Handling Typos and Variations with RapidFuzz

Day 7 - Hash Table Deep Dive The answer is O(1) AMORTIZED - and the 'amortized' part is what trips people up. In the best case, hash lookups are O(1). But with hash collisions, worst case is O(n). The key insight: with a good hash function and load factor below 0.75, the AVERAGE case stays O(1). Python dicts use open addressing with random probing, keeping collisions rare. This is why interviewers ask 'average' vs 'worst case' - they want to see if you understand the nuance. Drop your answer! Heart for correct ones. Follow DatascienceBro for Week 2! #datastructures #hashtable #timecomplexity #python #codinginterview #algorithms #bigO #programming #techinterview #softwareengineering

Starting a Python interpreter takes ~1 microsecond with Monty. A sandbox call takes ~1 second. That's 6 orders of magnitude. And no external service, no cgroups, no VM. Samuel Colvin explains why he built a Python interpreter in Rust from scratch for AI agents on his PyAI talk. Link in comments.

📊 Day 6 | K-Nearest Neighbors (KNN) 🤝📍 Today, I learned about K-Nearest Neighbors (KNN), a simple and intuitive Machine Learning algorithm. KNN works on the idea of distance — it classifies a data point based on the majority class of its nearest neighbors. 📌 In simple terms: “Similar data points are close to each other.” Example: ✔ Recommending products ✔ Classifying customers To understand this, I implemented KNN using Python and observed how it predicts based on nearby data points 💻 KNN is simple but powerful for many classification problems. #MachineLearning #KNN #DataScience #LearningInPublic #Python

Day 37 / #120DaysOfCode – LeetCode Challenge ✅ Problem Solved: • Search a 2D Matrix 💻 Language: Python 📚 Key Learnings: • Applied Binary Search on a 2D matrix • Learned how to treat matrix as a flattened sorted array • Practiced converting 1D index → 2D index (row, col) • Improved understanding of search space reduction • Strengthened logarithmic time complexity (O(log n)) thinking Better logic → Faster execution 🚀 🔗 LeetCode Profile: https://lnkd.in/gbeMKcv5 #LeetCode #Python #DSA #BinarySearch #Algorithms #CodingJourney #Consistency #120DaysOfCode

Claude Add-In for Excel Apparently Claude for Excel is powerful because it uses python execution layer behind the scenes. Instead of forcing everything in a formula it translates everything into a python script. This gives it alot of flexibility to handle messier datasets than formulas and is definately more reliable for complex logic. Its like having a python engine for your spreadsheet, since its release about a month ago I was hooked and have not made another excel formula since. Give it a try its extremely powerful #Anthropic #Claude #Excel #AI #Automation

Group Anagrams: Frequency Signature Beats Sorting for Keying Sorting each string as key costs O(k log k) per string. Character frequency array is O(k) and creates same signature for anagrams. Fixed 26-element array converted to tuple serves as hashable HashMap key — cleaner, faster grouping. Frequency as Key: Character counts uniquely identify anagram groups. Tuple conversion enables using array as dict key (lists aren't hashable). This pattern applies to document clustering, duplicate detection. Time: O(n × k) vs O(n × k log k) with sorting | Space: O(n × k) #FrequencySignature #HashMap #Anagrams #KeyOptimization #Python #AlgorithmDesign #SoftwareEngineering

𝐌𝐞𝐬𝐬𝐲 𝐜𝐨𝐥𝐮𝐦𝐧 𝐧𝐚𝐦𝐞𝐬. 𝐄𝐱𝐭𝐫𝐚 𝐬𝐩𝐚𝐜𝐞𝐬. 𝐌𝐢𝐱𝐞𝐝 𝐔𝐏𝐏𝐄𝐑𝐂𝐀𝐒𝐄 𝐚𝐧𝐝 𝐥𝐨𝐰𝐞𝐫𝐜𝐚𝐬𝐞. 𝐒𝐨𝐮𝐧𝐝 𝐟𝐚𝐦𝐢𝐥𝐢𝐚𝐫? These 12 Python string methods can fix all of that — sometimes in just one line of code. While learning Python for data analytics, I realized that small string methods like .𝐬𝐭𝐫𝐢𝐩(), .𝐥𝐨𝐰𝐞𝐫(), .𝐫𝐞𝐩𝐥𝐚𝐜𝐞(), .𝐬𝐩𝐥𝐢𝐭(), 𝐚𝐧𝐝 .𝐣𝐨𝐢𝐧() are extremely useful for cleaning text data before analysis. Strong fundamentals make advanced work easier. #Python #DataAnalytics #DataCleaning #PythonForDataAnalysis #LearningPython #AspiringDataAnalyst #PythonTips #DataScience #CodingJourney

[🐍-Python-Update] -061- [Transcription] Exporting Transcription Results to Excel https://lnkd.in/gM7BUHBy Continuing from last time, we’ll introduce the Python version of the script that uses the “transcription feature” implemented in SILVER-1.4.0. In this post, we’ll cover how to perform transcription and write the results into an Excel file prepared as a script.💻📑 ◆Required Plan ・RIGDOCKS -BRONZE- ・RIGDOCKS -SILVER- ・RIGDOCKS -GOLD- #AZSTOKE #RIGDOCKS #Reaper #Reascript

🚀 Solved: Find a String (Substring Count) Challenge Just solved another problem on HackerRank under the Python Strings section! ✅ 🧠 Problem Overview: Count how many times a substring appears in a string — including overlapping occurrences. 🔍 Key Learnings: Practiced string traversal techniques Understood why built-in methods like count() may not always work (no overlapping support) Strengthened concepts of slicing and iteration in Python 💡 Example Insight: For string "ABCDCDC" and substring "CDC", the answer is 2 (overlapping counts matter!). ⚡ Approach Used: Iterated through the string Compared substrings using slicing Counted valid matches efficiently 📈 Problems like this help build strong fundamentals in string manipulation, which is crucial for coding interviews and real-world applications. #Python #HackerRank #Coding #Strings #ProblemSolving #DSA #LearningJourney #AI link of #Solution :- https://lnkd.in/gtqcy8fX