Dynamic Programming with Python

Lab Steps

Connecting to the Python Web IDE
Exercise 1 - Dynamic Classes
Exercise 2 - Monkey Patching

The hands-on lab is part of this learning path

Ready for the real environment experience?

This is a long-running lab that you can pause for up to 1 hour

You can pause this lab for
up to 1h
Time Limit1h


Lab Overview

Knowing how to write code in a Pythonic style is an important skill that should be mastered!

This lab is designed to show you how to exploit various Python language features to produce code that is considered to be Pythonic - being clear, concise, readable and maintainable. 

  • Exercise 1 - DynamicClasses: Complete the code required to dynamically create classes at runtime using the built-in type() function
  • Exercise 2 - MonkeyPatching: Complete the code required to Monkey Patch a custom module - swapping out an existing function for an updated function at runtime

Note: Each exercise has a provided solution to consult with when needed.

Lab Objectives

Upon completion of this lab, you will be able to:

  • Write Python code that can dynamically generate new classes at runtime programmatically
  • Write Python code that can monkey patch existing Python code at runtime - thereby changing the behaviour of code 
  • Use the terminal to launch and debug Python scripts

You should:

  • Be comfortable with using a browser-based IDE

Lab Environment

This lab will start with the following AWS resources provisioned automatically for you:

  • A single EC2 instance, named ide.python.platform.instance, which will have a public IP address attached. This instance will host a web-based Python IDE (based on the Visual Code editor).

To achieve the lab end state, you will be walked through the process of:

  • Using your local browser, access the web-based Python IDE served from the ide.python.platform.instance
  • Completing the following lab exercises:
    • Exercise 1 - DynamicClasses
    • Exercise 2 - MonkeyPatching


About the Author
Learning paths179

Jeremy is a Content Lead Architect and DevOps SME here at Cloud Academy where he specializes in developing DevOps technical training documentation.

He has a strong background in software engineering, and has been coding with various languages, frameworks, and systems for the past 25+ years. In recent times, Jeremy has been focused on DevOps, Cloud (AWS, Azure, GCP), Security, Kubernetes, and Machine Learning.

Jeremy holds professional certifications for AWS, Azure, GCP, Terraform, Kubernetes (CKA, CKAD, CKS).

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