Python Variables & Objects: Understanding Identity vs Equality

🚀 Day 26 – The 30-Day AI & Analytics Sprint Python supports multiple inheritance, which allows a class to inherit from multiple parent classes. However, this can create ambiguity in method resolution. Question? 🔍Explain: 1)What is MRO (Method Resolution Order) in Python? 2)How does Python decide which parent method to call first? 3)Why does Python use the C3 Linearization algorithm? 4)Give a real example where multiple inheritance may cause confusion. ✅Answers 1️⃣(Method Resolution Order)? MRO (Method Resolution Order) is the order in which Python searches for a method or attribute in a class hierarchy when a method is called. 2️⃣How does Python decide which parent method to call first? Python follows the MRO list to determine the order of method lookup. 3️⃣ Why does Python use the C3 Linearization algorithm? Python uses the C3 Linearization algorithm to compute the MRO. This algorithm ensures: Consistency in the order of method resolution Preservation of the inheritance hierarchy No conflicts in complex multiple inheritance structures The C3 algorithm guarantees that the method search order is logical, predictable, and conflict-free. 4️⃣Example A common confusion occurs in the Diamond Problem, where two classes inherit from the same parent class: class A:   def show(self):     print("A") class B(A):   def show(self):     print("B") class C(A):   def show(self):     print("C") class D(B, C):   pass obj = D() obj.show() Python resolves this using MRO: D → B → C → A → object So the program calls B.show(). 👌Pro Tip: Use ClassName.mro() to debug your inheritance tree and avoid unexpected bugs! 🙏Thanks for: Muhammed Al Reay , Mariam Metawe'e and Instant Software Solutions #Python #OOP #MachineLearning #AI #DataScience #Programming #Analytics

Why does Python store *args in a tuple instead of a list? When we define a function using *args, Python allows us to pass a variable number of positional arguments. Example: def check_type(*args): print(type(args)) check_type(1, 2, 3, 4) Output: <class 'tuple'> This confirms that *args stores the values in a tuple, not a list. All passed positional arguments are automatically packed into a tuple, and the variable args becomes a reference to that tuple. But why does Python choose a tuple instead of a list? There are two main reasons: 1️⃣ Immutability and Data Safety Tuples are immutable, meaning their contents cannot be modified after creation. Function arguments are generally expected to remain constant within the function’s scope. By storing them in an immutable structure, Python ensures that the original input values cannot be accidentally altered (e.g., appended, removed, or reordered). This design choice promotes safer and more predictable code while preserving data integrity. 2️⃣ Performance and Memory Efficiency Tuples are more memory-efficient and slightly faster than lists because they have a fixed size at creation. Lists, on the other hand, are dynamically resizable and may allocate extra memory to support future growth. Since most functions do not modify their input arguments, using a tuple provides better efficiency without affecting the required behavior. 🔹In summary: *args stores values in a tuple because tuples are immutable, safer, and more memory-efficient, making them the most appropriate structure for handling variable positional arguments. #Python #MachineLearning #SoftwareDevelopment #CodingTips #AI

Last week I shared a simple causal inference agent I built where Claude is the brain and Python is the hands (https://lnkd.in/gAUxCj8c). This week I want to pull back the curtain on how it actually works because understanding the architecture changed how I think about when agents are worth building at all. --- The agent has 3 layers: Layer 1: Tool Definitions Claude never sees your Python code. It sees JSON descriptions of what tools exist. Based on those descriptions + the data, it decides which tool to call and what arguments to pass. That's it. Layer 2: The Dispatcher When Claude says "call run_did," the dispatcher translates that into an actual Python function call. It also manages state — storing results, tracking where we are in the analysis, handling errors. Layer 3: Python Tools Each tool is a standalone module doing real statistical work — A/B test, DiD, Synthetic Control, report generation. Claude reasons about them. Python executes them. --- The benefits I'm seeing so far: ·     Clean separation — Claude handles reasoning, Python handles computation ·     Easily extensible — adding a new method is just adding a new tool ·     Auditable — every decision is logged and traceable ·     Testable — each tool works independently of the agent and can be changed/tested --- The biggest thing I'm still working through is identifying when an agent actually beats just writing a Python script? Current answer: Use a script when you already know which method to run. Use an agent when the right method depends on results you haven't seen yet – at the very least it can help reason a proper method that you can then explore. I'm genuinely still learning here, and the questions being asked of the agent are super basic and somewhat black and white. But one thing I'm fairly convinced of is that if you want consistent, reliable behavior from an agent, you need to give it structure — clear tool definitions, explicit guidelines in the system prompt, well-defined fallback logic. Without that, you're just asking a general LLM to figure it out, which is a bit scary and weaker in my opinion. Curious if others have found this to be true — or where you'd push back. #MarketingAnalytics #CausalInference #AgenticAI #DataScience #BuildingInPublic

🚀 Day 14 – The 30-Day AI & Analytics Sprint 💡 Python Discussion In Python, variables can be categorized into two important types: 🔹 Mutable 🔹 Immutable But what do they actually mean? 🤔 🔹 Immutable Objects Immutable objects cannot be changed after they are created. If you try to modify them, Python will create a new object instead of modifying the existing one. Examples: int float string tuple Python Copy code x = 10 y = x y = y + 5 print(x) # 10 print(y) # 15 Here, x did not change because integers are immutable. 🔹 Mutable Objects Mutable objects can be modified after creation. Examples: list dictionary set Python Copy code numbers = [1, 2, 3] def add_item(lst): lst.append(4) add_item(numbers) print(numbers) # [1, 2, 3, 4] Here the original list changed because lists are mutable. ⚡ How This Affects Functions When passing data to a function: ✅ Mutable objects can be modified inside the function and the change affects the original data. ❌ Immutable objects cannot be changed directly; any modification creates a new object. 💭 Discussion Question Why do you think Python keeps some objects immutable? And when would immutability be useful in AI or data pipelines? Let’s discuss in the comments 👇 #Python #AI #DataScience #MachineLearning #Analytics #Programming #100DaysOfCode

⚖️ Day 4 of 180 — Python can do Math, Compare AND Think Logically! Yesterday Python listened to me via input(). Today? I gave Python a brain. 🧠 Meet the 3 types of Operators that power literally every program ever written. 👇 🧮 Part 1 — Arithmetic Operators (Basic Math) python 10 + 3 # 13 → Addition 10 - 3 # 7 → Subtraction 10 * 3 # 30 → Multiplication 10 / 3 # 3.33 → Division (gives float!) 10 // 3 # 3 → Floor Division (integer only!) 10 % 3 # 1 → Modulus (remainder) 2 ** 3 # 8 → Power (2 to the power 3) The one that confused me most? // vs / 😅 / gives you decimals → 10/3 = 3.33 // gives you whole numbers only → 10//3 = 3 Use // when you want a clean integer result. Simple! ✅ 🔍 Part 2 — Comparison Operators (True or False) These compare two values and ALWAYS return True or False 👇 python 5 == 5 # True → Equal to 5 != 3 # True → Not equal 5 > 3 # True → Greater than 3 < 5 # True → Less than 5 >= 5 # True → Greater than or equal 3 <= 5 # True → Less than or equal These are used EVERYWHERE — in if conditions, loops, filters... 🔄 🧠 Part 3 — Logical Operators (The Decision Makers) This is where it gets really interesting! 🤯 and → True ONLY if BOTH conditions are True python age >= 18 and score > 80 # True only if BOTH pass ✅ or → True if AT LEAST ONE condition is True python is_weekend or is_holiday # True if either one works 🎉 not → Flips the result completely python not (age < 18) # If age < 18 is False → this becomes True 🔄 💡 Putting it ALL together: python age = 20 score = 85 print(age >= 18 and score > 80) # True 🎉 print(not (age < 18)) # True (he's an adult!) print(score % 2 == 0) # False (85 is odd) 🎯 Today's Big Insight: Arithmetic does the math. Comparison asks the questions. Logical operators make the decisions. Together? They're the brain behind every if, while and for loop you'll ever write. 🧠⚡ 📅 Day 4 / 180 — Done! ✅ Slowly turning from a developer into an AI Engineer — one concept at a time. 💪 Part of my 6-Month GenAI & Agentic AI Certificate Program 🏛️ IIT Patna Certified 💬 Which operator confused YOU the most when you started? // or %? 😂 Drop it below! ♻️ Repost to help a fellow beginner understand operators! #Python 🐍 #Operators #Day4of180 #GenAI 🤖 #IITPatna 🏛️ #LearningInPublic #100DaysOfCode #PythonBasics #ArithmeticOperators #LogicalOperators #CodingJourney #AgenticAI #NeverStopLearning 🚀 #BuildInPublic

*Day 26 - The 30-Day AI & Analytics Sprint* 🚀 Python supports multiple inheritance, which allows a class to inherit from multiple parent classes. However, this can create ambiguity in method resolution. Question? Explain: What is MRO (Method Resolution Order) in Python? How does Python decide which parent method to call first? Why does Python use the C3 Linearization algorithm? Give a real example where multiple inheritance may cause confusion. MRO ==> is the order in which Python searches for a method or attribute in a class hierarchy This becomes important when multiple inheritance is used Python determines this order using the .mro() method or the __mro__ attribute When you call a method on an object, Python needs to know which class to check first Example: class A: def show(self): print("A") class B(A): pass class C(B): pass print(C.mro()) Output: [C, B, A, object] Python will search for the method in this order - Python decide which parent method to call Python follows the MRO list from left to right - Why does Python use the C3 Linearization Algorithm? Python uses C3 Linearization to create a consistent and predictable order for method lookup. It guarantees three important rules: 1- Child classes come before parents 2- The order of parent classes is respected 3- A class appears only once in the hierarchy - Real Example of Confusion (Diamond Problem) class A: def show(self): print("A") class B(A): pass class C(A): def show(self): print("C") class D(B, C): pass obj = D() obj.show() Let's check the MRO: print(D.mro()) Output: [D, B, C, A, object] Result: C How??? Python checks: D B C ✅ (method found) A object Even though B inherits from A, Python does not go to A first. It follows the C3 MRO order MRO determines the order Python searches for methods. Python checks classes from left to right based on the MRO list. Python uses C3 Linearization to avoid ambiguity in multiple inheritance Mariam Metawe'e Muhammed Al Reay Instant Software Solutions

What really happens when we pass an integer vs a list to a function? In Python, passing an integer to a function does not behave the same way as passing a list. Let’s see why. Example 1: Passing an Integer def change_value(x): x = x + 10 return x num = 5 change_value(num) print(num) ➡️Output: 5 At first glance, this might seem confusing. Here’s what actually happens: 1. num references the integer object 5 in memory. 2. When we call change_value(num), the value 5 is passed to the function. 3. Inside the function, a new local variable x is created, referencing the same object 5. At this moment: num → 5 x → 5 Now comes the important part: x = x + 10 Integers in Python are immutable, meaning they cannot be modified after creation. So Python does not change the value 5. Instead: 1. It calculates 5 + 10. 2. It creates a new integer object 15. 3. It makes x reference this new object. Now: num → 5 x → 15 The original 5 is still in memory, unchanged. This is called rebinding (the variable x was redirected to a new object). And because we did not assign the returned value back to num: ❌ num = change_value(num) ➡️ num remains 5. Example 2: Passing a List def add_item(lst): lst.append(10) numbers = [1, 2, 3] add_item(numbers) print(numbers) ➡️Output: [1, 2, 3, 10] Why did this one change? Because lists are mutable. When we pass numbers to the function: 1. lst references the same list object. 2. When we call lst.append(10), we modify the same object in place. No new list is created. Before: numbers → [1, 2, 3] After: numbers → [1, 2, 3, 10] The variable still points to the same object, but that object was directly modified. This is called modification (in place). 💡 The Core Difference When passing data to a function in Python: • If the object is immutable (like int),the function cannot modify it. Any change creates a new object (rebinding). • If the object is mutable (like list),the function can modify the same object in place. That is why the integer stayed 5, while the list was successfully updated. Understanding this difference is essential to understanding how Python handles memory and variable references. #Python #SoftwareDevelopment #CodingTips #MachineLearning #AI

Episode 9: Mastering Python Tuples — The Secure Data Structure! 🔒🐍 How do you store data that should never be changed? In Episode 9 of our Python Zero to Pro series, we are exploring Tuples—the immutable and high-performance cousins of Lists. While lists are great for data that grows and changes, Tuples are the backbone of data integrity. Whether you're storing geographical coordinates, fixed student records, or configuration settings for an AI model, Tuples ensure your data remains "read-only" and protected throughout your code's execution. What’s inside today’s module: ✅ Introduction to Tuples: Learn how to group multiple items into a single, secure variable using round brackets (). ✅ The Power of Immutability: Discover why being "unchangeable" makes Tuples faster and safer than lists. ✅ Indexing & Access: Master the art of pulling specific data points from your Tuples (remember, we still start at 0!). ✅ Data Integrity: See what happens when you try to modify a Tuple and how Python protects your fixed datasets. ✅ Packing & Unpacking: Unlock the Pythonic way to assign Tuple values to multiple variables in a single line of code. ✅ Real-World Use Cases: Understand when to choose a Tuple over a List for better performance and security. 🔗 Access the Ecosystem Here: 📂 GitHub (Code & Roadmaps): https://bit.ly/4utEK8m 🧪 Kaggle (Research Lab & Datasets): https://bit.ly/4sBjImu 🌐 Official Website: https://ailearner.tech 📺 Full Video Course (YouTube): https://bit.ly/4bmOW9J 📖 Exact Notebook Folder: https://bit.ly/3PAWNt5 How to Level Up with Us: 1️⃣ Follow my profile for daily modules as we march toward AI mastery in 2026. 2️⃣ Star the GitHub repo to keep your "AI Engineer Roadmap" updated and accessible. 3️⃣ Comment "TUPLE" below once you’ve completed today's exercises! I’ll be jumping in to check your progress and answer questions. Let’s keep building the future, one secure data point at a time. 💻🔥 #Python #AiLearner #AI2026 #MachineLearning #PythonSeries #DataScience