Given this test code below (it reconstructs how I receive my datasets so ignore the complicated part at the beginning, just look at the variable concat), a ValueError gets thrown.

``` import xarray as xr import numpy as np import pandas as pd import seaborn as sns # pandas aware plotting library from copy import deepcopy import time

np.random.seed(123)

def strTimeProp(start, end, format, prop): """Get a time at a proportion of a range of two formatted times.

start and end should be strings specifying times formated in the
given format (strftime-style), giving an interval [start, end].
prop specifies how a proportion of the interval to be taken after
start.  The returned time will be in the specified format.
"""

stime = time.mktime(time.strptime(start, format))
etime = time.mktime(time.strptime(end, format))

ptime = stime + prop * (etime - stime)

return time.strftime(format, time.localtime(ptime))

def randomDate(start, end): return strTimeProp(start, end, '%d/%m/%Y %I:%M %p', np.random.random())

n_stars = 100 stars = list(range(n_stars)) pointings_targets = {0: [0], 1: [1], 2: [2, 3]}

pointing_exposures = {0: {'iband': 30, 'ha1':34}, 1: {'iband': 29, 'ha1':31}, 2: {'iband': 24, 'ha2': 33}} detection_efficiency = 0.95

columns = list('abcdefghi')

pointings = list(pointings_targets.keys()) targets = [j for i in pointings_targets.values() for j in i] n_pointings = len(pointings) n_targets = len(targets)

what stars are in each pointing

pointing_stars = {} for pointing in pointings: pointing_stars[pointing] = pointings_targets[pointing] # contains the targets stars_to_choose_from = list(set(stars) - set(pointing_stars[pointing])) # other stars to choose chosen_stars = np.random.choice(stars_to_choose_from, np.random.randint(5, len(stars_to_choose_from)), replace=False).tolist() pointing_stars[pointing] += chosen_stars # list(set(chosen_stars) | set(pointing_stars[pointing])) # stars in a pointing

data = [] indices = [] for pointing, bands in pointing_exposures.items(): for band_name, n_exps in bands.items(): dates = sorted([randomDate('1/1/2015 1:00 AM', '30/1/2015 1:00 AM') for _ in range(n_exps)]) for i, date in enumerate(dates): stars_detected = np.random.choice(pointing_stars[pointing], int(detection_efficiency*len(pointing_stars[pointing])), replace=False) values = np.random.randn(len(stars_detected), len(columns)) table = pd.DataFrame(values, columns=columns, index=stars_detected) # as if from file table.index.name = 'objid' arr = table.to_xarray().to_array() indices.append((pointing, band_name, date)) data.append(arr) concat = xr.concat(data, pd.MultiIndex.from_tuples(indices, names=['pointing', 'band', 'date'])).to_dataset('variable')

o = concat.coords['objid'].values other = pd.DataFrame(np.random.normal(0, 1, size=(len(o), 2)), index=o, columns=['ra', 'dec']) other.index.name = 'objid' concat = concat.merge(other.to_xarray()) # dataframe concat.to_netcdf('test') ValueError Traceback (most recent call last) <ipython-input-3-bd4eb2732e50> in <module>() ----> 1 concat.to_netcdf('test2.cdf')

C:\Anaconda3\envs\test_xarray\lib\site-packages\xarray\core\dataset.py in to_netcdf(self, path, mode, format, group, engine, encoding, unlimited_dims) 975 return to_netcdf(self, path, mode, format=format, group=group, 976 engine=engine, encoding=encoding, --> 977 unlimited_dims=unlimited_dims) 978 979 def unicode(self):

C:\Anaconda3\envs\test_xarray\lib\site-packages\xarray\backends\api.py in to_netcdf(dataset, path_or_file, mode, format, group, engine, writer, encoding, unlimited_dims) 571 try: 572 dataset.dump_to_store(store, sync=sync, encoding=encoding, --> 573 unlimited_dims=unlimited_dims) 574 if path_or_file is None: 575 return target.getvalue()

C:\Anaconda3\envs\test_xarray\lib\site-packages\xarray\core\dataset.py in dump_to_store(self, store, encoder, sync, encoding, unlimited_dims) 914 915 store.store(variables, attrs, check_encoding, --> 916 unlimited_dims=unlimited_dims) 917 if sync: 918 store.sync()

C:\Anaconda3\envs\test_xarray\lib\site-packages\xarray\backends\common.py in store(self, variables, attributes, args, kwargs) 242 # All NetCDF files get CF encoded by default, without this attempting 243 # to write times, for example, would fail. --> 244 cf_variables, cf_attrs = cf_encoder(variables, attributes) 245 AbstractWritableDataStore.store(self, cf_variables, cf_attrs, 246 args, **kwargs)

C:\Anaconda3\envs\test_xarray\lib\site-packages\xarray\conventions.py in cf_encoder(variables, attributes) 1087 """ 1088 new_vars = OrderedDict((k, encode_cf_variable(v, name=k)) -> 1089 for k, v in iteritems(variables)) 1090 return new_vars, attributes

C:\Anaconda3\envs\test_xarray\lib\site-packages\xarray\conventions.py in <genexpr>(.0) 1087 """ 1088 new_vars = OrderedDict((k, encode_cf_variable(v, name=k)) -> 1089 for k, v in iteritems(variables)) 1090 return new_vars, attributes

C:\Anaconda3\envs\test_xarray\lib\site-packages\xarray\conventions.py in encode_cf_variable(var, needs_copy, name) 739 var = maybe_default_fill_value(var) 740 var = maybe_encode_bools(var) --> 741 var = ensure_dtype_not_object(var) 742 return var 743

C:\Anaconda3\envs\test_xarray\lib\site-packages\xarray\conventions.py in ensure_dtype_not_object(var) 707 data[missing] = fill_value 708 else: --> 709 data = data.astype(dtype=_infer_dtype(data)) 710 var = Variable(dims, data, attrs, encoding) 711 return var

ValueError: setting an array element with a sequence ```