Pagination in Django
https://testdriven.io/blog/django-pagination/
Looks at how to add pagination to a Django project.
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://xn--r1a.website/DataScience4
π5π₯1
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 Djangoβs powerful tools to build scalable, efficient, and secure web applications.
Enroll Free: https://www.coursera.org/learn/django-features-libraries
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 Djangoβs 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://xn--r1a.website/DataScience4
π6
Data Management With Python, SQLite, and SQLAlchemy
In this tutorial, youβll learn how to use:
1β£ Flat files for data storage
π’ SQL to improve access to persistent data
π’ SQLite for data storage
π’ SQLAlchemy to work with data as Python objects
Enroll Free: https://realpython.com/python-sqlite-sqlalchemy/
In this tutorial, youβll learn how to use:
Enroll Free: https://realpython.com/python-sqlite-sqlalchemy/
#python #programming #developer #programmer #coding #coder #softwaredeveloper #computerscience #webdev #webdeveloper #webdevelopment #pythonprogramming #pythonquiz #ai #ml #machinelearning #datascience #django #SQLAlchemy #SQLite #SQL
https://xn--r1a.website/DataScience4
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π8
Build a Blog From Scratch With Django
π In this #Django beginner project, you'll build a blog from scratch with the Django web #framework. You'll leverage the Django admin site and explore how to work with forms so your visitors can comment on your posts.
Link: https://realpython.com/build-a-blog-from-scratch-django/
β
https://xn--r1a.website/addlist/8_rRW2scgfRhOTc0
Link: https://realpython.com/build-a-blog-from-scratch-django/
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π7
https://xn--r1a.website/DataScience4
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Topic: Django Models and ORM β From Basics to Advanced Queries
---
What is a Model in Django?
β’ 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.
β’ Django uses its ORM (Object-Relational Mapping) to interact with the database using Python code instead of SQL.
---
Creating Your First Model
---
Making Migrations
β’ Create and apply migrations to sync models with the database:
---
Using the Model
---
Model Field Types
β’ CharField, TextField, IntegerField, FloatField, DateField, DateTimeField, BooleanField, EmailField, and more.
---
Meta Class for Model Options
---
Relationships Between Models
β’ One-to-Many (ForeignKey)
β’ Many-to-Many (ManyToManyField)
β’ One-to-One (OneToOneField)
---
Advanced ORM Queries
---
Summary
β’ Django models define your database structure.
β’ The ORM allows you to query and manipulate data using Python.
β’ Supports relationships, complex filtering, ordering, and aggregation.
---
Exercise
β’ Create two models:
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://xn--r1a.website/DataScience4
---
What is a Model in Django?
β’ 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.
β’ 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
β’ 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
β’ 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
β’ One-to-Many (ForeignKey)
β’ Many-to-Many (ManyToManyField)
β’ 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
β’ Django models define your database structure.
β’ The ORM allows you to query and manipulate data using Python.
β’ Supports relationships, complex filtering, ordering, and aggregation.
---
Exercise
β’ 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
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β€5
Topic: Django ORM β Advanced Queries, Aggregations, and Query Optimization (Part 2)
---
1. Aggregation Functions
β’ Django provides built-in functions for aggregating data.
---
2. Grouping and Annotating
β’ annotate() is used to compute values for each row (e.g., totals per group).
---
3. Complex Lookups with Q Objects
β’ Use Q for OR, AND, and NOT conditions.
---
4. Selecting Specific Fields
β’ Use values() or values\_list() to retrieve specific fields.
---
5. Related Model Queries
β’ Use select\_related and prefetch\_related to optimize related data access.
---
6. Raw SQL Queries (When Necessary)
---
7. Performance Tips
β’ Use only() or defer() to limit retrieved fields.
β’ Avoid chaining queries in loops.
β’ Use bulk\_create, bulk\_update for inserting/updating many records.
---
Summary
β’ Use aggregate(), annotate(), and Q objects for powerful filtering.
β’ Fetch only what you need using values, only, and select\_related.
β’ Optimize queries by reducing database hits and using Djangoβs ORM efficiently.
---
Exercise
β’ 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
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---
1. Aggregation Functions
β’ 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
β’ 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
β’ 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
β’ 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
β’ 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
β’ Use only() or defer() to limit retrieved fields.
books = Book.objects.only("title")β’ Avoid chaining queries in loops.
β’ Use bulk\_create, bulk\_update for inserting/updating many records.
---
Summary
β’ Use aggregate(), annotate(), and Q objects for powerful filtering.
β’ Fetch only what you need using values, only, and select\_related.
β’ Optimize queries by reducing database hits and using Djangoβs ORM efficiently.
---
Exercise
β’ 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
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β€2π2
Topic: Django ORM β Transactions, Subqueries, and Custom Managers (Part 3)
---
1. Working with Transactions
β’ Django supports atomic transactions to ensure database integrity β either all operations succeed, or none do.
β’ Use atomic() as a decorator or context manager.
---
2. Subqueries and OuterRef
β’ Use Subquery and OuterRef to perform queries that depend on other queries.
---
3. Exists() and Conditional Logic
β’ Use Exists for optimized existence checks.
---
4. Custom Model Managers
β’ Add custom query logic to models via custom managers.
---
5. QuerySet Methods: Update, Delete, Bulk Operations
β’ update() modifies multiple records efficiently.
β’ delete() removes objects in bulk.
β’ bulk\_create() inserts many records at once.
---
6. Using Database Functions
β’ Django provides built-in SQL functions like Lower, Upper, Length, Concat, etc.
---
Summary
β’ Use transactions to maintain data integrity.
β’ Leverage subqueries, OuterRef, and Exists for complex logic.
β’ Create custom managers to encapsulate reusable query logic.
β’ Apply bulk operations and DB functions for performance and flexibility.
---
Exercise
β’ Create a custom manager for the
---
#Django #ORM #Transactions #Subqueries #CustomManagers #AdvancedDjango
https://xn--r1a.website/DataScience4
---
1. Working with Transactions
β’ Django supports atomic transactions to ensure database integrity β either all operations succeed, or none do.
from django.db import transaction
@transaction.atomic
def create_author_and_book():
author = Author.objects.create(name="New Author")
Book.objects.create(title="New Book", author=author)
β’ Use atomic() as a decorator or context manager.
with transaction.atomic():
# multiple operations that must succeed together
...
---
2. Subqueries and OuterRef
β’ Use Subquery and OuterRef to perform queries that depend on other queries.
from django.db.models import Subquery, OuterRef
# Get latest book for each author
latest_books = Book.objects.filter(author=OuterRef('pk')).order_by('-published_date')
authors = Author.objects.annotate(latest_book=Subquery(latest_books.values('title')[:1]))
---
3. Exists() and Conditional Logic
β’ Use Exists for optimized existence checks.
from django.db.models import Exists
recent_books = Book.objects.filter(published_date__year=2023)
authors = Author.objects.annotate(has_recent_books=Exists(recent_books.filter(author=OuterRef('pk'))))
---
4. Custom Model Managers
β’ Add custom query logic to models via custom managers.
from django.db import models
class PublishedBookManager(models.Manager):
def get_queryset(self):
return super().get_queryset().filter(is_published=True)
class Book(models.Model):
title = models.CharField(max_length=200)
is_published = models.BooleanField(default=False)
objects = models.Manager() # Default manager
published = PublishedBookManager() # Custom manager
# Usage
Book.published.all()
---
5. QuerySet Methods: Update, Delete, Bulk Operations
β’ update() modifies multiple records efficiently.
Book.objects.filter(author__name="Alice").update(pages=300)
β’ delete() removes objects in bulk.
Book.objects.filter(published_date__year__lt=2000).delete()
β’ bulk\_create() inserts many records at once.
Book.objects.bulk_create([
Book(title="Book A", author=author),
Book(title="Book B", author=author),
])
---
6. Using Database Functions
β’ Django provides built-in SQL functions like Lower, Upper, Length, Concat, etc.
from django.db.models.functions import Upper
books = Book.objects.annotate(upper_title=Upper('title'))
---
Summary
β’ Use transactions to maintain data integrity.
β’ Leverage subqueries, OuterRef, and Exists for complex logic.
β’ Create custom managers to encapsulate reusable query logic.
β’ Apply bulk operations and DB functions for performance and flexibility.
---
Exercise
β’ Create a custom manager for the
Book model to return only books published in the last 5 years. Then use this manager in a view to list all recent books along with their authors.---
#Django #ORM #Transactions #Subqueries #CustomManagers #AdvancedDjango
https://xn--r1a.website/DataScience4
β€6
π Comprehensive Guide: How to Prepare for a Django Job Interview β 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
Are you ready to get a job: https://hackmd.io/@husseinsheikho/django-mcq
#DjangoInterview #Python #WebDevelopment #Django #BackendDevelopment #RESTAPI #Database #Security #Scalability #DevOps #InterviewPrep
β€6
Django REST Framework and Vue versus Django and HTMX
https://testdriven.io/blog/drf-vue-vs-django-htmx/
Learn how the development process varies between working with Django REST Framework and Vue versus #Django and #HTMX.
https://xn--r1a.website/DataScience4π
https://testdriven.io/blog/drf-vue-vs-django-htmx/
Learn how the development process varies between working with Django REST Framework and Vue versus #Django and #HTMX.
https://xn--r1a.website/DataScience4
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β€1
# 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 π
#Python #ORM #SQLAlchemy #Django #Database #BackendDevelopment #CodingInterview #WebDevelopment #TechJobs #SystemDesign #SoftwareEngineering #DataEngineering #CareerGrowth #APIs #Microservices #DatabaseDesign #TechTips #DeveloperTools #Programming #CareerTips
β€3
β’ Fetch related
β’ Fetch related
β’ Load only specific model fields.
β’ Defer loading of specific model fields.
β’ Execute raw, unmanaged SQL.
β’ Get results as a list of tuples.
XV. Transactions
β’ Import the transaction module.
β’ Run a block of code within a database transaction.
XVI. Managers & Model Methods
β’ Create a custom
β’ Add a custom method to a
β’ Add a custom method to a model for object-specific logic.
#Python #Django #ORM #Database #Backend
βββββββββββββββ
By: @DataScience4 β¨
ForeignKey objects in the same query.entries = Entry.objects.select_related('author').all()β’ Fetch related
ManyToManyField objects in a separate efficient query.entries = Entry.objects.prefetch_related('tags').all()β’ Load only specific model fields.
entries = Entry.objects.only('headline')β’ Defer loading of specific model fields.
entries = Entry.objects.defer('body_text')β’ Execute raw, unmanaged SQL.
authors = Author.objects.raw('SELECT * FROM myapp_author')β’ Get results as a list of tuples.
Entry.objects.values_list('headline', 'pub_date')XV. Transactions
β’ Import the transaction module.
from django.db import transaction
β’ Run a block of code within a database transaction.
with transaction.atomic():
# All database operations here are either committed together or rolled back.
author.save()
entry.save()
XVI. Managers & Model Methods
β’ Create a custom
Manager for common queries.class PublishedEntryManager(models.Manager):
def get_queryset(self):
return super().get_queryset().filter(status='published')
β’ Add a custom method to a
QuerySet via its Manager.Entry.objects.get_queryset().by_author("John Doe")β’ Add a custom method to a model for object-specific logic.
class Entry(models.Model):
#...
def is_recent(self):
return self.pub_date > timezone.now() - timedelta(days=1)
#Python #Django #ORM #Database #Backend
βββββββββββββββ
By: @DataScience4 β¨
β€1
Forwarded from Machine Learning with Python
A huge cheat sheet for Python, Django, Plotly, Matplotlib, P.pdf
741 KB
Many topics are covered inside:
https://xn--r1a.website/CodeProgrammer
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β€3π₯1