Python's round() Function: Banker's Rounding Explained

In C, an integer takes 4 bytes. In Python, the number 1 takes 28 bytes. Why? As python is a "Dynamically Typed" language, it needs to store more than just the value 1. It needs to store "metadata" about that value so the interpreter knows what to do with it. How? In the CPython source code, every single thing is a PyObject. So when we create x = 1, following structure is created with it: 1. ob_refcnt (8 bytes): The Reference Counter. It tracks how many variables point to this object. 2. ob_type (8 bytes): A pointer to the "Type Object" (telling Python the datatype). 3. ob_size (8 bytes): For variable-sized objects (like lists) 4. The Actual Value (4-8 bytes): The actual number 1 Understanding this overhead explains why Python is memory-intensive being a dynamically typed language. I am trying to learn Python Internals in detail and will share my learnings. Do follow along and tell your experiences in comments. #Python #PythonInternals #SoftwareEngineering #BackendDevelopment

Most people overcomplicate Python. That’s why they struggle. The 80/20 rule in Python is simple: 20% of concepts help you solve 80% of real problems. When I started, I tried learning everything. Libraries. Edge cases. Fancy tricks. Bad move. The shift happened when I focused on the essentials. Here’s the 20% that actually matters ⬇️ → Variables, loops, and conditionals → Lists, dictionaries, sets, tuples → Functions (inputs, outputs, reuse) → List comprehensions → File handling + basic error handling → One core library for your role (Pandas, Requests, Flask, etc.) Once you master these, Python stops feeling “hard.” You start building instead of memorizing. Advanced concepts? Important — but only after this foundation is solid. Python rewards clarity, not complexity. 📣 Are you learning Python by stacking concepts… or by solving problems? #Python #LearnToCode #Programming #TechCareers

🧠 Python Trick : Chained Comparisons Most people write this 👇 x > 5 and x < 10 But Python lets you write this 😲 ✅ The Python Way 5 < x < 10 ✔️ Same meaning. Cleaner. More readable. 🧒 Simple Explanation Imagine checking if a number is between two walls 🧱 Python checks both sides at the same time. No extra thinking needed 🧠✨ 💡 Why This Is Special ✔ Easier to read ✔ Fewer logical mistakes ✔ Unique to Python (not common in many languages) ⚠️ One Thing to Remember This works only for comparisons, not math: 5 < x < 10 ✅ 5 + x + 10 ❌ 💯 Python doesn’t just work… it reads like English. 💯 Small features like this are why developers love it 🐍💙 #Python #PythonTricks #CleanCode #LearnPython #DeveloperTips #Programming

👨🏿💻 It's all C Underneath Python isn’t just “interpreted.” Here’s what actually happens when you run a line of Python 👇 Every time Python runs code, it does four things: Read → Parse → Compile → Execute • Read (REPL) Python reads your input and knows you’re done when you press Enter. • Parse (AST) It checks syntax and turns your code into an internal structure in what we call the Abstract Syntax Tree. If it’s invalid, it never runs. • Compile (Bytecode) Python does compiling but just not to machine code. It compiles to bytecode like: LOAD_CONST 10 → STORE_NAME x • Execute (Virtual Machine) A built-in VM runs that bytecode, using C Code Underneath handling memory, objects, and dynamic typing. So when you type: >>> 2 + 3 Python compiles, executes, and prints 5 all in milliseconds. #Python #Programming #SoftwareEngineering #Tech

Time Complexity in Python Operations In Python, performance issues rarely come from syntax. They come from misunderstanding how common operations scale as data grows. Key complexity considerations: - List access by index: constant time, but insertions and deletions in the middle are linear - Dictionary and set lookups: constant time on average, dependent on hashing - Membership checks: linear for lists, constant time for sets and dictionaries Sorting operations: typically O(n log n), regardless of data structure - Understanding these behaviors helps avoid hidden bottlenecks and supports writing code that scales predictably. Good performance starts with knowing how your code grows. 🚀 #Python #DataStructures #Algorithms #CodeOptimization #Performance

Project Euler's First Five Questions are very tame, but if you are looking at levelling up your python skills, these are a great introduction to lists, loops, breaks, continues, string manipulation and many other useful operations. #Python #Data #Mathematics #DataScience #DataEngineering

Worked on comprehensions in Python, covering list, dictionary, and set comprehensions to write concise, readable, and efficient data transformations 🐍 Practiced generating derived data structures, applying conditional filters, creating nested lists, and extracting unique values from text. This approach highlights how Python enables expressive logic without sacrificing clarity. Key takeaways: Using list comprehensions for clean data generation and filtering 🔁 Building nested lists for structured outputs Applying dictionary comprehensions to transform and filter key–value data Leveraging set comprehensions to extract unique elements from text Writing compact logic without compromising readability ✨ #Python #Comprehensions #DataStructures #ProgrammingFundamentals #SoftwareDevelopment #CleanCode

Came across a video that helped clarify something I had long used in Python, but never fully understood under the hood — multithreading and the role of the GIL. In short, when using Python’s default CPython interpreter, threads are protected by a Global Interpreter Lock (GIL). This lock ensures thread safety and prevents race conditions when multiple threads try to access shared memory. However, the trade-off is that only one thread executes Python bytecode at a time, meaning true CPU-bound parallelism isn’t achieved even if multiple threads exist. On reading more about this, it became clear why Python multithreading works well for I/O-bound tasks but struggles with CPU-bound workloads. When a thread is waiting on I/O (like network calls, file reads, or database queries), it releases the GIL, allowing another thread to run. This makes multithreading effective for I/O-heavy applications. However, for CPU-intensive operations, threads continuously compete for the GIL, so execution remains effectively single-core, limiting true parallelism despite multiple threads. Interestingly, this sheds light on why performance-critical libraries like NumPy, Pandas, etc. are largely implemented in C/C++ (and increasingly Rust). These languages operate outside the GIL, allowing actual parallel execution and helping bypass interpreter-level bottlenecks. There are also alternative Python interpreters (and Rust-based extensions) that approach concurrency differently and can utilize multi-core CPUs more effectively — depending on the use case. For a clear visual explanation, here’s the video that sparked this learning: https://lnkd.in/gx8tx82J #Python #Multithreading #GIL #CPython #Concurrency #Parallelism #SoftwareEngineering #BackendEngineering #SystemDesign #Programming

🐍 90 Days of Python – Day 21 Sets in Python Today, I learned about sets in Python, a data structure used to store unique and unordered elements. Sets are especially useful when working with data that should not contain duplicates and when checking membership efficiently. 🔹 Key concepts I explored today: • Creating sets using set() • Understanding how sets handle unique values • Adding and removing elements • Using sets for membership testing and data cleaning Sets are commonly used in data preprocessing, analytics, and performance-critical operations where uniqueness matters. I’m practicing these concepts to better understand how Python handles collections efficiently. 📌 Day 21 completed. Managing unique data with sets. 👉 In which scenario do you think sets are more useful than lists? #90DaysOfPython #PythonLearning #LearningInPublic #PythonSets #PythonDeveloper #BTechCSE

Boolean Logic in Python — No if, no else, just clean decisions 🧠🐍 Today I’m sharing a small Python example that shows how boolean logic can replace traditional if / elif / else blocks in a clean and expressive way. The program asks three simple questions: Is it raining? Do you have an umbrella? Can you wait? From there, it makes a decision using only: and or not No conditionals. Just pure logic. Why is this interesting? Because Python’s boolean operators don’t just return True or False — they can return values. Combined with short-circuit evaluation, this allows you to describe decisions declaratively instead of controlling the flow step by step. This approach is especially useful when: Conditions are mutually exclusive Logic is clear but branching would be verbose You want readable, compact decision rules In the video, I break down: How user input becomes booleans How and, or, and not actually behave in Python Why the first “true” expression wins When this pattern is elegant — and when it’s not Sometimes the cleanest decision-making code has zero if statements. If you’re learning Python or revisiting fundamentals, this is a great example of how understanding the basics deeply can level up your code. #Python #BooleanLogic #Programming #SoftwareDevelopment #CleanCode #PythonTips #LearningPython #CodeExamples