Data Bugging: loc vs iloc in Pandas

🚀 Today’s Learning Update – Doubly Linked List (Data Structures) Today, I deepened my understanding of Doubly Linked List in Data Structures and Algorithms. 🔹 Learned how each node contains: data prev pointer next pointer 🔹 Implemented the following operations in Python: ✔️ Insertion at beginning ✔️ Insertion at end ✔️ Insertion at middle ✔️ Deletion of head, end and middle node ✔️ Forward and backward traversal Consistency in DSA practice is helping me strengthen my problem-solving skills step by step. 💪 #DSA #DataStructures #LinkedList #Python #CodingJourney #LearningEveryday #SoftwareDevelopment

Spent today exploring pandas while starting work with the MovieLens dataset for a recommendation systems project. A few small observations from the process: • pandas makes it incredibly easy to move from raw CSV files to structured data exploration • building a user–movie matrix is just a pivot operation away • debugging environments in VS Code can be surprisingly tricky when working with virtual environments The most interesting part for me was realizing how quickly you can move from: raw rating logs → structured dataset → matrices suitable for recommendation algorithms. Next step: experimenting with similarity-based recommendations using the dataset. Small progress today, but the foundation for something much bigger. Challenge : what pandas method gave the output in the terminal 🙃 🙃 #MachineLearning #DataScience #Python #RecommenderSystems

DSA Tip: Binary Trees (Part 1) If you're storing hierarchical data in flat lists and struggling with fast lookups… there’s a better way. Use Binary Trees. Each node connects to up to two children: left and right. No rigid structure. No long chains of data. Just organized relationships. Insight: The right structure can turn slow searches into powerful ones. Challenge: Insert these values into a tree: [5, 3, 7, 1, 4, 6, 9] Then search for 4. Drop “Done” when you complete it. Also this post has issues, let's know who first notice it. FOLLOW FOR MORE DSA TIPS & INSIGHTS #DSA #Python #BinaryTree #CodingChallenge #LearnToCode

🚀 Day-77 of #100DaysOfCode 📊 NumPy Practice – Finding Smallest Element Today I worked on finding the minimum value in an array using NumPy. 🔹 Concepts Practiced ✔ Array operations ✔ Using np.min() ✔ Basic data analysis 🔹 Key Learning Finding minimum values is a simple yet important operation used in data analysis, optimization problems, and real-world datasets. Small steps every day → Big progress 🚀 #Python #NumPy #DataScience #CodingPractice #100DaysOfCode #PythonDeveloper

Learning from Data Cleaning: Handling Mixed Date Formats in a DataFrame While working with a dataset recently, I noticed that the date column contained multiple formats. Because of this, converting the column to datetime was causing errors and incorrect parsing. To handle this, I used pandas to_datetime() with: format="mixed" – which allows pandas to parse multiple date formats within the same column errors="coerce" – which converts invalid or unrecognizable dates into NaT instead of breaking the code After applying this approach, most of the date values were parsed correctly, making the dataset much cleaner and ready for analysis. Key takeaway: Real-world datasets rarely come perfectly formatted. Using parameters like format="mixed" and errors="coerce" can significantly improve data quality and preprocessing efficiency. #DataAnalytics #Python #Pandas #DataCleaning #DataScience #DataPreparation

𝗢𝗢𝗣𝗦 𝘄𝗮𝘀 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗼𝘀𝗲 𝘁𝗼𝗽𝗶𝗰𝘀 𝗜 𝗸𝗲𝗽𝘁 𝗿𝗲𝘃𝗶𝘀𝗶𝘁𝗶𝗻𝗴 — 𝗻𝗼𝘁 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝗜 𝗳𝗼𝗿𝗴𝗼𝘁 𝗶𝘁, 𝗯𝘂𝘁 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝗜 𝗻𝗲𝘃𝗲𝗿 𝗳𝘂𝗹𝗹𝘆 𝘁𝗿𝘂𝘀𝘁𝗲𝗱 𝗺𝘆 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗼𝗳 𝗶𝘁. Read about it from multiple sources. Understood the syntax, could follow the examples. But the mental model was always shaky. College lectures sorted that — not by covering something new, but by connecting things in the right order. Inheritance types, method resolution, how one class builds on another — it started making structural sense rather than just syntactic sense. Revisited it all. Coded through single, multi-level, hierarchical, and hybrid inheritance. And for the first time it felt like I actually owned the concept, not just recognized it. #Python #OOP #CS #DataScience

Mastering Pandas Made Easy 📊 If you are learning data analysis, this image is a quick guide you should save. It covers the most useful Pandas functions in one place. From reading data using read_csv() to cleaning with drop_duplicates(), everything is explained in a simple way. These functions help you: ✔ Understand your data ✔ Clean messy data ✔ Combine and reshape datasets ✔ Create powerful summaries Instead of feeling confused, you can now learn step by step with clarity. Save this post for later and keep practicing. Small daily learning can make you a data expert. Start simple. Stay consistent. Grow fast. #DataScience #Python #Pandas #Learning #CareerGrowth

Building a simple data ingestion API using FastAPI. The idea is simple: • Upload a dataset (CSV) • Parse it using pandas • Automatically inspect columns • Return metadata like data types and missing values It’s interesting how quickly useful APIs can be built with FastAPI. Next step: adding querying and simple data exploration endpoints. Learning by building. #Python #FastAPI #BackendDevelopment #DataEngineering #BuildInPublic

🚀 Day-74 of #100DaysOfCode 📊 NumPy Practice – Replacing Negative Values Today I worked on replacing negative values with zero using NumPy. 🔹 Concepts Practiced ✔ Boolean indexing ✔ Array filtering ✔ Data cleaning techniques 🔹 Key Learning NumPy makes it easy to modify data efficiently without loops, which is very useful in real-world data preprocessing tasks. Step by step improving my data handling and NumPy skills 🚀 #Python #NumPy #DataScience #MachineLearning #100DaysOfCode #PythonProgramming

Organizing Data Lists & Tuples Day 5: If variables are containers, today I learned how to build a warehouse. I dove deep into Lists and Tuples. Lists: Perfect for items that need to change, grow, or be sorted (Mutable). Tuples: The "set it and forget it" version (Immutable). Learning how to slice through a list felt like a superpower. It’s amazing how much more organized your logic becomes when you know how to group your data properly. A Python tuple is an ordered and immutable collection of elements enclosed in parentheses, commonly used for grouping related data that should not change. #DataStructures #PythonLists #LearningInPublic #TechJourney #Pythonday5