Python Polymorphism: Flexible Code with Many Forms

An exercise to help build the right mental model for Python data. The “Solution” link uses memory_graph to visualize execution and reveals what’s actually happening: - Solution: https://lnkd.in/ewgqV4SD - Explanation: https://lnkd.in/ebPVvnhx - More exercises: https://lnkd.in/eQSdJdaW It's instructive to compare with this earlier exercise (tuple with list, instead of list with tuple): https://lnkd.in/edprkC5Y

An exercise to help build the right mental model for Python data. The “Solution” link uses memory_graph to visualize execution and reveals what’s actually happening: - Solution: https://lnkd.in/e8rk8Da3 - Explanation: https://lnkd.in/ebPVvnhx - More exercises: https://lnkd.in/eQSdJdaW It's instructive to compare with this earlier exercise (tuple with lists, instead of list with lists): https://lnkd.in/eiFiiYRj

Strings are everywhere in Python, and mastering string methods makes your code cleaner, faster, and more efficient. This visual covers commonly used Python string methods like: Case conversion (upper(), lower(), title()) Searching & counting (find(), index(), count()) Validation checks (isdigit(), isalpha(), islower()) Formatting & alignment (format(), center(), ljust(), rjust()) Cleaning & splitting (strip(), replace(), split()) These methods are extremely useful in Data Analytics, Data Cleaning, and Text Processing. #Python #DataAnalytics #PythonProgramming #LearningPython #DataScience #Coding #Developer #Analytics

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

𝗣𝘆𝘁𝗵𝗼𝗻 𝗗𝗮𝗶𝗹𝘆 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 | 𝗛𝗮𝗰𝗸𝗲𝗿𝗥𝗮𝗻𝗸 – 𝗡𝗲𝘀𝘁𝗲𝗱 𝗟𝗶𝘀𝘁𝘀 | 𝗗𝗮𝘆 𝟭𝟬 This Python problem exposes weak data-handling instantly. Day 10 of my Python Daily Challenge 🚀 Today’s task wasn’t about lists. It was about thinking in layers. 👉 Store names + scores 👉 Find the second lowest score 👉 Print names in alphabetical order Where most people slip 👇 • Forgetting duplicate scores exist • Mixing sorting logic with filtering • Ignoring output order requirements 💡 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗽𝗮𝘁𝘁𝗲𝗿𝗻 𝗳𝗿𝗼𝗺 𝗗𝗮𝘆 𝟭𝟬: Before writing code, separate the steps: collect → filter → sort → print Clear steps = clean solutions. That’s why I’m focusing on Python patterns, not shortcuts — one problem a day, stronger logic every time. Which part confuses you more — filtering or sorting? 👇 #Python #HackerRank #DailyCoding #ProblemSolving #InterviewPrep #LearnInPublic #Consistency

KGTK is a Python library for easy manipulation with #KnowledgeGraphs. It provides a flexible framework that allows chaining of common graph operations, such as: extraction of subgraphs, filtering, computation of graph metrics, validation, cleaning, generating embeddings, and more. https://lnkd.in/eEme__5v

𝗣𝘆𝘁𝗵𝗼𝗻 𝗗𝗮𝗶𝗹𝘆 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 | 𝗛𝗮𝗰𝗸𝗲𝗿𝗥𝗮𝗻𝗸 – 𝗧𝗵𝗲 𝗠𝗶𝗻𝗶𝗼𝗻 𝗚𝗮𝗺𝗲 | 𝗗𝗮𝘆 𝟮𝟭 This Python game looks innocent—but brute force fails hard. Day 21 of my Python Daily Challenge 🚀 At first glance, this felt like a substring problem: 👉 Generate all substrings 👉 Count scores 👉 Decide the winner 𝗧𝗵𝗮𝘁 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵 𝘁𝗶𝗺𝗲𝘀 𝗼𝘂𝘁 😅 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗶𝗻𝘀𝗶𝗴𝗵𝘁 👇 • Don’t build substrings — count positions • Each index contributes (n - i) points • Logic > loops > brute force 💡 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗽𝗮𝘁𝘁𝗲𝗿𝗻 𝗳𝗿𝗼𝗺 𝗗𝗮𝘆 𝟮𝟭: Great solutions change how you think, not just how you code. This problem rewards mathematical thinking over syntax. Did you try brute force first on this one? 👇 #Python #HackerRank #DailyCoding #ProblemSolving #InterviewPrep #LearnInPublic #Consistency

🌱 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐏𝐲𝐭𝐡𝐨𝐧 𝐁𝐚𝐬𝐢𝐜𝐬 – 𝐕𝐚𝐫𝐢𝐚𝐛𝐥𝐞𝐬, 𝐍𝐮𝐦𝐛𝐞𝐫𝐬 & 𝐒𝐭𝐫𝐢𝐧𝐠𝐬 Today I strengthened my understanding of Python fundamentals: # 𝐕𝐚𝐫𝐢𝐚𝐛𝐥𝐞𝐬 are used to store data values and make code reusable and readable. # 𝐍𝐮𝐦𝐛𝐞𝐫𝐬 in Python (int & float) help perform mathematical and logical operations. # 𝐒𝐭𝐫𝐢𝐧𝐠𝐬 are used to handle text data and support operations like concatenation and formatting. Building strong basics in Python is the first step toward mastering data analysis and automation. 🚀 #Python #LearningJourney #ProgrammingBasics #DataAnalytics #ContinuousLearning #Codebasics

I’m happy to share a quick tip from my data cleaning experience: When imputing missing quantitative values with skewed distributions, I bin by quantiles (pd.qcut()) and fill within-bin medians. This preserves distribution, reduces outlier impact, and keeps everything traceable . #DataScience #DataCleaning #Python #MachineLearning #SeniorPro #Storytelling

Used Python (pandas & matplotlib) to analyse the QVI Transaction Data & Purchase Behaviour dataset. Handled nulls, duplicates, outliers, merged datasets, created metrics, and generated insights all before touching any dashboard tool. Python truly shines in data preparation and exploration. #Data_Analysis#First_python_project