assign values from `xr.groupby_bins` to new `variable`

Code Sample, a copy-pastable example if possible

A “Minimal, Complete and Verifiable Example” will make it much easier for maintainers to help you: http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports

# Your code here
import pandas as pd
import numpy as np
import xarray as xr

time = pd.date_range('2010-01-01','2011-12-31',freq='M')
lat = np.linspace(-5.175003, -4.7250023, 10)
lon = np.linspace(33.524994, 33.97499, 10)
precip = np.random.normal(0, 1, size=(len(time), len(lat), len(lon)))

ds = xr.Dataset(
    {'precip': (['time', 'lat', 'lon'], precip)},
    coords={
        'lon': lon,
        'lat': lat,
        'time': time,
    }
)

variable = 'precip'

# calculate a cumsum over some window size
rolling_window = 3
ds_window = (
    ds.rolling(time=rolling_window, center=True)
    .sum()
    .dropna(dim='time', how='all')
)

# construct a cumulative frequency distribution ranking the precip values
# per month
rank_norm_list = []
for mth in range(1, 13):
    ds_mth = (
        ds_window
        .where(ds_window['time.month'] == mth)
        .dropna(dim='time', how='all')
    )
    rank_norm_mth = (
        (ds_mth.rank(dim='time') - 1) / (ds_mth.time.size - 1.0) * 100.0
    )
    rank_norm_mth = rank_norm_mth.rename({variable: 'rank_norm'})
    rank_norm_list.append(rank_norm_mth)

rank_norm = xr.merge(rank_norm_list).sortby('time')

# assign bins to variable xarray
bins = [20., 40., 60., 80., np.Inf]
decile_index_gpby = rank_norm.groupby_bins('rank_norm', bins=bins)
out = decile_index_gpby.assign()  # assign_coords()

Problem description

[this should explain why the current behavior is a problem and why the expected output is a better solution.]

I want to calculate the Decile Index - see the ex1-Calculate Decile Index (DI) with Python.ipynb.

The pandas implementation is simple enough but I need help with applying the bin labels to a new variable / coordinate.

Expected Output

<xarray.Dataset>
Dimensions:   (lat: 10, lon: 10, time: 24)
Coordinates:
  * time      (time) datetime64[ns] 2010-01-31 2010-02-28 ... 2011-12-31
  * lat       (lat) float32 -5.175003 -5.125 -5.075001 ... -4.7750015 -4.7250023
  * lon       (lon) float32 33.524994 33.574997 33.625 ... 33.925003 33.97499
Data variables:
    precip    (time, lat, lon) float32 4.6461554 4.790813 ... 7.3063064 7.535994
    rank_bin  (lat, lon, time) int64 1 3 3 0 1 4 2 3 0 1 ... 0 4 0 1 3 1 2 2 3 1

Output of xr.show_versions()

# Paste the output here xr.show_versions() here INSTALLED VERSIONS ------------------ commit: None python: 3.7.0 | packaged by conda-forge | (default, Nov 12 2018, 12:34:36) [Clang 4.0.1 (tags/RELEASE_401/final)] python-bits: 64 OS: Darwin OS-release: 18.2.0 machine: x86_64 processor: i386 byteorder: little LC_ALL: en_US.UTF-8 LANG: en_US.UTF-8 LOCALE: en_US.UTF-8 libhdf5: 1.10.4 libnetcdf: 4.6.2

xarray: 0.12.1 pandas: 0.24.2 numpy: 1.16.4 scipy: 1.3.0 netCDF4: 1.5.1.2 pydap: None h5netcdf: None h5py: 2.9.0 Nio: None zarr: None cftime: 1.0.3.4 nc_time_axis: None PseudonetCDF: None rasterio: 1.0.17 cfgrib: 0.9.7 iris: None bottleneck: 1.2.1 dask: 1.2.2 distributed: 1.28.1 matplotlib: 3.1.0 cartopy: 0.17.0 seaborn: 0.9.0 setuptools: 41.0.1 pip: 19.1 conda: None pytest: 4.5.0 IPython: 7.1.1 sphinx: 2.0.1

[rabernat]

If you just want different coordinates for the result of groupby_bins, you can pass the labels keyword. See example here: http://xarray.pydata.org/en/stable/groupby.html#binning

[tommylees112]

Perfect thankyou!