Python Generators for Memory Optimization

🔁 Python Revision – Sets Continuing my Python fundamentals revision 🐍 In this session, I focused on: ✔️ Sets (creation and properties) ✔️ Unique elements and unordered nature ✔️ Set methods (add, remove, discard, etc.) ✔️ Set operations (union, intersection, difference) Practiced using sets to handle unique data and perform efficient operations like finding common or different elements between datasets. Documented my practice in a Jupyter Notebook and shared it as a PDF to track my progress. Understanding sets is helping me work better with data and avoid duplicates 📊 Next: dictionaries and real-world data handling 🚀 #Python #Revision #Sets #Programming #DataAnalytics #LearningJourney #Coding

Python if Statement — Making Decisions in Code The if statement in Python is used to execute code based on conditions. It helps control the flow of a program. 🔹 Basic Syntax if condition: # code block 🔹 Example age = 18 if age >= 18: print("Eligible to vote") 🔹 if–else num = 5 if num % 2 == 0: print("Even") else: print("Odd") 🔹 if–elif–else marks = 75 if marks >= 90: print("Grade A") elif marks >= 70: print("Grade B") else: print("Grade C") 🔹 Multiple Conditions age = 22 salary = 25000 if age > 18 and salary > 20000: print("Eligible") if statements are essential for: ✔ Decision making ✔ Validations ✔ Filtering logic ✔ Conditional execution #Python #PythonBasics #Coding #LearnPython #DataAnalytics #Programming #IfStatement

Same Data, Different Memory — Python Data Types Comparison We often choose Python data structures based on ease of use… But rarely think about memory. That can quietly impact performance — especially with large datasets. So I tested how different Python data types behave when storing the same data. Here’s what stood out: • sys.getsizeof() helps measure object memory. • Tuples use less memory than Lists. • Sets consume more memory due to hashing. • String size varies based on content. One important note: - sys.getsizeof() shows memory used by the object itself (in bytes), not the full picture. - Small choices in data structures can lead to big differences in performance. - Something I’ve started paying more attention to while writing code. Do you consider memory usage when choosing data structures, or focus mostly on readability? #Python #Programming #Developers #Coding #SoftwareEngineering #PythonTips #BackendDevelopment #LearningToCode

📂 Moving beyond the basics: Automating file system analysis with Python. I recently tackled a challenge to build a directory analyzer that goes deeper than a simple ls command. Using the os module, I developed a script that provides a comprehensive audit of any given path. What it does: 1) Recursively traverses directories using os.walk(). 2) Aggregates total file counts and folder structures. 3) Calculates total storage footprint with formatted sizing. 4) Identifies the "heavy hitters" (largest files). 5) Uses Python dictionaries to map and group files by extension. This project was a great exercise in handling file metadata and organizing unstructured data into a clean, readable summary. Check out the screen recording below to see it in action! 👇 #Python #Coding #Automation #SoftwareDevelopment #Programming

🧠 Python Concept: __new__ vs __init__ Object creation vs initialization 😳 ❌ What most people think 👉 __init__ creates the object ✅ Reality class Demo: def __new__(cls): print("Creating instance") return super().__new__(cls) def __init__(self): print("Initializing instance") obj = Demo() 👉 Output: Creating instance Initializing instance 🧒 Simple Explanation 👉 __new__ → creates object 👉 __init__ → initializes object 💡 Why This Matters ✔ Used in immutable types ✔ Important for metaclasses ✔ Helps in advanced object control ✔ Asked in advanced interviews ⚡ Real-World Use ✨ Singleton pattern ✨ Custom object creation ✨ Immutable objects 🐍 Creation first, then initialization 🐍 Understand object lifecycle #Python #AdvancedPython #OOP #SoftwareEngineering #BackendDevelopment #Programming #DeveloperLife

⚡ Meet Ty: The New Generation Python Type Checker by Astral If you're still using traditional type checkers and feeling the slowdown 👉 it might be time to look at ty Built by the team behind ruff and uv, ty is a blazingly fast Python type checker and language server written in Rust  💡 Why Ty is getting attention ✅ Extremely fast compared to traditional tools ✅ Works as both a type checker and a language server ✅ Rich and actionable diagnostics ✅ Handles partially typed codebases well ✅ Near-instant feedback with incremental analysis 🔍 What makes it really interesting Ty is not just about speed It also introduces advanced typing capabilities like • Intersection types • Smarter type narrowing • Better reachability analysis 🔥 The bigger picture Astral is building a full Python tooling ecosystem ruff for linting uv for packaging ty for type checking 📦 If you care about performance and modern Python tooling, this is definitely one to watch 👉 GitHub repo: https://lnkd.in/eNB37cVa #Python #DataEngineering #TypeChecking #DeveloperTools #Programming #Astral

𝗪𝗵𝘆 𝗱𝗼𝗲𝘀 𝗣𝘆𝘁𝗵𝗼𝗻 𝗰𝗼𝗱𝗲 𝗳𝗲𝗲𝗹𝘀 𝘀𝗹𝗼𝘄 𝗱𝗲𝘀𝗽𝗶𝘁𝗲 𝘂𝘀𝗶𝗻𝗴 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝘁𝗵𝗿𝗲𝗮𝗱𝘀 ? The secret lies in how Python handles execution. I’ve put together a 12-slide deep dive into Python Concurrency, moving from absolute basics to the future of Python 3.13. What’s inside? ✅ Synchronous vs. Async: Why "𝘄𝗮𝗶𝘁𝗶𝗻𝗴" is the biggest bottleneck. ✅ The Event Loop: How 𝗮𝘀𝘆𝗻𝗰𝗶𝗼 manages thousands of tasks on a single thread. ✅ The 𝗚𝗜𝗟 (𝗚𝗹𝗼𝗯𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝗽𝗿𝗲𝘁𝗲𝗿 𝗟𝗼𝗰𝗸): Why traditional Python threading isn't always "parallel." ✅ The 𝗙𝘂𝘁𝘂𝗿𝗲 (𝗙𝗿𝗲𝗲-𝗧𝗵𝗿𝗲𝗮𝗱𝗶𝗻𝗴): How Python 3.13+ finally enables true multi-core parallelism. 🟪 𝗧𝗵𝗲 "𝗞𝗶𝘁𝗰𝗵𝗲𝗻" 𝗔𝗻𝗮𝗹𝗼𝗴𝘆: Think of a single cook (Thread) multitasking between a gas stove (I/O) and a cutting board. That’s Async. Now imagine a kitchen with multiple cooks and multiple gas stoves. That’s Modern Free-Threading. Whether you're building 𝘄𝗲𝗯 𝘀𝗰𝗿𝗮𝗽𝗲𝗿𝘀 (𝗜/𝗢-𝗯𝗼𝘂𝗻𝗱) or 𝗵𝗲𝗮𝘃𝘆 𝗱𝗮𝘁𝗮 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 (𝗖𝗣𝗨-𝗯𝗼𝘂𝗻𝗱), choosing the right model is key to performance. Check out the slides below! #Python #Programming #SoftwareEngineering #Concurrency #AsyncIO #Multithreading #Python313 #TechLearning

DAY-13 PYTHON SERIES What is Encapsulation? Encapsulation is the concept of wrapping data (variables) and methods (functions) together into a single unit (class) and restricting direct access to some of the data. Data hiding + controlled access. 🔹 Why is it important? ✔ Protects data from accidental changes ✔ Improves security ✔ Makes code more maintainable 🔹 Example in Python: class BankAccount: def __init__(self, balance): self.__balance = balance # private variable def deposit(self, amount): self.__balance += amount def get_balance(self): return self.__balance acc = BankAccount(1000) acc.deposit(500) print(acc.get_balance()) 🔹 Real-world example: Think of an ATM machine — you can deposit or withdraw money, but you cannot directly access the internal system. 💡 Key Idea: Hide internal data and allow access only through methods. #Python #OOP #Encapsulation #Coding #Programming #LearnPython #Developer #SoftwareEngineering #100DaysOfCode #Tech

🧠 Python Concept: lambda functions Write quick functions in one line 😎 ❌ Traditional Way def square(x): return x * x print(square(5)) ❌ Problem 👉 Extra lines 👉 Not always needed ✅ Pythonic Way square = lambda x: x * x print(square(5)) 🧒 Simple Explanation Think of lambda like a mini function ⚡ ➡️ No name needed ➡️ One-line function ➡️ Quick & simple 💡 Why This Matters ✔ Less code ✔ Useful for short operations ✔ Works great with map(), filter() ✔ Cleaner for small tasks ⚡ Bonus Example nums = [1, 2, 3, 4] even = list(filter(lambda x: x % 2 == 0, nums)) print(even) 🐍 Small functions, big impact 🐍 Keep it simple & Pythonic #Python #PythonTips #CleanCode #LearnPython #Programming #DeveloperLife #100DaysOfCode