Python Walrus Operator: Simplify Code with := Assignment Expressions

Python list vs array — When Working with Homogeneous Unsigned Integers In Python, we often default to using list for collections. But when dealing with homogeneous unsigned integers, the built-in array module can be a more memory-efficient and type-safe option. Let’s compare. Using a Python List numbers = [1, 2, 3, 4, 255] print(numbers) print(type(numbers)) • Can store mixed data types (even if we don’t use that feature). • Flexible and convenient. • Higher memory overhead since each element is a full Python object. ⸻ Using array for Unsigned Integers import array # 'I' = unsigned int (typically 4 bytes) numbers = array.array('I', [1, 2, 3, 4, 255]) print(numbers) print(type(numbers)) • Enforces homogeneous data. • More memory-efficient. • Faster for large numeric datasets. • Ideal when interfacing with binary data, files, or low-level systems. ⸻ Key Difference numbers = array.array('I', [1, 2, 3]) numbers.append(10) # ✅ Works # numbers.append(-5) # ❌ ValueError (unsigned constraint) With array, the type code ('I') ensures all values are unsigned integers. That constraint provides both safety and performance benefits. ⸻ When to Use What? • Use list when flexibility matters. • Use array when working with large, homogeneous numeric data and memory efficiency is important. • Consider numpy for heavy numerical computation. Understanding these distinctions helps write more efficient and intentional Python code. #Python #DataStructures #SoftwareEngineering #Performance #BackendDevelopment

Most Python code works. Very little Python code scales. The difference? 👉 Object-Oriented Programming (OOPS). As part of rebuilding my Python foundations for Data, ML, and AI, I’m now focusing on OOPS — the layer that turns scripts into maintainable systems. Below are short, practical notes on OOPS — explained the way I wish I learned it 👇 (No theory overload, only what actually matters) 🧠 Python OOPS — Short Notes (Practical First) 🔹 1. Class & Object A class is a blueprint. An object is a real instance. class User: def __init__(self, name): self.name = name u = User("Anurag") Used to model real-world entities (User, File, Model, Pipeline) 🔹 2. __init__ (Constructor) Runs automatically when an object is created. Used to initialize data. def __init__(self, x, y): self.x = x self.y = y 🔹 3. Encapsulation Keep data + logic together. Control access using methods. class Account: def get_balance(self): return self.__balance Improves safety & maintainability 🔹 4. Inheritance Reuse existing code instead of rewriting. class Admin(User): pass Used heavily in frameworks & libraries 🔹 5. Polymorphism Same method name, different behavior. obj.process() Makes systems flexible and extensible 🔹 6. Abstraction Expose what a class does, hide how it does it. from abc import ABC, abstractmethod Critical for large codebases & APIs OOPS isn’t about syntax. It’s about thinking in systems, not scripts. #Python #OOPS #DataEngineering #LearningInPublic #SoftwareEngineering #AIJourney

🚀 Day 5: Understanding Data Types in Python | Python Full Stack Series Data types are the foundation of any programming language. In Python, understanding how to work with different data types is crucial for building robust applications. 📊 Core Data Types: Numeric Types: int: Whole numbers (e.g., 42, -17, 1000) float: Decimal numbers (e.g., 3.14, -0.5, 2.0) complex: Complex numbers (e.g., 3+4j) Text Type: str: Strings for text data (e.g., "Hello, World!") Sequence Types: list: Mutable, ordered collections [1, 2, 3] tuple: Immutable, ordered collections (1, 2, 3) range: Sequence of numbers range(0, 10) Mapping Type: dict: Key-value pairs {"name": "John", "age": 30} Set Types: set: Unordered, unique elements {1, 2, 3} frozenset: Immutable set Boolean Type: bool: True or False values 💡 Quick Example: python # Numeric age = 25 price = 99.99 # String name = "Python Developer" # List skills = ["Python", "Django", "React"] # Dictionary user = {"username": "dev123", "active": True} # Type checking print(type(age)) # <class 'int'> 🎯 Pro Tip: Python is dynamically typed, meaning you don't need to declare data types explicitly. Use type() to check variable types and isinstance() for type validation. Tomorrow: We'll dive into Type Conversion and Casting! #Python #FullStackDevelopment #100DaysOfCode #Programming #WebDevelopment #LearnToCode #DataTypes #PythonProgramming #TechEducation #CodingJourney Alternative shorter version: Day 5/100: Python Data Types 📊 Every variable in Python has a type. Here's your quick reference guide: Numbers: int, float, complex Text: str Collections: list, tuple, dict, set Boolean: True/False Understanding these is essential for full stack development. Master the basics, build anything! What's your most-used Python data type? Drop it in the comments! 👇 #Python #FullStack #Day5 #100DaysOfCod

🚀 Revisiting Python Fundamentals Day 2: Data Types Have you ever wondered how Python knows the difference between 21, "Alex", or ["Python", "SQL"]? It’s not magic. It’s data types. When you give data to Python, it doesn’t panic… it asks questions 🧠 👉 Is this a single value or multiple values? 👉 If it’s multiple, is it ordered or unordered? That’s how Python understands your data. Let me explain it like a story 👇 Imagine Python as a smart organizer. 🟦 Step 1: Single Value If there’s only one piece of information, Python stores it here: int → whole numbers (age = 21) float → decimal numbers (price = 99.5) str → text ("Alex") bool → True / False Simple. Clean. One value = one box. 🟨 Step 2: Multiple Values If there’s more than one value, Python looks a bit deeper 👀 📌 Sequential (order matters) list → changeable collection tuple → fixed collection skills = ["Python", "SQL", "ML"] 📌 Unordered (order doesn’t matter) set → unique values only dict → key–value pairs Python doesn’t just store data — it categorizes it intelligently. That’s why choosing the right data type really matters. 💭 Question: Which data type confused you the most when you first learned Python? #Python #DataTypes #LearnPython #ProgrammingBasics #CodingJourney

🌦️ Built one more script using Python - **DuckDuckGo Top Search Result CLI Tool** (last project of edureka). I made a script using Python that fetches real-time top search result data of DuckDuckGo search engine for any query using "ddgs library of python" as one of the final project given by Edureka The program accepts a argument as query to search in duckduckgo search engine, and returns the required top search results, not only title and urls - it will provide you the top videos and all corresponding details like published_date, publisher, embed_urls, thumbnail_images of different resolutions and many more and at last you can save these data in a file. Tech Used: Python, JSON parsing, duckduckgo-search (ddgs), AsyncIO Features: ✅ accepting query by CLI ✅ current top required search results ✅ You can choose how many results you want to fetch ✅ Get all data - Title, URL, Publisher, Publishing date, images, Provider, Uploader, Video urls and many more ✅ Facility to save these data into a file (with the timestamp and the query) ✅ Clean terminal formatting ✅ Async execution for better structure This project strengthened my understanding of working with external services, handling responses, and building practical automation tools. All scripts available in my github profile - https://lnkd.in/gJGmqXme

🚀 From String Splits to Structured Data: A Quick Python Evolution Ever watched a simple Python script evolve? 😄 Started with extracting first names from a list: names = ["Charles Oladimeji", "Ken Collins"] fname = [] for i in names: fname.append(i.split()[0]) # Result: ['Charles', 'Ken'] Then flipped to last names: fname.append(i.split()[1]) # Result: ['Oladimeji', 'Collins'] Finally transformed it into clean, structured dictionaries: names = ["Charles Oladimeji", "Ken Collins", "John Smith"] fname = [] for i in names: parts = i.split() fname.append({"first": parts[0], "last": parts[1]}) # Result: [{'first': 'Charles', 'last': 'Oladimeji'}, ...] Why I love this progression: 1. Shows how small tweaks solve different problems 2. Demonstrates data structure thinking (list → list of dicts) 3. Real-world applicable for data cleaning/API responses 4. Sometimes the most satisfying code journeys start with a simple .split()! #DataEngineer #Python #Coding #DataTransformation #Programming

Day 36 – Hash Tables in Python (What’s really behind dict) 🐍 Today, we’re starting with Hash Tables — the idea behind one of Python’s most-used tools: the dict. If you’ve ever written: user = {"name": "John", "age": 25} then you’ve already used a hash table (even if you didn’t realize it). So why start here? Because hash tables help us store and retrieve data fast. Instead of looping through a list item by item, we can jump straight to what we need. That’s why they show up everywhere: user profiles settings and configurations caching quick lookups in real applications Why Python? Python makes this concept very approachable. Dictionaries look simple on the surface, but there’s a lot of smart engineering underneath. Once you understand how they work, you stop writing “just working” code and start writing efficient, intentional code. And yes — this matters for full-stack development too: Backends use hash tables to manage users, sessions, and data Frontends rely on key-value structures for state and UI logic Performance often comes down to how well you organize and access data We’re starting here because this is foundational. When this clicks, many other data structures and algorithms start to make sense. More coming from tomorrow — challenges, breakdowns, and practical thinking. 🚀 #Day36 #Python #DataStructures #HashTables #SoftwareEngineering #FullStackDevelopment #LearningInPublic

Why Is My Code So Slow? A Guide to Py-Spy Python Profiling frustrating issues to debug in data science code aren’t syntax errors or logical mistakes. Rather, they come from code that does exactly what it is supposed to do, but takes its sweet time doing it. Functional but inefficient code can be a massive bottleneck in a data science workflow. In this article, I will provide a brief introduction and walk-through of…...

Exploring Python Context Managers under the hood Context managers are one of Python's most powerful features for resource management. I’ve been diving into how the context protocol works, and it’s fascinating to see how the with statement actually operates. To implement a context manager from scratch, you need two dunder methods: __enter__: Sets up the environment. If you’re opening a file or a database connection, this method prepares the object and returns it. __exit__: Handles the cleanup. It ensures that regardless of whether the code succeeds or crashes, the resources (like file handles or network sockets) are properly closed. As a fun experiment, I wrote this helper class to redirect print statements to a log file instead of the console: import sys class MockPrint: def __enter__(self): # Store the original write method to restore it later self.old_write = sys.stdout.write self.file = open('log.txt', 'a', encoding='utf-8') # Redirect stdout.write to our file logic sys.stdout.write = self.file.write return self def __exit__(self, exc_type, exc_value, traceback): # Restore the original functionality and close the file sys.stdout.write = self.old_write self.file.close() # Usage with MockPrint(): print("This goes to log.txt instead of the console!") While Python’s standard library has tools like contextlib.redirect_stdout for this exact purpose, building it manually really helped me understand how the protocol manages state and teardown. It’s a simple concept, but it's exactly what makes Python code so clean and safe. #Python #SoftwareEngineering #Backend

🐍 Ever wondered how Python actually works behind the scenes? We write Python like this: print("Hello World") And it just… works 🤯 But there’s a LOT happening in the background. Let me break it down simply 👇 🧠 Step 1: Python compiles your code Your .py file is NOT run directly. Python first converts it into: ➡️ Bytecode (.pyc) This is a low-level instruction format, not machine code yet. ⚙️ Step 2: Python Virtual Machine (PVM) The bytecode is executed by the PVM. Think of PVM as: 👉 Python’s engine that runs your code line by line This is why Python is called: 🟡 An interpreted language (with a twist) 🧩 Step 3: Memory & objects Everything in Python is an object. • Integers • Strings • Functions • Even classes Variables don’t store values. They store references 🔗 That’s why: a = b = 10 points to the SAME object. ⚠️ Step 4: Global Interpreter Lock (GIL) Only ONE thread executes Python bytecode at a time 😐 ✔ Simple memory management ❌ Limits CPU-bound multithreading That’s why: • Python shines in I/O • Struggles with heavy CPU tasks 💡 Why this matters Understanding this helped me: ✨ Debug performance issues ✨ Choose multiprocessing over threads ✨ Write better, scalable backend code Python feels simple on the surface. But it’s doing serious work underneath. Once you know this, Python stops feeling “magic” and starts feeling **powerful** 🚀 #Python #BackendDevelopment #SoftwareEngineering #HowItWorks #DeveloperLearning #ProgrammingConcepts #TechExplained