Lesson 1: Setting Up Python Development Environment in VS Code

Introduction

Welcome to the first lesson of our course: "Learn how to use Python within VS Code, efficiently build Python applications, and deploy them to live websites." In this lesson, we'll cover the essential steps to set up your Python development environment in Visual Studio Code (VS Code). Whether you're a seasoned developer or a beginner, a well-configured environment is crucial for efficient coding and debugging.

Prerequisites

Before we get started, make sure you have the following installed on your machine:

1. Python: Ensure you have Python 3.x installed.
2. VS Code: The latest version of Visual Studio Code.
3. Pip: Python's package installer should be installed. It usually comes with Python but check just in case.

Step-by-Step Setup Guide

Step 1: Install Python

If you haven't installed Python yet, download it from the official Python website. Follow the installation instructions provided on the site for your specific operating system (OS).

Step 2: Install VS Code

Download VS Code from the official Visual Studio Code website. Install it by following the instructions for your OS.

Step 3: Install the Python Extension

1. Open VS Code.
2. Go to the Extensions view by clicking on the Extensions icon in the Activity Bar on the side of the window or by pressing Ctrl+Shift+X.
3. In the search box, type Python and look for the extension provided by Microsoft.
4. Click Install.

Step 4: Verify Python Installation

1. Open a new terminal in VS Code by going to Terminal -> New Terminal.
2. Type python --version (or python3 --version on some systems) and press Enter.
3. You should see the Python version number. If not, ensure Python is added to your system's PATH.

Step 5: Create a Python Project

1. Open a new folder in VS Code by going to File -> Open Folder.
2. Create a new file in this folder named app.py.
3. Open app.py and add a simple Python script to test your setup:

print("Hello, VS Code!")

Step 6: Configure the Python Interpreter

1. Open the Command Palette by pressing Ctrl+Shift+P.
2. Type Python: Select Interpreter.
3. Choose the Python interpreter you installed. It might look like Python 3.x.x [location].

Step 7: Run Your Python Code

1. With app.py open, right-click anywhere in the editor window and select Run Python File in Terminal.
2. You should see "Hello, VS Code!" printed in the terminal.

Step 8: Install Additional Tools (Optional)

For enhanced development experience, consider installing additional tools:

• Linting: Tools like pylint can help you identify programming errors.
• Jupyter Notebooks: Useful for data science projects.
• Virtual Environments: Manage project-specific dependencies.

Install pylint:

pip install pylint

Enable linting in VS Code:

1. Open the Command Palette (Ctrl+Shift+P).
2. Type Python: Enable Linting.
3. Choose pylint.

Conclusion

By following these steps, you should now have a fully functional Python development environment in VS Code. This setup will streamline your development process, allowing you to write, debug, and run Python code efficiently. In the next lessons, we'll dive deeper into building Python applications and deploying them to live websites. Stay tuned!

Lesson 2: Optimizing VS Code for Efficient Python Development

Welcome to Lesson 2 of our course: "Learn how to use Python within VS Code, efficiently build Python applications, and deploy them to live websites." Now that you have your Python development environment set up in VS Code, it's time to dive into optimizing your setup for maximum productivity and efficiency.

Table of Contents

1. Introduction
2. Key Extensions for Python Development
   • Python Extension by Microsoft
   • Pylint
   • IntelliCode
   • Jupyter
3. Useful Configurations and Settings
   • Python Interpreter Configuration
   • Formatting and Linting
   • Code Snippets
4. Integrated Terminal and Debugger
   • Using the Integrated Terminal
   • Efficient Debugging with Breakpoints
   • Conditional Breakpoints and Watch Variables
5. Version Control Integration
   • Git Integration
   • Using GitLens
6. Tips for Productivity
   • Keyboard Shortcuts
   • Workflow Optimization

1. Introduction

Visual Studio Code (VS Code) is a powerful code editor that, when optimized correctly, can significantly boost your productivity in Python development. This lesson will guide you through essential extensions, configurations, and integrated tools that streamline your workflow and help you write efficient Python code.

2. Key Extensions for Python Development

Python Extension by Microsoft

The core Python extension developed by Microsoft is indispensable for Python development in VS Code. It provides features like IntelliSense (code completion), linting, debugging, and more.

Pylint

Pylint is a popular linting tool that helps to identify errors and enforce a coding standard in your Python code. It integrates seamlessly with VS Code.

IntelliCode

IntelliCode enhances your development workflow by providing AI-assisted IntelliSense. It suggests code completions based on your coding patterns, making writing repetitive code faster and more accurate.

Jupyter

For data science and machine learning projects, the Jupyter extension allows you to work with .ipynb notebooks directly within VS Code. It supports running cells, inline visualizations, and exporting notebooks.

3. Useful Configurations and Settings

Python Interpreter Configuration

Ensure that your Python interpreter is properly configured. You can select the interpreter by invoking the Command Palette (Ctrl+Shift+P or Cmd+Shift+P on macOS) and selecting Python: Select Interpreter.

Formatting and Linting

Configuring auto-formatting tools like black and isort, and linters like Pylint can enhance code consistency. Add these settings to your settings.json for a seamless experience:

{
    "python.formatting.provider": "black",
    "editor.formatOnSave": true,
    "python.linting.pylintEnabled": true,
    "python.linting.enabled": true
}

Code Snippets

Custom code snippets can save time. Define useful snippets for common patterns or boilerplate code in the snippets directory within your workspace or in global settings.

4. Integrated Terminal and Debugger

Using the Integrated Terminal

VS Code's integrated terminal allows you to execute commands within the context of your workspace. Open the terminal using Ctrl+ (backtick) or from the View menu.

Efficient Debugging with Breakpoints

Set breakpoints in your code by clicking in the left margin next to the line number. Launch the debugger using the Run and Debug icon or F5.

Conditional Breakpoints and Watch Variables

For more nuanced debugging, use conditional breakpoints by right-clicking an existing breakpoint and providing an expression. Watch variables to monitor their values during debugging sessions, accessible from the Debug sidebar.

5. Version Control Integration

Git Integration

VS Code has built-in Git support. Access it from the Source Control view (icon with the branch). Common git commands like commit, pull, push, and branch management are easily accessible.

Using GitLens

GitLens supercharges the built-in Git capabilities. It provides insights into your repositories such as blame annotations and commit history, within the editor.

6. Tips for Productivity

Keyboard Shortcuts

Mastering VS Code's keyboard shortcuts can greatly enhance your efficiency. For example:

• Ctrl+P: Quick Open
• Ctrl+Shift+F: Find in files
• Ctrl+B: Toggle Sidebar

Workflow Optimization

Consistent workspace settings per project, leveraging tasks.json for frequent tasks automation, and using VS Code's multi-root workspaces can optimize your workflow.

This concludes Lesson 2. By optimizing VS Code with the aforementioned tools and configurations, you'll find your Python development process to be significantly streamlined. In our next lesson, we will explore advanced debugging techniques in VS Code. Happy coding!

Lesson #3: Building and Testing Your Python Application

Welcome to Lesson #3 of our course on using Python within VS Code. In this lesson, we'll cover the essential practices for building and testing your Python applications efficiently. Proper build and test processes ensure your applications are robust, maintainable, and ready for deployment.

Building Your Python Application

Structuring Your Code

A well-structured project is crucial for maintainability and scalability. Here's a common project structure:

your_project/
│
├── your_app/
│   ├── __init__.py
│   ├── module1.py
│   ├── module2.py
│   └── ...
│
├── tests/
│   ├── __init__.py
│   ├── test_module1.py
│   ├── test_module2.py
│   └── ...
│
├── .gitignore
├── README.md
└── requirements.txt

• your_app/: Contains the main source code.
• tests/: Contains test cases for your application.
• .gitignore: Specifies files and directories to ignore in version control.
• README.md: Provides an overview and usage instructions for your project.
• requirements.txt: Lists the dependencies required by your project.

Managing Dependencies

Managing dependencies ensures your application has all the necessary packages for execution. Use requirements.txt to list dependencies:

flask==2.0.1
requests==2.25.1
numpy==1.21.0

Create this file using the following command:

pip freeze > requirements.txt

To install dependencies from this file:

pip install -r requirements.txt

Using Virtual Environments

It's essential to run your application in a virtual environment to manage dependencies and avoid conflicts. Here’s how to set up and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

Deactivate the virtual environment using:

deactivate

Writing Tests for Your Python Application

Testing is a vital aspect of software development. It ensures your code performs as expected and helps identify bugs early. We'll cover unit testing using the unittest framework.

Creating Unit Tests

A typical unit test might look like this in test_module1.py:

import unittest
from your_app.module1 import add

class TestModule1(unittest.TestCase):

    def test_add(self):
        self.assertEqual(add(2, 3), 5)
        self.assertEqual(add(-1, 1), 0)
        self.assertEqual(add(-1, -1), -2)

if __name__ == '__main__':
    unittest.main()

Running Tests

In VS Code, you can run tests using the integrated terminal or a dedicated testing extension. Here’s how you can run tests using the command line:

python -m unittest discover -s tests

Mocking

For more complex applications, you may need to mock objects and functions. The unittest.mock module provides tools to mock objects in unit tests.

Example:

from unittest.mock import patch
from your_app.module1 import fetch_data

class TestModule1(unittest.TestCase):

    @patch('your_app.module1.requests.get')
    def test_fetch_data(self, mock_get):
        mock_get.return_value.status_code = 200
        self.assertEqual(fetch_data('http://example.com'), 'Success')

Continuous Integration (CI)

Integrating a CI tool ensures that every change in your codebase is tested and validated automatically. Popular CI services include GitHub Actions, Travis CI, and CircleCI.

Example of GitHub Actions Workflow

Create a .github/workflows/python-app.yml file in your project repository:

name: Python application

on: [push]

jobs:
  build:

    runs-on: ubuntu-latest

    steps:
    - uses: actions/checkout@v2
    - name: Set up Python
      uses: actions/setup-python@v2
      with:
        python-version: '3.x'
    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        pip install -r requirements.txt
    - name: Run tests
      run: |
        python -m unittest discover -s tests

Conclusion

Efficiently building and testing your Python application ensures high code quality and robustness. Here's a quick recap:

• Utilize a proper project structure.
• Manage dependencies through virtual environments and requirements.txt.
• Write comprehensive unit tests using unittest.
• Employ mocking for complex scenarios.
• Use CI tools to automate your build and test process.

In our next lessons, we will dive deeper into deploying your application to live websites. Keep up the excellent work, and let's build something amazing!

Lesson 4: Version Control and Collaboration with Git

Introduction

Version control systems are essential tools for managing and collaborating on projects. Git is a distributed version control system that allows multiple developers to work on the same codebase without overwriting each other's work. This lesson will cover the basics of using Git for versioning and collaboration.

Why Version Control?

• Track Changes: Version control systems keep a history of changes, allowing developers to see what changes were made, by whom, and when.
• Collaboration: Multiple developers can work on different parts of a project simultaneously and merge their work seamlessly.
• Branching and Merging: Branching enables developers to work on features or fixes independently, and merging allows these branches to be integrated back into the main codebase.
• Backup: A remote repository can act as a backup of the project.

Basic Git Commands

Initializing a Repository

To start using Git within a project, you need to initialize a repository.

git init

Cloning a Repository

To start working on an existing repository, you need to clone it.

git clone 

Committing Changes

1. Stage Changes: Add files to the staging area.

   git add 

   To add all changes:

   git add .

2. Commit Changes: Commit the staged changes with a message.

   git commit -m "Commit message describing the change"

Viewing History

To view the commit history:

git log

Branching and Merging

1. Create a Branch:

   git checkout -b 

2. Switching Between Branches:

   git checkout 

3. Merging Branches:

   git checkout main
   git merge 

Collaboration with Remote Repositories

1. Pushing Changes: Send committed changes to the remote repository.

   git push origin 

2. Fetching Updates: Fetch changes from the remote repository without merging.

   git fetch

3. Pulling Changes: Fetch and merge changes from the remote repository.

   git pull

Real-Life Example: Collaborative Feature Development

Consider a scenario where two developers are working on a website feature. Developer A focuses on the front-end UI, while Developer B works on back-end logic.

1. Both start by cloning the main repository.

   git clone https://github.com/example/project.git

2. Developer A creates a branch named frontend-feature.

   git checkout -b frontend-feature

3. Developer B creates a branch named backend-feature.

   git checkout -b backend-feature

4. Developer A makes changes and commits them.

   git add .
   git commit -m "Initial UI implementation"

5. Developer B does the same for their changes.

   git add .
   git commit -m "Backend logic implementation"

6. Both developers push their branches to the remote repository.

   git push origin frontend-feature
   git push origin backend-feature

7. A code review and testing are performed. Once done, each branch is merged back into the main branch.

   git checkout main
   git merge frontend-feature
   git merge backend-feature

8. Finally, the main branch is pushed to update the remote repository.

   git push origin main

Best Practices

• Commit Frequently: Make small, frequent commits with descriptive messages.
• Use Branches: Isolate features, bug fixes, and experiments in separate branches.
• Code Reviews: Ensure all code is reviewed before merging into the main branch.
• Stay Updated: Regularly fetch and pull changes to stay up-to-date with the main repository.
• Resolve Conflicts: Address merge conflicts immediately to keep the codebase clean.

Conclusion

Understanding and efficiently using Git for version control and collaboration is vital for modern software development. The techniques covered in this lesson will enable you to manage your codebase effectively and work seamlessly with other developers. By adhering to best practices, you can ensure that your projects are well-organized and maintainable.

Deploying Your Python Application to a Live Website

Welcome to Lesson #5! In this lesson, we will focus on deploying your Python application to a live website. By the end of this tutorial, you should be able to understand the concepts and steps necessary to take your application live. Let's get started!

Table of Contents

1. Introduction to Web Deployment
2. Choosing a Hosting Service
3. Preparing Your Application for Deployment
4. Deployment Process
5. Handling Post-Deployment

1. Introduction to Web Deployment

Web deployment is the process of making your application available on a web server, accessible via a domain name or IP address. This involves transferring your code and setting up various configurations.

2. Choosing a Hosting Service

Different applications have varying requirements, so you must choose the right hosting environment. Some popular options include:

• Shared Hosting: Suitable for small projects with low traffic.
• Virtual Private Server (VPS): Offers more control and scalability.
• Platform-as-a-Service (PaaS): Services like Heroku, AWS Elastic Beanstalk, and Google Cloud Platform manage most of the setup for you.

For simplicity, let's consider deploying on Heroku.

3. Preparing Your Application for Deployment

Here are key steps to prepare your Python application:

Project Structure

Organize your project files in a standard structure:

/myapp
  /app
    __init__.py
    views.py
    models.py
  /static
  /templates
  run.py
  requirements.txt
  Procfile

Virtual Environment

Ensure your virtual environment is ready and all dependencies are listed in requirements.txt.

pip freeze > requirements.txt

Configuration Files

• Procfile: Tells Heroku how to run your application:

  web: python run.py

• runtime.txt: Specifies Python version:

  python-3.9.1

Database

If using a database, adjust settings to connect to the production database. For example, configure your application to use PostgreSQL on Heroku.

4. Deployment Process

Deploying to Heroku involves several steps:

Step 1: Install the Heroku CLI

Install the Heroku Command Line Interface (CLI) if you haven't:

brew tap heroku/brew && brew install heroku

Step 2: Login to Heroku

heroku login

Step 3: Create a New Application

heroku create myapp

Step 4: Configure Environment Variables

Set up any necessary environment variables:

heroku config:set SECRET_KEY=mysecretkey

Step 5: Deploying Code

Initialize a Git repository, add Heroku as a remote, and deploy:

git init
git add .
git commit -m "Initial commit"
git remote add heroku https://git.heroku.com/myapp.git
git push heroku master

Step 6: Scale the App

Make sure your application is running:

heroku ps:scale web=1

5. Handling Post-Deployment

Logging

Monitor logs for errors or issues:

heroku logs --tail

Maintenance

• Regularly update dependencies.
• Monitor application performance.
• Backup databases on schedule.
• Apply security patches promptly.

Conclusion

Deploying a Python application to a live website might seem daunting at first, but with careful preparation and the right tools, the process becomes straightforward. Now you have a better understanding of choosing a hosting service, preparing your application, and executing the deployment. With practice, deploying applications can become a natural part of your development workflow.

Happy coding and deploying!