Python Coding Challange - Question With Answer(01200225)

 

Step-by-Step Execution:

1. Function Definition:

   python
   def extend_list(lst):
       lst.extend([10])

   • The function extend_list(lst) takes a list lst as an argument.
   • It uses .extend([10]), which appends 10 to the existing list.

2. List Initialization:

   python
   b = [5, 10, 15, 20]

   • A list b is created with four elements: [5, 10, 15, 20].
   • Initially, len(b) = 4.

3. Function Call:

   python
   extend_list(b)

   • The list b is passed to extend_list(lst).
   • Inside the function, lst.extend([10]) adds 10 to the end of b.
   • Now, b becomes [5, 10, 15, 20, 10].

4. Printing the Length:

   python
   print(len(b))

   • The updated list has 5 elements: [5, 10, 15, 20, 10].
   • len(b) returns 5.

Final Output:

5

---

Key Points:

1. .extend([10]) appends 10 to the list.
2.  Since lists are mutable, b is modified directly.
3. The function does not return a new list; it updates the existing list.
4. len(b) increases from 4 to 5 after the function call.

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10 Python Tricks That Prove You’re a Python Expert

 

Python is a powerful and versatile language, but mastering its hidden tricks can take your skills to the next level. If you know these Python tricks, you’re absolutely a Python pro!

1. Swap Variables in One Line

No need for a temporary variable—swap values like a pro:

a, b = b, a

2. List Comprehensions for Quick Iterations

Instead of using loops, use list comprehensions:

squares = [x**2 for x in range(10)]

3. Use zip() to Pair Elements

Efficiently iterate over multiple lists in parallel:

names = ["Alice", "Bob", "Charlie"]
ages = [25, 30, 35]
for name, age in zip(names, ages): 
    print(name, age)

4. Dictionary Comprehension for Cleaner Code

Create dictionaries in a single line:

squares_dict = {x: x**2 for x in range(5)}

5. Use enumerate() Instead of Range-Based Loops

Instead of using range(len()), use enumerate():

items = ["apple", "banana", "cherry"]
for index, item in enumerate(items, start=1): 
    print(index, item)

6. Unpacking Multiple Values from a List

Assign multiple values effortlessly:

data = ["Python", 3.9, "Pro"]
language, version, level = data

7. collections.Counter for Quick Frequency Count

Count occurrences in a list easily:

from collections import Counter
nums = [1, 2, 2, 3, 3, 3, 4]
print(Counter(nums))

8. Use *args and **kwargs for Flexible Functions

Allow functions to accept any number of arguments:

def greet(*names):
    for name in names:
        print(f"Hello, {name}!")
greet("Alice", "Bob", "Charlie")

9. setdefault() for Handling Missing Keys

Avoid key errors when working with dictionaries:

data = {}
data.setdefault("name", "Unknown")
print(data["name"])

10. Use any() and all() for Logical Checks

Check conditions in one line:

numbers = [0, 1, 2, 3]
print(any(numbers)) # True if any value is non-zero
print(all(numbers)) # True if all values are non-zero

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Python Coding Challange - Question With Answer(01190225)

 

Step-by-Step Execution

1. Define outer() function

   • Inside outer(), a variable x is initialized with the value 2.
   • inner() is defined inside outer() and modifies x using nonlocal.

2. Define inner() function

   • inner() accesses the x variable from outer() using nonlocal.
   • The x *= 2 operation doubles the value of x.
   • inner() returns the updated value of x.

3. Return the inner() function

   • When outer() is called, it returns inner() instead of executing it immediately.

---

Execution of closure = outer()

• outer() is executed, and inner() is returned.
• The x variable remains inside the closure and is remembered.

---

First Call: print(closure())

• inner() is called.
• x = 2 (from outer()).
• x *= 2 → Now x = 4.
• Returns 4, so it prints 4.

---

Second Call: print(closure())

• inner() is called again.
• x = 4 (previously modified value).
• x *= 2 → Now x = 8.
• Returns 8, so it prints 8.

---

Final Output

4
8

Key Concepts Used

1. Closure: The function inner() remembers the variable x from outer(), even after outer() has finished execution.
2. nonlocal keyword: This allows inner() to modify x in outer(), instead of creating a new local variable.
3. State Retention: The value of x persists between function calls.

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Top Python Coding Errors and How to Fix Them Like a Pro

 

Python is a powerful yet easy-to-learn programming language. However, even experienced developers encounter common coding errors that can be frustrating. In this guide, we'll cover some of the most frequent Python mistakes and how to resolve them efficiently.

1. Syntax Errors

Error:

print "Hello, World!"

Fix:

print("Hello, World!")

Python requires proper syntax, including parentheses for print statements in Python 3.

2. Indentation Errors

Error:

if True:

print("This is a test")

Fix:

if True:
   print("This is a test")

Python relies on indentation for defining code blocks, so always ensure proper spacing.

3. Name Errors

Error:

print(message)

Fix:

message = "Hello"

print(message)

Ensure variables are defined before using them.

4. Type Errors

Error:

num = 5 + "10"

Fix:

num = 5 + int("10")

Always match data types correctly before performing operations.

5. Index Errors

Error:

my_list = [1, 2, 3]

print(my_list[5])

Fix:

my_list = [1, 2, 3]
if len(my_list) > 5:
   print(my_list[5])

else:

    print("Index out of range")

Always check the length of a list before accessing an index.

6. Key Errors

Error:

data = {"name": "Alice"}

print(data["age"])

Fix:

data = {"name": "Alice"}

print(data.get("age", "Key not found"))

Use .get() to avoid missing key errors in dictionaries.

7. Attribute Errors

Error:

num = 10

num.append(5)

Fix:

num_list = [10]

num_list.append(5)

Ensure the object supports the method being used.

8. Value Errors

Error:

num = int("hello")

Fix:

try: 

   num = int("hello")

except ValueError: 

    print("Invalid input for conversion")

Use exception handling to catch invalid data conversions.

9. ZeroDivisionError

Error:

result = 10 / 0

Fix:

num = 0 

if num != 0:

   result = 10 / num
else: 

    result = "Cannot divide by zero"

Always check for division by zero before performing the operation.

10. Module Not Found Error

Error:

import non_existent_module

Fix:

# Install missing module using:
# pip install module_nameimport math

Ensure the module is installed and correctly spelled before importing.

Conclusion

By understanding and fixing these common Python errors, you can write more reliable and error-free code. Always read error messages carefully and debug systematically for efficient problem-solving!

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PRACTICAL DATA SCIENCE WITH R AND PYTHON: A Hands-On Approach to Solving Data Problems with R and Python

 

Practical Data Science with R and Python: A Hands-On Guide to Real-World Data Problems

Practical Data Science with R and Python: A Hands-On Approach to Solving Data Problems with R and Python Master the art of data science with Practical Data Science with R and Python, a comprehensive guide for aspiring data scientists, analysts, and professionals eager to leverage the power of R and Python to solve real-world data challenges. This hands-on book combines the strengths of these two leading data science tools to provide practical solutions to common data problems, helping you make data-driven decisions with confidence .In today’s data-driven world, businesses and organizations rely on data science to uncover insights, make informed decisions, and drive innovation. If you’re looking for a practical and hands-on approach to solving data problems, "PRACTICAL DATA SCIENCE WITH R AND PYTHON" is an excellent guide that bridges the gap between theory and application.

Why This Book?

This book is unique because it teaches both R and Python, two of the most widely used programming languages in data science. It’s perfect for professionals and students who want to compare and leverage the strengths of each language in different scenarios.

Comprehensive yet practical – Learn data science by working on real-world problems.

Side-by-side R and Python examples – Compare implementations in both languages.

Covers the entire data science workflow – From data wrangling to machine learning.

Ideal for all levels – Whether you're a beginner or an experienced professional.

Key Topics Covered in the Book

1. Introduction to Data Science

The role of data science in business and research.

How R and Python differ and complement each other.

Setting up your data science environment.

2. Data Wrangling and Exploration

Data cleaning and preprocessing techniques.

Working with Pandas (Python) and dplyr (R).

Data visualization with ggplot2 (R) and Matplotlib/Seaborn (Python).

3. Statistical Analysis and Hypothesis Testing

Descriptive and inferential statistics.

A/B testing and experimental design.

Using statsmodels (Python) and base R functions.

4. Machine Learning with R and Python

Implementing supervised and unsupervised learning.

Regression, classification, and clustering techniques.

Using Scikit-learn (Python) and caret (R).

5. Practical Applications of Data Science

Predictive modeling for business intelligence.

Sentiment analysis and text mining.

Time series forecasting and recommendation systems.

Why Learn Both R and Python?

While Python is widely used for machine learning and automation, R excels in statistical analysis and data visualization. Learning both allows you to:

Leverage Python's power for AI and deep learning

Use R's statistical capabilities for in-depth data analysis

Work across different teams and industries

Many companies prefer data scientists who are proficient in both, as it provides flexibility in handling diverse projects.

What You’ll Learn:

Getting Started with R and Python: Set up your environment and learn the basics of both R and Python for data science.

Data Wrangling and Cleaning: Handle messy datasets with Pandas in Python and dplyr in R to prepare data for analysis.

Exploratory Data Analysis (EDA): Uncover patterns, trends, and insights in data using visualization libraries like ggplot2 (R) and Matplotlib (Python).

Statistical Analysis: Perform descriptive and inferential statistics to test hypotheses and summarize data.

Machine Learning Basics: Build and evaluate predictive models using scikit-learn (Python) and caret (R).

Data Visualization: Create compelling and interactive visualizations with R’s Shiny and Python’s Plotly libraries.

Big Data Processing: Learn to work with large datasets using tools like SparkR, Dask, and PySpark.

Time-Series Analysis: Analyze and forecast time-series data with ARIMA and Prophet in both R and Python.

Text Mining and Natural Language Processing: Extract insights from unstructured text data using libraries like tidytext (R) and NLTK (Python).

Handling Geospatial Data: Visualize and analyze geospatial data with GeoPandas (Python) and sf (R).

Integration and Automation: Combine R and Python workflows to streamline repetitive tasks and maximize efficiency.

Model Deployment: Learn to deploy your data science models into production environments with Flask (Python) and RStudio Connect.

Real-World Applications: Work through projects including fraud detection, customer segmentation, and recommendation systems.

Collaborating and Version Control: Utilize Git and GitHub to manage and share your projects effectively.

Who Is This Book For?

Who Should Read This Book?

Data Analysts & Scientists – Expand your skill set with both R and Python.

Business Professionals – Learn data-driven decision-making techniques.

Students & Researchers – Apply real-world data science methodologies.

Programmers & Engineers – Transition into the field of data science.

Hard Copy : PRACTICAL DATA SCIENCE WITH R AND PYTHON: A Hands-On Approach to Solving Data Problems with R and Python

Kindle : PRACTICAL DATA SCIENCE WITH R AND PYTHON: A Hands-On Approach to Solving Data Problems with R and Python

Conclusion : 

If you want a hands-on, practical guide to solving real-world data science problems, PRACTICAL DATA SCIENCE WITH R AND PYTHON is the perfect book. It not only teaches theory and coding but also focuses on problem-solving approaches used in industry.

With side-by-side examples in R and Python, you’ll gain a strong foundation in data science and be prepared for real-world challenges.

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MACHINE LEARNING WITH PYTHON: A Comprehensive Guide To Algorithms, Deep Learning Techniques, And Practical Applications

 

Mastering Machine Learning with Python: A Deep Dive into Algorithms, Deep Learning, and Practical Applications

Machine Learning (ML) is transforming industries, driving innovation, and shaping the future of technology. If you're looking for a comprehensive guide that bridges the gap between theoretical concepts and real-world applications, then "MACHINE LEARNING WITH PYTHON: A Comprehensive Guide To Algorithms, Deep Learning Techniques, And Practical Applications" is an essential read. Machine Learning with Python in this all-in-one guide designed for beginners and experienced developers alike!  Whether you're diving into supervised and unsupervised learning, exploring neural networks, or mastering real-world applications, this book provides step-by-step explanations, hands-on examples, and expert insights.

Why This Book?

This book stands out as an all-in-one resource for beginners and experienced professionals alike. Whether you’re just starting out or looking to deepen your expertise, this book provides:

 Fundamental ML concepts – Understand the building blocks of machine learning.

 Hands-on coding examples – Apply what you learn with Python-based implementations.

 Deep learning insights – Explore advanced topics like neural networks and AI.

 Practical applications – Work on real-world projects that enhance your portfolio.

Key Topics Covered in the Book

1. Introduction to Machine Learning

• Understanding the basics of ML and its real-world impact.
• Supervised vs. unsupervised learning.
• The importance of data preprocessing.

2. Python for Machine Learning

• Why Python is the go-to language for ML.
• Essential libraries: NumPy, Pandas, Matplotlib, and Scikit-learn.
• Setting up your ML environment.

3. Core ML Algorithms

• Linear and logistic regression.
• Decision trees and random forests.
• Support vector machines (SVM).
• Clustering techniques (K-Means, DBSCAN).

4. Deep Learning Fundamentals

• Neural networks explained.
• Backpropagation and optimization.
• Introduction to TensorFlow and PyTorch.

5. Practical ML Applications

• Natural Language Processing (NLP).
• Image classification and object detection.
• Predictive analytics in business.
• Reinforcement learning in AI.

What You'll Learn:

Fundamentals of Machine Learning – Understand key concepts and algorithms 

Supervised vs. Unsupervised Learning – Learn how models make predictions 

Deep Learning & Neural Networks – Build intelligent AI systems 

Data Preprocessing & Feature Engineering – Prepare your data for success 

Practical Applications – Solve real-world problems using Python 

AI Ethics & Best Practices – Implement responsible AI solutions 

Why Python for Machine Learning?

Python’s simplicity and vast ecosystem of ML libraries make it an ideal choice for both beginners and experts. Libraries like TensorFlow, Scikit-learn, and PyTorch simplify complex tasks, allowing you to focus on innovation rather than reinventing the wheel.

Who Should Read This Book?

 Aspiring Data Scientists – Learn ML from the ground up.

 Software Engineers – Enhance your skill set with AI knowledge.

 Researchers & Analysts – Utilize ML for data-driven insights.

 Tech Enthusiasts – Stay ahead in the AI revolution.

Hard Copy : MACHINE LEARNING WITH PYTHON: A Comprehensive Guide To Algorithms, Deep Learning Techniques, And Practical Applications

Kindle : MACHINE LEARNING WITH PYTHON: A Comprehensive Guide To Algorithms, Deep Learning Techniques, And Practical Applications

Conclusion:

Machine learning with python serves as an indispensable resource for anyone looking to master machine learning. It combines theory, coding exercises, and real-world applications, ensuring that you gain both knowledge and practical experience.

If you’re serious about building a career in AI/ML, this book will be your roadmap to success. 

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API Design and Fundamentals of Google Cloud's Apigee API Platform

 

API Design and Fundamentals of Google Cloud's Apigee

The "API Design and Fundamentals of Google Cloud’s Apigee" course on Coursera is designed to help learners understand API design principles and how to manage APIs using Google Cloud’s Apigee platform. This course provides hands-on experience in building, securing, and deploying APIs at scale.

Why Learn API Design with Apigee?

APIs (Application Programming Interfaces) are the backbone of modern digital ecosystems, enabling seamless communication between services and applications. Apigee, Google Cloud’s API management platform, provides powerful tools to:

Design Scalable APIs: Create and manage APIs with best practices.

Secure API Endpoints: Implement authentication, authorization, and encryption.

Monitor API Performance: Track API usage, latency, and error rates.

Enhance Developer Experience: Provide comprehensive API documentation and versioning.

Monetize APIs: Enable API monetization models for businesses.

Course Structure

The "API Design and Fundamentals of Google Cloud’s Apigee" course follows a structured approach to teaching API management and design principles. Below is a breakdown of the key topics covered:

1. Introduction to APIs and API Management

Understanding API-first development

Overview of RESTful API architecture

Why API management is essential

2. Getting Started with Apigee

Introduction to Google Cloud’s Apigee API Management Platform

Setting up an Apigee environment

Exploring Apigee Edge and its components

3. API Design Best Practices

Principles of API-first development

Designing APIs with OpenAPI Specification (OAS)

Implementing API versioning and documentation

4. Securing APIs with Apigee

Implementing authentication and authorization (OAuth 2.0, API keys, JWTs)

Rate limiting and quota management

API security best practices

5. API Traffic Management and Caching

Understanding rate limiting, quotas, and throttling

Implementing caching strategies for improved performance

Handling API errors and exceptions

6. API Monitoring and Analytics

Using Apigee analytics to track API performance

Logging and debugging API requests

Setting up alerts and notifications

7. API Monetization and Developer Portal

Enabling API monetization models

Creating and managing API product bundles

Building a developer portal for API consumers

8. Deploying and Scaling APIs

Deploying APIs on Google Cloud

Using Apigee hybrid and multi-cloud deployment options

Scaling APIs for high availability and reliability

Learning Outcomes

• By the end of this course, learners will be able to:
• Design and develop high-performance APIs using Apigee
• Secure and monitor APIs effectively
• Implement API traffic management strategies
• Create API documentation and developer portals
• Deploy and manage APIs on Google Cloud

Who Should Take This Course?

This course is ideal for:

Developers and software engineers working with APIs

Cloud architects and DevOps professionals

IT professionals interested in API management

Business leaders looking to monetize APIs

Join Free : API Design and Fundamentals of Google Cloud's Apigee API Platform

Conclusion:

The "API Design and Fundamentals of Google Cloud’s Apigee" course is an excellent resource for anyone looking to master API development and management. With hands-on labs and real-world use cases, this course equips learners with the knowledge to design, secure, and scale APIs effectively.

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Django Features and 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 Django’s powerful tools to build scalable, efficient, and secure web applications.

Why Choose Django?

Django is one of the most popular web frameworks for Python developers due to its scalability, security, and ease of use. Here are some key benefits of Django:

Batteries-Included Approach: Comes with built-in features for authentication, database management, and security.

Rapid Development: Enables developers to build web applications quickly without reinventing the wheel.

Scalability: Suitable for projects of all sizes, from small apps to enterprise-level solutions.

Security: Protects against common web vulnerabilities such as SQL injection, CSRF, and XSS.

ORM (Object-Relational Mapping): Simplifies database interactions with Django’s built-in ORM.

Course Structure

The "Django Features and Libraries" course follows a structured approach to ensure learners gain practical experience with Django’s advanced features. Below is a breakdown of what the course covers:

1. Introduction to Django’s Core Features

• Understanding the Django framework and MVC architecture
• Setting up a Django project and exploring key configurations
• Overview of Django’s built-in admin interface

2. Working with Django’s ORM (Object-Relational Mapping)

• Introduction to Django models and migrations
• Querying databases with Django ORM
• Implementing model relationships (One-to-One, Many-to-One, Many-to-Many)

3. Authentication and User Management

• Implementing user authentication and authorization
• Using Django’s built-in authentication system
• Managing user sessions and permissions

4. Django’s Built-in Libraries and Utilities

• Using Django’s messaging framework for notifications
• Implementing caching for performance optimization
• Managing static and media files in Django projects

5. Working with Django Forms and Validation

• Creating and customizing Django forms
• Implementing form validation and error handling
• Handling file uploads with Django forms

6. Django’s Middleware and Security Features

• Understanding Django middleware and its role in request/response processing
• Implementing CSRF protection, XSS protection, and clickjacking protection
• Managing security best practices in Django applications

7. Advanced Django Features and Third-Party Integrations

• Using Django REST Framework (DRF) for API development
• Integrating Django with Celery for asynchronous task processing
• Working with Django Channels for real-time applications

8. Testing and Deployment of Django Applications

• Writing unit tests and integration tests in Django
• Debugging and optimizing Django applications
• Deploying Django projects on cloud platforms like AWS and Heroku

Learning Outcomes

By the end of this course, learners will be able to:

• Utilize Django’s built-in features and libraries effectively
• Work with Django’s ORM to manage databases efficiently
• Implement authentication and user management in Django applications
• Optimize web applications using caching and middleware
• Integrate Django with third-party tools for enhanced functionality
• Deploy Django applications in production environments

Who Should Take This Course?

This course is ideal for:

• Beginner and intermediate Python developers looking to master Django
• Web developers interested in building scalable web applications
• Software engineers who want to enhance their backend development skills
• Anyone interested in learning Django’s advanced features and best practices

Join Free : Django Features and Libraries

Conclusion:

The "Django Features and Libraries" course is an excellent resource for developers who want to take their Django skills to the next level. With its structured approach, hands-on exercises, and real-world applications, this course equips learners with the knowledge needed to build efficient, secure, and scalable Django applications.

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Introduction to FastAPI framework

 

Introduction to FastAPI Framework: A Comprehensive Course Review

The "Introduction to FastAPI Framework" course  is a beginner-friendly guided project designed to introduce learners to FastAPI, a modern web framework for building APIs with Python. This course offers hands-on experience in creating and deploying APIs efficiently, leveraging the speed and simplicity of FastAPI. If you're interested in web development, API design, or backend development, this course is an excellent starting point.

Why Choose FastAPI?

FastAPI has gained immense popularity among developers due to its speed, ease of use, and automatic interactive API documentation. Here are some key benefits of using FastAPI:

High Performance: FastAPI is one of the fastest web frameworks available, comparable to Node.js and Go.

Type Hints and Validation: Built-in support for type hints ensures robust input validation and better developer experience.

Auto-generated Documentation: FastAPI provides OpenAPI and Swagger documentation out-of-the-box.

Asynchronous Support: Designed to handle async programming with ease, improving API responsiveness.

Ease of Use: Minimal setup and concise syntax make it accessible to both beginners and experienced developers.

Course Structure

The "Introduction to FastAPI Framework" course follows a structured approach, ensuring that learners gain practical skills in API development. Below is a breakdown of what the course covers:

1. Introduction to FastAPI

Understanding the basics of REST APIs

Introduction to FastAPI and its advantages over other frameworks

2. Setting Up the Environment

Installing FastAPI and Uvicorn (ASGI server)

Setting up a virtual environment in Python

3. Building Your First FastAPI Application

Creating a simple API endpoint

Using decorators (@app.get, @app.post, etc.) to define routes

4. Request and Response Handling

Handling query parameters and request bodies

Implementing data validation using Pydantic

5. Error Handling and Responses

Managing error responses with exception handling

Using status codes for effective API communication

6. API Documentation and Testing

Exploring the built-in Swagger UI and ReDoc

Testing APIs using FastAPI’s interactive documentation

7. Deploying FastAPI Applications

Running FastAPI with Uvicorn in production

Deployment options: Cloud platforms and Docker integration

Learning Outcomes

By the end of this course, learners will be able to:

• Develop REST APIs using FastAPI
• Implement request validation and error handling
• Utilize FastAPI’s built-in documentation tools
• Deploy FastAPI applications efficiently

Who Should Take This Course?

This course is ideal for:

• Beginners in API development
• Python developers looking to build scalable APIs
• Backend developers transitioning to modern frameworks
• Anyone interested in learning a fast and efficient way to develop APIs

Join Free : Introduction to FastAPI framework

Conclusion:

The "Introduction to FastAPI Framework" course is a great hands-on guide for anyone looking to dive into API development with Python. Whether you're a beginner or an experienced developer, this course provides practical skills that will help you build, document, and deploy APIs effectively. With its interactive approach and real-world applications, this guided project is a valuable addition to your programming skill set.

If you're ready to explore FastAPI, enroll in the course today on Coursera and start building high-performance APIs effortlessly!

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Advanced Django: Introduction to Django Rest Framework

The "Advanced Django: Introduction to Django REST Framework (DRF)" course on Coursera is designed to help learners build powerful web APIs using Django's robust framework. This course provides hands-on experience in API development, authentication, and deployment using DRF. If you are looking to extend your Django knowledge into API development, this course is an excellent choice.

Why Choose Django REST Framework?

Django REST Framework (DRF) is a powerful toolkit for building Web APIs. Here are some reasons why DRF is widely used:

Comprehensive Features: Provides authentication, serialization, and request parsing out-of-the-box.

Browsable API: Automatically generates a web interface for easy API testing and interaction.

Flexible Authentication: Supports token-based authentication, OAuth, and custom authentication classes.

Serialization and Validation: Built-in serializers simplify data representation and validation.

Scalability and Security: Designed to handle high loads while ensuring robust security.

Wide Adoption: Used by companies and developers worldwide for building scalable APIs.

Course Structure

The "Advanced Django: Introduction to Django REST Framework (DRF)" course follows a structured approach to ensure learners gain practical experience in API development. Below is a breakdown of what the course covers:

1. Introduction to Django REST Framework

Understanding REST architecture and API concepts

Why use DRF for building APIs?

Differences between Django and DRF in API handling

2. Setting Up Django and DRF

Installing Django and Django REST Framework

Creating a Django project and app

Configuring Django settings for API development

3. Building Your First API with DRF

Creating API endpoints with Django views

Understanding function-based views (FBV) vs. class-based views (CBV)

Using Django serializers for data transformation

Implementing CRUD operations using Django ORM

4. Authentication and Permissions

Implementing authentication with Django’s built-in user model

Setting up permissions and access control for APIs

Implementing JWT-based authentication

Role-based access control (RBAC) in Django

5. Viewsets and Routers

Utilizing Django ViewSets for efficient API development

Implementing routers for clean URL management

Understanding mixins and generic views for streamlined API development

6. API Testing and Documentation

Testing APIs with Django’s built-in test framework

Writing unit tests and integration tests for APIs

Generating API documentation using DRF’s built-in tools

Using Postman for manual API testing

7. Deploying Django APIs

Running Django APIs in production

Deploying on cloud platforms such as AWS, Heroku, and DigitalOcean

Using Docker for containerization and scalability

Managing database migrations in production environments

Learning Outcomes

By the end of this course, learners will be able to:

Develop RESTful APIs using Django REST Framework

Implement authentication and authorization in Django APIs

Utilize serializers for efficient data handling

Optimize API performance and security

Write unit tests and integration tests for APIs

Deploy and scale Django APIs efficiently

Who Should Take This Course?

This course is ideal for:

Python developers looking to build web APIs

Django developers aiming to expand into API development

Backend developers working with Django

Anyone interested in mastering Django REST Framework

Developers working on scalable microservices architectures

Join Free : Advanced Django: Introduction to Django Rest Framework

Conclusion:

The "Advanced Django: Introduction to Django REST Framework (DRF)" course is an excellent resource for developers who want to build scalable and secure APIs using Django. With its structured approach, practical assignments, and real-world applications, this course provides the necessary knowledge to create, secure, and deploy high-performance APIs.

If you're ready to take your Django skills to the next level, enroll in the course today on Coursera and start building professional-grade web APIs effortlessly!

 

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Hollow Square Pattern Plot using python

 

import matplotlib.pyplot as plt

rows = 5

cols = 5

plt.figure(figsize=(6, 6))

for i in range(rows):

    for j in range(cols):

        if i == 0 or i == rows - 1 or j == 0 or j == cols - 1:

            plt.scatter(j, -i, s=800, c='orange')

           plt.xlim(-0.5, cols - 0.5)

plt.ylim(-rows + 0.5, 0.5)

plt.axis('off')

plt.gca().set_aspect('equal', adjustable='datalim')

plt.title("Hollow Square Pattern Plot", fontsize=14)

plt.show()

#source code --> clcoding.com 

Code Explanation:

1. Importing Matplotlib

import matplotlib.pyplot as plt

This imports matplotlib.pyplot, which is used to create plots.

2. Defining the Dimensions of the Square

rows = 5

cols = 5

rows = 5: Sets the height of the square.

cols = 5: Sets the width of the square.

3. Initializing the Plot

plt.figure(figsize=(6, 6))

Creates a figure with a size of 6x6 inches.

Generating the Hollow Square

4. Using Nested Loops to Plot the Dots

for i in range(rows):

    for j in range(cols):

        if i == 0 or i == rows - 1 or j == 0 or j == cols - 1:

            plt.scatter(j, -i, s=800, c='red')

The outer loop (i) iterates over the rows.

The inner loop (j) iterates over the columns.

The if condition ensures that only border points are plotted:

i == 0 → Top row

i == rows - 1 → Bottom row

j == 0 → Left column

j == cols - 1 → Right column

plt.scatter(j, -i, s=800, c='red') places a red dot at the coordinates (j, -i).

s=800: Controls the size of the dots.

-i: Ensures that rows go downward.

Adjusting the Plot Appearance

5. Setting Axis Limits

plt.xlim(-0.5, cols - 0.5)

plt.ylim(-rows + 0.5, 0.5)

plt.xlim(-0.5, cols - 0.5): Ensures the square is centered horizontally.

plt.ylim(-rows + 0.5, 0.5): Ensures the square is fully visible vertically.

6. Removing Axes and Adjusting Aspect Ratio

plt.axis('off')

plt.gca().set_aspect('equal', adjustable='datalim')

plt.axis('off'): Removes grid lines and axis labels for a clean display.

plt.gca().set_aspect('equal', adjustable='datalim'): Ensures equal spacing between points.

7. Adding a Title

plt.title("Hollow Square Pattern Plot", fontsize=14)

Displays the title of the plot.

8. Displaying the Pattern

plt.show()

Renders and displays the hollow square pattern.

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Diamond Pattern plot using python

 

import matplotlib.pyplot as plt

rows = 5

plt.figure(figsize=(6, 6))

for i in range(rows):

    for j in range(rows - i - 1, rows + i):

        plt.scatter(j, -i, s=800, c='purple')

for i in range(rows - 2, -1, -1):

    for j in range(rows - i - 1, rows + i):

        plt.scatter(j, -(2 * rows - i - 2), s=800, c='purple')

plt.xlim(-0.5, 2 * rows - 1.5)

plt.ylim(-2 * rows + 1.5, 0.5)

plt.axis('off')

plt.gca().set_aspect('equal', adjustable='datalim')

plt.title("Diamond Pattern Plot", fontsize=14)

plt.show()

#source code --> clcoding.com 

Code Explanation:

1. Importing Matplotlib

import matplotlib.pyplot as plt

This imports matplotlib.pyplot, which allows us to create plots.

2. Setting the Number of Rows

rows = 5

The variable rows controls the height of the upper half of the diamond.

3. Creating the Figure

plt.figure(figsize=(6, 6))

Initializes a figure with dimensions 6x6 inches.

Generating the Diamond Shape

The diamond consists of two triangular halves:

Upper half (top to middle).

Lower half (middle to bottom).

4. Creating the Upper Part of the Diamond

for i in range(rows):

    for j in range(rows - i - 1, rows + i):

        plt.scatter(j, -i, s=800, c='red')

The outer loop (i) iterates over the rows.

The inner loop (j) controls the number of dots per row.

The range rows - i - 1 to rows + i ensures that dots expand outward as i increases.

plt.scatter(j, -i, s=800, c='red') places red dots at calculated positions.

5. Creating the Lower Part of the Diamond

for i in range(rows - 2, -1, -1):

    for j in range(rows - i - 1, rows + i):

        plt.scatter(j, -(2 * rows - i - 2), s=800, c='red')

The outer loop (i) iterates in reverse to form the lower half.

The inner loop (j) places dots in a shrinking pattern.

-(2 * rows - i - 2) correctly positions dots below the center.

Adjusting the Plot Appearance

6. Setting Axis Limits

plt.xlim(-0.5, 2 * rows - 1.5)

plt.ylim(-2 * rows + 1.5, 0.5)

plt.xlim(-0.5, 2 * rows - 1.5): Ensures the diamond is centered horizontally.

plt.ylim(-2 * rows + 1.5, 0.5): Ensures the full diamond is visible vertically.

7. Removing Axes and Adjusting Aspect Ratio

plt.axis('off')

plt.gca().set_aspect('equal', adjustable='datalim')

plt.axis('off'): Removes grid lines and axis labels for a clean display.

plt.gca().set_aspect('equal', adjustable='datalim'): Ensures equal spacing between dots.

8. Adding a Title

plt.title("Diamond Pattern Plot", fontsize=14)

Displays the title of the plot.

9. Displaying the Pattern

plt.show()

Renders and displays the diamond pattern

Read More