Python Basics: Variables, Loops & Functions Explained

New Skill Unlocked: NumPy Basics! ✅ I've just wrapped up the fundamental concepts of the NumPy library. It's incredible to see how this tool serves as the foundation for almost every data-heavy python project Onward to Pandas! 🐼 #DataAnalytics #NumPy #Python #Programming Creating & Reshaping Data In data science, we often need to change the shape of our data (like turning a long list of numbers into a grid or matrix). NumPy makes this a one-liner. import numpy as np Create a 1D array of 12 numbers (0 to 11) data = np.arange(12) Reshape it into a 3x4 matrix (3 rows, 4 columns) matrix = data.reshape(3, 4) print(matrix) # Output: # [[ 0 1 2 3] # [ 4 5 6 7] # [ 8 9 10 11]] #DataAnalytics #NumPy #Python #Programming #machinelearning #dataScience #pandas

📅 Day 75 – Higher Order Functions using filter() 🚀🐍 Today I practiced filter() function in Python to select required data from lists 💡 🔹 Filtered even numbers from a list ⚖️🔢 🔹 Filtered odd numbers from a list 🔀🔢 🔹 Selected numbers greater than 10 ⬆️🔢 🔹 Applied condition-based filtering on numeric data ✔️ 🔹 Filtered words that start with vowels (a, e, i, o, u) 🔤 🔹 Learned string-based filtering using conditions 📏 💡 Understood how filter() helps in extracting only required elements from a dataset ⚡ 💡 Improved logic building with lambda functions + conditions 🧠 🔥 Feeling more confident in functional programming and data filtering in Python! #Day75 #Python #FilterFunction #HigherOrderFunctions #CodingJourney #LearnPython #WomenInTech #FutureEngineer 🚀✨

🚨 This behavior of Python might look like a BUG… but it isn’t actually. a = 10 b = 10 print(id(a)) print(id(b)) 👉 Same memory location 😲 “Why do we have two variables pointing to the same memory location?!” Here comes the second one and things get interesting 👇 a = [1, 2, 3] b = a b.append(4) print(a) # [1, 2, 3, 4] 🔥 👉 Hmmm… why did ‘a’ change?! 💡 Explanation: ⭐ id() returns the identity of an object ⭐ Python reuses memory locations for immutable values ⭐ For mutable objects however, there is no copying, just pointers! ⚠️ The misconception: Most people believe ‘=’ copies objects in variables. 👉 Nope! ✅ Solution: b = a.copy() Now the two variables are separate ✅ 🔥 Consequence: It can seriously mess up your program’s logic! Ever got caught by such a ghost bug in Python? 👇 #CodeWithSujith #Python #Programming #Coding #PythonTricks #LearnPython #PythonBeginner #100DaysOfCode #DeveloperJourney

🧠 Python Concept: __new__ vs __init__ Object creation vs initialization 😳 ❌ What most people think 👉 __init__ creates the object ✅ Reality class Demo: def __new__(cls): print("Creating instance") return super().__new__(cls) def __init__(self): print("Initializing instance") obj = Demo() 👉 Output: Creating instance Initializing instance 🧒 Simple Explanation 👉 __new__ → creates object 👉 __init__ → initializes object 💡 Why This Matters ✔ Used in immutable types ✔ Important for metaclasses ✔ Helps in advanced object control ✔ Asked in advanced interviews ⚡ Real-World Use ✨ Singleton pattern ✨ Custom object creation ✨ Immutable objects 🐍 Creation first, then initialization 🐍 Understand object lifecycle #Python #AdvancedPython #OOP #SoftwareEngineering #BackendDevelopment #Programming #DeveloperLife

The Python Collections Cheat Sheet Choosing the right data structure is 50% of the job. Pick the wrong one, and your code gets slow or buggy. Pick the right one, and it becomes elegant. My quick guide: ✅ List: When order matters ✅ Tuple: When data must stay constant ✅ Set: When you need uniqueness and speed ✅ Dict: When you need to map labels to data Day 16/30 #Python #Day16 #BuildinginPublic #DataStructures #CodingCommunity #PythonCheatSheet

I didn’t really understand NumPy until I asked a simple question: Why was it even created in the first place? Python, by design, is flexible and easy to use… but that flexibility comes at a cost. When developers started using Python for scientific computing and data-heavy tasks, they ran into real problems: * Working with large numerical data was slow * Memory usage was inefficient * Simple operations required too many loops and too much code And that’s exactly where NumPy came in. It wasn’t created to “add features” to Python — it was created to fix a bottleneck. NumPy introduced a new way of handling data: A structured, typed array that allows computations to happen at a much lower level (closer to C speed), while still writing code in Python. So instead of telling Python how to loop through every element… you just tell NumPy what operation you want — and it handles the rest efficiently. That shift is the real innovation. NumPy is not just about arrays. It’s about changing the way computation is done in Python — from step-by-step instructions to vectorized thinking. And that’s why it became the foundation of everything that came after it. #Python #NumPy #DataScience #MachineLearning #SoftwareEngineering

Real world data is messy. So I built my own 100-record JSON dataset to practice cleaning it with Python. The dataset included: • Duplicate entries • Missing values • Ratings in mixed formats like "five", "4", and "3.5" • Different types of customer feedback • Inconsistent formatting Using Python, I cleaned the data, removed duplicates, standardised ratings, and generated basic insights. Big takeaway: data cleaning is just as important as analysis. GitHub Repo: https://lnkd.in/gYr-4kkm #Python #DataScience #DataAnalytics #Projects #Coding

🔁 Python Revision – Sets Continuing my Python fundamentals revision 🐍 In this session, I focused on: ✔️ Sets (creation and properties) ✔️ Unique elements and unordered nature ✔️ Set methods (add, remove, discard, etc.) ✔️ Set operations (union, intersection, difference) Practiced using sets to handle unique data and perform efficient operations like finding common or different elements between datasets. Documented my practice in a Jupyter Notebook and shared it as a PDF to track my progress. Understanding sets is helping me work better with data and avoid duplicates 📊 Next: dictionaries and real-world data handling 🚀 #Python #Revision #Sets #Programming #DataAnalytics #LearningJourney #Coding

Hello Everyone, I used to think Python was complex… But it all starts with simple basics. Here’s what I learned: ⚡ What Python is & why it’s used in data analysis (page 1) ⚡ Variables & Data Types → int, float, string, list (page 3–4) ⚡ Type Casting & type() → understanding data correctly (page 5–6) ⚡ Operators & Logic → building conditions (page 6–7) ⚡ Input, Output & f-strings → clean and readable output (page 8–10) Big realization: 👉 Python is not hard… it’s logical. 💬 What was your first challenge in Python? #Python #DataAnalytics #LearningJourney #DataScience #Upskilling #Programming

Start learning Python the right way → https://lnkd.in/dBMXaiCv Most people stay stuck watching tutorials Few people build Only builders get hired This roadmap fixes that ⬇️ Step 1 Python basics • Variables • Loops • Functions ⬇️ Step 2 Data handling • Lists • Dictionaries • Files ⬇️ Step 3 Libraries • Pandas • Matplotlib ⬇️ Step 4 Build projects • Automation scripts • Data analysis • Simple apps Rule Stop consuming Start building You don’t need more tutorials You need output ⬇️ Related resources Python Courses https://lnkd.in/dtFbRP96 Data Science Path https://lnkd.in/dz3AXtmy Best AI Courses https://lnkd.in/dqQDSEEA Ask yourself What did you build this week #ProgrammingValley #Python #Coding #LearnToCode #BuildInPublic