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Khamisi Kibet

Khamisi Kibet

Software Developer

I am a computer scientist, software developer, and YouTuber, as well as the developer of this website, spinncode.com. I create content to help others learn and grow in the field of software development.

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7 Months ago | 54 views

**Course Title:** Modern Python Programming: Best Practices and Trends **Section Title:** Web Development with Python **Topic:** Building RESTful APIs with Flask/Django **Introduction** In this topic, we will explore the process of building RESTful APIs using two popular Python frameworks: Flask and Django. Representational State of Resource (REST) is an architectural style for designing networked applications. RESTful APIs are designed to take advantage of existing protocols, reducing the need for additional infrastructure. They are also stateless, meaning that each request contains all the information necessary to complete the request. **RESTful API Design Principles** Before we dive into building RESTful APIs with Flask and Django, let's cover some key design principles: 1. **Resource-based**: Everything in REST is a resource (e.g., users, orders, products). 2. **Client-server architecture**: Separate the client from the server. 3. **Stateless**: Each request contains all the information necessary to complete the request. 4. **Cacheable**: Responses from the server should be cacheable by the client. 5. **Uniform interface**: The API follows a uniform interface (e.g., HTTP methods, URI). 6. **Layered system**: The API follows a layered system (e.g., authentication, encryption). **Building a RESTful API with Flask** Flask is a lightweight Python web framework that is ideal for building small to medium-sized web applications. Here's a step-by-step guide to building a RESTful API with Flask: 1. **Install Flask**: `pip install flask` 2. **Create a new Flask app**: `flask new my_app` 3. **Create a new route**: `app.route('/api/users', methods=['GET'])` 4. **Define a function to handle the request**: `def get_users(): return jsonify([{'id': 1, 'name': 'John Doe'}])` 5. **Return the data in JSON format**: `return jsonify([{'id': 1, 'name': 'John Doe'}])` Here's a more complete example: ```python from flask import Flask, jsonify, request app = Flask(__name__) @app.route('/api/users', methods=['GET']) def get_users(): users = [{'id': 1, 'name': 'John Doe'}, {'id': 2, 'name': 'Jane Doe'}] return jsonify(users) @app.route('/api/users/<int:user_id>', methods=['GET']) def get_user(user_id): user = {'id': user_id, 'name': 'John Doe'} return jsonify(user) if __name__ == '__main__': app.run(debug=True) ``` **Building a RESTful API with Django** Django is a high-level Python web framework that is ideal for building complex web applications. Here's a step-by-step guide to building a RESTful API with Django: 1. **Install Django**: `pip install django` 2. **Create a new Django project**: `django-admin startproject my_project` 3. **Create a new Django app**: `python manage.py startapp my_app` 4. **Create a new model**: `python manage.py makemigrations && python manage.py migrate` 5. **Define a view to handle the request**: `from rest_framework import status from rest_framework.response import Response from rest_framework.views import APIView` Here's a more complete example: ```python from django.shortcuts import render from rest_framework import status from rest_framework.response import Response from rest_framework.views import APIView from .models import User from .serializers import UserSerializer class UserList(APIView): def get(self, request): users = User.objects.all() serializer = UserSerializer(users, many=True) return Response(serializer.data) class UserDetail(APIView): def get(self, request, pk): user = User.objects.get(pk=pk) serializer = UserSerializer(user) return Response(serializer.data) ``` **Conclusion** In this topic, we covered the principles of building RESTful APIs and built a simple example with Flask and Django. Remember to follow RESTful API design principles and use tools like Postman to test your APIs. **What to do next?** 1. **Practice building RESTful APIs**: Try building a RESTful API with Flask and Django. 2. **Read the official documentation**: Read the official documentation for Flask and Django to learn more about building RESTful APIs. 3. **Use Postman to test your APIs**: Use Postman to test your RESTful APIs and ensure they are working correctly. **Leave a comment or ask for help**: If you have any questions or need help with this topic, please leave a comment below. **Next topic**: Connecting to databases using SQLAlchemy (Flask) or Django ORM. **Recommended reading**: * [Flask Documentation](https://flask.palletsprojects.com/en/2.0.x/) * [Django Documentation](https://docs.djangoproject.com/en/4.0/) * [RESTful API Tutorial](https://flask-restful.readthedocs.io/en/latest/quickstart.html)
Course
Python
Best Practices
Data Science
Web Development
Automation

Building RESTful APIs with Flask/Django.

**Course Title:** Modern Python Programming: Best Practices and Trends **Section Title:** Web Development with Python **Topic:** Building RESTful APIs with Flask/Django **Introduction** In this topic, we will explore the process of building RESTful APIs using two popular Python frameworks: Flask and Django. Representational State of Resource (REST) is an architectural style for designing networked applications. RESTful APIs are designed to take advantage of existing protocols, reducing the need for additional infrastructure. They are also stateless, meaning that each request contains all the information necessary to complete the request. **RESTful API Design Principles** Before we dive into building RESTful APIs with Flask and Django, let's cover some key design principles: 1. **Resource-based**: Everything in REST is a resource (e.g., users, orders, products). 2. **Client-server architecture**: Separate the client from the server. 3. **Stateless**: Each request contains all the information necessary to complete the request. 4. **Cacheable**: Responses from the server should be cacheable by the client. 5. **Uniform interface**: The API follows a uniform interface (e.g., HTTP methods, URI). 6. **Layered system**: The API follows a layered system (e.g., authentication, encryption). **Building a RESTful API with Flask** Flask is a lightweight Python web framework that is ideal for building small to medium-sized web applications. Here's a step-by-step guide to building a RESTful API with Flask: 1. **Install Flask**: `pip install flask` 2. **Create a new Flask app**: `flask new my_app` 3. **Create a new route**: `app.route('/api/users', methods=['GET'])` 4. **Define a function to handle the request**: `def get_users(): return jsonify([{'id': 1, 'name': 'John Doe'}])` 5. **Return the data in JSON format**: `return jsonify([{'id': 1, 'name': 'John Doe'}])` Here's a more complete example: ```python from flask import Flask, jsonify, request app = Flask(__name__) @app.route('/api/users', methods=['GET']) def get_users(): users = [{'id': 1, 'name': 'John Doe'}, {'id': 2, 'name': 'Jane Doe'}] return jsonify(users) @app.route('/api/users/<int:user_id>', methods=['GET']) def get_user(user_id): user = {'id': user_id, 'name': 'John Doe'} return jsonify(user) if __name__ == '__main__': app.run(debug=True) ``` **Building a RESTful API with Django** Django is a high-level Python web framework that is ideal for building complex web applications. Here's a step-by-step guide to building a RESTful API with Django: 1. **Install Django**: `pip install django` 2. **Create a new Django project**: `django-admin startproject my_project` 3. **Create a new Django app**: `python manage.py startapp my_app` 4. **Create a new model**: `python manage.py makemigrations && python manage.py migrate` 5. **Define a view to handle the request**: `from rest_framework import status from rest_framework.response import Response from rest_framework.views import APIView` Here's a more complete example: ```python from django.shortcuts import render from rest_framework import status from rest_framework.response import Response from rest_framework.views import APIView from .models import User from .serializers import UserSerializer class UserList(APIView): def get(self, request): users = User.objects.all() serializer = UserSerializer(users, many=True) return Response(serializer.data) class UserDetail(APIView): def get(self, request, pk): user = User.objects.get(pk=pk) serializer = UserSerializer(user) return Response(serializer.data) ``` **Conclusion** In this topic, we covered the principles of building RESTful APIs and built a simple example with Flask and Django. Remember to follow RESTful API design principles and use tools like Postman to test your APIs. **What to do next?** 1. **Practice building RESTful APIs**: Try building a RESTful API with Flask and Django. 2. **Read the official documentation**: Read the official documentation for Flask and Django to learn more about building RESTful APIs. 3. **Use Postman to test your APIs**: Use Postman to test your RESTful APIs and ensure they are working correctly. **Leave a comment or ask for help**: If you have any questions or need help with this topic, please leave a comment below. **Next topic**: Connecting to databases using SQLAlchemy (Flask) or Django ORM. **Recommended reading**: * [Flask Documentation](https://flask.palletsprojects.com/en/2.0.x/) * [Django Documentation](https://docs.djangoproject.com/en/4.0/) * [RESTful API Tutorial](https://flask-restful.readthedocs.io/en/latest/quickstart.html)

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Modern Python Programming: Best Practices and Trends

Course

Objectives

  • Gain a deep understanding of Python fundamentals and its modern ecosystem.
  • Learn best practices for writing clean, efficient, and scalable Python code.
  • Master popular Python libraries and frameworks for data science, web development, and automation.
  • Develop expertise in version control, testing, packaging, and deploying Python projects.

Introduction to Python and Environment Setup

  • Overview of Python: History, popularity, and use cases.
  • Setting up a Python development environment (Virtualenv, Pipenv, Conda).
  • Introduction to Python's package manager (pip) and virtual environments.
  • Exploring Python's basic syntax: Variables, data types, control structures.
  • Lab: Install Python, set up a virtual environment, and write your first Python script.

Data Structures and Basic Algorithms

  • Understanding Python’s built-in data types: Lists, tuples, dictionaries, sets.
  • Working with iterators and generators for efficient looping.
  • Comprehensions (list, dict, set comprehensions) for concise code.
  • Basic algorithms: Sorting, searching, and common patterns.
  • Lab: Implement data manipulation tasks using lists, dictionaries, and comprehensions.

Functions, Modules, and Best Practices

  • Defining and using functions: Arguments, return values, and scope.
  • Understanding Python’s module system and creating reusable code.
  • Using built-in modules and the Python Standard Library.
  • Best practices: DRY (Don’t Repeat Yourself), writing clean and readable code (PEP 8).
  • Lab: Write modular code by creating functions and organizing them into modules.

Object-Oriented Programming (OOP) in Python

  • Introduction to Object-Oriented Programming: Classes, objects, and methods.
  • Inheritance, polymorphism, encapsulation, and abstraction in Python.
  • Understanding magic methods (dunder methods) and operator overloading.
  • Design patterns in Python: Singleton, Factory, and others.
  • Lab: Implement a class-based system with inheritance and polymorphism.

File Handling and Working with External Data

  • Reading and writing files (text, CSV, JSON) with Python.
  • Introduction to Python’s `pathlib` and `os` modules for file manipulation.
  • Working with external data sources: APIs, web scraping (using `requests` and `BeautifulSoup`).
  • Error handling and exception management in file operations.
  • Lab: Build a script that processes data from files and external APIs.

Testing and Debugging Python Code

  • Importance of testing in modern software development.
  • Unit testing with Python’s `unittest` and `pytest` frameworks.
  • Mocking and patching external dependencies in tests.
  • Debugging techniques: Using `pdb` and logging for error tracking.
  • Lab: Write unit tests for a Python project using `pytest` and practice debugging techniques.

Functional Programming in Python

  • Understanding the functional programming paradigm in Python.
  • Using higher-order functions: `map()`, `filter()`, `reduce()`, and `lambda` functions.
  • Working with immutability and recursion.
  • Introduction to Python’s `functools` and `itertools` libraries for advanced functional techniques.
  • Lab: Solve real-world problems using functional programming principles.

Concurrency and Parallelism

  • Introduction to concurrent programming in Python.
  • Using threading and multiprocessing for parallel tasks.
  • Asynchronous programming with `asyncio` and coroutines.
  • Comparing synchronous vs asynchronous workflows: When to use each.
  • Lab: Build a program that handles multiple tasks concurrently using `asyncio` and threading.

Data Science and Visualization with Python

  • Introduction to NumPy for numerical computing.
  • Pandas for data manipulation and analysis.
  • Visualizing data with Matplotlib and Seaborn.
  • Exploratory data analysis (EDA) using real-world datasets.
  • Lab: Perform data analysis and visualization on a dataset using Pandas and Matplotlib.

Web Development with Python

  • Introduction to web development frameworks: Flask vs Django.
  • Building RESTful APIs with Flask/Django.
  • Connecting to databases using SQLAlchemy (Flask) or Django ORM.
  • Best practices for securing web applications.
  • Lab: Create a RESTful API with Flask/Django and interact with it using Python.

Automation and Scripting

  • Introduction to scripting for automation (shell scripts, cron jobs).
  • Automating repetitive tasks with Python.
  • Interacting with system processes using `subprocess` and `os` modules.
  • Working with Python for network automation and web scraping.
  • Lab: Write scripts to automate tasks like file handling, data extraction, and network operations.

Packaging, Version Control, and Deployment

  • Introduction to Python packaging: `setuptools` and `wheel`.
  • Creating and publishing Python packages (PyPI).
  • Version control with Git: Managing and collaborating on Python projects.
  • Deploying Python applications: Using Docker and cloud platforms.
  • Lab: Package a Python project and deploy it using Docker and Git.

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