Auto copy paste using Python

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https://t.iss.one/DataScience4

Automatically Generate Image CAPTCHAs with Python for Enhanced Security

Unlock the power of Python to automatically generate image CAPTCHAs, adding an extra layer of security to online platforms. This advanced solution leverages Python's robust libraries to create dynamic, hard-to-crack CAPTCHA images that protect against bots and unauthorized access. With customizable features, such as text distortion, background noise, and color variations, these CAPTCHAs ensure a unique challenge for every user session. Elevate your system’s defense mechanisms while maintaining a seamless user experience.

#PythonProgramming #CAPTCHA #CyberSecurity #Automation #WebDevelopment #SecureAuthentication #TechInnovation

https://t.iss.one/DataScience4

Barcode creation using Python

#PythonProgramming #CAPTCHA #CyberSecurity #Automation #WebDevelopment #SecureAuthentication #TechInnovation

https://t.iss.one/DataScience4

Working With Linked Lists in Python (Course)

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

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https://t.iss.one/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.iss.one/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

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https://t.iss.one/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.iss.one/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.iss.one/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", "[email protected]")
    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 ✨