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Containerize Python applications with Docker. Deploy Flask/FastAPI apps with Docker Compose. Dockerfile FROM python:3.13-slim WORKDIR /app COPY requirements.txt . RUN pip install –no-cache-dir -r requirements.txt COPY . . CMD [“uvicorn”, “main:app”, “–host”, “0.0.0.0”] docker-compose.yml version: ‘3.8’ services: web: build: . ports: – “8000:8000” redis: image: redis:7-alpine Commands docker-compose up –build docker-compose down Conclusion Docker simplifies … Read more

Process background tasks with Celery. Handle long-running tasks asynchronously. Setup # tasks.py from celery import Celery app = Celery(‘tasks’, broker=’redis://localhost’) @app.task def send_email(to, subject): # Send email logic return “sent” Calling Tasks result = send_email.delay(“user@example.com”, “Hello”) print(result.get(timeout=10)) Docker Compose worker: command: celery -A tasks worker Conclusion Celery enables scalable background processing!

Write better tests with pytest. Master testing patterns for Python applications. Basic Test def add(a, b): return a + b def test_add(): assert add(2, 3) == 5 assert add(-1, 1) == 0 Fixtures import pytest @pytest.fixture def client(): return TestClient(app) def test_index(client): response = client.get(“/”) assert response.status_code == 200 Parametrization @pytest.mark.parametrize(“input,expected”, [ (1, 1), (2, … Read more

Build REST APIs with FastAPI step by step. Create high-performance APIs with automatic documentation. Setup pip install fastapi uvicorn Basic API from fastapi import FastAPI from pydantic import BaseModel app = FastAPI() class Item(BaseModel): name: str price: float @app.post(“/items/”) async def create_item(item: Item): return item Run Server uvicorn main:app –reload Documentation Open http://localhost:8000/docs Conclusion FastAPI … Read more

Build ML models with scikit-learn. Learn supervised and unsupervised learning. Classification from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y) model = RandomForestClassifier() model.fit(X_train, y_train) accuracy = model.score(X_test, y_test) Pipelines from sklearn.pipeline import Pipeline from sklearn.preprocessing import StandardScaler pipeline = Pipeline([ (‘scaler’, StandardScaler()), (‘classifier’, RandomForestClassifier()) ]) Conclusion scikit-learn makes … Read more

Master data manipulation with Pandas and NumPy. Analyze data efficiently in Python. NumPy Basics import numpy as np arr = np.array([1, 2, 3, 4, 5]) arr.mean() # 3.0 arr.std() # 1.414 Pandas DataFrames import pandas as pd df = pd.read_csv(‘data.csv’) df.head() df.describe() Data Cleaning df.dropna() df.fillna(0) df[‘column’].str.lower() Conclusion Pandas and NumPy are essential for data … Read more

Learn asynchronous programming with asyncio. Build high-performance Python applications. Basic async import asyncio async def fetch_data(): await asyncio.sleep(1) return “data” async def main(): result = await fetch_data() print(result) asyncio.run(main()) Concurrent Tasks async def fetch_all(urls): tasks = [fetch_url(u) for u in urls] return await asyncio.gather(*tasks) aiohttp import aiohttp async with aiohttp.ClientSession() as session: async with session.get(url) … Read more

Build web applications with Django 5. Learn the latest Django features and best practices. Setup pip install django django-admin startproject myproject python manage.py startapp blog Models class Post(models.Model): title = models.CharField(max_length=200) content = models.TextField() published = models.DateTimeField(auto_now_add=True) Views class PostListView(ListView): model = Post template_name = ‘blog/post_list.html’ Conclusion Django makes web development efficient!

Compare FastAPI and Flask for Python web development. Choose the best framework for your project. FastAPI Example from fastapi import FastAPI app = FastAPI() @app.get(“/users/{user_id}”) async def get_user(user_id: int): return {“user_id”: user_id} Flask Example from flask import Flask app = Flask(__name__) @app.route(“/users/“) def get_user(user_id): return {“user_id”: user_id} When to Use FastAPI ✅ API development ✅ … Read more

Explore Python 3.13’s new capabilities. Learn about the latest Python improvements. New Features ✅ Experimental JIT compiler ✅ Improved error messages ✅ Free-threaded CPython ✅ Tab completion in REPL JIT Preview # Enable with PYTHON_JIT=1 import sys print(sys.version) Better Errors # More helpful tracebacks def process(data): return data.upper() Conclusion Python 3.13 brings significant improvements!