I would like to implement a model based on some cleaned and prepared data set. I already have a bit of experience with PySpark, but from a data scientist's perspective it can be cumbersome to work with it. Therefore I would like to try Koalas. I have a lot of experience with Pandas and hope this API will help me to leverage my skills. My question is: does the Koalas library allow to use all Pandas machine learning libraries like Scikit-Learn, XGBoost, and TensorFlow ? As the Koalas API is relatively new, what draw-backs can be expected besides the lack of documentation ?
I think you have misunderstood the koalas library. You can say its Pandas on Distributed System. You can use Koalas similar to pandas. There are few drawbacks with respect to APIs which is documented in their docs and few articles already written on medium.
You can do your EDA and straight away use them in all the libraries you have mentioned.
Recent pandas 1.0 is faster compared to older versions. It also uses Numba Behind the scene.
Vaex is another library which is available and you can use for EDA but the api names are different from pandas.
Also you can use Modin and dask and use it like pandas with few limitations again.
Clearly these libraries have no dependencies on Sklearn, XGBOOST or TF. You can split train and run your model.