You can't use a list as a #Python dict key: mylist = ['a', 'b'] d = {mylist: 10} # TypeError! Why not? Dicts run "hash" on a key to choose a pair's storage location. To avoid pairs getting lost, mutable builtins (list, set, dict) cannot be used as dict keys.

Most data is inefficient you just don’t see it. I built Entropy Engine to make it visible. Drop in any JSON or CSV file and it: -detects what’s wasting space (duplicates, repeated strings, whitespace) -explains why it matters (storage, compression, performance) -lets you simulate fixes before touching your data The rotating ASCII cube in the center represents your dataset watch it shrink in real time as each optimization runs. Built with Python, FastAPI, React, and real-time SSE streaming. GitHub: https://lnkd.in/gmsiXj9u #python #react #fastapi #opensource #softwaredeveloper

Day 71 of 365 Days of code qn 1) koko eats banana approach: binary search 1) set low=1 and high= max(piles) 2) find mid=low+(high-low) 3) traverse the entire array and check if the number of hours taken by the current rate of consumption is less than the threshold hours if yes set high=mid-1 4) else low=mid+1 #365daysOfCode #NeetCode  #leetcode  #DSA  #python #LeetCode #ProblemSolving  #Algorithms #365dayschallenge

🚀 Built a Movie Recommendation System using Collaborative Filtering! I recently developed a movie recommendation system that suggests movies based on user preferences and similarity patterns. The model analyzes user–movie interactions to uncover hidden patterns and deliver personalized recommendations. 👉 Simply select a movie, and the system instantly suggests similar movies you’re likely to enjoy! ✨ Key Features: • Collaborative filtering–based recommendations • User–movie similarity analysis • Instant top movie suggestions • Clean and interactive web interface 🎥 Check out the demo to see it in action! 💻 Tech Stack: Python | Scikit-learn | Pandas | NumPy | Streamlit 🔗 GitHub Repository: https://lnkd.in/g-d3DwSa 🔗 Live Demo: https://lnkd.in/dnigN_7e #MachineLearning #RecommenderSystem #Python #AIProject #DataScience #Streamlit #CollaborativeFiltering #AIML

FastAPI Depends() — the pattern I learned 7 weeks too late Tutorials show you Depends() in week 1. Nobody shows you composition until your auth is already a mess. The 4-day refactor I could have skipped. Full breakdown (7 min read) → https://lnkd.in/g9F9EWKT #Python #Backend #FastAPI #SoftwareEngineering #WebDev

Want to check whether a #Python #Pandas series contains another string? Use .str.contains: df['x'].str.contains('a') This returns a boolean series, whose index matches that of df. Keep only those rows containing 'a': df.loc[ pd.col('x').str.contains('a') ] # Pandas 3 syntax

𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻 𝗶𝘀𝗻'𝘁 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻 𝗮𝗻𝘆𝗺𝗼𝗿𝗲.  𝗟𝗹𝗮𝗺𝗮𝗜𝗻𝗱𝗲𝘅 𝗶𝘀𝗻'𝘁 𝗷𝘂𝘀𝘁 𝗥𝗔𝗚 𝗮𝗻𝘆𝗺𝗼𝗿𝗲. 2026 changed everything. Most developers haven't caught up. I put together the updated decision framework — which tool to use, when, and how the smartest teams are combining them in production. Swipe through 👉 Which stack are you running? 👇 #LangGraph #LlamaIndex #RAG #AIAgents #LLMOps #Python

Want to check whether a #Python #Pandas string series only contains digits? Use str.isdigit: df['x'].str.isdigit() This returns a boolean series, with df's index. You can then convert the digit-containing strings to integers: df.loc[pd.col('x').str.isdigit(), 'x'].astype(int)

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

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