For developers, the choice of an editor is a very personal one. You have idiosyncratic needs and wants that are unique to you as a developer. This book will help you learn how to customize Visual Studio Code to meet your needs and Python development workflow.

Visual Studio Code for Python Programmers

Visual Studio® Code for Python® Programmers

Preface

When you began your Python development journey, you were most likely introduced to Python's Integrated Development and Learning Environment (IDLE). IDLE's simplicity is ideal for newcomers but leaves much to be desired by those who are more comfortable with the language and are in need of an efficient and productive workflow. A range of code editors and integrated development environments (IDEs) are available for Python development—some for general development with multilanguage support (such as Atom or Sublime) and others built exclusively for Python (such as PyCharm). Selecting a development environment is a matter of personal preference. As an experienced programmer, you might have already tried a few editors and thus are aware of what features you most desire. If you're in need of an extensible code editor that provides ample flexibility, efficiency, and productivity for managing Python source code, then Visual Studio Code is well worth your consideration.

Getting Started

As mentioned in Chapter 1, “Getting Started,” support for most languages in Visual Studio Code, including Python, comes through means of extensions. To use Python, then, you must install the extension along with a suitable Python interpreter. Creating and running your first Hello World Python file sets the stage for upcoming features and examples explored in this book. As you will learn in this chapter, there are various ways to perform the same task, such as creating, saving, and executing programs. Consider using the approaches explored as guidance to determine which options are best for your programming style.

Hello World for Python

Visual Studio Code provides a number of standard editing features that work for all programming languages.

Editing Code

Managing source code within the context of a project requires being able to navigate and maintain files, work within the appropriate Python environment, and maintain changes with source control. Each of these tasks is available in Visual Studio Code as either standard functionality or with the installation of an extension. In this chapter, the core features for managing projects are explored in a chatbot application created with the ChatBotAI library. (To learn more about the ChatBotAI library, visit pypi.org/project/chatbotAI .) The application uses the Wikipedia library to search for information from Wikipedia articles. (To learn more about the Wikipedia library, visit pypi.org/project/wikipedia/ .) The exercises within this chapter are completed within the Wikipeida_Chatbot folder.

Managing Projects and Collaboration

When the terminal outputs an error while running a program, you can refer to the Problems panel in the editor to resolve the issue you're experiencing. However, not all bugs result in errors. It may be the case that your program executes successfully, but the output is not what you expected. When such a scenario occurs, the next step is to identify and remove errors in the program. This process is referred to as debugging .

Debugging

Visual Studio Code and the Python extension provide a great interface for testing within the Test Explorer view. You can use Visual Studio Code to work with unit tests written in unittest, pytest, and nose. This chapter walks through unit testing for unittest and pytest with a simple example from the Python Koans repository.

Unit Testing

A Jupyter Notebook provides a shell for performing computation and data analysis and is often used by data scientists and others in scientific fields. The notebook consists of cells, each of which is a multiline text input field. The output of the cell can be HTML, rich text, values, and charts, as well as tables. The Python extension for Visual Studio Code provides Jupyter Notebook support. You can open, create, and modify .ipynb files directly in the editor. Within Visual Studio Code, you can take advantage of all its editing and debugging features that aren't typically available for notebooks in a browser.

Jupyter Notebook

GitHub is a platform that provides hosting for software development and version control using Git. Ranked as the number-one collaboration tool in the Stack Overflow Developer Survey 2020, GitHub is used by many developers for maintaining repositories and collaborating with others. The platform provides an interface that mirrors Git commands that are used within the terminal. Although you could maintain repositories within the GitHub interface, the built-in source control features and GitHub Pull Requests and Issues extension brings the same GitHub features and functionality inside Visual Studio Code.

Using Git and GitHub with Visual Studio Code

Django is a free open-source Python web framework for developing web apps. The framework encourages rapid development by providing the architecture to create database-connected web apps and thus enabling developers to focus more on writing the app. The framework's primary strength is how it manages data models and database connectivity. Each model maps to a single database table that can be leveraged to store data for your app. By default, the database configuration uses SQLite. However, Django also supports the following relational databases: PostgreSQL, MariaDB, MySQL, and Oracle.

Deploy a Django App to Azure App Service with the Azure App Service Extension

Flask is a lightweight Web Server Gateway Interface (WSGI) web application framework. Often referred to as a microframework , Flask is designed to keep the core of the application simple and scalable. Though Flask offers suggestions, the framework does not enforce any dependencies or project layouts. Instead, developers have the ability to add new functionality to their app with extensions provided by the community.

Create and Debug a Flask App

A container is a reliable solution for running a project in any computing environment. Inside a container is a packaged project and its dependencies, which can be run anywhere. Thus, a containerized Python project enables you to run a single service or an entire application environment while keeping everything inside the container isolated from the host system. Attempting to run a project locally that isn't containerized may result in project dependencies that conflict with what's installed on your computer; you can anticipate this happening if you're working with a team of developers, as no two developers have the same computer configurations.

Create and Deploy a Container with Azure Container Registry and Azure App Service

Suppose you were tasked to develop a mobile e-commerce app to support your employer's omnichannel approach to engaging with customers. Like most e-commerce apps, customers can view products, make purchases, and manage their customer profile. Depending on how a customer engages with the app, a workflow is triggered, whether that's writing data to a data table or providing a notification to the warehouse to kick off the supply chain workflow. You can anticipate app usage to dynamically change and would, therefore, need a hosting solution to scale to meet customer demand. Not to mention, spending less time managing servers and configuring the flow of data would be ideal so that you can focus more on the business logic. Serverless computing provides such a solution.

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