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Documentation Index

Fetch the complete documentation index at: https://python.datalumina.com/llms.txt

Use this file to discover all available pages before exploring further.

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What is object-oriented programming?

Object-oriented programming (OOP) is a way to organize code by grouping related data and functions together. Instead of having separate variables and functions scattered around, you bundle them into objects. Think of it like organizing a toolbox:
  • Without OOP: Tools scattered everywhere
  • With OOP: Tools organized in labeled compartments
Classes are a more advanced Python concept. Don’t worry if they feel confusing at first - they’ll click with practice. Many Python programs work perfectly fine without classes, but they become valuable as your projects grow.

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What is a class?

A class is a blueprint for creating objects. It defines:
  • Attributes: What data the object stores
  • Methods: What the object can do
# Without classes - data and functions separate
name = "OpenAI"
model = "gpt-4o-mini"

def generate_response(prompt):
    # Process prompt...
    return response

# With classes - everything bundled together
class OpenAIClient:
    def __init__(self, name, model):
        self.name = name
        self.model = model
    
    def generate_response(self, prompt):
        # Process prompt...
        return response

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Why use classes?

Classes help you write more understandable programs as they grow. Here’s the typical progression of a Python developer: 1. Single file scripts (where you started): 
# everything.py - All code in one file
api_key = "sk-..."
prompt = "Explain Python"
response = make_api_call(api_key, prompt)
print(response)
2. Functions (where you are now): 
# main.py - Organized with functions
def setup_api(key):
    return {"key": key, "base_url": "https://api.openai.com"}

def generate_response(api_config, prompt):
    # Make API call
    return response

api = setup_api("sk-...")
result = generate_response(api, "Explain Python")
3. Multiple files (getting organized): 
# api_utils.py
def setup_api(key):
    return {"key": key, "base_url": "https://api.openai.com"}

# main.py
from api_utils import setup_api
api = setup_api("sk-...")
4. Classes (where we are now): 
# client.py
class OpenAIClient:
    def __init__(self, api_key):
        self.api_key = api_key
        self.base_url = "https://api.openai.com"
    
    def generate(self, prompt):
        # All logic encapsulated here
        return response

# main.py
from client import OpenAIClient
client = OpenAIClient("sk-...")
response = client.generate("Explain Python")
In the context of AI, classes specifically help you:
  • Build clean interfaces to APIs
  • Manage complex data pipelines
  • Create reusable components
  • Organize stateful operations

Your first class

Create a simple class

Methods and attributes

Add functionality to classes

Inheritance

Build on existing classes

When to use classes

Best practices and guidelines