Python Morse Code Decryption with Recursive Backtracking

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

🐍 Day 4: Python Full-Stack Journey - Multiple Variable Initialization Today I explored one of Python's elegant features that makes code cleaner and more readable: initializing multiple variables in a single line! What I Learned: Python allows us to assign values to multiple variables simultaneously, which can make our code more concise and expressive. Examples from my practice: python # Basic multiple assignment x, y, z = 10, 20, 30 # Swapping values (no temp variable needed!) a, b = 5, 10 a, b = b, a # Now a=10, b=5 # Unpacking from lists/tuples name, age, city = ["Alice", 25, "New York"] # Same value to multiple variables x = y = z = 0 # Unpacking with * operator first, *middle, last = [1, 2, 3, 4, 5] # first=1, middle=[2,3,4], last=5 Why This Matters: This feature demonstrates Python's philosophy of writing clean, readable code. It's especially useful when working with functions that return multiple values or when processing data structures in full-stack applications. Key Takeaway: Python's multiple assignment isn't just syntactic sugar—it's a powerful tool that can make code more maintainable and Pythonic! What's your favorite Python feature that makes coding more elegant? Drop it in the comments! 👇 #Python #100DaysOfCode #FullStackDevelopment #LearnInPublic #PythonProgramming #CodingJourney #TechLearning

24th's Python Class – Modules, Packages & __main__ In a recent Python session, we learned how Python programs are organized using modules and packages, and how code behaves when imported versus executed directly. 🔹 Modules Learned that every Python file is a module Used the import keyword to access functions and variables from another file Called functions and accessed data from custom modules 🔹 Packages Understood that a package is a directory containing multiple Python modules Learned the role of __init__.py in package initialization Discussed examples of popular packages like NumPy and Pandas 🔹 Libraries Learned that libraries can contain multiple packages and modules Understood the difference between: Standard libraries Third-party libraries 🔹 Importing Data from Modules Imported functions, lists, and dictionaries from a custom module Accessed dictionary values using module references 🔹 __name__ == "__main__" Learned how Python identifies whether a file is: Run directly as a script Imported as a module Used conditional execution to control what code runs in each case 🔹 Script vs Module Behavior Observed different outputs when a program is executed directly versus imported Used functions to demonstrate module reusability This class gave me a strong foundation in writing reusable, well-structured Python programs 🚀 #Python #Modules #Packages #PythonBasics #main #CodingPractice #StudentLearning #ProgrammingConcept Pooja Chinthakayala

Day 3 of Python. Writing code once. Using it everywhere. Today’s focus was functions and modules. This is where Python stopped feeling like a scripting language and started feeling like a system tool. What I worked on: Writing reusable functions Passing data through parameters Returning predictable outputs Organizing logic into modules The key realization: Repetition is a design problem. If the same logic appears in multiple places: Bugs multiply Fixes become risky Pipelines turn fragile Functions solve logic. Modules protect structure. This is how Python scales from notebooks to production: One function. One responsibility. Shared utilities across files. Clean imports instead of copy-paste. This mindset is critical before touching Pandas or building pipelines. Tomorrow: applying this structure to real datasets. If you work with Python: What was the first function you automated that saved you real time? #datawithanurag #dataxbootcamp

🚀 pip vs uv: Python Dependency Management Is Changing For years, pip has been the default way to manage Python dependencies. It works — but modern Python projects need speed, reproducibility, and structure, not just installs. I just published an article breaking down: pip vs uv uv’s pip-compatible mode vs native workflow How uv init, uv add, and uv run actually work Whether you really need to activate .venv (spoiler: usually no) If you’re building Python projects in 2025 — APIs, services, or production systems — this is worth understanding. 📖 Read here: https://lnkd.in/g33mw9m7 Would love to hear: 👉 Are you still using pip / requirements.txt, or have you started experimenting with uv? #Python #SoftwareEngineering #DeveloperTools #BackendDevelopment #OpenSource

📌 Data Types in Python | Complete Fundamentals with Examples | Informational Share Sharing a clear and structured Python reference that explains all built-in Python data types with real-world examples and code snippets, making it ideal for beginners, interview preparation, and quick revision. 🔹 What this document covers: • Python as a dynamically typed language & use of type() • Text type: str with practical examples • Numeric types: int, float, complex • Sequence types: list, tuple, range • Mapping type: dict (key–value pairs) • Set types: set, frozenset • Boolean type: bool for logical conditions • Binary types: bytes, bytearray, memoryview • NoneType and its real-world usage • Type casting: implicit vs explicit conversion with examples 📄 The document also includes simple explanations, real-life use cases, and hands-on Python code, making complex concepts easy to understand. 📢 I’ll continue sharing high-value programming fundamentals, Python references, and interview-oriented content. Follow Pulimi Bala sankararao for more. #Python #PythonBasics #DataTypes #ProgrammingFundamentals #PythonInterview #LearningPython #TechInformation

🐍 90 Days of Python – Day 19 List Comprehensions Today, I learned about list comprehensions in Python, a more concise and Pythonic way to create lists. List comprehensions help combine loops, conditions, and expressions into a single readable line, making code cleaner and easier to understand. 🔹 Key things I learned today: • Basic syntax of list comprehensions • Creating lists using expressions • Adding conditional logic inside comprehensions • Replacing simple for loops with more compact code List comprehensions are especially useful in data processing and transformations, where readability and efficiency matter. I’m practicing these concepts to write cleaner and more expressive Python code. 📌 Day 19 completed. Writing more Pythonic code with list comprehensions. 👉 Do you prefer list comprehensions or traditional for loops, and why? #90DaysOfPython #PythonLearning #LearningInPublic #ListComprehension #PythonDeveloper #BTechCSE

⚠️ Python Gotcha: Defining the Same Method Twice in a Class Did you know that Python does NOT support method overloading by definition order inside a class? Consider this scenario 👇 You define the same method name twice inside a class, expecting both to exist… Only the LAST definition survives. What actually happens? Python reads the class top to bottom When it sees the second func1, it completely overwrites the first one The first method is lost and ignored No warning. No error. Just replacement. Example outcome Nirmal.func1(2, 4) Runs the second version only Output: Good Morning Result is: 6 🚨 Key Takeaways Python does not support traditional method overloading Method names inside a class must be unique If you need different behaviors: Use different method names Or use default parameters / *args / conditional logic 🧠 Pro Tip If your logic seems to “mysteriously change” — check whether a method name was accidentally redefined. Learning these small details makes a big difference in writing clean, predictable Python code 🐍 #Python #OOP #ProgrammingTips #LearningPython #Developers #CodeSmart

Day 7 of Python 🐍 | Understanding Lists & Memory Today I dove deep into one of Python's most powerful data structures - Lists! Here's what I explored today :✅ 📌 Indexing - Accessing elements is easier than I thought. Python's zero-based indexing means the first element is at index [0], and negative indexing lets you work backwards from the end . 📌 List Operations - Lists are incredibly flexible. Unlike some languages, Python lists can hold different data types in one container, making them super versatile for real-world applications. 📌 Memory Allocation - This was eye-opening! Python allocates memory dynamically for lists. When a list grows, Python doesn't just add one slot - it over-allocates to optimize performance. Understanding this helps write more efficient code. 📌 The len() Function - Simple but essential. len() returns the number of elements, and it's O(1) time complexity because Python stores the list size internally. 🎯Key Takeaway: Lists aren't just arrays - they're dynamic, flexible, and optimized for Python's philosophy of making code readable and efficient. What's your favorite Python data structure? Drop it in the comments! 👇 #Python #100DaysOfCode #DataStructures #PythonProgramming #LearnInPublic #CodingJourney #TechLearning #DeveloperCommunity