Python Pickling Limitations: Serialization and Security

Recursion can feel confusing when you first learn about it in Python. But don't worry: here, Bala teaches you how recursive functions work, how the call stack handles them, and when recursion makes more sense than iteration. You'll also learn how to work with nested data, traverse trees, use memoization, and avoid recursion limits. https://lnkd.in/gZSF8Vea

Day 19/30 – Numerical Computing with NumPy Today was all about getting comfortable with NumPy and actually understanding why people use it instead of plain Python. I focused on: • Working with arrays instead of lists • Performing fast calculations without writing long loops • Using built-in functions to simplify complex operations What I realized: NumPy isn’t just about speed — it reduces unnecessary code and forces you to think in a cleaner, more structured way. Two quick takeaways: 1. Instead of looping through values one by one, NumPy lets you operate on entire datasets at once. 2. Small problems feel simple, but NumPy really shows its value when data size increases. Still a lot to explore, but this feels like a solid step toward data handling and analysis. #Day19 #Python #NumPy #LearningJourney #DataSkills

🚀 Day 50 of My Python Journey Today I solved Complement of Base 10 Integer on LeetCode. 🔍 Problem Overview: The task is to find the bitwise complement of a given base-10 integer. The complement is obtained by flipping all bits in its binary representation — changing every 0 to 1 and every 1 to 0. 🧠 Approach: 1️⃣ Convert the integer into its binary representation. 2️⃣ Traverse the binary string and flip each bit (1 → 0, 0 → 1). 3️⃣ Convert the resulting binary string back to a decimal integer. ⚡ Key Learnings: • Practiced binary representation and bit manipulation • Improved understanding of number systems (binary ↔ decimal) • Strengthened string manipulation and logical thinking in Python 📊 Complexity: • Time Complexity: O(n) • Space Complexity: O(n) Under the Guidance of : Rudra Sravan kumar and Manoj Kumar Reddy Parlapalli #Day50 #Python #LeetCode #DataStructures #Algorithms #CodingJourney #ProblemSolving #100DaysOfCode 🚀

Just launched: Open Telemetry in Python — Part 1 If you've ever stared at logs at 2 AM wondering "which" service broke and "why" — this tutorial is for you. Part 1 covers the foundations: → What observability actually is (and how it differs from monitoring) → The three pillars: Traces, Metrics, and Logs → What OpenTelemetry is — and what it isn't → Your first instrumented Python app with real spans No fluff. Just clear explanations, working code, and a cheat sheet you'll actually use. 📖 Read it free at www.getspanforge.com 🔗 https://lnkd.in/geWXMD2w Parts 2–6 (Flask, Jaeger, Prometheus, the Collector) are coming soon. Follow along if you don't want to miss them. Share your comments, thank you. #OpenTelemetry #Python #Observability #SoftwareEngineering #DevOps

Day 3 of my Build in Public journey 🚀 Today’s focus: Advanced Python concepts and aptitude practice. Here’s what I worked on today 👇 💻 Python • Explored lambda functions, map(), filter(), and reduce() • Practiced recursion (tail and non-tail) • Learned about decorators and how functions act as first-class objects 🧠 Aptitude • Solved questions to improve logical thinking ⚠️ Note: Didn’t work on development today — focusing on strengthening fundamentals. 💡 Lesson: Understanding concepts deeply takes time, but it makes everything easier later. Back again tomorrow to keep building. #BuildInPublic #100DaysOfCode #LearningInPublic #TechJourney

Started my #30DaysOfPython journey today. 🚀 Here are some of the basics I picked up: 1. Python is a high-level, interpreted, open-source, object-oriented language created by Guido van Rossum. 2. Unlike many languages, Python uses indentation instead of curly brackets to define blocks of code. 3. I also revisited core data types like: (i) Numbers (ii) Strings (iii) Booleans (iv) Lists (v) Dictionaries (vi) Tuples (vii) Sets 4. One simple but useful thing: type() helps check the data type of any value. Day 1 done. One step closer to becoming more confident with Python. 🐍 #Python #30DaysOfPython #SoftwareDevelopment #LearningInPublic #CareerTransition

Learn in Public — Day 15 Today I studied Selection Sort and implemented it in Python. 🔹 Key Idea: Selection Sort repeatedly finds the minimum element from the unsorted part of the array and places it at the correct position in the sorted part. 🔹 How it works: 1️⃣ Start from the first element 2️⃣ Find the smallest element in the remaining array 3️⃣ Swap it with the current position 4️⃣ Repeat for the rest of the array 🔹 Complexity: Time Complexity: O(n²) Space Complexity: O(1) (in-place sorting) Even though Selection Sort is not efficient for large datasets, it’s a great algorithm for understanding how sorting works internally. Every small step builds the foundation for mastering algorithms. #LearnInPublic #100DaysOfCode #Python #Algorithms #DataStructures

Still slicing DataFrames the hard way? Here are the three essentials: grab one column with df['col'], pick multiple with df[['a','b']], then filter rows by building a boolean mask like df[df['age'] >= 18]. Master these and inspecting/subsetting data becomes effortless.#pandas #python #datascience #dataanalysis

I made myself an invisibility cloak! This was a fun project I created in a few hours with Python. This is the tech I used to build it: → Python 3 → OpenCV → NumPy And here is exactly how it works: 1. Takes 30 frames and creates a clean background image. 2. Converts frames to HSV color space and detects yellow-colored objects. 3. Uses morphological operations to clean up the detection mask. 4. Combines the current frame with the background using bitwise operations. If you are curious to see the code behind it, here is the link to my GitHub repo: → https://lnkd.in/eNziSv9E