Python Data Structures: List, Tuple, Dictionary Fundamentals

Python Challenge #1 – Answer Revealed! b = a[:] ---> creates a shallow copy of a. So a and b are separate lists. Appending 4 to b changes only b, not a. #python #code #programmer #challenge #trending #list

🚀 Day 3 of Learning Python Today was all about writing smarter and more efficient code: ✅ `filter()` function ✅ `lambda` functions ✅ `return` statement The `filter()` function helped me understand how to extract specific data from a list based on conditions. With `lambda` functions, I learned how to write short, one-line functions — super useful for quick operations without defining full functions. And the `return` statement showed me how functions give back results, making them reusable and powerful. Each concept is small on its own, but together they really change how you think about problem-solving in code. Staying consistent and building every day 💪 #Python #CodingJourney #LearningInPublic #100DaysOfCode #TechSkills #StudentDeveloper

Most beginners start learning Python… but get confused about what to learn next. So we created a simple Python Roadmap that covers everything step-by-step: * Python Basics * OOP Concepts * Data Structures & Algorithms * Automation * Web Frameworks * Data Science Libraries If you're starting your Python journey in 2026, this roadmap can save you months of confusion. 📌 Save this roadmap for later. 🌐 Visit our website: thevinia.com Follow #thevinia for more interview prep resources and coding guides. Having Doubts in technical journey? 🚀 Book 1:1 demo with me : https://thevinia.com 🚀 Subscribe and stay up to date: https://lnkd.in/g-Rf8EgT follow instragram page : https://lnkd.in/g5jfDRxy 🚀 Get Complete React JS Interview Q&A Here: https://lnkd.in/gCs_jvJf #PythonDeveloper #CodingJourney #LearnProgramming

That simple x = 10 in Python? It’s doing much more than storing the number 10. Under the hood, a Python integer is a full object (implemented in C) that includes: -- The actual value (stored as a dynamic array of digits) -- Type information -- A size field -- A reference count for memory management Unlike C, where an integer is just fixed-size raw bytes, Python stores a reference to an object. This is why a Python list of 1000 integers is actually 1000 separate objects in memory, each with its own overhead. And this is exactly where NumPy shines: -- Fixed-type arrays -- Contiguous memory -- No per-object overhead More flexibility = more memory. That’s the trade-off Python makes for you silently, every single day. #Python #SoftwareEngineering #BackendDevelopment #NumPy #PythonInternals

Day 3 of #90DaysPythonChallenge 🚀 Today I focused on the fundamentals — Variables and Strings in Python. It may sound basic, but I’m realizing something important: Strong foundations create strong developers. Here’s what I learned today: 🔹 What variables are and how they store data 🔹 Naming conventions and why clean variable names matter 🔹 Understanding strings as a data type 🔹 String operations (concatenation, indexing, formatting) 🔹 The importance of writing readable code The more I learn, the more I understand that programming is not about memorizing syntax — it’s about thinking clearly and logically. Every small concept I master today is one step closer to becoming job-ready and confident in my skills. No rush. No comparison. Just consistent improvement. #Python #LearningInPublic #CSE #FutureDeveloper #Consistency #90DaysOfCode

Hot take: Python is fast enough for most things. For the other things? 🦀 Rust. Just published a guide on using PyO3 v0.28 + maturin to drop Rust into your Python stack — the same approach Polars, Ruff, and Pydantic v2 use. One function. Native speed. Still pip install-able. 👉 https://lnkd.in/g794MZxa #Rust #Python #PyO3 #Engineering #Performance

Day 2 / 14 — Python Foundation Reset Today I continued rebuilding my Python fundamentals, focusing on comparison operators, logical operators, and truth tables. Key concepts reviewed: • Chained comparisons in Python • Operator precedence (and vs or) • Truthiness and how logical operators return operands • Short-circuit evaluation One important reminder from today: Logical conditions can easily lead to silent errors if they are not carefully structured. Understanding how Python evaluates conditions helps prevent mistakes in filtering logic and data processing workflows. Small steps, but consistent progress. #Python #DataScience #LearningInPublic

Python Clarity Series – Episode 9 Topic: Dictionary get() vs Direct Access 📌 Why does this code crash sometimes? d = {"a": 10} print(d["b"]) Error ❌ KeyError Better way: print(d.get("b")) Output: None 👉 d["key"] → Throws error if key missing 👉 d.get("key") → Returns None (safe access) 💡 Smart Tip: Use .get() when key might not exist. In real-world coding, this prevents crashes. This is beyond exam — this is practical Python. Have you used .get() before? #PythonTips #CodingClarity #FutureProgrammers

Building the future of Python is a community effort! Nathan Goldbaum shares the story behind reaching 50% free-threaded wheel support across PyPI's most-downloaded packages. This milestone covers everything from low-level C extension porting to pure-Python test coverage, and it shows what's possible when a community moves together. Whether you're a maintainer, contributor, or curious developer, there's a role for you in the next 50%. See how you can help in our latest blog 🔗 https://lnkd.in/dnsfUvh2 #Python #FreethreadedPython #OpenSource #PyPI #Quansight #PythonCommunity

📌 Nested Loops in Python Building on my loop concepts, I practiced Nested Loops in Python. A nested loop means placing one loop inside another. The inner loop executes completely for every single iteration of the outer loop. In this example, I used two lists: • One containing properties • One containing fruits The program combines each property with every fruit, showing how nested loops help generate all possible pair combinations. Nested loops are useful when: • Working with multiple lists • Creating combinations • Handling rows and columns • Solving pattern-based problems Step by step, improving my logical thinking and Python fundamentals. 🚀 #Python #Programming #CodingJourney #LearningPython #DataAnalytics