Mastering Lists & Tuples in Python

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

Day 3 — Data Structures in Python Today I learned: • Lists • Tuples • Sets • Dictionaries Practiced these concepts with real-world examples to understand how data is stored and managed Key takeaway: Data structures make it easier to organize, access, and manage data efficiently. Example: {"name": "Rahul", "marks": 85} Small step, but feels powerful already. GitHub: https://lnkd.in/gNxJa4TR #Python #DataStructures #CodingJourney #LearningInPublic #Consistency

Day 65 of the #three90challenge 📊 Today I learned about File Handling in Python — working with external data files. This is a big step because real-world data doesn’t live inside code — it comes from files like .txt, .csv, etc. What I practiced today: • Opening files using open() • Reading data (read(), readline()) • Writing data to files • Understanding file modes (r, w, a) • Closing files properly Example thinking: Instead of hardcoding data, I can now read data from files, process it, and even write results back. Example: with open("data.txt", "r") as file: content = file.read() print(content) This makes Python much more powerful for handling real datasets. From working with code → to working with real data 🚀 GeeksforGeeks #three90challenge #commitwithgfg #Python #DataAnalytics #LearningInPublic #Consistency #Upskilling #PythonBasics

Stop the Excel vs. Python war. Here is the actual answer: Use Excel when: ✅ Your audience only knows Excel ✅ The dataset fits in rows you can see ✅ Speed of delivery beats reproducibility Use Python when: ✅ The same report runs every week ✅ Data has 100k+ rows ✅ You need auditability and version control Use BOTH when: ✅ You want a job in 2025 The best analysts do not pick sides. They pick the right tool. Tool tribalism is the enemy of good analysis. Master both. Charge more. Ship faster. Which tool do YOU default to — and why? Let's debate 👇 #Excel #Python #DataAnalysis #DataScience #Analytics

Week 4 of #100DaysOfCode — done! 🎉 Last week I learned how to build classes. This week I learned how to make them talk to each other. Topics covered: 🧹 Inheritance → Superclass, subclass, base class — and what they actually mean → How Python finds attributes (spoiler: it walks up the chain) → Overriding methods — and extending them with super() → isinstance, issubclass, type — when to use which → Multiple inheritance + MRO (Method Resolution Order) 🏗️ Abstract Classes & Interfaces → ABC + @abstractmethod — enforcing a contract on subclasses → Duck typing — "if it has a speak() method, it speaks" → Subclassing built-ins (list, dict, iterator) → Mixins — plugging in behaviour without full inheritance I’ve structured my learning into notes and practical examples to better understand the concepts : https://lnkd.in/epaBymnJ #100DaysOfCode #Python #OOP #LearningInPublic #Programming

𝐎𝐧𝐞 𝐭𝐡𝐢𝐧𝐠 𝐈 𝐮𝐧𝐝𝐞𝐫𝐞𝐬𝐭𝐢𝐦𝐚𝐭𝐞𝐝 𝐢𝐧 𝐝𝐚𝐭𝐚 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬: 𝐦𝐢𝐬𝐬𝐢𝐧𝐠 𝐯𝐚𝐥𝐮𝐞𝐬 While exploring a dataset in Python recently, I noticed how often real datasets contain missing values. At first it seems like a small issue, but it can actually affect the entire analysis. Using pandas functions like isnull() and fillna() made it easier to detect and handle those gaps before doing any calculations or visualizations. It made me realize that a big part of data analysis isn’t just analyzing the data — it’s preparing the data properly so the results actually make sense. Still learning, but these small steps are starting to make the workflow clearer. #Python #Pandas #DataAnalytics #DataCleaning

🚀 Simplifying Trees in DSA! 🌳💻 While Arrays and Linked Lists are great linear structures, hierarchical data requires a Non-Linear approach—like Trees! To make revising easier, I created this visual cheat sheet. Just like a real-world tree has a Root and Leaves, a Tree data structure starts at the Root Node and branches out to Intermediate and Leaf Nodes. Here is what I have visually summarized in these notes: ✅ The core difference between Linear and Non-Linear structures ✅ 7 Types of Trees (including BST, Strict, Complete, and Skew Trees) ✅ Array Representation vs. Logical View ✅ Tree Traversal logic (Pre-order, In-order, Post-order) complete with Python code! 🐍 Visualizing the flow from the root down to the leaf nodes is a game-changer for understanding algorithms. Take a look and let me know in the comments—what is your favorite data structure to work with? 👇 #DSA #DataStructures #Algorithms #Python #CodingJourney #TechNotes #SoftwareEngineering #LearnInPublic

𝗗𝗮𝘆 𝟮 | 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗵𝗼𝘄 𝗣𝘆𝘁𝗵𝗼𝗻 𝗵𝗮𝗻𝗱𝗹𝗲𝘀 𝗱𝗮𝘁𝗮 Today’s learning was focused on how data is stored and used in Python, which is an important base for data analysis. 𝗧𝗼𝗽𝗶𝗰𝘀 𝗰𝗼𝘃𝗲𝗿𝗲𝗱: 💠 Variables and assigning values 💠Data types such as int, float, string, and boolean 💠Using type() to check and understand data types I tried a few small examples to see how different data types behave. Even though this topic looks simple, it is clear that everything in programming depends on how well we handle data. Taking time here feels important before moving forward. #PythonBasics #DataTypes #DataAnalysis #LearningInPublic #CodingJourney

Days 68-69 of the #three90challenge 📊 Today I explored NumPy operations — specifically indexing and slicing arrays. After understanding NumPy basics, this step made it easier to access and manipulate data efficiently. What I practiced today: • Accessing elements using indexing • Extracting subsets of data using slicing • Working with multi-dimensional arrays • Performing operations on selected data Example thinking: Instead of looping through data manually, I can directly select and operate on specific parts of an array. Example: import numpy as np arr = np.array([10, 20, 30, 40, 50]) print(arr[1:4]) # Output: [20 30 40] This makes data manipulation faster and more intuitive. From handling data → to controlling it efficiently 🚀 GeeksforGeeks #three90challenge #commitwithgfg #Python #NumPy #DataAnalytics #LearningInPublic #Consistency #Upskilling