Python Access Modifiers Explained

Stop writing clunky Python code. 🐍 Most developers treat Python like C++ or Java, but the "Zen of Python" reminds us: Simple is better than complex. I just finished Benjamin Bennett Alexander’s latest guide, and these 3 "tricks" are absolute game-changers for your daily workflow: 1️⃣ Merge Dictionaries with 1 Character: Forget .update(). In 2026, the | operator is the cleanest way to combine data. 2️⃣ Stop Guessing Memory Usage: Use sys.getsizeof() to see exactly how much RAM your objects are eating. (Spoiler: Tuples are almost always more efficient than lists) . 3️⃣ Speed Up String Joins: Stop using + to concatenate. Using .join() is significantly faster because Python only has to create one string object in memory. Python isn't just about making it work; it's about making it "Pythonic." Which of these is new to you? Let’s discuss in the comments. 👇 #PythonProgramming #SoftwareDevelopment #CodingTips #Technology #AI

Tips for larger Python programs! (I am not referring to small scripts, but program that are over 20,000) lines of code! 1 - modularize your design, this means split it into different python files for the different functional parts of your overall program! SUPER Important! 2 - if you cram everything into a single python file - maintenance will be a nightmare! How are you going to find anything ? the Human brain has limitation in very large program files no matter how smart you are! It doesn't matter if you have 50 years of experience or even if you invented the language! 3 - compiling a single large python file into a C code to protect you IP will take a very long time to run as the GCC compiler has limitations! even if you have a powerful computer - the rule is 1 python file to 1 core when compiling, you can not split that into Parallel, which will take forever to compile and you will waste a lot of time!!! 4 - after implementing modules, implement a Cache system / example you have 20 modules, but only made change to 1 Python file, why would you recompile the other 19 if there was zero change ? "BIG waste of time" and Unproductive! 5 - I written this, not used AI #python #tips #large #programs

🚀 Python Basics Every Beginner Should Know Starting your journey in Python? 🐍 Here are some must-know basic commands that every beginner should master 👇 🔹 1. Print Output print("Hello World") 🔹 2. Take Input name = input("Enter your name: ") 🔹 3. Variables x = 10 name = "Python" 🔹 4. Data Types int, float, str, bool, list, tuple, dict 🔹 5. Conditional Statements if x > 5: print("Greater") else: print("Smaller") 🔹 6. Loops for i in range(5): print(i) 🔹 7. Functions def greet(): print("Hello!") 🔹 8. Lists fruits = ["apple", "banana", "mango"] 🔹 9. Dictionaries data = {"name": "John", "age": 25} 🔹 10. Import Libraries import math 💡 Mastering these basics is the first step towards becoming a Python Developer or Automation Tester. ✨ Consistency > Perfection 💬 What was the first Python command you learned? #Python #Programming #CodingForBeginners #AutomationTesting #QA #TechLearning #100DaysOfCode #Developers #LearnPython

🐍 The developer just had to name it python 🐍 Respect ✊ *Guido van Rossum* He was Python’s “Benevolent Dictator For Life” (BDFL) until stepping down in 2018. Now Python’s run by a steering council It could have been given any other name but he just settled for python 🐍 😁😂😂😂😂😂😂😂😂😂😂 The chances of seriously listening to phython code will make you a star in programming, try it. This example uses SMTP (works with Gmail, Outlook, or most mail servers). import smtplib from email.mime.text import MIMEText from email.mime.multipart import MIMEMultipart from datetime import datetime # Email configuration SMTP_SERVER = "smtp.gmail.com" SMTP_PORT = 587 EMAIL_ADDRESS = "your_email@gmail.com" EMAIL_PASSWORD = "your_app_password" # Recipient TO_EMAIL = "recipient@example.com" def generate_report(): # Replace this with your real report logic today = datetime.now().strftime("%Y-%m-%d") report = f""" Daily Report - {today} - System Status: OK - Users Active: 120 - Errors Logged: 2 Regards, Automated System """ return report def send_email(): report = generate_report() msg = MIMEMultipart() msg["From"] = EMAIL_ADDRESS msg["To"] = TO_EMAIL msg["Subject"] = "Daily Automated Report" msg.attach(MIMEText(report, "plain")) try: server = smtplib.SMTP(SMTP_SERVER, SMTP_PORT) server.starttls() server.login(EMAIL_ADDRESS, EMAIL_PASSWORD) server.send_message(msg) server.quit() print("Email sent successfully!") except Exception as e: print("Error:", e) if __name__ == "__main__": send_email()

Switching from Python to Java: Coming from a Python-heavy background, working with Java has been a real shift in perspective. In Python, a lot is taken care of for you through powerful high-level abstractions. You can move quickly, write less code, and focus on solving problems. But Java? It makes you slow down in a good way. You start paying attention to details you might have overlooked before: type definitions, structure, and the mechanics behind what your code is actually doing. It demands more explicitness, more discipline, and a deeper level of understanding. And that’s the beauty of it. Different languages, different strengths, but stepping outside your comfort zone is where real growth happens. https://lnkd.in/deNbabM5 #Java #Python #SoftwareEngineering #CodingJourney #LearningToCode

Python Learning Journey Today I explored some core fundamentals that build a strong foundation in Python development: ◆ Installed VS Code and set up the Python environment ◆ Learned about different Python flavors: CPython (default implementation) Jython (Java integration) IronPython (.NET framework) PyPy (fast execution with JIT) Anaconda Python (data science ecosystem) RubyPython (experimental) ◆ Understood Python versions and compatibility ◆ Compared Java vs Python with real examples ◆ Practiced basic syntax like printing messages using print() Key concepts covered: Identifiers in Python Data Types & their types (int, float, list, tuple, dict, etc.) Typecasting Operators in Python eval() function Conditional statements (if, else, elif) Range data type and its variants Every day is a step closer to mastering Python. Consistency is the key! #Globalquesttechnologies #G R Narendra Reddy #Python #Coding Journey #Learning Python #VSCode #Programming #Developer #100DaysOfCode #TechSkills

In Java, private means private. In Python, it means: “I trust you not to look.” I was exploring encapsulation and discovered something interesting. In Java, access is enforced. In Python, it’s… negotiated. A double underscore (__attr) doesn’t truly hide anything. It just renames it. Which means: You *can* still access it — if you know how. That realization changed how I think about class design. Java protects the code. Python trusts the developer. Two different philosophies. Which one do you prefer? 👇 Curious to hear your perspective #Python #Java #OOP #SoftwareEngineering #LearningInPublic

I used to hate Python. Coming from C++ and Java, it felt fragile, inconsistent, and way too forgiving. Indentation defines scope, types are optional, performance isn’t great… and don’t get me started on packaging. The interesting part is: most of those things are still true. In today’s video, I talk about why I still use Python anyway, and the bigger lesson behind it. At some point, you realize it’s not about finding the “best” language. It’s about understanding trade-offs and choosing the right tool for the problem you’re solving. If you want to grow as a developer, that shift in thinking matters much more than the language you use. 👉 Watch here: https://lnkd.in/eXAPr3wq. #python #softwareengineering #programming #developers #careergrowth

As a long-time Java engineer, I continue to be impressed by how much Python has evolved. What once felt like a simple scripting language has grown into a remarkably capable ecosystem: C-backed libraries like NumPy, performance-oriented tooling in Rust, native coroutine support with async and await, and multiple concurrency models for very different workloads. One thing I find especially interesting is Python’s concurrency toolbox. Choosing the right model usually comes down to one question: What is your code actually waiting on? If your program is mostly waiting on the network, a database, or disk, you are likely dealing with an I/O-bound problem. In that case, asyncio can be a strong fit when the surrounding stack is async-native. If your program spends most of its time computing, parsing, or transforming data, you are likely dealing with a CPU-bound problem. In standard CPython, threads usually do not speed up pure Python CPU work because of the GIL. For that, multiprocessing is often the better fit. A few practical rules I keep in mind: • asyncio for high-concurrency I/O with async-native libraries • threads for blocking libraries or simpler concurrency • multiprocessing for CPU-heavy pure Python workloads • threads with native libraries when heavy work runs in C or Rust A good example is PyArrow and PyIceberg. PyArrow’s Parquet reader supports multi-threaded reads. That means you can get parallelism without rewriting everything around asyncio, because the heavy work happens in native code rather than Python bytecode. PyIceberg builds on this ecosystem. From the Python caller’s point of view, the workflow is still synchronous, while file access and data processing can benefit from native parallelism underneath. The key lesson for me: Not every high-performance I/O workflow in Python needs asyncio. If the underlying engine is native and already parallelizes efficiently, threads can be the right tool. If the stack is async-native, asyncio becomes much more compelling. A simple mental model: Async-native I/O → asyncio Native libraries parallelizing outside Python → threads CPU-heavy pure Python → multiprocessing Not sure → profile first That mindset is often more useful than memorizing any specific framework. #Python #Java #Concurrency #AsyncIO #Threading #Multiprocessing #Performance #SoftwareEngineering #DataEngineering

I used to hate Python. Coming from C++ and Java, it felt fragile, inconsistent, and way too forgiving. Indentation defines scope, types are optional, performance isn’t great… and don’t get me started on packaging. The interesting part is: most of those things are still true. In today’s video, I talk about why I still use Python anyway, and the bigger lesson behind it. At some point, you realize it’s not about finding the “best” language. It’s about understanding trade-offs and choosing the right tool for the problem you’re solving. If you want to grow as a developer, that shift in thinking matters much more than the language you use. 👉 Watch here: https://lnkd.in/eJtP_jHF. #python #softwareengineering #programming #developers #careergrowth