🕯 Enrico Frumento’s Post

In plain English, "and" and "or" are just connective words. In Python, they are strict gatekeepers. 🧐 ⠀ Confusing them is the easiest way to break the logic of your application. ⠀ Let's look at a real-world example: Going to the cinema. 🍿 ⠀ 🎬 The "Flexible" Approach (OR): Imagine regular entry to a movie. You write: `if has_paper_ticket OR has_digital_app:` ⠀ The `or` operator is chill. It opens the door if *either* condition is met. Did you forget your paper ticket but have your phone? No problem. You're in. ⠀ 🔞 The "Strict" Approach (AND): Now imagine entry to an age-restricted screening (18+). You write: `if has_ticket AND is_over_18:` ⠀ The `and` operator is the strict manager. It demands *both* requirements be met simultaneously. Have a ticket but forgot your ID? You aren't getting in. ⠀ Logical operators aren't just syntax; they define the rules of your digital world. ⠀ Don't accidentally lock your users out (or let the wrong ones in) because you chose the wrong conjunction. ⠀ We turn boring Python documentation into a friendly, 3-minute daily habit. ☕ ⠀ 👇 Subscribe to the website for free: https://lnkd.in/ducXvs-y ⠀ #Python #Logic #CodingTips #SoftwareDevelopment #LearnToCode #PyDaily

“Python(Basic) Questions from a HackerRank Assessment I Recently Cleared" 🔹 Problem Statement You are given a sentence consisting of space-separated words with upper and lower case English letters. Each word must be transformed using the following rules: The first character of each word remains unchanged For every next character: Compare it with the previous character (case-insensitive) If the previous character comes earlier in the alphabet → convert current character to uppercase If the previous character comes later → convert current character to lowercase If both characters are the same → keep it unchanged Spaces should remain as they are 🔹 Logic / Approach Traverse the sentence character by character Reset comparison whenever a space is encountered Keep track of the previous character Compare characters using lower() to avoid case issues Apply transformation rules and build the result step by step This ensures accurate transformation while preserving word boundaries. 🔹 Code Implementation (Python) def transformSentence(sentence): result = [] prev_char = None for ch in sentence: if ch == " ": result.append(" ") prev_char = None else: if prev_char is None: result.append(ch) else: if prev_char.lower() < ch.lower(): result.append(ch.upper()) elif ch.lower() < prev_char.lower(): result.append(ch.lower()) else: result.append(ch) prev_char = ch return "".join(result) 🔹 Sample Input a Blue MOON 🔹 Output a BLUe MOOn #Python #HackerRank #CodingPractice #ProblemSolving #LearningJourney #SoftwareEngineering

Quick check: what’s the type of bin(10)? int? float? Something else? It’s str. bin(), oct(), and hex() always return strings. That’s why bin(10) + 5 fails and why you use int(bin(10), 2) when you need a number again. I wrote a complete guide to Python base conversion functions so you can: ✓ Use bin(), oct(), and hex() correctly ✓ Know they return strings (and convert back when needed) ✓ Avoid float/complex (only int and bool work) ✓ Fix the usual mistakes and read the common errors ✓ See practical use (e.g. colors, permissions) and try exercises ~23 min read, with examples and solutions. Link: https://lnkd.in/dnUxjXBB #Python #LearnPython #Programming #Tech

Day 13 – Practice with Recursion, Loops, and String Logic in Python Day 13 was all about hands-on problem solving. I focused on strengthening my logical thinking by implementing common programming problems using functions, loops, recursion, and strings. What I practiced today: Recursion Concepts: Calculated the sum of N natural numbers using recursion Example: input = 5 → output = 15 Understood how recursive calls work and when to stop using base conditions Loop-Based Logic (Without Recursion): Printed N Fibonacci numbers using loops Learned why loops are more memory-efficient than recursion in some cases String Processing Problems: Found the frequency of each character in a string using dictionaries Counted occurrences of characters without using built-in methods Compared two strings and printed common characters Avoided duplicate counting using a checked/reference string Pattern & Combination Problems: Printed all possible character pairs from a given string Example: "abc" → ab, ac, bb, bc, ca, cb, cc Practiced nested loops to generate combinations List & String Manipulation: Reversed each string inside a list without using slicing Converted: ["apple", "banana", "orange", "mango", "grapes"] into reversed word format Key Takeaways: Improved understanding of nested loops and condition checks Learned how to avoid repeated computations Practiced writing logic without relying on shortcuts or built-in functions Strengthened confidence in solving interview-style problems Consistent practice like this is helping me think more clearly about how code actually works step by step. #Python #PythonPractice #ProblemSolving #Recursion #Loops #StringManipulation #LogicalThinking #DailyLearning #CodingPractice #ComputerScience

3 Performance Mistakes Python Developers Make in Production Your code works locally. It passes tests. It even gets deployed. But in production? It slows down. Here are 3 common mistakes I keep seeing: 1. Using a List Instead of a Set for Lookups if x in my_list: Lists search one by one → O(n) If lookup is frequent, use: my_set = set(my_list) if x in my_set: Sets use hashing → O(1) average time Small change. Massive impact at scale. 2. Ignoring Time Complexity Nested loops feel harmless… Until data grows 100x. Quadratic logic in small datasets becomes a production bottleneck. If you don’t know the Big-O of your solution, you’re coding blind. 3. Ignoring Memory Usage Creating unnecessary copies: new_list = old_list[:] Loading huge datasets fully into memory instead of streaming. Using lists where generators would work. Performance isn’t just speed — it’s also memory efficiency. Real Engineering Insight: Production performance problems rarely come from “bad Python.” They come from weak algorithmic thinking. Code that works is beginner level. Code that scales is professional level. Which performance mistake did you learn the hard way? #Python #Performance #SoftwareEngineering #DSA #Programming #Developers #CleanCode

Python weirdness — 500 "None" values but only ONE object in memory I ran a small experiment today. I created a list: lst = [None for _ in range(500)] len(lst) Output: 500 So Python created 500 "None" objects… right? No. Now check this: all(x is None for x in lst) Output: True Every element is the SAME "None". Let’s inspect memory: len({id(x) for x in lst}) Output: 1 Only ONE memory address Python does NOT create new "None" objects. There is exactly one "None" in the entire interpreter. Whenever you write: x = None you are just referencing a pre-existing object. This is why Python developers always write: if value is None: and not: if value == None: Because "is" checks identity, and "None" has a guaranteed single identity (a language-level singleton). Other singleton objects in Python: • True • False • NotImplemented • Ellipsis (...) Takeaway: Python isn’t just a scripting language. It’s a carefully designed object system. Sometimes a tiny keyword like "None" teaches more about memory than a whole textbook. #Python #Programming #SoftwareEngineering #CodingTips #BackendDevelopment

Type Hinting, Type Checking & Validation — Know the Difference in Python I’ve noticed many developers use these terms interchangeably, but they solve different problems. Here’s the clear distinction: Type Hinting Annotating your code with expected types (parameters, variables, return values). It improves readability, maintainability, and IDE autocomplete. It does not enforce types at runtime — your code can still run with wrong types. Type Checking Static analysis of your code based on those hints. Tools like mypy, Pyright, and the newer Rust-based Ty catch mismatched types before execution. They warn you, but they don’t stop your program from running. Validation Runtime enforcement of rules in your application (e.g., checking if a DB record exists). Validation can raise exceptions and interrupt execution. In short: Type Hinting → clarity Type Checking → early warnings Validation → runtime enforcement Strong Python systems use all three together.

Python is a high-level, easy-to-read programming language widely used in web development, data science, AI, and automation. In Python, a data type defines the kind of value a variable can store and how that value is handled in memory. Python automatically assigns a data type at runtime based on the value given to a variable. It offers built-in data types grouped as Numeric, Sequence, Set, Mapping, Boolean, and None. Common data types include int, float, complex, str, list, tuple, set, dict, bool, and NoneType. Some are mutable (list, dict, set) while others are immutable (int, float, str, tuple). This dynamic typing makes Python flexible, beginner-friendly, and powerful. 🚀 #Python #PythonBasics #DataTypes #Programming #LearningPython #CodingJourney

🚀 LeetCode Practice 📌 Problem: Number of Steps to Reduce a Number in Binary Representation to One 🔗 LeetCode Problem #1404 🧠 Problem Statement Given a binary string s, return the number of steps required to reduce it to "1" using: ✅ If the number is even → divide it by 2 ✅ If the number is odd → add 1 It is guaranteed that we can always reach "1". 🔎 Example Input: s = "1101" Output: 6 Explanation: 13 (1101) → +1 → 14 14 → /2 → 7 7 → +1 → 8 8 → /2 → 4 4 → /2 → 2 2 → /2 → 1 💡 Key Insight The length of s can be up to 500 bits, so converting directly to an integer might not be ideal in some languages. Instead, we: Traverse from right to left Simulate division and addition Maintain a carry variable Count operations efficiently ⚡ Optimized Approach (Greedy + Carry Handling) 🔥 Core Observations If last bit is '0' → number is even → 1 step (divide) If last bit is '1' → number is odd → 2 steps (add 1 + divide) Handle carry propagation carefully 🧑💻 Python Implementation (O(n) Time | O(1) Space) class Solution: def numSteps(self, s: str) -> int: steps = 0 carry = 0 # Traverse from right to left (ignore MSB) for i in range(len(s) - 1, 0, -1): bit = int(s[i]) # If bit + carry == 1 → odd if bit + carry == 1: steps += 2 carry = 1 else: steps += 1 return steps + carry 📊 Complexity Analysis ⏱ Time Complexity: O(n) 📦 Space Complexity: O(1) Where n is the length of the binary string. #LeetCode #ProblemSolving #Python #DSA #CodingInterview #BitManipulation #TechGrowth

🐍 Python Functions — Rules & How to Use Them ⚡ Functions let you reuse code instead of writing the same logic again and again 👇 ✅ Basic Function Syntax def greet(): print("Hello, world!") greet() 👉 Output: Hello, world! 💡 Function Rules (Beginner Friendly) ✔️ Use def keyword to create a function ✔️ Function name should be meaningful ✔️ Parentheses () are required ✔️ Indentation is mandatory ✔️ Must call the function to run it ✅ Function with Parameters (Inputs) def greet(name): print(f"Hello, {name}!") greet("Danial") 👉 Output: Hello, Danial! ✅ Function with Return Value def add(a, b): return a + b result = add(3, 5) print(result) 👉 Output: 8 🔑 Why Functions Are Important • Avoid repeating code • Make programs organized • Easier to debug • Used in every real application 🔥 Simple Idea: Function = A machine Input → Process → Output 🚀 Master functions, and you move from beginner code to real programming skills. #Python #Coding #Programming #LearnToCode #Developer