Python Hybrid Execution Model: Compiled to Bytecode

🐍 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

Day 37 – Understanding How Python Stores Data Today, I am continuing on hash tables in Python, which is what powers dictionaries (dict). In simple terms: Python uses a smart system to store and find data almost instantly, instead of searching line by line. That’s why dictionaries are fast and used everywhere — from logins to APIs to caching. Today, I didn’t just read about how Python dictionaries work — I built a simple hash table from scratch in VS Code. What I did: Created a basic HashTable class Used Python’s hash() function to decide where data should live Stored values in buckets (lists) to safely handle collisions Retrieved values using keys, just like a real Python dict Even tested collisions by inserting keys that land in the same bucket I learned: Why dictionary keys must not change What a hash is (Python’s way of knowing where to store data) Why this concept matters for building fast and scalable systems This might look small, but it’s one of the ideas behind efficient backend and full-stack development. Slow progress is still progress. Understanding beats rushing. Which of the terms or concepts used here sounds too scary and unusual for you? Let me know, let's learn together 😊 #Day37 #LearningInPublic #Python #DataStructures #BackendBasics #Consistency

Today’s Python focus was 𝗠𝗼𝗱𝘂𝗹𝗲𝘀. I worked on understanding how Python lets you organize code into reusable files instead of writing everything in one script. 𝗪𝗵𝗮𝘁 𝗜 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝗱 𝘁𝗼𝗱𝗮𝘆: • Importing built in modules like math and calendar • Using functions from the math module such as sqrt() and ceil() • Working with the calendar module to generate month level calendars • Creating a custom module to store reusable functions • Importing and using functions from a user defined module • Separating logic into different files for better structure and readability 𝗞𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀: • Modules help break large programs into smaller, manageable pieces • Built in modules save time and prevent rewriting common logic • Custom modules make code reusable across multiple scripts • Organizing functions into modules improves maintainability Working with modules made it clear how real Python projects are structured. Code is written once, organized properly, and reused when needed. If you are learning Python, are you already using modules in your practice or still keeping everything in a single file? #Python #PythonLearning #PythonModules #ProgrammingBasics #LearningInPublic #DataAnalytics #Upskilling

Why 60 * 60 * 24 costs the same as 86400 in Python? TL;DR: Python evaluates it once at compile time! You might think writing out the math makes the code slower because Python has to do the multiplication every time. When Python compiles source code into Bytecode, it uses an optimiser (Peephole) that looks for constants. This is called Constant Folding. It’s not just for numbers! Python also folds small strings. "Deep" + "Tech" becomes "DeepTech" in the bytecode. Takeaway: -> Never sacrifice readability for a "assumed" performance gain in constants. -> Python’s compiler is not just a translator, it’s an optimiser. -> Compiler handles the math smartly, so you can focus on writing code that humans can actually read! I’m deep-diving into Python internals and performance. Do follow along and tell your experiences in comments. #Python #PythonInternals #SoftwareEngineering #BackendDevelopment

Jan 19th Python Class – Global & Local Variables In one of our Python sessions, we learned how Python handles variables inside and outside functions and how scope affects program behavior. 🔹 Global Variables Defined variables outside functions and accessed them inside functions Observed that global variables can be read inside a function without modification Understood how global values remain unchanged outside the function unless explicitly updated 🔹 Local Variables Created variables inside functions Learned that local variables exist only within the function scope Verified that changes to local variables do not affect global variables with the same name 🔹 Global vs Local Interaction Practiced multiple cases where: A local variable shadows a global variable Calculations inside functions do not modify global values Compared values printed inside and outside functions to clearly understand scope behavior 🔹 Why Scope Matters Prevents accidental data modification Helps write cleaner, safer, and more predictable code Improves debugging and code readability This class strengthened my understanding of variable scope, which is essential for writing reliable Python programs 🚀 #Python #GlobalVariables #Local scope #LocalVariables #PythonBasics #ProgrammingConcepts #StudentLearning #CodingJourney 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

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

🐍 90 Days of Python – Day 12 Today, I learned about modules and imports in Python, which help in organizing code and reusing functionality efficiently. As programs grow, writing everything in a single file becomes hard to manage. Modules allow us to split code into logical parts and reuse them whenever needed. Key concepts I explored today: • What a module is in Python • Using import to access built-in and custom modules • Importing specific functions using from ... import • Understanding why modular code is easier to maintain Modules encourage clean, structured, and reusable code, which is essential for real-world applications. I’m practicing these concepts to write more organized programs and avoid unnecessary repetition. 📌 Day 12 completed. Writing modular and reusable code. 👉 Which Python module do you use most often in your projects? #90DaysOfPython #PythonLearning #LearningInPublic #ProgrammingBasics #BTechCSE #MachineLearning

🚀 GitHub Project | Morse-Like Message Decryption in Python 🐍 I’m excited to share a recent Python project that solves a Morse-like message decryption challenge using recursive backtracking and variable-length encoding logic. 🔍 Problem Summary Given an encrypted message of dots (.) and underscores (_), decode it into all possible valid plaintext messages based on a defined encoding scheme. 🧠 Solution Highlights ✔ Handles variable-length encodings ✔ Uses recursive backtracking to explore all combinations ✔ Generates all valid decodings efficiently ✔ Clean, modular Python implementation 💻 Repository Structure solution.py — Python implementation question.md — Full problem description README.md — Instructions & usage ▶ How to Run python solution.py 📌 Check out the code here: 👉 https://lnkd.in/g9HvrdBQ This project helped me reinforce core concepts such as: ✨ Recursion & Backtracking ✨ String parsing & algorithmic thinking ✨ Designing clean, maintainable Python code I’d love to hear your thoughts and feedback! 👇 #Python #Algorithms #Recursion #Backtracking #Coding #GitHub #SoftwareEngineering #ProblemSolving #DeveloperJourney

Jan 20th's Python Class – Generators & yield In a recent Python class, we explored Generators and how they differ from lists and normal functions. 🔹 Generator Expressions Compared list comprehensions and generator expressions Learned that: List comprehensions store all values in memory Generator expressions produce values one at a time Converted generator output into list, tuple, and set 🔹 Generators using yield Understood that a generator is a special function that works as an iterator Used the yield keyword to produce values step-by-step Learned that generators pause execution and resume from the last state 🔹 yield vs return return terminates the function immediately yield returns a value and continues execution in the next iteration Observed how multiple yield statements produce a sequence of outputs 🔹 Iterating Generators Used for loops and next() to fetch generator values Learned that calling next() after iteration ends raises an error 🔹 Built-in Functions Practiced using max(), min(), and sum() with iterable data types This class helped me understand how generators make Python programs more memory-efficient and powerful, especially when working with large data 🚀 #Python #Generators #Yield #PythonBasics #Iterators #MemoryEfficient #CodingPractice #StudentLearning Pooja Chinthakayala