and manipulate lists in a single line of code. Building a solid foundation in these data structures is making me a more confident programmer every day. If you’re a fellow learner or a Python pro, what’s your favorite "hidden gem" tip for working with dictionaries? Let’s discuss! 👇 #Python #CodingJourney #DataScience #WebDevelopment #ContinuousLearning #Sololearn #Programming #TechCommunity #PythonDeveloper

I was today years old when I found out random() in Python is NOT actually random. 😭 I've been using it for years. Never questioned it once. Turns out Python uses something called the Mersenne Twister, a math algorithm from 1997. It just PRETENDS to be random. Give it the same seed → same output. Every. Single. Time. random.seed(42) print(random.random()) # 0.6394... random.seed(42) print(random.random()) # 0.6394... again 😭 And the scary part? If you've ever used random() for passwords or tokens -> an attacker can predict your entire sequence after seeing ~624 outputs. The fix is one word: secrets # ❌ This token = random.randint(100000, 999999) # ✅ This token = secrets.randbelow(900000) + 100000 Sharing the full breakdown- how it works, when to use what, and a cheat sheet to save you from my mistakes 😅 Was this new to you too? Drop a 🎲 in the comments! #Python #Programming #LearnInPublic #CodingTips #SoftwareEngineering

Earlier this week, I wrote about the developer loop - learn, practice, apply, repeat. So I put it into practice. Just staying sharp, working through DSA problems in Python. Came across max_subarray_sum. Solved it. Moved on. Then I went back to see how other people approached it… and ran into Kadane’s algorithm : ``` def max_subarray_sum(nums): max_sum = current_sum = nums[0] for num in nums[1:]: current_sum = max(num, current_sum + num) max_sum = max(max_sum, current_sum) return max_sum ``` Same problem. Cleaner logic. O(n) instead of brute force. Here's what I'm trying to say: going back to problems you’ve already solved might feel like you’re wasting time… but you’re not. It isn't. Sometimes it’s only when you revisit something with a fresh mind that you actually see better approaches you missed the first time. There’s almost always more than one way to solve a problem. Which one is "best"? That's a conversation for another day. What’s something you revisited and understood completely differently the second time? #Python #DSA #SoftwareEngineering #DeveloperLife #TechCanada

If Python feels inconsistent… you’re probably missing this. Day 15 — Polymorphism & Method Overloading Quick recap: Explored how the same method can behave differently based on context. Here’s what clicked: → Polymorphism isn’t theory — it’s flexibility in design One interface, multiple behaviors = cleaner, scalable code → Python doesn’t support traditional method overloading But it simulates it using default arguments & dynamic typing → Real power = writing code that adapts without rewriting logic The struggle? I kept trying to force Java-style overloading. Didn’t work. Breakthrough came when I stopped fighting Python… and started thinking in Pythonic design patterns. That shift changes everything. Showing up daily. No skips. No shortcuts. If you’re building real skills, consistency > intensity. What confused you the most about polymorphism? Or what should I break down next?

🚀 Python Essentials: Range, For Loop, Enumerate & List Comprehension   💡"Write cleaner, smarter, and more Pythonic code."   🔢 range Definition: Generates a sequence of numbers. Syntax: range(start, stop, step) Example: for i in range(1, 6):    print(i)  # 1 to 5 🔄 for loop Definition:  Iterates over a sequence. Syntax: for variable in sequence: Example: fruits = ["apple", "banana", "cherry"] for fruit in fruits:    print(fruit) 🏷️ enumerate Definition: Adds a counter to an iterable. Syntax:  enumerate(iterable, start=0) Example: for index, fruit in enumerate(fruits, start=1):    print(index, fruit) ⚡ List Comprehension Definition: Concise way to build lists. Syntax: [expression for item in iterable if condition] Example: squares = [x**2 for x in range(1, 6)] print(squares)  # [1, 4, 9, 16, 25] ✨ These four tools are the backbone of writing efficient loops and data transformations in Python. Master them, and your code will be cleaner, faster, and more elegant.  "Python isn’t just about writing code—it’s about writing it beautifully.” 🔖#PythonProgramming #LearningJourney #CodingInPublic #EntriLearning #CodeNewbie #Python #ProgrammingBasics #DataAnalytics #CareerGrowth #LinkedInLearning #LearnWithMe #BeginnerFriendly #AnalyticsInAction #CodeSmart

🚀 Python Series – Day 12: List Comprehension (Write Short & Smart Code!) Till now, we used loops to create lists. But what if you can do it in one clean line? 🤔 👉 That’s where List Comprehension comes in! 🧠 What is List Comprehension? List comprehension is a short and powerful way to create lists. 👉 It replaces loops with a single line of code 🔧 Basic Syntax: [expression for item in iterable] ▶️ Example (Using Loop): numbers = [] for i in range(5): numbers.append(i) print(numbers) ⚡ Same Using List Comprehension: numbers = [i for i in range(5)] print(numbers) 👉 Output: [0, 1, 2, 3, 4] 🔥 With Condition: even = [i for i in range(10) if i % 2 == 0] print(even) 👉 Output: [0, 2, 4, 6, 8] 🎯 Why Use List Comprehension? ✔️ Short & clean code ✔️ Faster than loops ✔️ Easy to read (once you practice) 🔥 Pro Tip: Don’t overuse it 😄 👉 Use it when it makes code simple, not confusing ⚡ Quick Challenge: What will be the output? x = [i*i for i in range(4)] print(x) 👇 Comment your answer! 📌 Tomorrow: Lambda Functions (Anonymous Functions in Python) Follow me to learn Python step-by-step from basics to advanced 🚀 #Python #DataScience #Coding #Programming #LearnPython #Beginners #Tech #MustaqeemSiddiqui

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🚀 Day 20 of My Python Journey – Solved Find First and Last Position of Element! 💻✨ Today I worked on finding the first and last occurrence of an element in a sorted array ✅ This problem helped me understand how powerful Binary Search can be when slightly modified. 📌 Problem Statement Given a sorted array, find the starting and ending position of a target value. If the target is not found, return [-1, -1]. Example: nums = [5,7,7,8,8,10] target = 8 ✅ Output: [3,4] 🔍 My Approach Instead of using a normal binary search once, I used it twice: First → to find the first occurrence Second → to find the last occurrence The idea is: When target is found, don’t stop Keep searching on the left side (for first position) Keep searching on the right side (for last position) This small modification makes a big difference 🚀 💡 Key Learning This problem helped me understand: ✔ Advanced Binary Search techniques ✔ How to modify standard algorithms ✔ Handling edge cases carefully ✔ Writing efficient search logic ⏱ Complexity Time Complexity: O(log n) Space Complexity: O(1) Really liked this one because it shows how a basic concept can be extended to solve more complex problems 🔥 #Python #DSA #BinarySearch