Python Performance: List Comprehensions vs Loops

Here's a Python dictionary merge challenge from @dontmisstmr — can you get it right without running the code? dict1 = {"name": "Alice", "age": 25} dict2 = {"city": "Noida", "age": 26} merged = dict1 | dict2 print(merged) The | operator was introduced in Python 3.9. When both dicts share a key, which value wins? Drop your answer in the comments! #Python #SoftwareDevelopment #CodingChallenge #ProgrammingTips #TechCommunity

Day 4 – Python Deep Dive 🚀 Topic: Mutable vs Immutable Immutable: int, string, tuple Mutable: list, dictionary Example: x = "hello" x[0] = "H" ❌ (error) 👉 Key takeaway: Immutable objects cannot be changed after creation. This impacts memory and performance. #Python #DeveloperThinking

Most implementations of the State pattern in Python look very “clean”. Lots of small classes. A base interface. One class per state. But if you’ve ever worked with one in a real project, you know the downside: transitions are scattered, behaviour is hard to see in one place, and adding new states often means touching multiple files. In today’s video, I rebuild the State pattern in a very different way. Instead of relying on inheritance, I make the state machine explicit as data and use decorators to define transitions. The result is a small, reusable engine where the entire flow becomes visible at a glance. If you’re interested in writing Python that’s easier to reason about and extend, this is a pattern worth understanding. 👉 Watch here: https://lnkd.in/eg22yEHR. #python #softwaredesign #designpatterns #statemachine #cleancode

Python Challenge – Can you solve this? Today was all about deep-diving into Lists vs. Sets and I came across a common mistake that we can sometimes overlook. Let’s test your Python understanding👇 numbers = [1, 2, 3] numbers.append([4, 5]) print(len(numbers)) A) 3 B) 4 C) 5 D) Error It’s a classic interview question that tests if you truly understand how Python handles memory and lists. Day 15/30 #30DaysOfCode #DataStructures #Day15 #PythonQuiz

Day 1 of learning NumPy. I thought it was just a faster Python list. Nope. NumPy arrays store only one data type — that's why they're blazing fast. And this blew my mind: my_list + 5 → Error my_array + 5 → Adds 5 to everything instantly No loops. No extra code. Just math. Day 1 and I'm already Cooked. #NumPy #Python

✅ Python: 04 🎯 Nested loops Let's share an interesting concept of python. we've this concept called nested loop, here we can use one loop inside of an another loop. We can get some interesting results. Let's take a look- for x in range(2):  # outer loop   for y in range(3):  # inner loop      print(f"({x} , {y})")   # for co-ordinates 📌Code explanation: The outer loop will be executed 2 times & the inner loop will be executed 3 times, To begin with, the python interpreter will execute the outer loop first then it'll go to the inner loop and execute codes as follows, then it'll print as commanded and then jumps into the outer loop again, this will continue as per the range mentioned in the code. That's how nested loop works. #PythonProgramming #PythonDeveloper #Coding #python #nestedloopinpython #DataScience #pythondeveloper

Python Logic: Is this Math or Magic?🤔 Day 8 of my Python journey! C++ logic tells me: You can't add words to numbers. Python logic says: Hold my coffee ☕ Check out this snippet: result = True + True + False * True What do you think the print(result) output will be? Drop your guess in the comments! 👇 A) True B) 2 C) 3 D) Error Hint💡: It comes down to how Python stores Booleans as Integers! #Python #LearninginPublic #30DaysOfCode #ProgrammingLogic #Day8

Dunder methods (aka “double underscore” or “magic methods”) are what make Python objects behave like built-ins. From __init__ for initialization to __str__ for readable output and __add__ for operator overloading — this is where OOP in Python gets powerful. Learn these, and your classes stop being basic… and start being Pythonic. #Python #PythonProgramming #DunderMethods #MagicMethods #OOP #LearnPython #CodingJourney #SoftwareDevelopment #PythonTips #DeveloperLife # AadyaTechnovate

Here's a Python collections challenge from @dontmisstmr — can you get it right without running the code? from collections import Counter data = [1, 2, 2, 3, 3, 3, 4, 4] top_element = Counter(data).most_common(1) print(top_element) Counter is one of Python's most underrated built-ins. Do you know what format most_common(1) returns? Drop your answer in the comments! #Python #SoftwareDevelopment #CodingChallenge #ProgrammingTips #TechCommunity

Want to find which time period contains a #Python #Pandas datetime? Use to_period: pd.Timestamp.now().to_period('1D') pd.Timestamp.now().to_period('6W') This returns a Pandas "Period" object, useful (among other things) for checking if two dates are within the same period.