Merging Linked Lists and Handling Edge Cases

QK's power comes from its best-in-class data API — designed by people who raised the bar of how financial data is consumed around the world. A single line item gets augmented with 39 additional dimensions allowing for more consistent interpretation and depth of analysis. Oh, and the data is delivered directly into R or Python with an incredibly intuitive call. Fundamentals (fully auditable) Ownership (Beneficial Owners, Institutions, Insiders) #R #python #fundamentals #api

This week I spent 2 hours debugging a pipeline that broke because of a subtle mutable default argument. Last week I finished DataCamp's "Intermediate Python for Developers" - and guess what chapter was in there. Funny how that works sometimes. A few takeaways that'll stick with me: • Mutable defaults are a trap, even for people who "know Python" • Decorators aren't magic - they're just functions returning functions (but the mental model matters) • Comprehensions > loops, until they don't fit on one screen anymore Working with Python daily on dbt models, and data transformations, it's easy to get comfortable in a narrow slice of the language. Stepping back to revisit the fundamentals consistently makes my production code cleaner. What's your approach - do you block time for structured learning, or learn purely on the job? #Python #DataEngineering #LearningInPublic

I was cleaning a dataset — filtering rows, transforming values, the usual. My 5-line for loop worked fine. But I wanted to be "Pythonic." So I compressed it into a one-liner. Then I added another layer. The next morning I stared at it for two full minutes trying to decode my own logic. If I couldn't read it, my future teammates had no chance. This carousel breaks down: → The mental model that makes list comprehensions click instantly → The reading order most beginners get backwards → The exact rule for when to stop using them and write a real loop What's the longest you've stared at your own code before realizing you had no idea what it does? #Python #DataAnalytics #DataAnalyst #PythonTips #LearnInPublic #AHAMoments #DataAnalystJourney

One lesson that keeps coming up in my data analytics journey: the right data structure can outperform the most advanced algorithm 🧠 Python dictionaries have been a game-changer for me in real-time scenarios—especially for caching intermediate results and tracking session-level data 🔄 What makes them powerful?  Constant-time lookups ⚡  Flexible structure for dynamic data 🔀  Easy integration into pipelines 🔧 When you’re working with streaming or high-volume data, these advantages add up quickly 📈 It’s not always about doing more—it’s about doing things smarter 💡 What data structure do you rely on the most? #DataAnalytics #Python #DataStructures #RealTimeSystems #BigData #LearningInPublic #TechThoughts

🚀 Day 29 – LeetCode Journey Today’s problem: Combine Two Tables ✔️ Used Pandas merge() to join datasets ✔️ Applied left join to retain all records from the primary table ✔️ Selected only required columns for clean output 💡 Key Insight: Understanding how to work with dataframes and joins is essential for real-world data analysis. Using merge() makes combining structured data simple and efficient. This problem strengthened my skills in Pandas, data manipulation, and SQL-like operations in Python. From algorithms to data handling — growing every day 📊🔥 #LeetCode #Day29 #Pandas #DataAnalysis #Python #ProblemSolving #CodingJourney #100DaysOfCode

📈 Real Growth Starts When You Stop Copy-Pasting Today I realized something important: It’s not about writing code… It’s about understanding what the data is saying. So instead of just running code, I focused on: ✔ Why we clean data ✔ How to handle real-world datasets ✔ What insights can be extracted This mindset shift is changing everything. From learning → to thinking → to solving. And that’s where real opportunities begin 💼 #Mindset #DataAnalytics #LearningJourney #Python #Growth

💡 From idea → execution 🚀 Ever wondered how chatbots fetch real-time data? I built one! Introducing my Python Weather Chatbot 🌤️ It takes user input and instantly responds with live weather updates. 🔧 What I used: Python + Weather API 🎯 What I learned: Real-world problem solving & API integration Small project. Big learning. 💯 Let’s connect and grow together 🤝 #PythonDeveloper #Projects #Chatbot #LearningByDoing #TechJourney

✅ Day 92 of 100 Days LeetCode Challenge Problem: 🔹 #2011 – Final Value of Variable After Performing Operations 🔗 https://lnkd.in/gX-JQNUJ Learning Journey: 🔹 Today’s problem was about evaluating a sequence of increment and decrement operations. 🔹 I initialized a variable ans = 0 to track the value. 🔹 Used a hashmap to map each operation to its effect: • "++X" and "X++" → +1 • "--X" and "X--" → -1 🔹 Iterated through the operations and updated ans accordingly. 🔹 Returned the final computed value. Concepts Used: 🔹 HashMap / Dictionary 🔹 String Matching 🔹 Simple Simulation Key Insight: 🔹 Instead of using multiple condition checks, mapping operations to values simplifies logic and improves readability. Complexity: 🔹 Time: O(n) 🔹 Space: O(1) #LeetCode #Algorithms #DataStructures #CodingInterview #100DaysOfCode #Python #ProblemSolving #LearningInPublic #TechCareers

🐍 Moving beyond basic Pandas… When datasets get bigger, how you write Pandas code starts to matter a lot. Here are a few techniques I’ve been learning to make analysis faster, cleaner, and more scalable: ✔ Vectorization instead of loops ✔ Using .loc[] and .iloc[] correctly ✔ Choosing apply() vs map() wisely ✔ Writing readable pipelines with method chaining ✔ Handling missing data before analysis Small improvements → Huge impact on real-world datasets 📊 Which Pandas technique improved your workflow the most? 👇 #Python #Pandas #DataAnalytics #LearningInPublic #AspiringDataAnalyst

Most beginners try to learn Pandas by memorizing everything. That’s the wrong approach. Good analysts: 1. Focus on a few core functions 2. Apply them to messy real data and 3. Build intuition over time These 8 functions handle most real-world cleaning: dropna, drop_duplicates, fillna, astype, groupby, merge, apply, rename Master these → you’re already ahead. #Python #Pandas #DataAnalytics #SQL #DataScience #Analytics #LearnPython #DataCleaning