Drop duplicate times in xarray

Question

I'm reading NetCDF files with open_mfdataset, which contain duplicate times. For each duplicate time I only want to keep the first occurrence, and drop the second (it will never occur more often). The problem is quite similar to this Pandas question, but none of the solutions provided there seem to work with Xarray.

To reproduce the problem:

import numpy as np
import netCDF4 as nc4
import xarray as xr

# Create example NetCDF files
for t in range(2):
    nc    = nc4.Dataset('test{}.nc'.format(t), 'w')
    dim_t = nc.createDimension('time', None)
    var_t = nc.createVariable('time', 'f8', ('time',))
    var_s = nc.createVariable('var', 'f8', ('time',))
    var_t.setncattr('units', 'hours since 2001-01-01 00:00:00')
    var_t[:] = t*5+np.arange(6)
    var_s[:] = t*5+np.arange(6)+t
    nc.close()

# Read with xarray
f = xr.open_mfdataset(['test0.nc', 'test1.nc'])

The times in the resulting dataset are:

array(['2001-01-01T00:00:00.000000000', '2001-01-01T01:00:00.000000000',
       '2001-01-01T02:00:00.000000000', '2001-01-01T03:00:00.000000000',
       '2001-01-01T04:00:00.000000000', '2001-01-01T05:00:00.000000000',
       '2001-01-01T05:00:00.000000000', '2001-01-01T06:00:00.000000000',
       '2001-01-01T07:00:00.000000000', '2001-01-01T08:00:00.000000000',
       '2001-01-01T09:00:00.000000000', '2001-01-01T10:00:00.000000000'], dtype='datetime64[ns]')

Is there an easy way to remove the second ocurance of 2001-01-01T05:00:00.000000000? The real-life problem deals with multi-dimensional NetCDF files, so switching to Pandas is no option.

[update] The closest I get is following this answer; that works for my simple example as long as Dask is not used, if the files contain Dask arrays I get the error:

'last' with skipna=True is not yet implemented on dask arrays

But I don't see where I can/have to set skipna.

Answer 1

I think xarray does not have its own method for this purpose, but the following works,

In [7]: _, index = np.unique(f['time'], return_index=True)

In [8]: index
Out[8]: array([ 0,  1,  2,  3,  4,  5,  7,  8,  9, 10, 11])

In [9]: f.isel(time=index)
Out[9]: 
<xarray.Dataset>
Dimensions:  (time: 11)
Coordinates:
  * time     (time) datetime64[ns] 2001-01-01 2001-01-01T01:00:00 ...
Data variables:
   var      (time) float64 dask.array<shape=(11,), chunksize=(6,)>

Answer 2

Apparently stackoverflow won't let me comment... I wanted to add to Keisuke's answer. You can also use the get_index() function to get a pandas index.

f.sel(time=~f.get_index("time").duplicated())