---
title: How to Use the New O4 Mini for Data Visualization
date: "2025-04-20T09:44:27Z"
lastmod: "2025-04-20T09:44:29Z"
categories:
  - llms
  - visualisation
wp_id: 4050
description: "O4 Mini can replace quick Excel-style workflows by turning raw text data into iteratively refined charts in minutes, letting the human focus on interpretation instead of implementation."
keywords: [O4 Mini, data visualization, iterative charts, Excel replacement, quick analysis, LLMs]
---

![How to Use the New O4 Mini for Data Visualization](/blog/assets/output-3.webp)

[O3/O4 Mini](https://openai.com/index/introducing-o3-and-o4-mini/) are starting to replace Excel (or Tableau/Power BI) for quick analysis and visualizations. At least for me.

I normally open Excel when I need a fast chart or pivot. For instance, we track [outages](https://llmfoundry.site24x7statusiq.com/) of our semi‑internal server, [LLM Foundry](https://llmfoundry.straive.com/).

To grab the data I ran one line in the browser console:

```javascript
$$(".lh-base").map(d => d.textContent.trim()).filter(d => d.includes("From"));
```

This produced lines like:

```text
Apr 20, 2025 03:11:27 PM +08 to Apr 20, 2025 03:27:12 PM +08 (15 mins 45 secs)
Apr 19, 2025 10:03:15 PM +08 to Apr 19, 2025 10:05:45 PM +08 (2 mins 30 secs)
Apr 19, 2025 09:47:13 PM +08 to Apr 19, 2025 09:49:45 PM +08 (2 mins 32 secs)
Apr 19, 2025 08:49:00 PM +08 to Apr 19, 2025 08:51:51 PM +08 (2 mins 51 secs)
Apr 19, 2025 08:13:02 PM +08 to Apr 19, 2025 08:15:35 PM +08 (2 mins 33 secs)
...
```

Then [I told O4-Mini-High](https://chatgpt.com/share/6804bbef-149c-800c-ac74-5d2d1fad1273):

> Here are downtimes for llmfoundry.straive.com.
> Convert this to CSV and allow me to download it.
> Also, draw the downtimes on a grid, rows=hour of day, columns=date,
> cell contains 1 circle per outage in that time period,
> size of each circle is based on the duration of the outage.
>
> Apr 20, 2025 03:11:27 PM +08 to Apr 20, 2025 03:27:12 PM +08 (15 mins 45 secs)
> Apr 19, 2025 10:03:15 PM +08 to Apr 19, 2025 10:05:45 PM +08 (2 mins 30 secs)
> Apr 19, 2025 09:47:13 PM +08 to Apr 19, 2025 09:49:45 PM +08 (2 mins 32 secs)
> … (rest of the data - about 50 rows)

![](/blog/assets/output-711x1024.webp)

Here's the power of what a model like [O4 Mini High](https://chatgpt.com/?model=o4-mini-high) can do.

1. It can **reason**. So, it planned an approach. (Convert to CSV, transform into date and hour. create a grid-based plot, use a pandas DataFrame, save it to a CSV, etc.)
2. It can **code**. It is pretty good at coding, and this is not too hard a problem, so it got the code right in one shot.
3. I can **run code**. This is a powerful step. It executed the code and produced the visualization above.

All of this took **less than one minute**.

I did not look at the **code**. I just focused on the picture and suggested changes.

> This draws crosses, not circles, for each hour. Also, if there are multiple outages in an hour, I want multiple circles.

Here's the output that took less than 10 seconds:

![](/blog/assets/output-1-1024x949.webp)

Next iteration:

> Make the circles red with the same level of transparency.
> Set the title to "LLM Foundry Downtime (SGT)".
> Instead of jittering the circle, let the Y position be the middle of the outage time.

![](/blog/assets/output-2-891x1024.webp)

Next iteration:

> Change the red to a milder shade.
> Set alpha to 0.5 but add a stroke with alpha 0.9.
> Format the dates like "Sun 20 Apr", etc.

![](/blog/assets/output-3-891x1024.webp)

That's it! I never even looked at the code. The whole loop took 3 minutes - far faster than I could manage, though I'm good at code **and** data visualization!

More importantly, the model frees me to focus on the **real** problem, which is **why is the downtime high?**