4 Ways You Can’t Use the ChatGPT Code Interpreter That’ll Disrupt Your Analytics | by Ken Jee | Medium

I don’t think the Code Interpreter is as big a leap as people are hyping it up to be. Data professionals should beware of these problems. Can there be a workaround for them?

Ken Jee

Towards Data Science

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The Power of the Code Interpreter

The Limitations of the Code Interpreter

• • • Database Inaccessibility

• • • Python Versioning Inhibitions

• • • Unavailable Libraries

• • • GPU Constraint

Getting Around These Issues

• • • A Temporary Hybrid Fix


ChatGPT will replace data analysts. At least that’s what it seems like based on what everyone’s saying about the introduction of the Code Interpreter. I’ve had access to it for the last month or so. And, honestly, I haven’t been that impressed.

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Before I talk about why I think the Code Interpreter is overrated and how you may overcome these flaws, I should at least give it some credit.

It’s a solid advancement and it’s useful in many different ways. It can do some pretty cool stuff with basic data and it can be helpful for people with no exposure to coding. Plus, it allows multiple types of data to be uploaded and it can be particularly handy when it comes to basic data cleaning with regular expressions and iterating on simple data visualizations.

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However, in my opinion, this is where the benefits really end. Don’t get me wrong, I still think ChatGPT is revolutionary overall. I just don’t think the Code Interpreter is as big a deal as people are making it out to be.

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