@dcherian Thanks! That's simple ;) However, the groupby_bins method is a little different from binned_statistic.

binned_statistic:

All but the last (righthand-most) bin is half-open. In other words, if bins is [1, 2, 3, 4], then the first bin is [1, 2) (including 1, but excluding 2) and the second [2, 3). The last bin, however, is [3, 4], which includes 4.

groupby_bins:

right (bool, default: True) – Indicates whether the bins include the rightmost edge or not. If right == True (the default), then the bins [1,2,3,4] indicate (1,2], (2,3], (3,4].

So, let's check this shorter example: ``` from scipy.stats import binned_statistic import numpy as np import xarray as xr

--- scipy method ---

x = np.arange(10) values = x*5 statistics, _, _ = binned_statistic(x, values, statistic='min', bins=10, range=(0, 10))

--- xarray method ---

x = xr.DataArray(x) values = xr.DataArray(values) bin_res = values.groupby_bins('dim_0', bins=np.linspace(0, 10, 10), right=False, include_lowest=True).min()

print('scipy: \n', statistics) print('xarray: \n', bin_res) ```

Output: ``` scipy: [ 0. 5. 10. 15. 20. 25. 30. 35. 40. 45.]

xarray: <xarray.DataArray (dim_0_bins: 9)> array([ 0, 10, 15, 20, 25, 30, 35, 40, 45]) Coordinates: * dim_0_bins (dim_0_bins) object [0.0, 1.111) ... [8.889, 10.0) ```

The scipy method has one more value ...

Summary

These produce the same results: binned_statistic(x, values, statistic='min', bins=10, range=(0, 10)) values.groupby_bins('dim_0', bins=np.linspace(0, 10, 11), right=False, include_lowest=True).min()

Output: scipy: [ 0. 5. 10. 15. 20. 25. 30. 35. 40. 45.] xarray: <xarray.DataArray (dim_0_bins: 10)> array([ 0, 5, 10, 15, 20, 25, 30, 35, 40, 45]) Coordinates: * dim_0_bins (dim_0_bins) object [0.0, 1.0) [1.0, 2.0) ... [9.0, 10.0)