Working With Linked Lists in Python (Course)

Enroll Free: https://realpython.com/videos/working-linked-lists-overview/

#python #programming #developer #programmer #coding #coder #softwaredeveloper #computerscience #webdev #webdeveloper #webdevelopment #pythonprogramming #pythonquiz #ai #ml #machinelearning #datascience

https://t.me/DataScience4

Pagination in Django

https://testdriven.io/blog/django-pagination/

Looks at how to add pagination to a Django project.

#python #programming #developer #programmer #coding #coder #softwaredeveloper #computerscience #webdev #webdeveloper #webdevelopment #pythonprogramming #pythonquiz #ai #ml #machinelearning #datascience #django

https://t.me/DataScience4

Django Features and Libraries - course

Exploring Django Features and Libraries
The "Django Features and Libraries" course is designed to help learners deepen their understanding of Django by exploring its advanced features and built-in libraries. Django is a high-level Python web framework that promotes rapid development and clean, pragmatic design. This course provides hands-on experience in leveraging Django’s powerful tools to build scalable, efficient, and secure web applications.

Enroll Free: https://www.coursera.org/learn/django-features-libraries

#python #programming #developer #programmer #coding #coder #softwaredeveloper #computerscience #webdev #webdeveloper #webdevelopment #pythonprogramming #pythonquiz #ai #ml #machinelearning #datascience #django

https://t.me/DataScience4

Topic: Django Models and ORM — From Basics to Advanced Queries

---

What is a Model in Django?

• A model in Django is a Python class that defines the structure of your database table. Each model maps to a table, and each attribute represents a column.

• Django uses its ORM (Object-Relational Mapping) to interact with the database using Python code instead of SQL.

---

Creating Your First Model

from django.db import models

class Book(models.Model):
    title = models.CharField(max_length=200)
    author = models.CharField(max_length=100)
    published_date = models.DateField()
    pages = models.IntegerField()

---

Making Migrations

• Create and apply migrations to sync models with the database:

python manage.py makemigrations
python manage.py migrate

---

Using the Model

# Creating a new record
book = Book(title="1984", author="George Orwell", published_date="1949-06-08", pages=328)
book.save()

# Fetching all books
books = Book.objects.all()

# Filtering
orwell_books = Book.objects.filter(author="George Orwell")

# Getting one object
book = Book.objects.get(id=1)

# Updating
book.title = "Animal Farm"
book.save()

# Deleting
book.delete()

---

Model Field Types

• CharField, TextField, IntegerField, FloatField, DateField, DateTimeField, BooleanField, EmailField, and more.

---

Meta Class for Model Options

class Book(models.Model):
    title = models.CharField(max_length=200)

    class Meta:
        ordering = ['title']  # default ordering by title

---

Relationships Between Models

• One-to-Many (ForeignKey)
• Many-to-Many (ManyToManyField)
• One-to-One (OneToOneField)

class Author(models.Model):
    name = models.CharField(max_length=100)

class Book(models.Model):
    title = models.CharField(max_length=200)
    author = models.ForeignKey(Author, on_delete=models.CASCADE)

---

Advanced ORM Queries

# Complex filters
books = Book.objects.filter(published_date__year__gte=2000, pages__lte=300)

# Exclude
books = Book.objects.exclude(author="J.K. Rowling")

# Ordering
books = Book.objects.order_by("-published_date")

# Count
total = Book.objects.count()

---

Summary

• Django models define your database structure.

• The ORM allows you to query and manipulate data using Python.

• Supports relationships, complex filtering, ordering, and aggregation.

---

Exercise

• Create two models: Author and Book. Link them using a foreign key. Then, write views that:

1. Add a new book.
2. List all books by a specific author.
3. Delete books published before the year 2000.

---

#Django #WebDevelopment #ORM #DatabaseModels #DjangoTips

https://t.me/DataScience4

Topic: Django ORM – Advanced Queries, Aggregations, and Query Optimization (Part 2)

---

1. Aggregation Functions

• Django provides built-in functions for aggregating data.

from django.db.models import Avg, Sum, Max, Min, Count

# Average number of pages
avg_pages = Book.objects.aggregate(Avg("pages"))

# Total number of pages
total_pages = Book.objects.aggregate(Sum("pages"))

# Count of books per author
book_counts = Book.objects.values("author").annotate(total=Count("id"))

---

2. Grouping and Annotating

• annotate() is used to compute values for each row (e.g., totals per group).

# Number of books per author
from django.db.models import Count

authors = Author.objects.annotate(book_count=Count("book"))
for author in authors:
    print(author.name, author.book_count)

---

3. Complex Lookups with Q Objects

• Use Q for OR, AND, and NOT conditions.

from django.db.models import Q

# Books with title containing 'war' OR author name 'Leo Tolstoy'
books = Book.objects.filter(Q(title__icontains="war") | Q(author__name="Leo Tolstoy"))

# Books not published in 2023
books = Book.objects.filter(~Q(published_date__year=2023))

---

4. Selecting Specific Fields

• Use values() or values\_list() to retrieve specific fields.

# Dictionary of titles and authors
data = Book.objects.values("title", "author__name")

# List of titles
titles = Book.objects.values_list("title", flat=True)

---

5. Related Model Queries

• Use select\_related and prefetch\_related to optimize related data access.

# Optimized: Single JOIN query for ForeignKey
books = Book.objects.select_related("author")

# For ManyToMany or reverse relations
authors = Author.objects.prefetch_related("book_set")

---

6. Raw SQL Queries (When Necessary)

books = Book.objects.raw("SELECT * FROM myapp_book WHERE pages > %s", [300])
for book in books:
    print(book.title)

---

7. Performance Tips

• Use only() or defer() to limit retrieved fields.

books = Book.objects.only("title")

• Avoid chaining queries in loops.

• Use bulk\_create, bulk\_update for inserting/updating many records.

---

Summary

• Use aggregate(), annotate(), and Q objects for powerful filtering.

• Fetch only what you need using values, only, and select\_related.

• Optimize queries by reducing database hits and using Django’s ORM efficiently.

---

Exercise

• Write a Django query that returns all authors with more than 5 books, sorted by the number of books (descending). Then print their name and book count.

---

#Django #ORM #AdvancedQueries #QueryOptimization #WebDevelopment

https://t.me/DataScience4

🚀 Comprehensive Guide: How to Prepare for a Django Job Interview – 400 Most Common Interview Questions

Are you ready to get a job: https://hackmd.io/@husseinsheikho/django-mcq

#DjangoInterview #Python #WebDevelopment #Django #BackendDevelopment #RESTAPI #Database #Security #Scalability #DevOps #InterviewPrep

# Django ORM Comparison - Know both frameworks
# Django model (contrast with SQLAlchemy)
from django.db import models

class Department(models.Model):
    name = models.CharField(max_length=50)

class Employee(models.Model):
    name = models.CharField(max_length=100)
    email = models.EmailField(unique=True)
    department = models.ForeignKey(Department, on_delete=models.CASCADE)

# Django query (similar but different syntax)
Employee.objects.filter(department__name="HR").select_related('department')

# Async ORM - Modern Python requirement
# Requires SQLAlchemy 1.4+ and asyncpg
from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession

async_engine = create_async_engine(
    "postgresql+asyncpg://user:pass@localhost/db",
    echo=True,
)
async_session = AsyncSession(async_engine)

async with async_session.begin():
    result = await async_session.execute(
        select(Employee).where(Employee.name == "Alice")
    )
    employee = result.scalar_one()

# Testing Strategies - Interview differentiator
from unittest import mock

# Mock database for unit tests
with mock.patch('sqlalchemy.create_engine') as mock_engine:
    mock_conn = mock.MagicMock()
    mock_engine.return_value.connect.return_value = mock_conn
    
    # Test your ORM-dependent code
    create_employee("Test", "test@company.com")
    mock_conn.execute.assert_called()

# Production Monitoring - Track slow queries
from sqlalchemy import event

@event.listens_for(engine, "before_cursor_execute")
def before_cursor(conn, cursor, statement, params, context, executemany):
    conn.info.setdefault('query_start_time', []).append(time.time())

@event.listens_for(engine, "after_cursor_execute")
def after_cursor(conn, cursor, statement, params, context, executemany):
    total = time.time() - conn.info['query_start_time'].pop(-1)
    if total > 0.1:  # Log slow queries
        print(f"SLOW QUERY ({total:.2f}s): {statement}")

# Interview Power Move: Implement caching layer
from functools import lru_cache

class CachedEmployeeRepository(EmployeeRepository):
    @lru_cache(maxsize=100)
    def get_by_id(self, employee_id):
        return super().get_by_id(employee_id)
    
    def invalidate_cache(self, employee_id):
        self.get_by_id.cache_clear()

# Reduces database hits by 70% in read-heavy applications

# Pro Tip: Schema versioning in CI/CD pipelines
# Sample .gitlab-ci.yml snippet
deploy_db:
  stage: deploy
  script:
    - alembic upgrade head
    - pytest tests/db_tests.py  # Verify schema compatibility
  only:
    - main

# Real-World Case Study: E-commerce inventory system
class Product(Base):
    __tablename__ = 'products'
    id = Column(Integer, primary_key=True)
    sku = Column(String(20), unique=True)
    stock = Column(Integer, default=0)
    
    # Atomic stock update (prevents race conditions)
    def decrement_stock(self, quantity, session):
        result = session.query(Product).filter(
            Product.id == self.id,
            Product.stock >= quantity
        ).update({"stock": Product.stock - quantity})
        if not result:
            raise ValueError("Insufficient stock")

# Usage during checkout
product.decrement_stock(2, session)

By: @DATASCIENCE4 🔒

#Python #ORM #SQLAlchemy #Django #Database #BackendDevelopment #CodingInterview #WebDevelopment #TechJobs #SystemDesign #SoftwareEngineering #DataEngineering #CareerGrowth #APIs #Microservices #DatabaseDesign #TechTips #DeveloperTools #Programming #CareerTips

@app.post("/uploadfile/")
async def create_upload_file(file: UploadFile = File(...)):
    return {"filename": file.filename, "content_type": file.content_type}

• Set a response cookie.

from fastapi import Response
@app.post("/cookie/")
def create_cookie(response: Response):
    response.set_cookie(key="fakesession", value="fake-cookie-session-value")
    return {"message": "Cookie set"}

• Read a request cookie.

from fastapi import Cookie
@app.get("/read-cookie/")
def read_cookie(fakesession: Optional[str] = Cookie(None)):
    return {"session": fakesession}

X. Error Handling & Advanced Responses

• Handle HTTPException.

from fastapi import HTTPException
@app.get("/items/{item_id}")
def read_item(item_id: str):
    if item_id not in items_db:
        raise HTTPException(status_code=404, detail="Item not found")

• Return a custom JSONResponse.

from fastapi.responses import JSONResponse
@app.get("/custom/")
def custom_response():
    return JSONResponse(status_code=202, content={"message": "Accepted"})

• Return an HTMLResponse.

from fastapi.responses import HTMLResponse
@app.get("/page", response_class=HTMLResponse)
def read_page():
    return "<h1>Hello World</h1>"

• Define a WebSocket endpoint.

from fastapi import WebSocket
@app.websocket("/ws")
async def websocket_endpoint(websocket: WebSocket):
    await websocket.accept()
    await websocket.send_text("Hello from WebSocket")
    await websocket.close()

• Handle a path operation that may not exist.

@app.get("/{full_path:path}")
def read_catch_all(full_path: str):
    return {"path": full_path}

• Mount a static files directory.

from fastapi.staticfiles import StaticFiles
app.mount("/static", StaticFiles(directory="static"), name="static")

• Add a custom exception handler.

from fastapi import Request
from fastapi.responses import JSONResponse

class MyCustomException(Exception): pass

@app.exception_handler(MyCustomException)
async def custom_exception_handler(request: Request, exc: MyCustomException):
    return JSONResponse(status_code=418, content={"message": "I'm a teapot"})

• Read a request header.

from fastapi import Header
@app.get("/headers/")
async def read_headers(user_agent: Optional[str] = Header(None)):
    return {"User-Agent": user_agent}

• Get a raw Request object.

from fastapi import Request
@app.get("/request-info")
def get_request_info(request: Request):
    return {"client_host": request.client.host}

#Python #FastAPI #API #WebDevelopment #Pydantic

━━━━━━━━━━━━━━━
By: @DataScience4 ✨