Optimize Data Search with Hash Tables

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 13 | PCA (Dimensionality Reduction) 📉📊 Today, I explored Principal Component Analysis (PCA). PCA is used to reduce the number of features while preserving important information. This helps in: ✔ Reducing complexity ✔ Improving model performance ✔ Visualizing high-dimensional data I applied PCA using Python to transform data into fewer dimensions 💻 This helped me understand how large datasets can be simplified without losing key insights. #MachineLearning #PCA #DataScience #LearningInPublic #Python

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 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

Learn how to build a predictive model with Python and Scikit-Learn, including data preparation, model selection, and evaluation techniques, with expert tips and real-world examples https://lnkd.in/ge-CSTzq #PredictiveModelWithPython Read the full article https://lnkd.in/ge-CSTzq

Learn how to build a predictive model with Python and Scikit-Learn, including data preparation, model selection, and evaluation techniques, with expert tips and real-world examples https://lnkd.in/ge-CSTzq #PredictiveModelWithPython Read the full article https://lnkd.in/ge-CSTzq

Learn how to build a predictive model with Python and Scikit-Learn, including data preparation, model selection, and evaluation techniques, with expert tips and real-world examples https://lnkd.in/ge-CSTzq #PredictiveModelWithPython Read the full article https://lnkd.in/ge-CSTzq

Python Series — Day 3 🧠 Let’s level it up a bit 👇 What will be the output of this code? def modify_list(lst): lst.append(4) a = [1, 2, 3] modify_list(a) print(a) Options: A. [1, 2, 3] B. [1, 2, 3, 4] C. Error D. None Think carefully 👀 (Hint: It’s not about functions… it’s about how Python handles data) Drop your answer 👇 Answer tomorrow 🚀 #Python #CodingChallenge #LearningInPublic #DataEngineering #Tech

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

DSA Tip: Collision Handling What happens when two keys map to the same index? That’s called a collision. In hash tables, different keys can produce the same hash — but data must still be stored correctly. So we use collision handling techniques like: Chaining (store multiple values in one bucket) Open Addressing (find another empty slot) Insight: Efficiency isn’t just about speed, it’s about handling conflicts gracefully. Quick Challenge: If two keys hash to index 2, what should happen next? Drop your answer, I’ll review the best ones. FOLLOW FOR MORE DSA TIPS & INSIGHTS #DSA #HashTable #CollisionHandling #Python #CodingTips