Python Floating Point Precision Issue Explained

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

Python Clarity Series – Episode 25 Topic: Mutable vs Immutable Function Behavior 📌 Why did my list change after function call? def modify(lst): lst.append(100) a = [1, 2] modify(a) print(a) Output: [1, 2, 100] 👉 Lists are mutable → changes reflect outside Now: def modify(x): x = x + 10 a = 5 modify(a) print(a) Output: 5 👉 Integers are immutable → no change outside 💡 Rule: Mutable → changes persist Immutable → changes don’t This confusion causes logic errors. #PythonBasics #FunctionConcepts #StudentClarity #python #clarity

Day 43/100 – #100DaysOfCode 🚀 Solved LeetCode #2610 – Convert an Array Into a 2D Array With Conditions (Python). Today I practiced hashmap (frequency counting) to construct a 2D array based on given conditions. Approach: 1) Create a frequency map to count occurrences of each element. 2) Initialize an empty result list. 3) While the frequency map is not empty: 4)  Create a new row. 5)  Iterate through keys and add each number once to the row. 6)  Decrease its frequency and remove it if it becomes zero. 7) Add the row to the result. 8) Return the final 2D array. Time Complexity: O(n) Space Complexity: O(n) Learning how frequency maps help in structuring data efficiently 💪 #LeetCode #Python #DSA #HashMap #Arrays #ProblemSolving #100DaysOfCode

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

🐍 Day 22 of My 30-Day Python Learning Challenge Today I improved my Log File Analyzer by allowing user input (file name) instead of hardcoding. 📌 Code: filename = input("Enter file name: ") with open(filename, "r") as file: content = file.read().lower() print(content[:100]) # preview first 100 characters 📌 Why this matters? • Makes the program flexible • Works with any file • Closer to real-world usage 📊 Quick Question What happens if the user enters a wrong file name? A) Program crashes B) Empty output C) None D) Skips execution Answer tomorrow 👇 #Python #MiniProject #UserInput #LearningInPublic #SoftwareDeveloper

Learn how to build a predictive model with Python and Scikit-Learn, including data preparation, model selection, and evaluation techniques, with expert tips and real-world examples https://lnkd.in/ge-CSTzq #PredictiveModelWithPython Read the full article https://lnkd.in/ge-CSTzq

Learn how to build a predictive model with Python and Scikit-Learn, including data preparation, model selection, and evaluation techniques, with expert tips and real-world examples https://lnkd.in/ge-CSTzq #PredictiveModelWithPython Read the full article https://lnkd.in/ge-CSTzq

Learn how to build a predictive model with Python and Scikit-Learn, including data preparation, model selection, and evaluation techniques, with expert tips and real-world examples https://lnkd.in/ge-CSTzq #PredictiveModelWithPython Read the full article https://lnkd.in/ge-CSTzq

Day 106 Some problems feel simple when the pattern clicks. #Day106 🧩 17. Letter Combinations of a Phone Number How today went: • Used a digit → letters map • Built combinations using backtracking • Maintained a string path at each step • One recursive call per choice — no need for complex state handling What I realized: Once you understand the pattern: → choose a letter → move to next digit → build the path Backtracking becomes very natural. Simple problem, but great for building confidence. #LeetCode #DSA #Python #Backtracking #Recursion #LearningInPublic #Consistency

Working with messy real-world datasets taught me one thing: The cleaning step takes longer than the actual analysis. So I spent the last few weeks building dfdoctor - an open-source Python library that audits your DataFrame, tells you what’s wrong, and helps you fix it systematically instead of manually. It helps you quickly understand what’s broken and what to fix first. The part I'm most proud of: 5 correlation methods (including Kendall τ and Phi-k) implemented from scratch in pure numpy - no scipy dependency anywhere. 164 tests. CI passing across Python 3.9–3.12. Try it: pip install dfdoctor https://lnkd.in/e-ChV6mE #Python #OpenSource #DataEngineering #Pandas #EDA #DataScience