AI with GitHub Copilot for Python & Django REST Developers

Learn how GitHub Copilot, an AI pair programmer powered by OpenAI Codex, integrates with popular IDEs to provide context-aware code completions, snippets, debugging steps, and code documentation across languages.

Explore the limitations of GitHub Copilot, including code quality, context accuracy, internet dependency, prompt engineering, and licensing concerns, and learn to review, test, and secure generated code.

Set up GitHub Copilot in Visual Studio Code. Obtain an individual subscription with a free one-month trial, then install GitHub Copilot and Copilot chat extensions and sign in with GitHub.

Learn to generate Python code with GitHub Copilot in Visual Studio Code using an inline chat and Python comments to create and run a factorial function.

Learn to use GitHub Copilot for code explanations and debugging inside the editor, including a line-by-line recursion explanation of a factorial function and inserting fixes directly into your file.

Review and refactor code using GitHub Copilot chat to improve a factorial function, replacing recursion with iteration for better memory use and cleaner variable names.

Create a Python task manager using a Task class and a TaskManager with add, list, and mark-as-done methods, including a string representation and Copilot guided explanations.

Learn how to add and remove a task with GitHub Copilot, which scans code and proposes a remove task method, then checks the list and handles not found cases.

Generate Python unit tests with Copilot, create a new test file, and validate task manager methods like add and list tasks, including failure scenarios.

Learn to use GitHub Copilot chat slash commands to get help, explain code, generate unit tests, create notebooks, and navigate VS Code plugins and documentation for Python and Django REST.

Work on a data science use case by analyzing a customer purchase data set with Copilot, loading, cleaning, exploring, visualizing, and generating insights using pandas, matplotlib, and seaborn.

Develop data insights by loading and cleaning customer purchases data with pandas, then explore, visualize with matplotlib and seaborn, and identify top 25% customers using quantiles, all guided by Copilot.

Build an end-to-end Django rest framework backend and React frontend with GitHub Copilot to manage patients and their clinical data, with a lab technician registering patients and recording measurements.

Use Copilot as an AI pair programmer for planning and design, shaping a microservice stack with Django REST framework, Python, Postgres or MySQL, and React Redux front end.

Create and configure a Django project and clinical app, establish a virtual environment, install Django, rest framework, and MySQL client, and configure settings for a MySQL database.

Set up the database using MySQL Workbench by executing create database clinical underscore data, then inspect tables with show tables; initial tables are empty until Django migrations create them later.

Create a restful api quickly with Copilot by defining Django models for patient and clinical data, with auto generated keys, foreign keys, and date fields, then generate serializers and viewsets.

Configure urls for the rest API using django rest framework routers to register patient and clinical data view sets and expose CRUD endpoints at clinical services/api/patients and clinical services/api/clinical data.

Run makemigrations and migrate to create patient and clinical data tables in the clinical app, verified by show tables in MySQL workbench.

Learn to run and test a Django REST app, start the server on port 8000, and use the clinical data API to manage patients and their BP entries.

Learn to create Django rest unit tests for a patient view set using api test case and GitHub Copilot, covering create, read, update, and delete operations.

Run unit tests in a simulated Django environment and fix field name mismatches. Leverage Copilot to adjust patient attributes first_name, last_name, and age, and correct put update payload.

Add a test method to cover the get all list endpoint using GitHub Copilot, creating a patient, asserting a 200 status and correct count, and validating multiple test cases.

Create a React app with npx create-react-app clinicals-app, then install react-router-dom, axios, and react-toastify for routing, API calls, and notifications. Start npm start and open it in VS Code.

Configure routing for three placeholder components rendering the home page with a patient list, enabling add patient and add clinicals via browser router and routes for /, /add-patient, and /add-clinicals/:patientId.

Implement the home page to fetch patient details with Axios, render them in an HTML table, and handle CORS issues as you connect a React frontend to a Django API.

Fix a cors error using Copilot by installing Django cors headers, updating settings with installed apps and middleware, enabling cors allow all origins, restarting the server, and confirming data renders.

Implement the add patient component with a three-field form (first name, last name, age), routing to the add patient screen, and an axios post call to create a patient.

Implement toast notifications in a React app using react-toastify to show a 'patient added successfully' popup after creating a patient, with configurable auto close and bottom-center positioning.

Retrieve the patient ID from the URL, fetch details with Axios to render the patient’s first name, last name, and age, then add clinical data.

build a form to gather component name and value, then submit via axios to the API/clinical-data endpoint with the patient id and display a toast on success.

Add back links to navigate to the home screen from the add patient and add clinical forms by wrapping the forms in a div and importing link from react-router-dom.

Style a single page app with Copilot to enhance a patient data table, apply borders, th/td styling, alternating and hover colors, align the table, and refine form controls.

Learn to dockerize a Django clinical services app, create a dockerfile and docker compose yaml to run two services (MySQL database and the Django app) with Copilot.

Create a requirements.txt by running pip freeze in your virtual environment, redirecting the output to requirements.txt to capture all libraries for the clinical services Django project.

Create a dockerfile for a Django app, install Python dependencies from requirements.txt, copy project files, set a work directory, and run the Django server.

Create a Docker Compose file to launch a MySQL database and a Django web app, configure environment variables, ports, and volumes, and run migrations before starting the server.

Launch docker compose up to start the django and mysql containers, pull images, and apply migrations. Access the clinical services api at localhost:8000 and test the patients endpoint.

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