From def to lambda: The power of concise code

Day 4 of #100DaysOfLeetCode Problem: 219. Contains Duplicate II Category: Arrays / HashMap / Sliding Window Today’s challenge was about checking if an array contains duplicate elements within a specific index distance k. I solved this using a hashmap to track the most recent index of each element while iterating through the array. 🧠 Key Learnings: 1) Used a hashmap to perform quick lookups and avoid nested loops. 2) Checked if a duplicate element appeared again within k indices. 3) Optimized the approach to run in O(n) time and O(n) space. 🎯 Takeaway: Tracking element indices in a hashmap helps handle proximity-based duplicate problems efficiently. Staying consistent and learning something new every day! 💪 #LeetCode #100DaysOfCode #ProblemSolving #CodingJourney #HashMap #SlidingWindow #Python #AIEngineer #Consistency

Understanding NumPy Arrays — The Core of Data Analysis After exploring NumPy, let’s dive into its backbone — the NumPy Array. Unlike Python lists, arrays are faster, more memory-efficient, and built for numerical computation. From storing data efficiently to performing complex mathematical operations in just a line of code — arrays make data manipulation seamless! Stay tuned as I explore some key NumPy array operations in my next post. #Python #NumPy #DataAnalytics #LearningJourney #PythonForData

𝐇𝐚𝐩𝐩𝐲 𝐒𝐨𝐜𝐢𝐚𝐥 𝐒𝐚𝐭𝐮𝐫𝐝𝐚𝐲🥳🥳! Wrapping up my tasks early so I can dive into the fun part of the day! 🌟 𝐒𝐤𝐢𝐥𝐥 𝐮𝐩 𝐃𝐚𝐲𝟏𝟎 𝐮𝐩𝐝𝐚𝐭𝐞: 𝐹𝒾𝓁𝓉𝑒𝓇() 𝐹𝓊𝓃𝒸𝓉𝒾𝑜𝓃 𝒾𝓃 𝒫𝓎𝓉𝒽𝑜𝓃💻 The filter function is a built-in function that allows you filter out elements from an iterable (such as list, tuple, string, dictionary) based on a specific condition 𝐒𝐲𝐧𝐭𝐚𝐱: Filter(function, iterable) - The function applied the block of code to each element and return true or false - The iterable is the collection of data. I created a video chowing examples using lambda functions and multiple conditions. See full video👇 https://lnkd.in/eNVaHN3N Let's Connect! 🤝 Share your favorite Python tips and tricks in the comments below! 💬 #SocialSaturday #Python #SkillUp #FilterFunction #LambdaFunctions #CodingCommunity #LearningIsFun"

💡 Python Tip of the Day 🚀 The Power of * : Smart Value Capturing Use * to capture remaining items while unpacking — perfect for flexible data. #Python #PythonTips #CodingTips #PythonLearning #LearnPython #PythonDeveloper #100DaysOfCode #TechLearning #ProgrammingBasics 

🗓 Day 2 / 100 – #100DaysOfLeetCode 📌 Problem 1636: Sort Array by Increasing Frequency The task was to sort an array such that elements with lower frequency appear first, and if two elements have the same frequency, the larger number comes first. 🧠 My Approach: Counted element frequencies using a hash map. Sorted the elements by ascending frequency and then by descending value. Reconstructed the array based on sorted frequency order. ⏱ Time Complexity: O(n log n) 💾 Space Complexity: O(n) 💡 Key Learning: This problem reinforced how powerful custom sorting logic can be in Python, especially when handling multiple sort priorities using tuple-based keys in sorting functions. Each day is helping me refine how I think about data organization, sorting, and frequency analysis — small steps that build strong foundations. #100DaysOfLeetCode #LeetCodeChallenge #Python #ProblemSolving #Algorithms #DataStructures #DSA #Sorting #CodingJourney #CodingChallenge #SoftwareEngineering #CompetitiveProgramming #CodeEveryday #LearningInPublic #DeveloperJourney #TechStudent #CareerGrowth #CodingCommunity #KeepLearning

💡 Creating Arrays using NumPy! Just explored how to create and manipulate different types of arrays using NumPy — from 1D to multidimensional arrays, along with functions like arange(), zeros(), ones(), and linspace(). This helped me understand how NumPy makes numerical operations faster and more efficient. ⚡📊 🔗 GitHub Repository: https://lnkd.in/gsPj_hxs 👨🏫 Under the guidance of: Ashish Sawant #NumPy #Python #DataScience #MachineLearning #CodingJourney #LearningEveryday

Today I explored NumPy, one of the most powerful library of Python for numerical and scientific computing. Here’s what I practiced: Creating arrays with np.array() Using functions like zeros(), ones(), arange(), eye(), and linspace() Checking dimensions with .ndim Understanding array shapes using .shape I’m really enjoying how NumPy makes working with data so much easier and faster. #Python #NumPy #DataScience #LearningJourney #PythonForDataScience

#LearnPythonWithMe With the guidance of Dev BhushanSir Today, I practiced two key Python concepts — dictionary creation and pair sum logic. Topics Covered: Creating dictionaries dynamically using input() Mapping multiple values using split() and map() Nested loops for finding number pairs that match a target sum Applying logic to check conditions and append pairs to a list This session strengthened my understanding of how loops and data structures, such as lists and dictionaries, can interact dynamically in real-world scenarios. #PythonLearning #ProblemSolving #CodingJourney