Pandas EDA Tricks for Faster Insights

Working with messy real-world datasets taught me one thing: The cleaning step takes longer than the actual analysis. So I spent the last few weeks building dfdoctor - an open-source Python library that audits your DataFrame, tells you what’s wrong, and helps you fix it systematically instead of manually. It helps you quickly understand what’s broken and what to fix first. The part I'm most proud of: 5 correlation methods (including Kendall τ and Phi-k) implemented from scratch in pure numpy - no scipy dependency anywhere. 164 tests. CI passing across Python 3.9–3.12. Try it: pip install dfdoctor https://lnkd.in/e-ChV6mE #Python #OpenSource #DataEngineering #Pandas #EDA #DataScience

Starting something new 🚀 I’ve decided to start sharing daily polls on SQL, Python, and Excel. One thing I’ve realized is that we focus so much on advanced topics that we forget the basics, yet they’re the foundation of everything we build. So this is just a simple way to stay sharp: 👉 Quick questions 👉 Small “aha” moments 👉 From basics to advanced tips Nothing too heavy, just quick learning moments to keep your engine running. Because at the end of the day, consistency and daily practice are what really make the difference. Join in, vote, and let’s learn together 🧠 #SQL #PYTHON #EXCEL

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

I understood NumPy better when I applied it to real data 👇 Learning concepts is one thing… But using them on actual data is different. So I tried a simple example: 👉 Dataset: list of student marks Task: Add 5 bonus marks to every student Using Python list: - needed a loop - more lines of code Using NumPy: - converted list → array - added 5 in a single step That’s it. What I realized: NumPy is not just about syntax. It’s about handling data efficiently at scale. Even a small example made it clear: - less code - faster execution - cleaner logic Now I’m focusing more on applying concepts, not just learning them. If you're learning NumPy, try this: 👉 Take any small dataset and apply operations on it That’s where real understanding begins. What’s one concept you learned but haven’t applied yet? #NumPy #Python #DataScience #DataEngineering #MachineLearning #CodingJourney #TechLearning

Working with Pandas during EDA and came across something interesting today 🤔 df[df['salary'] > 50000]['bonus'] = 1000 At first glance, this looks correct. No error, no warning… but the DataFrame didn’t actually update. Took me a moment to realize what was going on. 👉 Question: Why doesn’t this modify the original DataFrame? What’s the correct way to handle this situation in Pandas? Curious to see how others approach this 👇 #Python #Pandas #DataAnalytics #EDA #MachineLearning

Day 109 Backtracking patterns are repeating again — and that’s a good sign. #Day109 🧩 78. Subsets How today went: • Used recursion to explore all elements • At each step, decide to include or skip the current element • Append current subset → explore → then pop to backtrack • Move to the next index and repeat What I’m noticing: Subsets is one of the cleanest backtracking patterns: → choose → explore → undo Another revision day, but clarity is improving. Consistency continues. #LeetCode #DSA #Python #Backtracking #Recursion #LearningInPublic #Consistency

Recursion confused me. So I built a visualizer for it. 🐍 Most beginners (including me) struggle with one thing: "What actually happens when a function calls itself?" So I wrote a Python program that shows you — step by step, with a delay so you can actually follow it. Watch it go DOWN the stack, hit the base case, then come back UP — adding numbers on the way. 👇 What I used: → Recursion — function calling itself → time.sleep() — to slow execution down visually → Print statements — to trace every step This isn't from a tutorial. I built this because I was confused. That's the best reason to build anything. 💡 Important: Dry run your code, It helps alot. #Python #Recursion #LearningInPublic #DataAnalytics #BBA #BuildInPublic

Most people default to Pandas. Works fine… until your data scales. That’s where Polars wins: > Similar syntax for most operations > Faster execution > Lazy evaluation (big performance boost) Don’t ditch Pandas. But ignoring Polars now? That’s a mistake. Learn both. Use what fits. Found Insightful? ♻️ Repost in your network and follow Sahil Alam for more. #DataEngineering #Python #Pandas #Polars #BigData #DataAnalyticsSahil Alam for more.

Day 5 of #30DaysOfPython ✅ Today I met two of Python's most powerful data structures. One of them already feels like home. The other? Slightly chaotic. Lists and dictionaries. Day 5. Lists made sense quickly — they're just ordered collections. I can store things, loop through them, sort them, slice them. Intuitive. Dictionaries? At first, the key-value pair concept felt abstract. The bug that got me today? I threw both strings and integers into the same list and tried to sort it. Python did not appreciate that. TypeError showed up like an old enemy. Day 5 done. 25 more to go! 👇 Lists vs dictionaries — when do you reach for one over the other? #Python #30DaysOfPython #DataStructures #StudentLife #AIML

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