issues

Would it be good to add a unique() method that mimics pandas?

import pandas as pd import xarray as xr pd.Series([0, 1, 1, 2]).unique() xr.DataArray([0, 1, 1, 2]).unique() # not implemented

Output: array([0, 1, 2]) AttributeError: 'DataArray' object has no attribute 'unique'

https://github.com/pydata/xarray/discussions/5865#discussioncomment-1636647

I wonder if it's possible to implement a built-in function like: da.str.format("%.2f") or xr.string_format(da, "%.2f)

To wrap: ``` import xarray as xr

da = xr.DataArray([5., 6., 7.]) das = xr.DataArray("%.2f") das.str % da

<xarray.DataArray (dim_0: 3)> array(['5.00', '6.00', '7.00'], dtype='<U4') Dimensions without coordinates: dim_0 ```

If I have a ds with time, lat, lon and I call the same command twice: python ds.to_zarr('test.zarr', append_dim='time') ds.to_zarr('test.zarr', append_dim='time') Can it raise an error since all the times already exist?

Kind of like: ```python import numpy as np import xarray as xr

ds = xr.tutorial.open_dataset('air_temperature') ds.to_zarr('test_air.zarr', append_dim='time') ds_tmp = xr.open_mfdataset('test_air.zarr', engine='zarr') overlap = np.intersect1d(ds['time'], ds_tmp['time']) if len(overlap) > 1: raise ValueError(f'Found overlapping values in datasets {overlap}') ds.to_zarr('test_air.zarr', append_dim='time') ```

Not sure if exactly bug and what performance implications there are but it'd be more user friendly if supported:

1.) comparing against datetime python import datetime import pandas as pd import xarray as xr ds = xr.Dataset(coords={'int': [0, 1, 2]}) ds['data'] = ('int', [0, 5, 6]) ds.coords['time'] = ('int', pd.date_range('2017-02-01', '2017-02-03')) ds = ds.where(ds['time'] > datetime.datetime(2017, 2, 2)) ds

TypeError: '>' not supported between instances of 'int' and 'datetime.datetime'

2.) pd.Timestamp python import datetime import pandas as pd import xarray as xr ds = xr.Dataset(coords={'int': [0, 1, 2]}) ds['data'] = ('int', [0, 5, 6]) ds.coords['time'] = ('int', pd.date_range('2017-02-01', '2017-02-03')) ds = ds.where(ds['time'] > pd.to_datetime('2017-02-02')) ds

This works though when converting to np.datetime64 python import datetime import pandas as pd import xarray as xr ds = xr.Dataset(coords={'int': [0, 1, 2]}) ds['data'] = ('int', [0, 5, 6]) ds.coords['time'] = ('int', pd.date_range('2017-02-01', '2017-02-03')) ds = ds.where(ds['time'] > pd.to_datetime(['2017-02-02']).values) ds

import xarray as xr import pandas as pd xr.DataArray(pd.date_range('2020-01-01', '2020-02-01').tolist() + [pd.NaT]).ffill('dim_0')

<xarray.DataArray (dim_0: 33)> array(['2020-01-01T00:00:00.000000000', '2020-01-02T00:00:00.000000000', '2020-01-03T00:00:00.000000000', '2020-01-04T00:00:00.000000000', '2020-01-05T00:00:00.000000000', '2020-01-06T00:00:00.000000000', '2020-01-07T00:00:00.000000000', '2020-01-08T00:00:00.000000000', '2020-01-09T00:00:00.000000000', '2020-01-10T00:00:00.000000000', '2020-01-11T00:00:00.000000000', '2020-01-12T00:00:00.000000000', '2020-01-13T00:00:00.000000000', '2020-01-14T00:00:00.000000000', '2020-01-15T00:00:00.000000000', '2020-01-16T00:00:00.000000000', '2020-01-17T00:00:00.000000000', '2020-01-18T00:00:00.000000000', '2020-01-19T00:00:00.000000000', '2020-01-20T00:00:00.000000000', '2020-01-21T00:00:00.000000000', '2020-01-22T00:00:00.000000000', '2020-01-23T00:00:00.000000000', '2020-01-24T00:00:00.000000000', '2020-01-25T00:00:00.000000000', '2020-01-26T00:00:00.000000000', '2020-01-27T00:00:00.000000000', '2020-01-28T00:00:00.000000000', '2020-01-29T00:00:00.000000000', '2020-01-30T00:00:00.000000000', '2020-01-31T00:00:00.000000000', '2020-02-01T00:00:00.000000000', 'NaT'], dtype='datetime64[ns]') Dimensions without coordinates: dim_0

```

~/anaconda3/envs/py3/lib/python3.7/site-packages/xarray/core/computation.py in apply_variable_ufunc(func, signature, exclude_dims, dask, output_dtypes, vectorize, keep_attrs, dask_gufunc_kwargs, args) 698 ) 699 --> 700 result_data = func(input_data) 701 702 if signature.num_outputs == 1:

~/anaconda3/envs/py3/lib/python3.7/site-packages/bottleneck/slow/nonreduce_axis.py in push(a, n, axis) 49 elif ndim == 0: 50 return y ---> 51 fidx = ~np.isnan(y) 52 recent = np.empty(y.shape[:-1]) 53 count = np.empty(y.shape[:-1])

TypeError: ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe'' ```