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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 | 58 views

**Course Title:** Modern Python Programming: Best Practices and Trends **Section Title:** Introduction to Python and Environment Setup **Topic:** Install Python, set up a virtual environment, and write your first Python script. (Lab topic) **Introduction:** Welcome to the hands-on portion of our course, Modern Python Programming: Best Practices and Trends. In this lab, we will walk through the process of installing Python, setting up a virtual environment, and writing your first Python script. By the end of this lab, you will have a solid foundation in the basics of Python development and be ready to dive deeper into the world of Python programming. **Step 1: Installing Python** Before we begin, make sure you have Python installed on your system. You can download the latest version of Python from the official Python website: <https://www.python.org/downloads/> Follow the installation instructions for your operating system: * For Windows: <https://docs.python.org/3/using/windows.html> * For macOS: <https://docs.python.org/3/using/mac.html> * For Linux: <https://docs.python.org/3/using/unix.html> Once you have installed Python, verify the installation by opening a terminal or command prompt and typing `python --version`. This should display the version of Python you just installed. **Step 2: Setting up a Virtual Environment** A virtual environment is a self-contained Python environment that allows you to manage dependencies and isolate your project from other Python projects on your system. We will use the `venv` module, which is part of the Python standard library, to create a virtual environment. Open a terminal or command prompt and navigate to the directory where you want to create your virtual environment. Then, run the following command: ```bash python -m venv myenv ``` This will create a new virtual environment called `myenv`. To activate the virtual environment, run: * On Windows: ```bash myenv\Scripts\activate ``` * On macOS and Linux: ```bash source myenv/bin/activate ``` Once activated, your command prompt should indicate that you are now operating within the virtual environment. **Step 3: Writing Your First Python Script** With your virtual environment set up, let's write a simple Python script to test everything. Create a new file called `hello.py` and add the following code: ```python # hello.py def greet(name: str) -> None: """Prints a personalized greeting message.""" print(f"Hello, {name}!") if __name__ == "__main__": greet("World") ``` This script defines a function called `greet` that takes a name as input and prints out a greeting message. The `if __name__ == "__main__":` block is used to ensure that the `greet` function is only executed when the script is run directly. **Running Your Script** To run your script, navigate to the directory where you created the `hello.py` file and type: ```bash python hello.py ``` You should see the following output: ``` Hello, World! ``` Congratulations! You have just written and run your first Python script. **Key Takeaways:** * Python can be downloaded from the official Python website. * Virtual environments can be created using the `venv` module. * Activating a virtual environment allows you to manage dependencies for your project. * Python scripts can be run using the `python` command. **What's Next:** In the next topic, we will explore Python's built-in data types, including lists, tuples, dictionaries, and sets. This will lay the foundation for more advanced topics in the course. If you have any questions or need help with this lab, please leave a comment below. We'd be happy to assist you.
Course
Python
Best Practices
Data Science
Web Development
Automation

Setting Up Python and Writing Your First Script

**Course Title:** Modern Python Programming: Best Practices and Trends **Section Title:** Introduction to Python and Environment Setup **Topic:** Install Python, set up a virtual environment, and write your first Python script. (Lab topic) **Introduction:** Welcome to the hands-on portion of our course, Modern Python Programming: Best Practices and Trends. In this lab, we will walk through the process of installing Python, setting up a virtual environment, and writing your first Python script. By the end of this lab, you will have a solid foundation in the basics of Python development and be ready to dive deeper into the world of Python programming. **Step 1: Installing Python** Before we begin, make sure you have Python installed on your system. You can download the latest version of Python from the official Python website: <https://www.python.org/downloads/> Follow the installation instructions for your operating system: * For Windows: <https://docs.python.org/3/using/windows.html> * For macOS: <https://docs.python.org/3/using/mac.html> * For Linux: <https://docs.python.org/3/using/unix.html> Once you have installed Python, verify the installation by opening a terminal or command prompt and typing `python --version`. This should display the version of Python you just installed. **Step 2: Setting up a Virtual Environment** A virtual environment is a self-contained Python environment that allows you to manage dependencies and isolate your project from other Python projects on your system. We will use the `venv` module, which is part of the Python standard library, to create a virtual environment. Open a terminal or command prompt and navigate to the directory where you want to create your virtual environment. Then, run the following command: ```bash python -m venv myenv ``` This will create a new virtual environment called `myenv`. To activate the virtual environment, run: * On Windows: ```bash myenv\Scripts\activate ``` * On macOS and Linux: ```bash source myenv/bin/activate ``` Once activated, your command prompt should indicate that you are now operating within the virtual environment. **Step 3: Writing Your First Python Script** With your virtual environment set up, let's write a simple Python script to test everything. Create a new file called `hello.py` and add the following code: ```python # hello.py def greet(name: str) -> None: """Prints a personalized greeting message.""" print(f"Hello, {name}!") if __name__ == "__main__": greet("World") ``` This script defines a function called `greet` that takes a name as input and prints out a greeting message. The `if __name__ == "__main__":` block is used to ensure that the `greet` function is only executed when the script is run directly. **Running Your Script** To run your script, navigate to the directory where you created the `hello.py` file and type: ```bash python hello.py ``` You should see the following output: ``` Hello, World! ``` Congratulations! You have just written and run your first Python script. **Key Takeaways:** * Python can be downloaded from the official Python website. * Virtual environments can be created using the `venv` module. * Activating a virtual environment allows you to manage dependencies for your project. * Python scripts can be run using the `python` command. **What's Next:** In the next topic, we will explore Python's built-in data types, including lists, tuples, dictionaries, and sets. This will lay the foundation for more advanced topics in the course. If you have any questions or need help with this lab, please leave a comment below. We'd be happy to assist you.

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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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