From the course: Enhancing Your Productivity as a Data Scientist with Generative AI
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Use case 8: Data cleaning copilot
From the course: Enhancing Your Productivity as a Data Scientist with Generative AI
Use case 8: Data cleaning copilot
- [Instructor] So knowing what data quality issues exist and what to do about them is great, but what's even better is to actually perform these actions and fix the issues. And this is where our data cleaning copilot comes in. Now, I'm still in the same code space, and if you're following along, switch to the 03_03b branch of this notebook. I'm heading over to 03_03e because this already includes the outputs. Now what are we going to do here? First of all, let's collapse the first use case. We don't need this right now and jump into the next one, use case 8. First of all, I'm loading my data here just as we did previously, and then also initializing Gemini, just as we did before. Now the new step is that we load in our dq_result.json that we just generated in the previous step. And just as a quick heads up, that's how it looks like. It does not contain any actual data, but instead, it's just a list of column names, whether they are relevant and what to do about the data quality…
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