Part 4: Python Mastery - From Scripting to Production-Grade Code

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1. Why Python for 2026?

Python remains the dominant language for backend engineering, AI/ML, data engineering, and automation in 2026. Its simplicity allows rapid prototyping while its ecosystem supports production-scale applications.

Python's Role in High-Paying Jobs

Role	Python Usage	Salary Premium
Backend Engineer	FastAPI, Django, Flask	10-20%
AI/ML Engineer	PyTorch, TensorFlow, Scikit-learn	30-50%
Data Engineer	Pandas, Airflow, Spark	20-35%
DevOps Engineer	Ansible, Scripting	15-25%

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2. Advanced Python Concepts

Object-Oriented Programming (OOP) Deep Dive

Classes and Objects

class DatabaseConnection:
    def __init__(self, host: str, port: int):
        self.host = host
        self.port = port
        self._connection = None
    
    def connect(self):
        # Connection logic
        self._connection = f"Connected to {self.host}:{self.port}"
        return self._connection
    
    def __str__(self):
        return f"DB Connection to {self.host}"

# Usage
db = DatabaseConnection("localhost", 5432)
print(db)  # DB Connection to localhost

Inheritance and Polymorphism

class BaseRepository:
    def __init__(self, model):
        self.model = model
    
    def save(self, data):
        raise NotImplementedError

class UserRepository(BaseRepository):
    def save(self, data):
        # User-specific save logic
        return {"id": 1, **data}

class ProductRepository(BaseRepository):
    def save(self, data):
        # Product-specific save logic
        return {"product_id": "p1", **data}

Encapsulation and Properties

class APIClient:
    def __init__(self, api_key: str):
        self._api_key = api_key
        self._base_url = "https://api.example.com"
    
    @property
    def api_key(self):
        return "***HIDDEN***"
    
    @api_key.setter
    def api_key(self, value):
        if not value:
            raise ValueError("API key required")
        self._api_key = value

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3. Decorators and Higher-Order Functions

Function Decorators

import functools
import time

def timing_decorator(func):
    @functools.wraps(func)
    def wrapper(*args, **kwargs):
        start = time.perf_counter()
        result = func(*args, **kwargs)
        end = time.perf_counter()
        print(f"{func.__name__} took {end - start:.4f} seconds")
        return result
    return wrapper

@timing_decorator
def fetch_data(url: str) -> dict:
    time.sleep(1)
    return {"data": "fetched"}

# Usage
fetch_data("https://api.example.com")

Class Decorators

def singleton(cls):
    instances = {}
    def get_instance(*args, **kwargs):
        if cls not in instances:
            instances[cls] = cls(*args, **kwargs)
        return instances[cls]
    return get_instance

@singleton
class DatabasePool:
    def __init__(self):
        self.pool = []

Property Decorators

class Temperature:
    def __init__(self, celsius: float = 0):
        self._celsius = celsius
    
    @property
    def celsius(self):
        return self._celsius
    
    @property
    def fahrenheit(self):
        return self._celsius * 9/5 + 32
    
    @fahrenheit.setter
    def fahrenheit(self, value):
        self._celsius = (value - 32) * 5/9

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4. Generators and Iterators

Generator Functions

def fibonacci_generator(limit: int):
    a, b = 0, 1
    while a < limit:
        yield a
        a, b = b, a + b

# Usage
for num in fibonacci_generator(100):
    print(num)

Generator Expressions

# Memory-efficient processing
squares = (x**2 for x in range(1000000))
print(sum(squares))  # Process without storing all values

Custom Iterators

class CountDown:
    def __init__(self, start: int):
        self.current = start
    
    def __iter__(self):
        return self
    
    def __next__(self):
        if self.current <= 0:
            raise StopIteration
        self.current -= 1
        return self.current + 1

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5. Context Managers

Using with Statement

# File handling
with open('data.txt', 'r') as f:
    content = f.read()

# Database connection
with database.connection() as conn:
    result = conn.execute("SELECT * FROM users")

Custom Context Managers

from contextlib import contextmanager

@contextmanager
def timer(name: str):
    start = time.perf_counter()
    yield
    end = time.perf_counter()
    print(f"{name} took {end - start:.4f} seconds")

# Usage
with timer("database query"):
    # Database operations
    pass

Class-Based Context Managers

class DatabaseTransaction:
    def __init__(self, db):
        self.db = db
    
    def __enter__(self):
        self.db.begin_transaction()
        return self.db
    
    def __exit__(self, exc_type, exc_val, exc_tb):
        if exc_type:
            self.db.rollback()
        else:
            self.db.commit()

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6. Type Hints and Static Analysis

Basic Type Hints

from typing import List, Dict, Optional, Union

def process_users(users: List[Dict[str, str]]) -> Optional[Dict[str, int]]:
    if not users:
        return None
    return {"count": len(users)}

# Union types
def parse_id(id_value: Union[str, int]) -> str:
    return str(id_value)

Protocol and Structural Subtyping

from typing import Protocol

class SupportsClose(Protocol):
    def close(self) -> None: ...

def cleanup(resource: SupportsClose) -> None:
    resource.close()

Generic Types

from typing import TypeVar, Generic

T = TypeVar('T')

class Cache(Generic[T]):
    def __init__(self):
        self._data: Dict[str, T] = {}
    
    def set(self, key: str, value: T) -> None:
        self._data[key] = value
    
    def get(self, key: str) -> Optional[T]:
        return self._data.get(key)

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7. Virtual Environments and Packaging

venv vs. conda

# venv (built-in, lightweight)
python -m venv myproject
source myproject/bin/activate  # Linux/Mac
myproject\Scripts\activate     # Windows

# conda (package management + environments)
conda create -n myproject python=3.11
conda activate myproject

pyproject.toml (Modern Packaging)

[build-system]
requires = ["setuptools>=45", "wheel"]
build-backend = "setuptools.build_meta"

[project]
name = "myproject"
version = "0.1.0"
dependencies = [
    "fastapi>=0.68.0",
    "pydantic>=1.8.0",
    "sqlalchemy>=1.4.0",
]

[tool.setuptools.packages.find]
where = ["src"]

Poetry (Alternative to pip)

# Install Poetry
curl -sSL https://install.python-poetry.org | python3 -

# Initialize
poetry init
poetry add fastapi uvicorn
poetry add pytest --group dev

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8. Resource Directory: Python Learning

Best Books

Book	Author	Price	Key Topics
Effective Python	Brett Slatkin	Paid	90 specific ways to write better Python
Fluent Python	Luciano Ramalho	Paid	Deep dive into Python internals
Python Tricks	Dan Bader	Paid	Practical tips and best practices
Architecture Patterns with Python	Harry Percival	Paid	Domain-driven design, CQRS

Best Udemy Courses

Course	Instructor	Price (INR)	Key Topics
100 Days of Code	Dr. Angela Yu	₹399-799	Complete Python from zero
Python for Everybody	Dr. Charles Severance	₹399-799	University-level Python
Python and Django	Jose Portilla	₹399-799	Web development with Django
Python for Data Science	Jose Portilla	₹399-799	Pandas, NumPy, visualization

Best O'Reilly Resources

Resource	Topic	Access
Python Cookbook	David Beazley	Paid
Mastering Python	Sam Sexton	Paid
Python Fundamentals	Barron Stone	Paid

Best LinkedIn Learning Courses

Course	Instructor	Access
Python Essential Training	Joe Marini	Paid
Python for Data Science	Michael Lester	Paid
Learning Python	Erin Allard	Paid
Python Security	Emmanuel Tsukerman	Paid

Free Resources

Platform	Resource	Link
Python.org	Official documentation	docs.python.org
Real Python	Tutorials and articles	realpython.com
Automate the Boring Stuff	Free online book	automatetheboringstuff.com
Python Design Patterns	GitHub repo	github.com/justin-chang/python-design-patterns

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9. Common Python Interview Questions

Question	Answer
Difference between list and tuple?	Lists are mutable, tuples are immutable. Use tuples for constants.
What is the GIL?	Global Interpreter Lock prevents multi-threading in CPU-bound tasks. Use multiprocessing instead.
**How does *args and kwargs work?	*args unpacks positional arguments, **kwargs unpacks keyword arguments.
What is a closure?	Inner function that remembers outer function's variables.
Difference between str and repr?	str is for readable output, repr is for debugging/unambiguous representation.

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10. Part Navigation

Previous Parts

Part 3: Developer Toolkit

Next Parts

Part 5: Async Python & FastAPI · Part 6: TypeScript & Node.js

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