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Abhay Palakkode

Why Python Lacks Traditional OOP Encapsulation

Python does not offer true OOP encapsulation because it follows a philosophy of trust and flexibility over strict access control, using naming conventions and name mangling instead of enforced private attributes. True OOP encapsulation completely hides internal implementation details and prevents external access to private members, while Python’s approach allows access to any attribute if […]

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Memory Optimization in Python: How slots Works

The __slots__ attribute in Python is a class-level attribute that explicitly declares which instance attributes a class can have, replacing the default dictionary-based storage with a more memory-efficient fixed structure. Memory optimization with __slots__ reduces memory usage by 20-50% and improves attribute access speed by preventing the creation of __dict__ for each instance and restricting

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Python’s Magic Methods: How to Overload Operators Properly

Operator overloading in Python is the ability to give custom behavior to built-in operators like +, -, *, and == when used with your own classes. Magic methods (also called dunder methods) are special methods that start and end with double underscores like __add__, __eq__, and __str__ that Python automatically calls when you use operators

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Understanding Python Descriptors and the __set_name__ Method

Custom descriptors are Python objects that control how attributes are accessed, set, or deleted on other objects. The __set_name__ method is a special hook introduced in Python 3.6 that automatically tells descriptors their name and owner class when the class is created. This eliminates the need for manual name passing or complex metaclass solutions. If

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Python @property: Getters, Setters, and Attribute Control Guide

Property decorators in Python allow you to combine the elegance of dot notation with the power of validation and control. Properties let you access methods like attributes while maintaining control over how data is accessed, set, and validated, giving you both the simplicity of direct attribute access and the safety of explicit getter and setter

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Python Method Decorators- @classmethod vs @staticmethod vs @property

Class methods, static methods, and property decorators are three powerful Python features that control how methods behave in classes. Class methods work with the class itself rather than instances, static methods are independent utility functions within a class, and property decorators turn methods into attributes that can validate and control access to data. These decorators

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How Python Handles Multiple Inheritance- MRO Explained

Multiple Inheritance in Python is when a class inherits from more than one parent class, allowing it to access methods and attributes from all parent classes. Method Resolution Order (MRO) is the system that determines which method gets called when a class inherits from multiple parent classes that have the same method name. If you’ve

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Python ABCs- The Complete Guide to Abstract Base Classes

Abstract Base Classes in Python are classes that cannot be instantiated directly and serve as blueprints for other classes. They define a common interface that subclasses must implement, ensuring consistency across your codebase. The abc module provides the tools to create these abstract classes using decorators like @abstractmethod. If you’ve ever wanted to enforce that

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Inheritance vs. Composition in Python- When to Use Which

Inheritance means creating new classes based on existing ones, where the new class automatically gets all the methods and attributes from the parent class. Composition means building classes by combining other objects, where you create instances of other classes and use them as components inside your class. If you’ve been writing Python classes, you’ve probably

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Build a Simple RAG System with CSV Files: Step-by-Step Guide for Beginners

RAG (Retrieval-Augmented Generation) with CSV files transforms your spreadsheet data into an intelligent question-answering system that can understand and respond to natural language queries about your data. Instead of manually searching through rows and columns, you can ask questions like “What were our top-performing products last quarter?” and get instant, contextual answers powered by AI.

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Hypothetical Document Embedding (HyDE) – A Smarter RAG method to Search Documents

Hypothetical Document Embeddings (HyDE) is an advanced technique in information retrieval (IR) for RAG systems designed to improve search accuracy when little or relevant documents exist in the dataset yet. It leverages large language models (LLMs) to generate “hypothetical” documents that might answer a query, then uses their embeddings for similarity search. Have you ever

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Simple RAG Explained: A Beginner’s Guide to Retrieval-Augmented Generation (RAG)

I’m going to walk you through creating a Simple RAG system. But what exactly is RAG? RAG stands for Retrieval-Augmented Generation. Think of it as giving your AI a specific relevant documents (or chunks) that it can quickly scan through to find relevant information before answering your questions. So, instead of searching the entire database

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Machine Learning A-Z™: Hands-On Python & R In Data Science

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Machine Learning A-Z™: Hands-On Python & R In Data Science

Machine Learning A-Z™: Hands-On Python & R In Data Science

Machine Learning A-Z™: Hands-On Python & R In Data Science

Machine Learning A-Z™: Hands-On Python & R In Data Science

Machine Learning A-Z™: Hands-On Python & R In Data Science

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