4 ms·
Not sure about the ethics of naming your project "<other project> AI" presumably without their endorsement? In general though, this is a good idea, definitely
by SilverBirch 3y ago
Not sure about the ethics of naming your project "<other project> AI" presumably without their endorsement?
In general though, this is a good idea, definitely some obstacles to overcome though - and I suspect many of them involve solving problems that have to be solved on the OpenAI side, not on the user side. For example "send my entire data frame to OpenAI" seems a bit like writing a blank cheque, at the least they could mitigate that on the user side by having a pandas_ai.estimate which could take a run command and analyze the tokens to come up with a cost estimate. The other thing is obviously you want to cache the dataframe on the OpenAI side but that's definitely going to be tricky for these devs.
I wonder if the authors have checked if the data analysis is actually correct too, I guess that the AI is generating the code, not the actual analysis so if the code is wrong you'll get the wrong analysis on the right data rather than the right analysis on the wrong data.
- laratied 3y agoIt is bullshit to call this pandas AI. I use pandas all the time and could give quite a bit of feedback but not when you are basically trying to conflate and leverage the hard work of another project for yourself. Not a big deal though because no one is going to use this tool as is.
- gventuri 3y agoHey SilverBirch, I'm the author of PandasAI. In terms of the naming, that's a very good point, and I must confess I didn't even consider that when I published the repo. It just made sense to me, since the goal was to add AI on the top of pandas, to call it "PandasAI" (pandas + AI). For it was more of a tribute to the library, but I totally get your point, and I thank you for pointing it out. I'm trying to reach out the pandas founder, and I'll definitely change the name of the repo if he prefers to. As of the quality of the output, we are constantly working on improving the quality of it. It provides good enough results in most of the cases, but it still suffers from some hallucinations, although we have already fixed many either programmatically or with prompt engineering. As of the dataframe, we don't pass the whole df, but only the head and 5 columns with shuffled data (also redacting the sensitive ones). Any questions, feel free to ask!