Superhash: binby, groupby, unique, value_counts and xarray support

[maartenbreddels]

This is the start of using hashmaps in vaex for

• groupby support
• binby support
• unique
• value_counts

Value count is 235x faster than the old implementation, 4.7x faster compared to pandas (benchmarked on 1e8 random integers between 0 and 99).

[maartenbreddels]

cc @rabernat

I've chosen to split 'groupby' into two:

• A 'traditional' groupby, that simply returns a new DataFrame,
• A new binby method, that returns an xarray DataArray, which is a much more friendly data structure to work with if you do higher dimensional 'groupbys'. Also, it seems like a perfect match for vaex, since it produces regular N-d arrays. It also makes visualization very easy to do:

[JovanVeljanoski]

I find this potential issue:

import vaex
import numpy as np

x = np.array(['a', 'a', 'a', 'b', 'b', 'c'], dtype=np.object)
ds = vaex.from_arrays(x=x)

ds.x.value_counts()

that returns

Series([], dtype: float64)

For big datasets this also sometimes kills the kernel, although I always reproduce it.

[maartenbreddels]

Due to fundamental issues with the aggregation code, this led to a complete refactor, which also gives a 20-100% speedup in certain cases for 1 or 2 binning for non-float (and even float in some cases).
This leaves a lot of legacy code in, but there are a few aggregations still using the old code path, once we fully migrated, we can cut out a lot of legacy code.
We'll do this in a separate PR.

[maartenbreddels]

I've squashed some commits (mainly CI debugging), but since it's a big change, I'm gonna merge this as is to have a fine grained history.