ibis

pandas is a great example of data science code at its best and its worst. If you look at the source code you will see every object and every function allows for way too many variations in input options and therefore about 20 conditional statements. For instance, I believe DataFrame's init method can take a dictionary, DataFrame, Series, etc. versus a class method for each one. Contrast that to requests where the public interface is a nice requests.get, requests.post. Yet, have a csv file you are only uploading once or twice to peak at? Then its super efficient. I think my biggest issue is all the effort that goes into pandas-like api's, i.e. https://github.com/ibis-project/ibis. To me, it doesn't make sense to take something stable and known (SQL) and build a complex DSL so it works like pandas.

Good to see I'm not crazy! I think this can be easier than maintain, and with tools like Datagrip now, seems to make a lot of sense. Coming from the data science side, I observed many talented people spending time on projects like https://github.com/cloudera/ibis to abstract SQL into something that I often thought was more verbose, less literal and less portible in reality.