Python Shallow vs Deep Copy: Understanding the Difference

🚀 Lecture 2 is Done! — Teaching Python to Think & Decide                                 Lecture 2 of my Introduction to Python course for MSBA students at SZABIST  Islamabad just wrapped up, and this is where things get really interesting!       In Lecture 1, we gave Python instructions. In Lecture 2, we taught Python how   to make decisions.  Here's what we covered:                                 🔹 Boolean Values                                 Every decision in programming boils down to: True or False? Python's Boolean  data type has just these two values. Every financial model that says "if      revenue exceeds threshold, then…" is powered by this idea.                                            🔹 Comparison Operators  We learned to let Python compare values:  ✅ Equal (==), Not equal (!=)  ✅ Greater than (>), Less than (<), and their or-equal variants          Key lesson: = assigns a value, == compares two values. Mixing these up is the   most common beginner mistake!                                                                     🔹 Boolean Operators                                Real decisions combine multiple conditions — "Approve the loan if credit score   > 700 and debt-to-income < 40%."                         📌 and — True when both conditions are True  📌 or — True when at least one is True                       📌 not — Flips True to False and vice versa  🔹 Blocks & Indentation                              Python uses indentation to define code blocks — forcing clean, readable code  from day one. The colon (:) signals that an indented block is coming next.                🔹 if / elif / else — The Core of Flow Control                   ✅ if — runs code only when a condition is True  ✅ elif — checks another condition when the previous was False  ✅ else — catches everything else                                            Python evaluates top to bottom and executes only the first match. Order      matters — just like structuring decision logic in risk models.                                                     🔹 From Flowcharts to Code  Flow control maps directly onto flowcharts. Diamonds are conditions,  rectangles are code blocks. If you can draw a decision flowchart, you can     write it in Python.                                            💡 Key Takeaway:  Flow control powers every automated decision in finance — credit scoring,  fraud detection, portfolio rebalancing. Python's if statement does what      Excel's IF() does, but with far more power and scalability.                                            📚 Resource: https://lnkd.in/dWM25WeX  Stay tuned — Lecture 3 is coming up next! 🐍  #Python #MSBA #BusinessAnalytics #Finance #PythonForFinance #Teaching #SZABIST   #DataScience #LearningPython #FlowControl

703 Python files. 300 Markdown files. I am a Python developer who writes 30% documentation. I pulled my language distribution from 19 days of AI-assisted work. The numbers did not match my identity. Python was first. Expected. But Markdown — planning docs, architecture decisions, agent instructions — was second. Not YAML. Not JSON. Prose. The AI needed written instructions more than it needed configuration files. Then the tools told a stranger story. 2,254 bash commands. 760 file reads. 416 edits. For every line I changed, I ran five commands investigating what to change. Before AI tools, my ratio was closer to 2:1. The AI made me a better reader, not a faster writer. That distinction matters for how you invest. If you evaluate AI coding tools on "lines generated per hour," you are measuring the tail, not the dog. The value is in investigation — 2,254 commands identifying what to change before a single edit. 📊 Three changes after seeing this: I optimized prompts for codebase exploration, not code generation. I wrote better CLAUDE.md instructions — Markdown, not Python. And I stopped timing "how fast can it write this function" and started timing "how fast can it understand this module." My exploration sessions — 8 out of 81 — produced zero code and the best decisions of the month. What does your read-to-edit ratio look like — and have you ever measured it? — Ernest Hemingway, Journalist AI #DeveloperProductivity #AITools #SoftwareEngineering

📘 Python for PySpark Series – Day 15 🎭 Polymorphism in Python ✨ What is Polymorphism? Polymorphism means “many forms”. It allows the same method or function to behave differently based on the object. ➡️ Promotes flexibility and dynamic behavior in code 🔹 Why Polymorphism? ✔ Improves code flexibility ✔ Reduces complexity ✔ Enhances readability ✔ Supports method reusability 🔹 Types of Polymorphism ✔ Method Overriding (Runtime) ✔ Method Overloading (Compile-time – limited in Python) ✔ Operator Overloading 🔹 Syntax (Method Overriding) class Parent: def show(self): print("Parent method") class Child(Parent): def show(self): print("Child method") 🔹 Example class Animal: def sound(self): print("Some sound") class Dog(Animal): def sound(self): print("Bark") class Cat(Animal): def sound(self): print("Meow") animals = [Dog(), Cat()] for a in animals: a.sound() ➡️ Same method sound() → different outputs 🔗 Why Polymorphism in PySpark? ✔ Same operations work on different data types (RDD, DataFrame) ✔ Functions behave differently based on input ✔ Helps in writing generic and reusable code 🏫 Real-Life Analogy (Remote Control 📺) One remote → multiple devices ➡️ Same button (action) → different behavior (TV, AC, etc.) 🧠 Interview Key Points ✔ Polymorphism = one interface, multiple implementations ✔ Achieved using method overriding ✔ Supports dynamic method dispatch ✔ Increases flexibility and scalability 🧠 Key Takeaway Polymorphism allows writing flexible and reusable code where the same operation behaves differently for different objects. 🔖 Hashtags #python #pyspark #dataengineering #oop #polymorphism #pythonbasics #learningjourney #coding

⚡ Writing Python loops the hard way? Let's fix that. ━━━━━━━━━━━━━━━━━━━━━━ Most Python beginners write this: result = [] for n in [1, 2, 3, 4, 5, 6]: if n % 2 == 0: result.append(n * 2) ▸ 4 lines. Repetitive. Easy to get wrong. ━━━━━━━━━━━━━━━━━━━━━━ Python gives you a cleaner tool — the List Comprehension: result = [n * 2 for n in numbers if n % 2 == 0] ────── ─────────────── ───────────── express iterate filter Both produce the same result: [4, 8, 12] ▸ 1 line. Readable. Faster to execute. ━━━━━━━━━━━━━━━━━━━━━━ How to read any list comprehension: [ WHAT YOU WANT → FROM WHERE → ONLY IF ] Once you see that pattern, every comprehension becomes obvious. This is the kind of insight Vaathiyaar teaches at PyMasters — building intuition, not just syntax. → Learn Python the right way at pymasters.net #Python #PythonTips #ListComprehensions #LearnPython #PyMasters

Today marks Day 2 of my Python Revision Series, where I am revisiting all core concepts with clean logic, structured thinking, and consistent problem-solving. I have completed Python multiple times, but this revision series is focused on: ✅ Strengthening fundamentals ✅ Improving coding speed ✅ Sharpening logic for AI & automation ✅ Creating content that beginners can follow and learn from 📌 Key Topics Revised Today 1️⃣ Functions (def) Reusable and modular code structure Parameters, return values, default arguments Importance of functions in large-scale projects & AI scripts 2️⃣ Loop-Based Logical Thinking Mathematical breakdown Step-by-step iteration Dry-run analysis Optimizing logic flow 🧩 Problem 1: Reverse an Integer (Without converting to string) Objective: Given a number, return its reversed form using pure mathematical logic. Input: 1234 Output: 4321 ✅ Python Code def reverse_number(n): rev = 0 while n > 0: digit = n % 10 rev = rev * 10 + digit n = n // 10 return rev print(reverse_number(1234)) 🔍 Explanation Extract the last digit Append it to the reversed number Remove the digit from the original number Repeat until the number becomes 0 This builds strong loop + math reasoning — very important for interviews. 🧩 Problem 2: Character Frequency Counter (Hashmap) Objective: Count how many times each character appears in a string. Input: "aabbccd" Output: {'a': 2, 'b': 2, 'c': 2, 'd': 1} ✅ Python Code def char_frequency(s): freq = {} for ch in s: if ch not in freq: freq[ch] = 1 else: freq[ch] += 1 return freq print(char_frequency("aabbccd")) 🔍 Explanation Dictionary used for fast lookup Each character is counted efficiently This pattern is heavily used in LeetCode and AI token analysis

Tell your AI that you are beginner when learning Python! I recently asked AI to generate some simple code to reverse two virtual spreadsheet columns called ‘Name’ and ‘Type’. Instead of simply suggesting code in which the column list [‘Name’, ‘Type] is replaced by [‘Type’, ‘Name’], AI suggested a complex indexing trick that looks like [::-1]. Succinct coding, to be sure, but I could not decipher it without additional prompts! In another case, my AI provided some sample code in which a variable was defined after that variable was used! The Horror! When I asked why it made such a fundamental mistake, the AI complimented me on my “good eye” for catching the error and that it did not necessarily provide code in execution order. So, if you are just learning Python, always start your session with a good prompt to set the stage such as the following: "I am a total beginner learning Python. Please follow these rules for ALL Python code you write for me: 1. Write code in execution order. 2. Break every task into small, discrete steps. 3. Use simple, obvious variable names that describe what they contain 4. Add plenty of comments explaining what it happening in plain English 5. Avoid shortcuts, clever one-liners, or condensed syntax that experienced coders use 6. If there are multiple ways to do something, choose the most readable one, not the most efficient one 7. Before showing me code, double-check it runs in the correct order from top to bottom" For more tips, consider my new book "Automate Excel with Python". My publisher @No Starch Press is offering a free review chapter in case you were interested: https://lnkd.in/eS-WAVyV Good luck and good coding! #Excel, #Python, #pandas, #dataframes,#Productivity, #DataAnalysis, #Automation

Today I explored some important concepts in NumPy, Image Processing, and CNN models. Here’s what I learned 👇 🔹 1. Gray Conversion Convert color image → black & white (only brightness used). Example: Python gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 🔹 2. Blur Smooths the image by averaging nearby pixels. Example: Python blur = cv2.GaussianBlur(img, (15,15), 0) 🔹 3. Edge Detection Finds the outlines/edges in an image (lines, shapes). Example: Python edges = cv2.Canny(img, 100, 200) 🔹 4. Crop Cut a small part from the image (like zoom into a region). Example: Python crop = img[50:200, 100:300] 🔹 5. Flip Flip image left–right or up–down (mirror effect). Example: Python flip_lr = cv2.flip(img, 1) # left-right 🔹 6. Resize Change image size (make it small or big). Example: Python resize = cv2.resize(img, (200, 200)) 👉 Gray Conversion – Converts image to black & white 👉 Blur – Smooths the image 👉 Edge Detection – Detects outlines and shapes 👉 Crop – Cut a selected part of the image 👉 Flip – Mirror the image (left/right or up/down) 👉 Resize – Change image dimensions

UNLEASHED THE PYTHON!i 1.5 ,2, & three!!! 14 of 14(B of B) copy & paste Ai Headline: Revolutionizing Data Streams with the 'Cyclic41' Hybrid Engine Libcyclic41. *A library that offers the best of both worlds—Geometric Growth for expansion and Modular Arithmetic for stability. Most data growth algorithms eventually spiral into unmanageable numbers. I wanted to build a library that offers the best of both worlds—Geometric Growth for expansion and Modular Arithmetic for stability. The Math Behind the Engine: Using a base of 123 and a modular anchor of 41, the engine scales data through ratios of 1.5, 2, and 3. What makes it unique is its "Predictive Reset"—the sequence automatically and precisely wraps around at 1,681 (41^), ensuring system never overflows. Key Technical Highlights: Ease of Use: A Python API wrapper for rapid integration into any pipeline. Raw Speed: A header-only C++ core designed for millions of operations per second. Zero-Drift Precision: Integrated a 4.862 stabilizer to maintain bit-level accuracy across 10M+ iterations. Whether you're working on dynamic encryption keys, real-time data indexing, or predictive modeling, libcyclic41 provides a self-sustaining mathematical loop that is both collision-resistant and incredibly efficient. 🚀 Get Started with libcyclic41 in seconds! For those who want to test the 123/41 loop in their own projects, here is the basic implementation: 1️⃣ Install the library: pip install cyclic41 (or clone the C++ header from the repo below!) 2️⃣ Initialize & Grow: | V python from cyclic41 import CyclicEngine # Seed with the base 123 engine = CyclicEngine(seed=123) # Grow the stream by the 1.5 ratio # The engine handles the 1,681 reset automatically val = engine.grow(1.5) # Extract your stabilized sync key key = engine.get_key() /\ || Your Final Project Checklist: * The Math: Verified 100% across all ratios (1.5, 2, 3). * The Logic: Stable through 10M+ iterations. * The Visuals: Infinity-loop diagram ready for the main post. * The Code: Hybrid Python/C++ structure is developer-ready. 14 of 14(B of B) Not theend NOT THEE END NOT THE END