Python Flashcards: Fundamentals to NumPy and Pandas

Python Clarity Series – Episode 13 Topic: *args and **kwargs Simplified 🤯 What are *args and **kwargs? Students fear this syntax. Let’s simplify. def total(*numbers): return sum(numbers) print(total(1, 2, 3)) 👉 *args collects multiple positional arguments into a tuple. Now: def student(**details): print(details) student(name="Ravi", marks=90) 👉 **kwargs collects named arguments into dictionary. 💡 Memory Trick: → Tuple ** → Dictionary This is heavily used in frameworks and advanced coding. Not hard. Just unfamiliar. #PythonConcepts #FutureDevelopers #LearnPython

In Harvard's CS50. One thing I genuinely appreciate about the course is the transition from C to Python. Starting with C forces you to understand the fundamentals of programming — memory, data structures, and how things work at a lower level. When the course later introduces Python, you suddenly realize how much abstraction and productivity a higher-level language gives you. That transition makes Python feel not just easier, but more meaningful, because you understand what’s happening behind the scenes. CS50 does a great job teaching not just syntax, but the thinking process behind programming. On to the Python problem sets. 🚀 #CS50 #Python #Programming #ComputerScience #LearningInPublic

Many students say they “know Python.” I decided to build it properly from the basics. In the past few days, I’ve covered: • Variables & Data Types • Arithmetic, Comparison & Logical Operators • Conditional Statements (if, elif, nested conditions) • Strings & String Functions • Indexing • Started working with Lists Instead of rushing into advanced topics, I’m focusing on strong fundamentals and structured learning. Next: Mastering lists and moving towards loops & problem-solving. Consistency > Speed. #Python #CSE #LearningJourney #Programming #PlacementPreparation

🚀 Python vs NumPy: Speed Test Experiment 🐍⚡ Today I conducted a simple yet revealing experiment to compare the performance of a Python list and a NumPy array for numerical operations. Here’s what I tested: - I created two datasets containing 1,000,000 elements. - I performed element-wise addition on these datasets. - I measured the execution time for both: - Python list using loops. - NumPy array using vectorised operations. The observation was striking: the speed difference was substantial. NumPy completed the operation significantly faster than the Python list, even though both were performing the same task. Key takeaway: This experiment clearly demonstrated the necessity of libraries like NumPy. While Python lists offer flexibility, NumPy arrays are optimised at a lower level and utilise vectorisation, making them far more efficient for numerical computations. Grasping this difference after building a solid foundation enhances the learning experience. Small experiments like these profoundly influence how we perceive performance and design choices in code. #Python #NumPy #Performance #LearningByDoing #Programming #ComputerEngineering #Foundations #DataScience #MLJourney

Day 69 Some problems look hard… until they teach you something valuable. #Day69 🧩 295. Find Median from Data Stream A challenging problem, but a great one for learning. What it teaches: • Using two heaps (min heap + max heap) together • Keeping the data stream balanced • Understanding how median changes with every insertion This problem really makes you think about the structure of data, not just the code. The more I analyze it from different angles, the clearer it becomes. Definitely one of those questions that deserves revision. Hard problems often become the best teachers. #LeetCode #DSA #Python #Heap #PriorityQueue #LearningInPublic #Consistency

I just published my article on Python Dictionaries. In this blog, I explained: ✔ what dictionaries are ✔ how key–value pairs work ✔ simple Python examples ✔ real-life uses like phone book, student records & inventory systems This topic helped me understand how real applications store and manage data. Thank you @Innomatics Research Labs for encouraging practical learning. 👉 Read here: https://lnkd.in/g7WGamc2 😊 Innomatics Research Labs #Python #Programming #DataStructures #Coding #EDA

Day 2 – Understanding Numbers in Python 🚀 Learning in public and rebuilding my foundations from scratch. Today I focused on Python’s numeric system: • Difference between integers and floats • Arithmetic operators: +, -, *, /, //, %, ** • Operator precedence and why 2 ** 3 ** 2 = 512 • Scientific notation (2.3e4, 2.3e-3) • Rounding vs math.floor() vs math.ceil() • Why negative modulus works differently in Python • How floor division and modulus follow the identity: a = (a // b) * b + (a % b) • Using the math module (sqrt, floor, ceil, pi) Big takeaway: Understanding number behavior removes 90% of beginner confusion in Python. Strong foundations. One layer at a time. 💪 #Python #DataScience #LearningInPublic

🚀 Python Learning Journey – Revision Day Today, I revised Day 12, Day 13, and Day 14 topics to strengthen my understanding. Here’s what I revised: ✅ Sets (unique elements, set operations like union, intersection, difference) ✅ Matrices (nested lists, accessing elements, basic operations) ✅ Star ⭐ pattern programs (nested loops and logic building) Revision helped me improve my confidence in loops and data structures. Step by step, my problem-solving skills are getting stronger 💪 Consistency is the key to mastery! #Python #LearningJourney #Revision #Sets #Matrix #StarPattern #Coding #KeepLearning

Like I mentioned in my last post, I am an inconsistent learner. To break this pattern, I have been into learning python for two days now. No one cares, but a small win for me for sure :) I have done operators of python quite a few times but there are three special operators that always leave me confusing. Back in school, teachers used to tell me “once you write it down, you will have it stored within your memory”. Hence, I am writing them down here, publicly. Modulus (%): gives you remainder as an output Exponentiation (**): helps you square a number Floor division (//): divides and rounds to the nearest integer Maybe it’s never too late to start something afresh. #Python #SkillDevelopment #QuantFinance