Author(s): John Loewen, PhD

Originally published on Towards AI.

Which framework reigns supreme in GPT-4, prompting simplicity?
Dall-E generated image: impresionist painting of two similar dashboards on two computer screens

For the past 8 months, I have been incessant on optimizing GPT-4 prompt engineering for Python data visualization code.

I now have a solid grasp on what works and what doesn’t when prompting for interactive data visualizations.

Two of the most used Python frameworks for multi-visual dashboards are Streamlit and Plotly dash. In my experience, GPT-4 has become quite good at handling Python code creation for multi-visual dashboards.

How good is each framework? Let’s put them side-by-side and prompt them with the same data set to create an interactive data-visual dashboard that has:

A dropdown menu (allowing the user to interact with the dataset)Two different types of maps (a choropleth map and a bubble map) that are updated based on the user’s choice.

And for each framework, let’s put GPT-4 to the test using a single prompt!

For this test, let’s use the UN food security data as it is up to date and provides a yearly snapshot of the global food security situation at the country level.

The UN food security dataset can be found HERE.

There are a few options to consider when downloading the dataset:

For the CSV file to download for this article,… Read the full blog for free on Medium.

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Published via Towards AI