R Kishore Reddy’s Post

Want to grab a part of datetime value in a #Python #Pandas data frame column? Use "dt": df['x'].dt.year df['x'].dt.hour df['x'].dt.minute df['x'].dt.day_of_week # or dayofweek or weekday day_of_week returns an integer, where 0 is Monday and 6 is Sunday.

DSA Tip: Hash Table If you’re searching through data one by one… there’s a faster way. Use a Hash Table. It uses a key -> index mapping to store and retrieve data instantly. No full scanning. No long searches. Just direct access. Used in: Dictionaries Caching Databases Insight: The right key can turn searching into instant access. Quick Challenge: What happens when two keys map to the same index? Drop your answer, I’ll review the best ones. FOLLOW FOR MORE DSA TIPS & INSIGHTS #DSA #HashTable #Python #CodingTips #LearnToCode

Most people use Pandas for EDA. 𝗩𝗲𝗿𝘆 𝗳𝗲𝘄 𝘂𝘀𝗲 𝗶𝘁 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁𝗹𝘆. That’s the difference between spending hours exploring data and getting insights in minutes. Over time, one thing has stood out to me: It’s not just about the insights - it’s about how efficiently you get there. I’ve put together a quick reference: 📊 10 Pandas EDA Tricks that help: • Write cleaner, more readable code • Speed up analysis • Build more reliable workflows 📌 Attached is a cheat sheet for easy reference. 𝗙𝗼𝗿 𝗮 𝗱𝗲𝘁𝗮𝗶𝗹𝗲𝗱 𝗯𝗿𝗲𝗮𝗸𝗱𝗼𝘄𝗻: 🔗 https://lnkd.in/gv6_TmUD What’s one Pandas tricks you use that saves you the most time? #DataAnalytics #DataScience #Python #Pandas #EDA

Day 106 Some problems feel simple when the pattern clicks. #Day106 🧩 17. Letter Combinations of a Phone Number How today went: • Used a digit → letters map • Built combinations using backtracking • Maintained a string path at each step • One recursive call per choice — no need for complex state handling What I realized: Once you understand the pattern: → choose a letter → move to next digit → build the path Backtracking becomes very natural. Simple problem, but great for building confidence. #LeetCode #DSA #Python #Backtracking #Recursion #LearningInPublic #Consistency

🚀 Day 6/30 of My LeetCode Journey (Python + SQL) Consistency is slowly turning into confidence 💪📈 🔹 **Python Problem of the Day** 👉 *Plus One* Given an integer represented as an array of digits, increment the number by one and return the resulting array. 💡 *Key Concept:* Handling carry from the last digit (especially edge cases like 9 → 10). 🔹 **SQL Problem of the Day** 👉 *Game Play Analysis I* Given a table of player activity, write a query to find the first login date for each player. 💡 *Key Concept:* GROUP BY with MIN() to extract earliest dates. Every day learning something new, refining logic, and improving speed ⚡ Day 6 done ✅ #LeetCode #30DaysChallenge #Python #SQL #CodingJourney #Consistency #ProblemSolving #Learning

🚀 Day 43 – LeetCode Journey Today’s problem: Minimum Depth of Binary Tree ✔️ Solved using recursion (DFS) ✔️ Carefully handled edge cases (null child nodes) ✔️ Calculated minimum depth correctly for uneven trees 💡 Key Insight: The minimum depth is the shortest path from the root node to a leaf node. We must handle cases where one subtree is missing — simply taking "min()" is not enough without checking null nodes. This problem improved my understanding of tree traversal and edge case handling in recursion 🌳 Learning to think deeper with trees every day 🔥💪 #LeetCode #Day43 #BinaryTree #DFS #Recursion #Python #ProblemSolving #CodingJourney #100DaysOfCode

Real world data is messy. So I built my own 100-record JSON dataset to practice cleaning it with Python. The dataset included: • Duplicate entries • Missing values • Ratings in mixed formats like "five", "4", and "3.5" • Different types of customer feedback • Inconsistent formatting Using Python, I cleaned the data, removed duplicates, standardised ratings, and generated basic insights. Big takeaway: data cleaning is just as important as analysis. GitHub Repo: https://lnkd.in/gYr-4kkm #Python #DataScience #DataAnalytics #Projects #Coding

Focus on the Process, Not the Project The dashboard gets finished. The model gets deployed. The output gets delivered. And then it's Next project. ✅ But how you explored the data — that stays. ✅ How you validated your results — that stays. ✅ How you questioned what didn't make sense — that stays. The project is temporary. The process is what compounds. Most people chase finished work as proof of progress. But finishing faster doesn't mean thinking better. 👉 Projects end. Your process stays. #DataAnalytics #Python #AnalyticsThinking #LearningInPublic

🚀 Day 85 of #100DaysOfLeetCode 🔍 Problem Solved: Ransom Note (LeetCode 383) Today’s problem was all about efficiently checking whether one string can be constructed from another — a classic hashing / frequency counting concept. ⚡ What I Learned: - Importance of frequency maps (hash tables) - Writing optimized solutions over naive approaches - How built-in methods can simplify logic but may impact performance 📊 Performance: ✅ Runtime: 0 ms (Beats 100%) ✅ Memory: Efficient usage 🔥 Takeaway: Small optimizations and choosing the right data structure can make a huge difference, even in easy problem #Day85 #LeetCode #CodingJourney #Python #DataStructures #ProblemSolving #100DaysOfCode

Python Clarity Series – Episode 23 Topic: Floating Point Precision Issue 🤯 Why does this happen? print(0.1 + 0.2) Output: 0.30000000000000004 ❗ 👉 This is NOT a Python bug. It’s due to how floating-point numbers are stored in binary. 💡 Fix (when needed): round(0.1 + 0.2, 1) Output: 0.3 💡 Concept: Computers approximate decimal values internally. Important in: ✔ Financial calculations ✔ Data Science Don’t ignore this. #PythonConcepts #FloatingPoint #RealWorldCoding #python #clarity