Python's Slow Reputation: Separating Fact from Fiction

🐍 Back to Basics: The Building Blocks of Python I recently reviewed some comprehensive notes on Python fundamentals, and it’s a great reminder of why this language—developed by Guido Van Rossum—remains so user-friendly and powerful. Whether you are just starting or brushing up on your skills, mastering the core vocabulary is key. Here is a breakdown of the essentials found in these notes: 1. The "Grammar" (Keywords) These are the reserved words that give Python its structure. The notes highlight how we use logic operators like and, or, and not to control flow, or def and class to build functions and objects. Did you know? You can use import keyword and print(keyword.kwlist) to see all of them in your current version! 2. The "Tools" (Built-in Functions) Python comes batteries-included with functions that handle heavy lifting immediately. Math & Numbers: abs(), round(), float(), and int(). Interaction: input() and print(). Sequence Helpers: len(), range(), and the powerful map() function for applying logic across iterables. 3. Data Manipulation (Methods) The notes dive deep into how we handle Lists and Strings: Lists: Modifying data is easy with append(), insert(), pop(), and sort(). Strings: Text processing is a breeze using split(), join(), capitalize(), and checks like isalpha(). Python’s "platform independence" and "open source" nature make it accessible, but its these built-in tools that make it efficient. Which built-in Python function do you find yourself using the most? 👇 #Python #Programming #CodingBasics #SoftwareDevelopment #TechSkills #Learning

ARE YOU STILL LEARNING PYTHON IN 2026 ⁉ Here are 10 tips you should know: Python is a general purpose programming language created by Guido van Rossum and first released in 1991, widely used for machine learning, data analysis, web development, and more. The Zen of Python is a set of guiding principles that emphasize readability and simplicity, such as 'Simple is better than complex.' Python is an interpreted language, meaning the source code is converted to bytecode and executed by the Python virtual machine, making development quick but execution moderately slower. Python source files end with the .py extension and are often called modules. These files contain your Python code. Variables in Python are created by assigning a name to a value using the equals sign. Python is strongly typed but does not require type annotations. Python uses indentation (usually four spaces) to define blocks of code instead of curly braces or semicolons, enforcing readable structure. Functions are defined with the def keyword followed by the function name and parentheses. The function body is indented beneath the definition line. Python supports multiple programming paradigms: procedural, functional (with lambda expressions), and object-oriented (with classes and inheritance). Common data structures in Python include tuples, lists, and dictionaries, which can be defined using literal syntax directly in the code. Python's vast ecosystem includes third-party libraries managed by PIP, such as TensorFlow for deep learning and OpenCV for image processing. #python #3MTT #dataengineers

Learning by doing is what I practice today! In software development, this is usually easy to do, especially when it involves only the programming language and your computer (and no resources to be deployed). Today I needed to do a "lab" to better understand yield and generators in #python to solve some misunderstandings I was struck yesterday while trying to stich #simpy, #plotly and #marimo. Not sure if generators is the way to go but I needed to clarify my thoughts. Base reference from Real Python - my proxy for good and accessible Python content: https://lnkd.in/dmsaZJt9 #lifelonglearning #softwaredevelopment

Today’s Python focus was 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀. I worked on understanding why functions exist and how they make code reusable, readable, and easier to manage instead of repeating the same logic again and again. 𝗪𝗵𝗮𝘁 𝗜 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝗱 𝘁𝗼𝗱𝗮𝘆: • Writing a simple function to calculate the volume of a cylinder instead of doing direct calculations • Passing parameters to functions and returning values • Calling the same function multiple times with the same inputs • Understanding the difference between built in functions, library functions, and user defined functions • Using functions to calculate total expenses from a list • Comparing custom logic with built in functions like sum() • Using functions from the math module such as sqrt() and ceil() • Working with *args to accept a variable number of arguments • Working with **kwargs to pass key value pairs into a function • Writing and using lambda functions for simple operations • Creating placeholder functions using pass 𝗞𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀: • Functions help avoid repetition and keep code clean • Parameters and return values make functions flexible • Built in and library functions save time and reduce errors • *args and **kwargs make functions more dynamic • Lambda functions are useful for short, simple logic Functions made it clear that Python is not just about writing code that works once, but about writing code that can be reused and maintained. If you are learning Python too, which function related concept took you some time to fully understand? #Python #PythonLearning #FunctionsInPython #ProgrammingBasics #LearningInPublic #DataAnalytics #Upskilling

I didn't understand why functions mattered when I first learned Python. My instructor kept saying: "Don't repeat yourself. Use functions" 🤔 I thought: Why? Copy paste works fine. Then I wrote some repeated logic in my code, the same check appeared more than once. When I found a mistake, I had to fix it in multiple places. That's when it clicked.💡 Functions aren’t just a Python feature. They’re a way to write smarter, maintainable code. The rule I follow now: If I’m writing the same logic twice → make it a function. Why functions matter: • One source of truth 📌 • Fewer bugs 🐞 • Easier updates 🔧 • Code that’s easier to read 👀 I wish I had understood this earlier instead of just memorizing syntax. 📚 Now, when I write code, I ask: “Will I need this logic again?” If yes → function. Functions didn’t just make my code shorter. They made it maintainable. If you’re learning Python, don’t skip functions thinking they’re “advanced.” They’ll save you hours of debugging later.⏱️ What Python concept took you time to really understand? #Python #Programming #CleanCode #LearnInPublic #SoftwareDevelopment

Python GIL explained in simple words Python has something called the Global Interpreter Lock (GIL). It means: only one thread can execute Python code at a time inside a single process. Now, why does Python do this? 🧠 The reason Python manages memory automatically (garbage collection, reference counting). If multiple threads modified memory at the same time, it could cause crashes and corrupted data. So the GIL: Protects memory Keeps Python simple and stable Makes single-thread execution very fast Yes, this safety comes with extra memory overhead, because Python needs bookkeeping to manage threads safely. ⚡ What about performance? Here’s the important part many people miss: I/O-bound tasks (API calls, database queries, file reads): 👉 Performance is excellent because threads release the GIL while waiting. CPU-bound tasks (heavy calculations, loops): 👉 Threads won’t scale — but Python gives alternatives: Multiprocessing Async programming Native extensions (C/C++) ✅ The takeaway The GIL is not a performance bug. It’s a design trade-off: Slight memory overhead In exchange for simplicity, safety, and great real-world performance Most backend systems are I/O-heavy — and for those, Python performs just fine 🚀 #Python #GIL #Concurrency #BackendEngineering #SoftwareDevelopment

Most beginners struggle with Python not because it’s hard but because they skip the basics. In my Python class yesterday, we slowed things down and focused on two foundations that make everything else easier. Numeric data type conversion We learnt how to convert; - Integers to floats - Floats to integers - Integers to complex numbers Why this matters: Python behaves differently based on data types. If you don’t understand conversions, your results will confuse you and your code will break in ways you can’t explain. Lists in Python What is a list ? A list allows you to store multiple values inside one variable instead of creating many separate ones. They are denoted by square brackets. How to access items in a list? Each item has an index, and Python starts counting from zero. You access items using square brackets. Why are lists important? Lists help you organize data, loop through values, and work with real-world datasets. If you understand lists, learning loops, functions, and data analysis becomes much easier. Python is not about rushing to advanced topics. It’s about building foundations that don’t break later. Are you still struggling with Python basics, or have they finally clicked for you? I teach data tools in simple ways for easy understanding. Follow for more lessons. #Python #LearningInPublic #DataAnalytics #Teaching #CareerGrowth

📌 Data Types in Python | Complete Fundamentals with Examples | Informational Share Sharing a clear and structured Python reference that explains all built-in Python data types with real-world examples and code snippets, making it ideal for beginners, interview preparation, and quick revision. 🔹 What this document covers: • Python as a dynamically typed language & use of type() • Text type: str with practical examples • Numeric types: int, float, complex • Sequence types: list, tuple, range • Mapping type: dict (key–value pairs) • Set types: set, frozenset • Boolean type: bool for logical conditions • Binary types: bytes, bytearray, memoryview • NoneType and its real-world usage • Type casting: implicit vs explicit conversion with examples 📄 The document also includes simple explanations, real-life use cases, and hands-on Python code, making complex concepts easy to understand. 📢 I’ll continue sharing high-value programming fundamentals, Python references, and interview-oriented content. Follow Pulimi Bala sankararao for more. #Python #PythonBasics #DataTypes #ProgrammingFundamentals #PythonInterview #LearningPython #TechInformation

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