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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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    infor@spinncode.com
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7 Months ago | 53 views

**Course Title:** Modern Python Programming: Best Practices and Trends **Section Title:** Object-Oriented Programming (OOP) in Python **Topic:** Implement a class-based system with inheritance and polymorphism.(Lab topic) **Objective:** In this lab, we will implement a class-based system that uses inheritance and polymorphism to demonstrate the power of Object-Oriented Programming (OOP) in Python. By the end of this exercise, you will have a solid understanding of how to design and implement a hierarchy of classes that inherit behavior from parent classes and override behavior using polymorphism. **Step 1: Define the Base Class** The base class is the foundation of our class-based system. It defines the common attributes and methods that are shared by all classes in the hierarchy. For this lab, let's create a simple base class called `Vehicle`: ```python # base_class.py class Vehicle: def __init__(self, make, model, year): self.make = make self.model = model self.year = year def description(self): return f"{self.year} {self.make} {self.model}" ``` **Step 2: Define Derived Classes** Derived classes inherit from the base class and add their own specific attributes and methods. For this lab, let's create two derived classes: `Car` and `Truck`. These classes will inherit from the `Vehicle` base class: ```python # derived_classes.py from base_class import Vehicle class Car(Vehicle): def __init__(self, make, model, year, num_doors): super().__init__(make, model, year) self.num_doors = num_doors def description(self): return f"{super().description()} with {self.num_doors} doors" class Truck(Vehicle): def __init__(self, make, model, year, bed_size): super().__init__(make, model, year) self.bed_size = bed_size def description(self): return f"{super().description()} with a {self.bed_size} bed" ``` **Step 3: Implement Polymorphism** Polymorphism is the ability of an object to take on multiple forms. In Python, we can achieve polymorphism using method overriding or method overloading. For this lab, let's use method overriding to demonstrate polymorphism. We will create a `Vehicle` method called `drive` that is overridden by the `Car` and `Truck` classes: ```python # base_class.py class Vehicle: ... def drive(self): return "The vehicle is moving" # derived_classes.py class Car(Vehicle): ... def drive(self): return "The car is driving" class Truck(Vehicle): ... def drive(self): return "The truck is hauling" ``` **Step 4: Test the System** Now that we have implemented the class-based system with inheritance and polymorphism, let's test it: ```python # test_system.py from derived_classes import Car, Truck car = Car("Toyota", "Corolla", 2020, 4) truck = Truck("Ford", "F-150", 2019, "6.5 ft") print(car.description()) print(car.drive()) print(truck.description()) print(truck.drive()) ``` This code will output: ``` 2020 Toyota Corolla with 4 doors The car is driving 2019 Ford F-150 with a 6.5 ft bed The truck is hauling ``` **Conclusion:** In this lab, we have successfully implemented a class-based system with inheritance and polymorphism. We have demonstrated how to define a base class and derived classes, and how to use method overriding to achieve polymorphism. These concepts are fundamental to Object-Oriented Programming (OOP) in Python. **Further Reading:** * [Python Documentation: Classes](https://docs.python.org/3/tutorial/classes.html) * [Python Documentation: Inheritance](https://docs.python.org/3/tutorial/classes.html#inheritance) * [Python Documentation: Polymorphism](https://docs.python.org/3/glossary.html#term-polymorphism) **What's Next:** In the next topic, we will learn about reading and writing files (text, CSV, JSON) with Python. This will include file handling, reading, and writing data to files. If you have any questions or need help with this topic, feel free to leave a comment or ask for help.
Course
Python
Best Practices
Data Science
Web Development
Automation

Implementing Inheritance and Polymorphism with Python Classes.

**Course Title:** Modern Python Programming: Best Practices and Trends **Section Title:** Object-Oriented Programming (OOP) in Python **Topic:** Implement a class-based system with inheritance and polymorphism.(Lab topic) **Objective:** In this lab, we will implement a class-based system that uses inheritance and polymorphism to demonstrate the power of Object-Oriented Programming (OOP) in Python. By the end of this exercise, you will have a solid understanding of how to design and implement a hierarchy of classes that inherit behavior from parent classes and override behavior using polymorphism. **Step 1: Define the Base Class** The base class is the foundation of our class-based system. It defines the common attributes and methods that are shared by all classes in the hierarchy. For this lab, let's create a simple base class called `Vehicle`: ```python # base_class.py class Vehicle: def __init__(self, make, model, year): self.make = make self.model = model self.year = year def description(self): return f"{self.year} {self.make} {self.model}" ``` **Step 2: Define Derived Classes** Derived classes inherit from the base class and add their own specific attributes and methods. For this lab, let's create two derived classes: `Car` and `Truck`. These classes will inherit from the `Vehicle` base class: ```python # derived_classes.py from base_class import Vehicle class Car(Vehicle): def __init__(self, make, model, year, num_doors): super().__init__(make, model, year) self.num_doors = num_doors def description(self): return f"{super().description()} with {self.num_doors} doors" class Truck(Vehicle): def __init__(self, make, model, year, bed_size): super().__init__(make, model, year) self.bed_size = bed_size def description(self): return f"{super().description()} with a {self.bed_size} bed" ``` **Step 3: Implement Polymorphism** Polymorphism is the ability of an object to take on multiple forms. In Python, we can achieve polymorphism using method overriding or method overloading. For this lab, let's use method overriding to demonstrate polymorphism. We will create a `Vehicle` method called `drive` that is overridden by the `Car` and `Truck` classes: ```python # base_class.py class Vehicle: ... def drive(self): return "The vehicle is moving" # derived_classes.py class Car(Vehicle): ... def drive(self): return "The car is driving" class Truck(Vehicle): ... def drive(self): return "The truck is hauling" ``` **Step 4: Test the System** Now that we have implemented the class-based system with inheritance and polymorphism, let's test it: ```python # test_system.py from derived_classes import Car, Truck car = Car("Toyota", "Corolla", 2020, 4) truck = Truck("Ford", "F-150", 2019, "6.5 ft") print(car.description()) print(car.drive()) print(truck.description()) print(truck.drive()) ``` This code will output: ``` 2020 Toyota Corolla with 4 doors The car is driving 2019 Ford F-150 with a 6.5 ft bed The truck is hauling ``` **Conclusion:** In this lab, we have successfully implemented a class-based system with inheritance and polymorphism. We have demonstrated how to define a base class and derived classes, and how to use method overriding to achieve polymorphism. These concepts are fundamental to Object-Oriented Programming (OOP) in Python. **Further Reading:** * [Python Documentation: Classes](https://docs.python.org/3/tutorial/classes.html) * [Python Documentation: Inheritance](https://docs.python.org/3/tutorial/classes.html#inheritance) * [Python Documentation: Polymorphism](https://docs.python.org/3/glossary.html#term-polymorphism) **What's Next:** In the next topic, we will learn about reading and writing files (text, CSV, JSON) with Python. This will include file handling, reading, and writing data to files. If you have any questions or need help with this topic, feel free to leave a comment or ask for help.

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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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