Revisiting Python Fundamentals for Data Engineers

🚀 Day 23 – The 30-Day AI & Analytics Sprint 🌙 💡 Python Question: What does self mean in Python classes? When we start learning Object-Oriented Programming in Python, we often see this word everywhere: self But what exactly does it mean? 🤔 Consider this simple example: class Person: def __init__(self, name): self.name = name def greet(self): print("Hello", self.name) p = Person("Ahmed") p.greet() 🧠 The idea is simple: self refers to the current instance (object) of the class. It allows each object to: ✔ Store its own data ✔ Access its attributes ✔ Call its own methods So when we write: self.name We are saying: ➡️ Use the name that belongs to this specific object. ⚠️ Interesting fact self is not a reserved keyword in Python, but it is a strong convention used by Python developers. ✅ Correct Answer Refers to the current instance of the class 💬 Discussion: When did you first start using Object-Oriented Programming in Python? And do you prefer functional programming or OOP for data projects? #Python #Programming #AI #DataAnalytics #MachineLearning #Coding #LearnPython #OOP

Building my Python foundation one small project at a time. Today I practiced writing a simple Python script that calculates total weekly hours worked based on daily hours and number of workdays. It’s a beginner exercise, but it helped me reinforce core concepts like variables, arithmetic operations, f‑strings, and writing clear comments. Here’s the script I pushed to GitHub: # This program calculates the total number of hours worked in a week. # - hours_worked: number of hours worked per day # - days: number of days worked in a week # - total_hours: total hours worked in the week (hours_worked * days) # The program prints the total weekly hours in a readable sentence. hours_worked = 8 days = 5 total_hours = hours_worked * days print(f"I worked {total_hours} hours this week.") #output I worked 40 hours this week. I’m sharing my progress as I continue strengthening my Python skills for data analytics. Consistency is the goal, and every small project builds toward the bigger picture. #Python #DataAnalytics #LearningInPublic #GitHubJourney #WomenInTech

Your Python optimization models are running slow. The problem isn't the solver. It's your code. Here's what CTOs need to know: Most teams blame solve time when model build time is the real killer. Common mistakes I see: 🚫 Nested Python loops building constraints one at a time 🚫 Inefficient data structures (lists instead of NumPy arrays) 🚫 Not profiling before optimizing The fix: FICO Xpress's Python API is designed for vectorized operations. See the example in the comments below. 👇️ Use them. ✅ addVariables() instead of loops ✅ NumPy arrays for coefficients ✅ Scipy sparse matrices for large problems Example impact: → Model build time: 5 minutes → 5 seconds → Same problem. Same decisions. 60x faster. The lesson: Python democratized optimization, but performance requires discipline. Invest in training your teams on efficient Python practices. FICO provides documentation, examples, and best practices to help. Is your team writing efficient optimization code? #DecisionIntelligence #DataScience #Python #Optimization #Performance #Technology #Leadership

Here’s your Day 11 LinkedIn post continuing your Python journey 👇 --- 🚀 ✨ 𝐃𝐀𝐘 11: 𝐖𝐎𝐑𝐊𝐈𝐍𝐆 𝐖𝐈𝐓𝐇 𝐓𝐔𝐏𝐋𝐄𝐒 ✨ Today, I explored another useful data structure in Python — 💻 𝐓𝐮𝐩𝐥𝐞𝐬. 🔹 📘 𝐖𝐡𝐚𝐭 𝐀𝐫𝐞 𝐓𝐮𝐩𝐥𝐞𝐬? Tuples are similar to lists but are 𝐢𝐦𝐦𝐮𝐭𝐚𝐛𝐥𝐞 (𝐜𝐚𝐧𝐧𝐨𝐭 𝐛𝐞 𝐜𝐡𝐚𝐧𝐠𝐞𝐝) once created. 🔹 ⚙️ 𝐖𝐡𝐚𝐭 𝐈 𝐋𝐞𝐚𝐫𝐧𝐞𝐝 ✔️ Creating and accessing 𝐭𝐮𝐩𝐥𝐞𝐬 ✔️ 𝐢𝐧𝐝𝐞𝐱𝐢𝐧𝐠 & 𝐬𝐥𝐢𝐜𝐢𝐧𝐠 ✔️ Difference between 𝐥𝐢𝐬𝐭𝐬 vs 𝐭𝐮𝐩𝐥𝐞𝐬 🔹 🧠 𝐖𝐡𝐲 𝐈𝐭 𝐌𝐚𝐭𝐭𝐞𝐫𝐬 Tuples are useful when we need 𝐟𝐢𝐱𝐞𝐝 𝐝𝐚𝐭𝐚 and ensure data remains unchanged. 💡 𝐈𝐦𝐦𝐮𝐭𝐚𝐛𝐥𝐞 𝐝𝐚𝐭𝐚 = 𝐌𝐨𝐫𝐞 𝐫𝐞𝐥𝐢𝐚𝐛𝐥𝐞 𝐩𝐫𝐨𝐠𝐫𝐚𝐦𝐬! 💪 𝐆𝐚𝐢𝐧𝐢𝐧𝐠 𝐦𝐨𝐫𝐞 𝐜𝐥𝐚𝐫𝐢𝐭𝐲 on data structures! 🚀 𝐊𝐞𝐞𝐩 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠, 𝐤𝐞𝐞𝐩 𝐠𝐫𝐨𝐰𝐢𝐧𝐠! #Python #Day11 #CodingJourney #Tuples #DataStructures #LearningPython #Consistency🚀

🐍 Python Basics Challenge – Empty Data Types Concept While practicing Python, I found a small question related to empty data types. It looks easy, but many beginners get confused here. Python set = {} tup = () list = [] string = "" 📌 Question: Which of the following is NOT an appropriate empty data type in Python? A) set = {} B) tup = () C) list = [] D) string = "" 💬 Comment your answer (A / B / C / D) Think carefully before answering. This question checks your understanding of: ✔ Empty set vs empty dictionary ✔ Tuple syntax ✔ List syntax ✔ String syntax ✔ Python fundamentals I will reveal the correct answer after a few responses. Let’s see who gets it right 👇 #Python #PythonBasics #CodingChallenge #Programming #DataAnalytics #LearnInPublic #TechCommunity

People say Python is just for data science. But it's quietly powering backends everywhere → REST APIs with Flask → Databases with SQLite & MySQL → Auth, validation, secure routes And the best part? You can go from zero to a working API in under 50 lines of code. That's why I chose Python as my primary language and I'm not looking back. Are you building with Python? Drop your stack below 👇 #Python #BackendDevelopment #Flask #WebDevelopment #RESTAPI #PythonDeveloper #LearnToCode #TechStudent #BuildInPublic #Programming #IndianDeveloper

💡 A common mistake many Python beginners make… They start building projects without understanding Python’s fundamental data types. But every Python application depends on how data is stored and structured. Core built-in types include: • int – Whole numbers • float – Decimal numbers • str – Text values • list – Ordered mutable collection • tuple – Immutable collection • set – Unique elements • dict – Key-value structure Mastering these fundamentals helps developers: ✔ Write cleaner code ✔ Avoid common logical errors ✔ Work efficiently with data ✔ Build stronger foundations for AI & ML Read more info: https://lnkd.in/d-ccb2Ta #Python #SoftwareDevelopment #MachineLearning #Programming