Implement groupby for continuous dimensions

HoloViews supports a groupby operation for discrete/categorical dimensions, but as far as I can see there is no support for grouping over a continuous dimension, which requires a specified bin width. The xarray interface might provide this already (https://github.com/ioam/holoviews/issues/804), but for pandas the separate cut method would seem to be needed. Philipp suggests adding a method:

def groupby_bin(dataset, dimension, bins=10):
    dimension = dataset.get_dimension(dimension)
    values = dataset.dimension_values(dimension)
    other_dims = [d for d in dataset.kdims if d is not dimension]
    cats, bins = pd.cut(values, bins, retbins=True)
    hmap = hv.HoloMap(kdims=[dimension])
    for i in range(1, len(bins)):
        start, end = bins[i-1], bins[i]
        mid = np.mean([start, end])
        hmap[mid] = dataset.select(**{dimension.name: (start, end)}).reindex(other_dims)
    return hmap

but I have not tested this.

[TylerTCF]

Would this resample allow users to take a datetime kdim and aggregate to a different sampling frequency like hourly measurements being aggregated into daily, monthly, yearly intervals?

[philippjfr]

This can now technically be done with the new transform method:

ds = hv.Dataset(np.random.randn(1000, 3), ['x', 'y'], 'z')
ds.transform(x=hv.dim('x').bin(np.linspace(-1, 1, 11))).groupby('x').apply(hv.Scatter)