Python Generators vs Lists for Large Data

Dashboard with DASH. Using a series of synthetic data, I created this dashboard using the DASH library in Python, but I must admit that it's easier to work with the Shainy library in R. Which do you prefer? The advantages of using these two libraries I just mentioned are that they are completely free and that you can also share and interact with the image in a professional meeting without having to pay a single cent. This is much more expensive with other solutions. #DataVisualization #PythonProgramming #BusinessIntelligence

Merging spreadsheets, cleaning exports, and splitting reports are necessary-but-boring tasks. These Python scripts handle the repetitive parts so you can focus on the actual work. https://lnkd.in/eJtC6Wae

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

🐍 New on wcblog.in: Python Basics — Variables, Data Types, Loops & Functions Explained If you're starting out with Python (or need a solid refresher), I just published a practical, engineer-focused guide covering everything you need to write real Python code from day one: ✅ Variables & data types (int, str, list, dict, set...) ✅ String manipulation & f-strings ✅ Loops — for, while & list comprehensions ✅ Functions, *args, **kwargs ✅ Error handling with try/except ✅ A mini pipeline project to tie it all together Python is the backbone of data engineering, ML, and automation — and it all starts with these fundamentals. 👉 Read the full guide: https://lnkd.in/g92XrVSU #Python #DataEngineering #PythonBasics #LearnPython #Programming #DataEngineer #TechBlog

📅 Day 75 – Higher Order Functions using filter() 🚀🐍 Today I practiced filter() function in Python to select required data from lists 💡 🔹 Filtered even numbers from a list ⚖️🔢 🔹 Filtered odd numbers from a list 🔀🔢 🔹 Selected numbers greater than 10 ⬆️🔢 🔹 Applied condition-based filtering on numeric data ✔️ 🔹 Filtered words that start with vowels (a, e, i, o, u) 🔤 🔹 Learned string-based filtering using conditions 📏 💡 Understood how filter() helps in extracting only required elements from a dataset ⚡ 💡 Improved logic building with lambda functions + conditions 🧠 🔥 Feeling more confident in functional programming and data filtering in Python! #Day75 #Python #FilterFunction #HigherOrderFunctions #CodingJourney #LearnPython #WomenInTech #FutureEngineer 🚀✨

An exercise to help build the right mental model for Python data. - Solution: https://lnkd.in/e55Pr8nJ - Explanation: https://lnkd.in/ebPVvnhx - More exercises: https://lnkd.in/eQSdJdaW The “Solution” link visualizes execution and reveals what’s actually happening using 𝗺𝗲𝗺𝗼𝗿𝘆_𝗴𝗿𝗮𝗽𝗵: https://lnkd.in/e3sUM7wG

Problem: 2.675 → 2.67 ❌ Fix: (value + 1e-9).round(2) ✅ Reason? Floating-point precision. Save this for later — it can save your data accuracy! #Python #QuickTip #DataAnalytics #Pandas #Tech

🐍 Python Basic Data Types – Quick Overview If you're starting your journey in Python, understanding basic data types is the first step 🚀 🔢 int → Whole numbers (10, -5) 🔣 float → Decimal numbers (10.5, 3.14) 🔤 str → Text ("Hello", "Python") ✔️ bool → True / False 📦 list → Ordered & changeable [1, 2, 3] 🔗 tuple → Ordered & unchangeable (1, 2, 3) 🧩 set → Unique values {1, 2, 3} 📘 dict → Key-value pairs {"name": "Satish"}

🅸🅽🅿🆄🆃 & 🅾🆄🆃🅿🆄🆃 🅵🆄🅽🅲🆄🆃🅸🅾🅽🆂 📦 Definition: In Python, Input is how we get data from the user into our program, and Output is how the program displays information back to the user. 🏠 Real-World Example: Think of a Vending Machine. Input: You press the buttons to tell the machine you want a "B3" (snack code). Output: The machine displays "Price: $1.50" on the screen and then drops your chips! 🍟 Without that type interaction, the machine is just a silent box of snacks. Here is how we do it in Python: 👉 input(): This function pauses the program and waits for you to type something. Python always treats input as a string by default, so if we want numbers, we will need to wrap it in an int() or float(). 👉 print(): It takes whatever is inside the parentheses and splashes it onto the screen for the world to see. #python #inputoutput #codingtips #pythonsimplified #datananalytics #learnpython

I built a Python metadata introspection tool for visualising attribute origination across:   • Classes   • Instances   • MRO   • Metaclasses   • Other objects It is useful for exploring Python’s object model beyond standard dir() style introspection. GitHub: https://lnkd.in/eniV5Yu8   YouTube: https://lnkd.in/e9fatuQt  #python #oop #metaprogramming #introspection Example output for a class: