Chronological isochrone maps focus on distance rather than time, created using Python and SQL, and visualized in Jupyter Notebooks for testing with various locations #isochrones https://lnkd.in/d9vDNUy2

I recently implemented the K-Medoids clustering algorithm from scratch in Python and visualized the clusters using graphs to understand how the algorithm groups data points. #MachineLearning #PythonProjects https://lnkd.in/gczybNGr

Day 7/100 – #100DaysOfCode 🚀 Solved LeetCode #27 – Remove Element (Python). Today I practiced the Two Pointer technique to remove all occurrences of a given value in-place and return the new length of the array. Approach: 1) Initialize a pointer k to track the position of valid elements. 2) Traverse the array using index i. 3) If nums[i] is not equal to the given value, assign it to nums[k]. 4) Increment k to expand the valid portion of the array. 5) Return k as the new length. Time Complexity: O(n) Space Complexity: O(1) – In-place solution Learning how in-place array manipulation works step by step 💪 #LeetCode #Python #DSA #Arrays #TwoPointers #ProblemSolving #100DaysOfCode

Still slicing DataFrames the hard way? Here are the three essentials: grab one column with df['col'], pick multiple with df[['a','b']], then filter rows by building a boolean mask like df[df['age'] >= 18]. Master these and inspecting/subsetting data becomes effortless.#pandas #python #datascience #dataanalysis

LeetCode #102 – Binary Tree Level Order Traversal | Python Implementation I implemented an iterative BFS approach using a queue to collect nodes level by level. Core Insight: The len(q) snapshot before the inner loop prevents mixing levels. New children added during iteration don't affect the current level's processing count, ensuring clean level separation. Time: O(n) | Space: O(w) where w = maximum tree width #LeetCode #DataStructures #Python #BinaryTree #BFS #LevelOrderTraversal #CodingInterview #SoftwareEngineering

An exercise to help build the right mental model for Python data. - Solution: https://lnkd.in/ewxef-EN - Explanation: https://lnkd.in/ebPVvnhx - More exercises: https://lnkd.in/eQSdJdaW The “Solution” link uses 𝗺𝗲𝗺𝗼𝗿𝘆_𝗴𝗿𝗮𝗽𝗵 to visualize execution and reveals what’s actually happening.

An exercise to help build the right mental model for Python data. - Solution: https://lnkd.in/etgeQqgF - Explanation: https://lnkd.in/ebPVvnhx - More exercises: https://lnkd.in/eQSdJdaW The “Solution” link uses 𝗺𝗲𝗺𝗼𝗿𝘆_𝗴𝗿𝗮𝗽𝗵 to visualize execution and reveals what’s actually happening.

Python moment that aged me 10 years: Spent 2 hours debugging why my Pandas merge was returning empty. merged = df1.merge(df2, on='user_id', how='inner') The problem? Column was actually named 'User_ID' (capital U and I). Python is case-sensitive. I am not emotionally stable. Now I triple-check column names like my life depends on it. #Python #Pandas #DataEngineering #Debugging #TechLife #Humour #WomenInTech #MomInTech #DataAnalyst #LearningEveryday

Stop using for loops to process data in Python. Do this instead. Day 6/47: Calculating Batting Strike Rates! 🏏 Processing millions of rows with a standard Python loop will freeze your system. The fix? Vectorization. Instead of looping row-by-row, NumPy aligns arrays in memory, executing math (runs / balls) * 100 instantly in parallel. Standard Lists = Slow & Iterative. NumPy Arrays = C-Speed & Parallel. 🔊 Watch my 43-second animated Masterclass to see the data flow! Ever crashed a notebook with a bad loop? Let me know! 👇 #DataScience #NumPy #Python #MachineLearning #CodingJourney #BSCIT