“List some Python libraries that can be used for [Use Case] (that don't relate to the finance industry or algorithmic trading) and describe what they do.”

1. [A] Application

**Key Takeaways** - It’s important to match your application to your specific use case. Online environments like Jupyter notebooks have a much lower barrier to entry that installing developer tools like Visual Studio Code.

**Pro Tip** - Use this prompt with an AI of your choice:

“I’m thinking of performing [use case] using a library like [library]. Is this the library you’d suggest? If so, which platform (e.g Google Colab or Visual Studio Code) would you use to deploy it?”

1. [W] Workflow

**Key Takeaway -** Python become infinitely more powerful when added to day to day workflows. Have a think about where it can slot into yours.

## Putting it into practise

The best way to get concepts to stick is to put them into practise.

Try this:

1. Login to an AI of your choice (ChatGPT Pro or [Copilot](https://copilot.microsoft.com/) preferred as they’re better with coding)

2. Enter this prompt:“Using the Matplotlib library, generate some Python code that someone in corporate finance could use in Google Colab to get an immediate impression of the power of Python - Use dummy data within the code to avoid the need for data upload or data connection - Exclude anything to do with the finance industry or algorithmic trading.”3. Copy the code

4. Create a new Notebook in Google Colab (login using [this link](https://colab.research.google.com/))

5. Copy the code and hit play (top left)

P.S - Don’t forget to head over to www.techforfinance.com and sign up to Framework Friday for 1 actionable tech framework you can use to stay ahead of the game.

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