Srini Kadamati

I reproduced the error internally in this Colab Notebook: https://colab.research.google.com/drive/1SW5WxJgU5Y2ykmP0t793a5OE-LxKsw5H?authuser=1#scrollTo=sHSODwrsjwZ9

Hi @ardulat without metadata, this might be challenging to debug but let's try! - Are the synthetic distributions uniform for context columns and non-context columns? - How many columns fall...

Hi @ardulat apologize for the delay! It seems like there are a few issues here to discuss: **Uniform distribution for some of the time series columns** I will attempt to...

Hi @ardulat unfortunately I wasn't able to recreate any of these issues with my own fake dataset that has matching metadata as yours. It's likely my fake dataset is too...

Awesome @ardulat I'll take a look today and circle back! Full disclosure though, some (or all) of these might just be issues we need to open, track, and eventually address.

Hi @ardulat when I tried the PARSynthesizer workflow with the `train.csv` dataset you shared with me, I noticed that the `steps` column seemed to be float values (e.g. 6979.0, 5104.0,...

@ardulat I see what you mean now! Even if a column contains only integer values, if it has missing values then usually pandas will assign the float dtype to it....

@ardulat after chatting internally, it looks like the experience around float values in the synthetic data is our intended experience. In pandas, integer columns that contain missing values are converted...

Hi @PaudGS 👋 At the moment, our single table and multi table synthesizers are definitely a bit more mature than PARSynthesizer, our sequential synthesizer. So this difference alone might be...

Thanks for sharing this @PaudGS I'll leave this thread open for now so we can collect more examples here from the community and hopefully invest in improving the experience over...