Python's Case Against Do-While Loops

In this session I learned about the topic is functions of python: *Functions of python: A function is a block of reusable code that performs a specific task.It help make programs modular, readable, and reusable. #Types functions of Python : 1. Built-in Functions: These are predefined functions available in Python. #Examples: print() input() len() type() range() 2. User-Defined Functions: Functions created by the programmer using the def keyword. #Syntax: Python def function_name(): statements #Example: Python def greet(): print("Hello") 3. Functions with Parameters: Functions that accept input values. #Example: Python def add(a, b): print(a + b) 4. Functions with Return Value: Functions that return a result using the return statement. #Example: Copy code Python def square(x): return x * x 5. Lambda Functions: Small anonymous functions written in one line. #Example: Copy code Python square = lambda x: x * x 6.Recursive Function: A recursive function is a function that calls itself to solve a problem by breaking it into smaller sub-problems. #General Syntax: Copy code Python def function_name(): if base_condition: return value else: return function_name(smaller_input). Lakshmi Tejaswi Akula, Ayesha parvin, Athota Amulya,Kadiyam Akanksha, AMULYA DOMA, Lakshmi Tirupatamma , Gunturu Sony, Thanuja Karnati, Konduru sai mounika,Ashok raj Sagana boyana, Rakshandhaa Syed , Chandu Sri Kavya, Polu chandana sri, Jyothika Namburu, Jhansi lakshmi Gopisetti, Veerla Meenakshi, Deevena Florence. Vemuru, vanikumari Buragadda, Sravani Chillara, Sravanthi Katta, Kakumanu Baby Honey , Golla Gayathri , NIKHITHA REPALLE, Nandini Kona,Mikkilineni Bhavana ,Madhavi Posam ,Mudigonda mukhesh,Supriya Nadakuduru,Sharon Samudrala ,Srinivas Yarlagadda, Shaik Afthaf, SHAIK BAJI, Srikar GCV (Trainer)

🚨 Python Gotcha That Every Developer Should Know! 🚨 “Default arguments in Python are evaluated once, not per function call.” Sounds harmless, right? 😄 But this single line has confused millions of Python developers — from beginners to seniors. Let’s break it down 👇 🐍 The Trap When you use a mutable default argument (like a list, dict, or set), Python creates it only once, at function definition time — not every time the function runs. That means 👀 ➡️ Changes made in one call ➡️ Stick around for the next call 🤯 Yes, your function can remember things even when you didn’t ask it to. 💡 Why This Matters in Real Life Unexpected bugs in production Shared state across API requests Data leakage between function calls Painful debugging sessions at 2 AM ☕😵 🛠️ The Golden Rule Never use mutable objects as default arguments. Use None, then initialize inside the function. 📌 Why Python Works This Way Because Python is efficient, not magical ✨ Default arguments are stored in memory once — faster, but dangerous if misunderstood. 👨💻 Senior Dev Insight This isn’t just a Python trick question — It’s a design decision that tests: Your understanding of memory Object mutability Function execution model 📈 Interviewers LOVE this question If you explain this clearly, you instantly stand out as someone who really knows Python. 🔥 Takeaway Python doesn’t bite… But if you don’t understand its internals, it can surprise you 😉 If this helped you, like ❤️, comment 💬, or share 🔁 so more devs avoid this classic pitfall! #Python #PythonTips #CodingGotchas #SoftwareEngineering #PythonDeveloper #DevCommunity #ProgrammingHumor #TechCareers #InterviewPrep #LearnPython

I love Python. But it's slow. So I wrote C code to value an option and call it from Python. C helps Python run up to 45X faster. Here's how: Before you ask, yes I know there are better ways to do this. Here's the problem: A lot of quant code is already written in C. So instead of re-writing it from scratch, most quants wrap existing code in Python. Enjoy: https://lnkd.in/gDnfmtGk

I love Python. But it's slow. So I wrote C code to value an option and call it from Python. C helps Python run up to 45X faster. Here's how: Before you ask, yes I know there are better ways to do this. Here's the problem: A lot of quant code is already written in C. So instead of re-writing it from scratch, most quants wrap existing code in Python. Enjoy: https://lnkd.in/ejFiHXdy

Your Python script is slow to start. And it’s probably not your code. Ever noticed how a tiny Python script still takes time to run? No heavy logic. No loops. Still slow. This usually has nothing to do with your machine. The real problem is your import tree. Most projects load heavy libraries at startup, even when they are not needed yet. Just one top level import can slow everything down without you realizing it. Libraries like pandas, numpy, or large internal modules get loaded before your program runs a single real line of logic. So the script looks simple, but the delay happens behind the scenes. Here’s how to see the problem clearly. Run your script like this: python -X importtime your_script.py You’ll get a full breakdown of every import and how long it takes to load. Most people are shocked when they see a single import taking hundreds of milliseconds. Now the fix. Use lazy imports. Instead of importing everything at the top, move heavy imports inside the functions that actually need them. Example: def run_report(): import pandas as pd … Now your script starts almost instantly. Heavy libraries load only when they are actually used. A few extra tips that help a lot: • avoid import * • keep heavy imports out of init.py • split large modules into smaller ones These changes take minutes, but your project instantly feels faster and cleaner. If you work with Python daily, try this on one script today. You’ll notice the difference.

Day 185/200 Importing files into Python. When importing files in Python, all we need is the file, its location, and the right Python keywords. The required syntax is to start by typing a “with” statement. The keyword “with” handles errors and manages external resources. “with” is often used in file handling to automatically close a file after reading it. After the “with” keyword has been called, to open the file, we use the open() function. open() is a function that opens a file in Python. The first parameter is the name of the file, the second parameter tells Python what action is to be performed on the file. If the file wants to be read, the letter “r” is used in the open() function as the second parameter, if the file wants to be written to, the “r” would be replaced with “w”. Example: with open(“login_attempts.txt”, “r”) as file: “file” in the above code is a variable that contains the file information and must be included in the code as long as we’re in the “with” statement. It basically instructs Python to store the file object in the “file” variable while inside the “with” statement. Lastly, there’s a column, the code that comes after the colon will tell Python what to do with the content of the file.

Day 460: 7/1/2026 Why Python Execution Is Slow? Python is expressive, flexible, and easy to use — but when performance matters, it often struggles. This is not because Python is “badly written,” but because of how Python executes code and accesses memory. Let’s break it down. ⚙️ 1. Python Works With Objects, Not Raw Data In Python, data is not stored contiguously like in C or C++. Instead: --> Each value is a Python object --> Objects live at arbitrary locations in memory --> Variables hold pointers to those objects When Python accesses a value: --> The pointer is loaded --> The CPU jumps to that memory location --> The Python object is loaded --> Metadata is inspected --> The actual value is read This pointer-chasing happens for every operation. 🔁 2. Python Is Interpreted, Not Compiled to Machine Code Python source code is not executed directly. Execution flow: --> Python source is compiled into bytecode --> Bytecode consists of many small opcodes --> The Python interpreter: fetches an opcode, decodes it, dispatches it to the corresponding C implementation --> This repeats for every operation --> Each step adds overhead. Even a simple arithmetic operation involves: --> multiple bytecode instructions --> multiple function calls in C --> dynamic type checks at runtime ⚠️ 3. Dynamic Typing Adds Runtime Checks Because Python is dynamically typed: --> Types are not known at compile time --> Every operation checks type compatibility --> Method lookups happen at runtime This flexibility makes Python powerful — but it prevents many low-level optimizations. Stay tuned for more AI insights! 😊 #Python #Performance #SystemsProgramming

🚀 My First “Doubt” in Python (Not Self-Doubt 😄) Many people start their coding journey with Python, but mine was a little different. I was a Java learner who transitioned to Python, and my very first doubt wasn’t self-doubt… it was about the (self ) doubt parameter in Python. In Java, when we write a method like: display(int n) we clearly see that it takes one parameter. But in Python, the same method looks like this: def display(self, n): And that made me wonder 🤔 👉 Why are there two parameters? Why do we need self? Later, I understood something interesting: In Java, when we call obj.display(n) behind the scenes, it actually works like: display(obj, n) The object is passed implicitly. In Python, this happens explicitly using self. So when we call: obj.display(n) Python internally treats it as: display(obj, n) Java hides the object using this. Python shows it clearly using self. Once I realized this, everything clicked 💡 And I truly appreciated how simple and transparent Python is. ✨ That first doubt helped me understand OOP concepts more deeply. 👉 What was the first doubt that came to your mind when you switched from one programming language to another? #Python #Java #LearningJourney #Programming #CodingLife #OOP #Developers #CareerGrowth

🚀 Full Stack Journey Day 37: Advanced Python - Overriding Behavior & Dynamic Duck Typing! 🦆🐍 Day 37 of my #FullStackDevelopment learning series took a deep dive into core OOP principles in Advanced Python: Method Overriding, the nuances of Constructor Overriding, and the elegance of Duck Typing! ✨ These concepts are vital for building adaptable and dynamically typed applications. Today's crucial advanced OOP topics covered: Method Overriding: Re-emphasized method overriding, where a subclass provides a specific implementation for a method already defined in its superclass. This is fundamental for polymorphism, allowing subclasses to specialize or alter inherited behavior while maintaining the same method signature. Constructor Overriding (Python's Approach): Explored how, while Python doesn't have explicit "constructor overriding" in the same way as methods, a subclass's __init__ method can extend or entirely replace its parent's __init__. We specifically looked at how to correctly call the parent's constructor using super().__init__() to ensure proper initialization of inherited attributes. Duck Typing in Python: Mastered Duck Typing, a key aspect of Python's dynamic nature. Understood the principle: "If it walks like a duck and quacks like a duck, then it must be a duck." This means Python focuses on what an object can do (its methods and properties) rather than its explicit type or class hierarchy. This leads to highly flexible and less coupled code. Understanding these concepts empowers you to write highly polymorphic and maintainable object-oriented code, crucial for any complex full-stack development! 📂 Access my detailed notes here: 👉 GitHub: https://lnkd.in/grVzxyDv #Python #AdvancedPython #OOP #ObjectOrientedProgramming #MethodOverriding #ConstructorOverriding #DuckTyping #Polymorphism #FullStackDeveloper #LearningToCode #Programming #TechJourney #SoftwareDevelopment #DailyLearning #CodingChallenge #Day37 LinkedIn Samruddhi P.

Ever wonder how much memory an empty list takes? How about how long it takes to add two integers in Python? How fast is adding an element to a Python list? How does that compare to opening a file does it usually take less than a millisecond? Are there hidden factors that make these operations slower than expected? When writing performance-sensitive code, which data structures are most appropriate? How much memory does a floating-point number consume in Python? What about a single character or an empty string? Came across a great write-up on this👇 https://lnkd.in/gdWieZhY