Python Input & Output Basics: Understanding Input() and Print()

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

The Python Collections Cheat Sheet Choosing the right data structure is 50% of the job. Pick the wrong one, and your code gets slow or buggy. Pick the right one, and it becomes elegant. My quick guide: ✅ List: When order matters ✅ Tuple: When data must stay constant ✅ Set: When you need uniqueness and speed ✅ Dict: When you need to map labels to data Day 16/30 #Python #Day16 #BuildinginPublic #DataStructures #CodingCommunity #PythonCheatSheet

Day 5 of #30DaysOfPython ✅ Today I met two of Python's most powerful data structures. One of them already feels like home. The other? Slightly chaotic. Lists and dictionaries. Day 5. Lists made sense quickly — they're just ordered collections. I can store things, loop through them, sort them, slice them. Intuitive. Dictionaries? At first, the key-value pair concept felt abstract. The bug that got me today? I threw both strings and integers into the same list and tried to sort it. Python did not appreciate that. TypeError showed up like an old enemy. Day 5 done. 25 more to go! 👇 Lists vs dictionaries — when do you reach for one over the other? #Python #30DaysOfPython #DataStructures #StudentLife #AIML

🚨 This behavior of Python might look like a BUG… but it isn’t actually. a = 10 b = 10 print(id(a)) print(id(b)) 👉 Same memory location 😲 “Why do we have two variables pointing to the same memory location?!” Here comes the second one and things get interesting 👇 a = [1, 2, 3] b = a b.append(4) print(a) # [1, 2, 3, 4] 🔥 👉 Hmmm… why did ‘a’ change?! 💡 Explanation: ⭐ id() returns the identity of an object ⭐ Python reuses memory locations for immutable values ⭐ For mutable objects however, there is no copying, just pointers! ⚠️ The misconception: Most people believe ‘=’ copies objects in variables. 👉 Nope! ✅ Solution: b = a.copy() Now the two variables are separate ✅ 🔥 Consequence: It can seriously mess up your program’s logic! Ever got caught by such a ghost bug in Python? 👇 #CodeWithSujith #Python #Programming #Coding #PythonTricks #LearnPython #PythonBeginner #100DaysOfCode #DeveloperJourney

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/e9Y3xGNF. #python #softwaredesign #designpatterns #statemachine #cleancode

Finally moved Python & AI project from local to live! 🎈 I’ve been experimenting with Python lately and finished building a simple Personal Assistant for my morning routines and productivity. It was fun to step outside .NET and try out some new tools: ⚡ Groq : Really impressed with how fast the responses are. 🐍 Python: Getting more comfortable with the syntax every day. 🖥️ Streamlit: Made it super easy to put a clean UI on top. It’s work in progress, but you can check out V1 here: https://lnkd.in/gwyuaGZt #Python #Streamlit #PersonalProject #BuildingInPublic #WomenInCode

Day 3/365: Comparing Two Strings Character by Character 🧵🧠 Today I worked on a simple but fundamental logic problem: checking if two strings are the same, without directly using a built-in equality check. First, I compare the lengths of both strings. If lengths differ, they can’t be the same. If lengths match, I loop through each index and compare characters one by one. If any character is different, I break and print that the strings are not the same. If the loop finishes without finding a mismatch, the else block of the for loop runs and prints that the strings are the same. The interesting part is the for-else in Python. The else only runs when the loop completes normally (no break). This makes it a clean way to express: “if I didn’t find any mismatch in the entire loop, then the strings are equal.” Day 3 done ✅ 362 more to go. #100DaysOfCode #365DaysOfCode #Python #LogicBuilding #StringComparison #ForElse #CodingJourney #LearnInPublic #AspiringDeveloper

Learn how to create predictive models with Python and Scikit-Learn. This comprehensive guide covers data preparation, model building, evaluation, and deployment. https://lnkd.in/ghAvtz8v #PredictiveModelingWithPython Read the full article https://lnkd.in/ghAvtz8v

📊 Comparing Two Outlier Removal Approaches in Python When cleaning datasets, how you remove outliers matters more than you think. I recently compared two common strategies: 1️⃣ Column-wise removal – Drop outliers sequentially, one column at a time. 2️⃣ Dataset-level removal – Flag all outliers across the entire dataset first, then remove them together. 🔍 What I found: The column-wise approach changes the IQR bounds after each removal, causing many non‑outlier rows to be wrongly filtered out (545 → 365 rows). The dataset-level approach respects original distributions, removes only true outliers (545 → 463 rows), and avoids over‑cleaning. ✅ Takeaway: Always identify outliers globally before removing them – your data will thank you. 📁 Used Python, pandas, IQR method, and a housing dataset. 🔗 Full code & notebook: https://lnkd.in/gheGYYEz #DataScience #Python #OutlierDetection #DataCleaning #Pandas #MachineLearning

Copying vs Reference 🐍 I thought this would create a copy: b = a It didn’t. a = [1, 2, 3] b = a b.append(4) print(a) 👉 [1, 2, 3, 4] Both variables point to the same list. No copy was made. In Python, variables are just labels, not containers. When you use =, you aren’t duplicating data, you’re just giving the same memory address a second name. To actually copy: b = a.copy() Looks the same. Behaves completely differently. 💡 And also: .copy() only goes one layer deep and that's why we need deep copy. ➡️ Assignment ≠ Copy Day 17/30 #Python #30DaysOfCode #SoftwareEngineering