"Exploring Python Modules: Built-in, Random, and Custom"

🚀 Day 18 of my #100DaysOfCode Journey – Exploring Python Modules 🐍 Today’s focus was on Python Modules, both built-in and custom! Here’s what I practiced: ✅ Standard Library (math module) – Calculated square root, factorial, and rounded pi value. ✅ Random Module – Generated random choices and shuffled lists dynamically. ✅ Custom Module – Created my own calculator.py with add() and sub() functions, then imported it into the main file. 💡 Key Takeaway: “Modules make Python more powerful, organized, and reusable — write once, use everywhere!” #Python #100DaysOfCode #Modules #LearningJourney #SoftwareDevelopment #CodingEveryday #Math #Random #CustomModules #CodeNewbie

Day 41 – Scope in Python: Local vs Global Variables Ever got a “NameError” and wondered why Python couldn’t find your variable? 🤔 It’s all about Scope — where a variable lives and breathes. x = 10 # Global variable def show(): x = 5 # Local variable print("Inside function:", x) show() print("Outside function:", x) 🧩 Output: Inside function: 5 Outside function: 10 ➡️ Local variables exist only inside the function. ➡️ Global variables exist everywhere. Use them wisely — globals are powerful but can cause confusion if overused. 👉 Tip: Prefer local scope for clean and bug-free code. #Python #Coding #Learning #100DaysOfCode #PythonTips

Ever noticed how Python behaves weirdly sometimes? a = 256 b = 256 print(a is b) # True x = 257 y = 257 print(x is y) # False Wait… what? Both pairs “look” the same, but Python only thinks the first pair is identical. Here’s why 👇 Python caches small integers from -5 to 256 in memory (a mechanism known as integer interning). This means whenever you create any number in that range, Python points to the same memory address. So a is b returns True. But for numbers outside that range, Python creates new integer objects, even if the values match. That’s why x is y returns False — they are equal in value, not identical in memory. This tiny detail showcases Python’s clever optimization tricks — saving memory and speeding up simple number operations! #Python #CodingTips #DataScience #MachineLearning #PythonInternals #LearningEveryday

🚀 Big news for the Power Platform ecosystem! Microsoft just enabled Python execution directly within Power Platform — a massive leap for automation, analytics, and AI integration. 💡 Why it matters: For business users: This means you can now access advanced data transformation, AI, and visualization capabilities without leaving Power Apps or Power Automate. Think predictive analytics, natural language models, and data science workflows — all in one low-code environment. For developers: It bridges low-code and pro-code like never before. You can now reuse Python scripts, leverage existing libraries (Pandas, NumPy, OpenAI, etc.), and supercharge Power Platform solutions with real computation power. For organizations: Expect faster innovation, less dependency on external pipelines, and more value extracted from your data — directly within Microsoft’s governance and security layer. This is the convergence of low-code + pro-code + AI, and it’s going to reshape how we build and automate business solutions. 🔥 #PowerPlatform #Python #AI #Automation #DataScience #LowCode

Nesting three loops and translating that into a single list comprehension can be a little bit tricky. Today I tackled a fun Python challenge using list comprehensions, and I found it super interesting! I generated all combinations [i, j, k] within given ranges where the sum isn’t equal to a target value n. It really stretched my thinking about nested loops and concise Python syntax. Example output (for x=1, y=1, z=2, n=3): [[0, 0, 0], [0, 0, 1], [0, 1, 0], [1, 0, 0], [1, 1, 0], [1, 0, 1], [0, 1, 1]] If you’ve solved similar combinatorics or Python list comprehension problems, I’d love to hear your approach! #Python #Coding #LearningInPublic #100DaysOfCode #ProblemSolving #ListComprehension #BeginnerToBuilder

I discovered this after 2 years of using Python... I can’t believe I missed it! Try the following in Python: 👉 round(4.5) will return 4 👉 round(5.5) will return 6 One rounds down, the other rounds up! “But wait, shouldn’t the rule always be the same?” “If it’s .5, it should round up!” => That used to be the case until Python 3… But then they switched to a logic called “banker’s rounding.” The idea is simple: If you always round up, over millions of calculations the result becomes biased. Python 3.0 (and later versions) eliminates this bias: → If the number is even, it rounds down → If it’s odd, it rounds up Just goes to show, we’re always learning, and it’s worth revisiting the basics! #python #data #datascience #dataengineering

Super interesting! This is what you get when you combine lack of mathematical rigor with under-specification of business problems: irrational tradeoffs in a language implementation. As far as I can tell we have a very clear and specific understanding of what Math.round is supposed to do. If that well-defined behavior leads to undesired aggregated effects in a specific context, then the problem domain should be further investigated. If the odd/even strategy is a valid solution, than this solution should be applied in that context (e.g. by overriding, or even better by providing a very clearly documented alternative). oh well, never a dull moment in the life of an engineer I guess 😄.

Python Tip – Day 6: Make Your Loops Cleaner with enumerate() Ever used range(len()) to get both index and value in a loop? There’s a better and more Pythonic way , use enumerate()! Here’s a quick example: x = [1, 2, 3, 4, 5, 6] s = 0 for i in enumerate(x): print(i[0], i[1]) enumerate() returns both the index and value as a tuple! That’s why i[0] gives the index and i[1] gives the list element. Try unpacking it directly for cleaner code: for index, value in enumerate(x): print(index, value) enumerate() improves readability and makes your code look more professional. Keep your loops clean and efficient! #Python #30DaysOfpythonCode #PythonTips #CodingJourney #LearnPython #CodeBetter

👩💻 “That’s not very Pythonic…” We’ve all heard that phrase before. But what does it actually mean? This week I took a small, messy Python script and refactored it step by step, each time applying a principle that makes the code more Pythonic: 🔁 Replacing classes with functions 🧼 Using @dataclass for structure 📎 Adding type annotations 🧘 Embracing the Zen of Python ... and more! 👉 Watch here: https://lnkd.in/gaCp34fC #python #cleancode #refactoring #zenofpython #pythonic #arjancodes

I continued my Python learning journey and explored some key fundamentals: 🔹 Understanding Data Types – strings, integers, floats, booleans, and how Python handles them. 🔹 Performing Type Checking & Type Conversion – using type(), int(), float(), and str() to manage data effectively. 🔹 Practiced Number Manipulation & f-Strings – improved how I format and display results cleanly in Python. To apply what I learned, I created two small practice tasks: ✅ BMI Calculator – to calculate Body Mass Index based on user input. ✅ Tip Calculator – to split bills smartly among friends. Every small project builds confidence and improves logical thinking. 🚀 #Learning #WebDevelopment #Python #KeepGrowing #100DaysOfCode