Harini M S’ Post

𝗠𝗔𝗖𝗛𝗜𝗡𝗘 𝗟𝗘𝗔𝗥𝗡𝗜𝗡𝗚 𝗙𝗢𝗥 𝗕𝗘𝗚𝗜𝗡𝗡𝗘𝗥𝗦 𝗣𝘆𝘁𝗵𝗼𝗻 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹𝘀: 𝗗𝗶𝗰𝘁𝗶𝗼𝗻𝗮𝗿𝗶𝗲𝘀, 𝗗𝗮𝘁𝗮 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝘀, 𝗖𝗼𝗺𝗽𝗿𝗲𝗵𝗲𝗻𝘀𝗶𝗼𝗻𝘀 & 𝗟𝗮𝗺𝗯𝗱𝗮 Good Python code is rarely about knowing more features — it’s about choosing the right tools. In this notebook, I dive into concepts that directly impact performance, clarity, and design: • How dictionaries power fast lookups • Why selecting the right data structure matters • Writing cleaner loops with list comprehensions • Simplifying logic with lambda functions #Python #PythonProgramming #LearnPython #Coding #DataStructures #SoftwareDevelopment #ProgrammingLife

Strengthening my problem-solving foundation with Data Structures and Algorithms in Python. Covered core concepts such as Big O analysis, linked lists, stacks, queues, hashmaps, recursion, and basic graph traversal. Moving from concept clarity to consistent hands-on practice. #DSA #PythonDeveloper #BackendDevelopmenr #ProblemSolving

Tuples might look simple… but they’re powerful! 👀✨ If you’re working with fixed data that shouldn’t change, tuples are your best friend 💡 ✔️ Ordered ✔️ Immutable ✔️ Supports heterogeneous data Understanding CRUD on tuples helps you clearly see why immutability matters in Python 🐍 Save this post for quick revision & follow for more Python concepts explained visually 🚀 #PythonProgramming #LearnPython #CodingJourney #SoftwareDevelopment #TechCareer

🐍 𝗔 𝗣𝘆𝘁𝗵𝗼𝗻 𝘁𝗿𝗶𝗰𝗸 𝗲𝘃𝗲𝗿𝘆 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝘀𝗵𝗼𝘂𝗹𝗱 𝗸𝗻𝗼𝘄 If you’re processing large datasets in Python, this small trick can save both memory and time. 👉🏻Instead of this: 𝗻𝘂𝗺𝘀 = [𝗶*𝗶 𝗳𝗼𝗿 𝗶 𝗶𝗻 𝗿𝗮𝗻𝗴𝗲(𝟭𝟬𝟬𝟬𝟬𝟬𝟬𝟬)] 𝘁𝗼𝘁𝗮𝗹 = 𝘀𝘂𝗺(𝗻𝘂𝗺𝘀) 🚀Use a generator expression: 𝘁𝗼𝘁𝗮𝗹 = 𝘀𝘂𝗺(𝗶*𝗶 𝗳𝗼𝗿 𝗶 𝗶𝗻 𝗿𝗮𝗻𝗴𝗲(𝟭𝟬𝟬𝟬𝟬𝟬𝟬𝟬)) 👉🏻Why? • 𝙉𝙤 𝙝𝙪𝙜𝙚 𝙡𝙞𝙨𝙩 𝙘𝙧𝙚𝙖𝙩𝙚𝙙 𝙞𝙣 𝙢𝙚𝙢𝙤𝙧𝙮 • 𝙑𝙖𝙡𝙪𝙚𝙨 𝙜𝙚𝙣𝙚𝙧𝙖𝙩𝙚𝙙 𝙤𝙣 𝙩𝙝𝙚 𝙛𝙡𝙮 • 𝙁𝙖𝙨𝙩𝙚𝙧 𝙛𝙤𝙧 𝙡𝙖𝙧𝙜𝙚-𝙨𝙘𝙖𝙡𝙚 𝙙𝙖𝙩𝙖 𝙥𝙧𝙤𝙘𝙚𝙨𝙨𝙞𝙣𝙜 𝘐𝘯 𝘥𝘢𝘵𝘢 𝘱𝘪𝘱𝘦𝘭𝘪𝘯𝘦𝘴, 𝘵𝘩𝘦𝘴𝘦 𝘴𝘮𝘢𝘭𝘭 𝘰𝘱𝘵𝘪𝘮𝘪𝘻𝘢𝘵𝘪𝘰𝘯𝘴 𝘤𝘢𝘯 𝘮𝘢𝘬𝘦 𝘢 𝘣𝘪𝙜 𝘥𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘤𝘦 𝘸𝘩𝘦𝘯 𝘥𝘢𝘵𝘢 𝙜𝘳𝘰𝘸𝘴 𝘶𝘯𝘱𝘳𝘦𝘥𝘪𝘤𝘵𝘦𝘥. Sometimes the best optimization is just removing unnecessary objects from memory. #Python #DataEngineering #DataEngineer #BigData #DataPipelines #CodingTips #TechTips #LearnPython #DataEngineeringTips

🐍 𝐏𝐲𝐭𝐡𝐨𝐧 𝐐𝐮𝐢𝐜𝐤 𝐓𝐢𝐩: 𝐋𝐢𝐬𝐭 𝐯𝐬 𝐓𝐮𝐩𝐥𝐞 Confused between 𝐋𝐢𝐬𝐭 𝐚𝐧𝐝 𝐓𝐮𝐩𝐥𝐞 in Python? ✅ 𝐋𝐢𝐬𝐭 → 𝐌𝐮𝐭𝐚𝐛𝐥𝐞 (can add/remove/change items) ✅ 𝐓𝐮𝐩𝐥𝐞 → 𝐈𝐦𝐦𝐮𝐭𝐚𝐛𝐥𝐞 (fixed, cannot be changed) 📌 Use 𝐋𝐢𝐬𝐭 when data changes 📌 Use 𝐓𝐮𝐩𝐥𝐞 when data must stay constant #Python #Coding #Programming #InterviewPrep #DataScience #KSRDatavizon | | Santosh J. | Mahesh | KONDA REDDY | Magudeswaran | Satya | Ajay | Basha | Gopi E | Sekhar | Gopi Krishna | Prasanna | Sourav | Shaik Arshad | Kamalaker | Indrajeet | Arvind | Harikrishna | Maureen | Ravindra Reddy | Manikanta Reddy | Niharika | RAMA | Sreethar M B | Khuddus | Mallikarjuna R

🐍 𝐏𝐲𝐭𝐡𝐨𝐧 𝐐𝐮𝐢𝐜𝐤 𝐓𝐢𝐩: 𝐋𝐢𝐬𝐭 𝐯𝐬 𝐓𝐮𝐩𝐥𝐞 Confused between 𝐋𝐢𝐬𝐭 𝐚𝐧𝐝 𝐓𝐮𝐩𝐥𝐞 in Python? ✅ 𝐋𝐢𝐬𝐭 → 𝐌𝐮𝐭𝐚𝐛𝐥𝐞 (can add/remove/change items) ✅ 𝐓𝐮𝐩𝐥𝐞 → 𝐈𝐦𝐦𝐮𝐭𝐚𝐛𝐥𝐞 (fixed, cannot be changed) 📌 Use 𝐋𝐢𝐬𝐭 when data changes 📌 Use 𝐓𝐮𝐩𝐥𝐞 when data must stay constant #Python #Coding #Programming #InterviewPrep #DataScience #KSRDatavizon | | Santosh J. | Mahesh | KONDA REDDY | Magudeswaran | Satya | Ajay | Basha | Gopi E | Sekhar | Gopi Krishna | Prasanna | Sourav | Shaik Arshad | Kamalaker | Indrajeet | Arvind | Harikrishna | Maureen | Ravindra Reddy | Manikanta Reddy | Niharika | RAMA | Sreethar M B | Khuddus | Mallikarjuna R

Conducted variable type verification in Python to ensure correct data classification (int, float, string, categorical). Strong analysis begins with proper data validation and structure. #PythonProgramming #DataScience #DataCleaning #AnalyticsSkills

LeetCode #572 – Subtree of Another Tree | Python Implementation I implemented a recursive DFS approach that checks every node in the main tree as a potential subtree root. Core Insight: Subtree verification is a nested recursion problem — outer recursion finds candidate positions, inner recursion validates exact matches. Reusing the same-tree helper keeps logic clean and modular. Time: O(m × n) worst case where m, n are tree sizes | Space: O(h) recursion depth #LeetCode #DataStructures #Python #BinaryTree #Recursion #DFS #CodingInterview #SoftwareEngineering

# Understanding Pandas and Semantic Link for Data Manipulation Navigating the world of data often involves manipulating dataframes, merging tables, and shaping information. Tools like Pandas provide robust solutions for these tasks in Python. Microsoft's Semantic Link extends these capabilities, offering a direct interface within Python notebooks to interact with semantic models. This integration streamlines the process of data analysis and model building. #DataScience #Python #Pandas #SemanticLink #DataAnalysis