Imageio: Read & Write Image Data with Python

🚀 Exploring Python Strings & Lists – Practice Day! Today I worked on list indexing, slicing, and string operations in Python 🐍 🔹 Sample Code: alist = ["praveen","ajay","san","kiran","chandru","fun","joy","rrrr"] cd = alist[1][1:4] + alist[4][4:7] print(cd) ✅ Key Learnings: 🔸 Accessing elements using index → alist[5] 🔸 String slicing → alist[5][1:3] 🔸 Combining slices from different elements 🔸 Finding length of list → len(alist) 🔸 Counting characters → count('e') 🔸 Finding position → index('e') 💡 Example Insight: "ajay"[1:4] → jay "chandru"[4:7] → dru 👉 Combined result: jaydru 📌 These small exercises are helping me strengthen my fundamentals in Python, which is very useful for automation and DevOps tasks. #Python #DevOps #Learning #CodingJourney #Automation #Programming #Beginners

Today I learned about Polymorphism in Python, and it completely changed how I think about writing flexible code. Polymorphism is all about using a single interface to work with different types of objects. In simple terms, the same method can behave differently depending on the object that calls it. For example, a speak() method can return “Woof” for a Dog and “Meow” for a Cat — same method name, different behavior. What I found really interesting is how it works behind the scenes. Python allows this through concepts like method overriding, duck typing, and operator overloading. Instead of writing separate logic for every type, we can write more general and reusable code that adapts automatically. The real-world usefulness is huge. Whether it's handling different types of files, working with multiple payment methods, or building scalable systems, polymorphism helps keep code clean, maintainable, and easy to extend. This is a powerful reminder that writing smart code isn’t about making it complex — it’s about making it adaptable. #Python #Programming #OOP #Learning #SoftwareDevelopment

🐍📰 Using ggplot in Python: Visualizing Data With plotnine In this tutorial, you'll learn how to use ggplot in Python to build data visualizations with plotnine. You'll discover what a grammar of graphics is and how it can help you create plots #python

Most people think Python iteration is just a for loop. But that’s not what’s really happening. Under the hood, Python isn’t “looping” the way most people imagine — it’s running a machine built on iterators. And once you see this, your mental model of Python completely changes. In my latest article, I break this down in a simple way: 👉 A for loop is just a wrapper 👉 Python actually uses iterators to fetch one value at a time 👉 Every iterable (list, file, generator) behaves like a data stream 👉 The loop ends not because of a condition — but because of a Stop Iteration signal That’s why: generators feel “lazy” large datasets don’t load fully into memory Python can scale iteration efficiently 💡 The shift is this: Stop thinking: “Loop through data” Start thinking: “Pull values from a stream until it ends” That one idea makes Python iteration finally click. I’ll drop the link in the first comment 👇 Quick question: When you learned Python, did iteration feel intuitive — or confusing at first? #Python #Programming #DataScience #Coding #Developers #TechLearning #ArtificialIntelligence

Writing code in C++ for a multi-target tracking system has some advantages over python such as speed. For me code development is much slower, and of course there is the unit test code. Since I already have a python real time multi target simulator with gui, data logger, performance metrics, data logging, post processing and analysis, I decided to create python bindings for the C++ code. It worked quite well. Numpy is mapped to the armadillo data structures. The python simulator allows me to see the overall high performance of the various tracking algorithms. https://lnkd.in/gRM_X3xA

A class is the blueprint for creating objects. Just as a house blueprint defines what every house built from it will look like and how it will behave, a Python class defines the attributes and methods that every instance of that class will possess. This blog takes you from defining your first class all the way through advanced topics like inheritance, dunder methods, class methods, static methods, properties, dataclasses, and abstract base classes — with working code examples at every step. #Python #DataEngineering https://lnkd.in/gZKd-jWk

Python gets easier when you realize it’s learned in layers. First comes the basics — variables, conditions, loops, functions, file handling, and understanding how data structures work. This is where you learn how Python thinks. Then the intermediate level — OOP, comprehensions, lambda functions, collections, modules, environments, async programming, and how real projects are structured. This is where you start writing cleaner and smarter code. Finally, the expert layer — decorators, generators, context managers, testing, parallelism, packages, and performance tools like Cython. This is where Python becomes powerful and scalable. It’s not about rushing the journey. It’s about building depth at every stage. That’s how Python turns from a skill into a superpower.

🐍 The most misunderstood line in Python is this: for item in [1, 2, 3]: Most developers think the for loop just "goes through the list". What it actually does: calls iter([1,2,3]) to get an iterator, then calls next() on it repeatedly until StopIteration is raised. That's the entire protocol. Once you understand that, generators click immediately. A generator function with yield IS an iterator — Python implements iter and next automatically. And the magic of yield is that the function pauses at each yield and resumes from there on the next call. Full guide: iterator protocol from scratch, generator functions vs expressions, yield from for delegation, lazy 5-stage file processing pipeline, context managers (enter/exit), @contextmanager, suppress, ExitStack, and send()/throw() for two-way generator communication. A generator expression uses 200 bytes. An equivalent list uses 8MB. For the same data. 📎 Free PDF. Zero pip installs — pure Python standard library. #Python #Generators #Iterators #ContextManagers #PythonProgramming #SoftwareEngineering #CleanCode #BackendDev #Programming

Mastering Tuples in Python – Simple yet Powerful! Today’s learning focused on one of the most efficient data structures in Python — Tuples 🔥 📌 Key Concepts Covered: 🔹 Tuple Packing Combining multiple values into a single tuple ➡️ Example: data = ('apple', 10, 3.5) 🔹 Tuple Unpacking Extracting values into variables easily ➡️ Example: a, b, c = data 🔹 Tuple using range() Generating sequences efficiently ➡️ Example: nums = tuple(range(1, 6)) 🔹 Tuple Comprehension (via generator) Creating tuples dynamically ➡️ Example: tuple(x*x for x in range(5)) ✨ Why Tuples? ✔️ Faster than lists ✔️ Immutable (safe & secure) ✔️ Useful for fixed data collections 📊 Learning tuples helps in writing clean, optimized, and professional Python code. Global Quest Technologies #Python #PythonProgramming #DataStructures #Tuples #CodingJourney #LearnPython #ProgrammingLife #DeveloperLife #TechSkills #Coding #PythonBasics #SoftwareDevelopment

🧠 Python Concept: List Comprehension Write loops in one clean line 😎 ❌ Traditional Way numbers = [1, 2, 3, 4, 5] squares = [] for num in numbers: squares.append(num * num) print(squares) ✅ Pythonic Way numbers = [1, 2, 3, 4, 5] squares = [num * num for num in numbers] print(squares) 🧒 Simple Explanation Think of it like a shortcut formula 🧮 ➡️ Take each item ➡️ Apply logic ➡️ Store result — all in one line 💡 Why This Matters ✔ Less code, more clarity ✔ Faster to write ✔ Easy to read once you learn it ✔ Widely used in real projects ⚡ Bonus Example (With Condition) even_squares = [num * num for num in numbers if num % 2 == 0] print(even_squares) 🐍 Python is all about writing clean and expressive code 🐍 Master list comprehension = level up 🚀 #Python #PythonTips #PythonTricks #AdvancedPython #CleanCode #LearnPython #Programming #DeveloperLife #DailyCoding #100DaysOfCode