issues

Hi, I've recently come across what it seems to be a bit annoying bug. After adding an offset to the time dimension using pd.add_offset(), the wrong times are selected when using .sel(). I've come across this behavior in recent versions of xarray, starting from 0.12. I've now worked around it by using ds.assign_coords(time = ...). I'm not sure however if this is a possible bug in Xarray or Pandas. Below a minimum working example.

MCVE Code Sample

```python import xarray as xr import pandas as pd import numpy as np

temp = 15 + 8 * np.random.randn(2, 2, 3) lon = [[-99.83, -99.32], [-99.79, -99.23]] lat = [[42.25, 42.21], [42.63, 42.59]] ds = xr.Dataset({'temperature': (['x', 'y', 'time'], temp)}, coords={'lon': (['x', 'y'], lon), 'lat': (['x', 'y'], lat), 'time': pd.date_range('2014-09-01', periods=3,freq='MS'), })

seltime = ds.time[2] ds.time.values = pd.to_datetime(ds.time.values) + pd.DateOffset(months=2) python print(ds.sel(time=seltime).time) python <xarray.DataArray 'time' ()> array('2015-01-01T00:00:00.000000000', dtype='datetime64[ns]') Coordinates: time datetime64[ns] 2015-01-01 python print(seltime) python <xarray.DataArray 'time' ()> array('2014-11-01T00:00:00.000000000', dtype='datetime64[ns]') Coordinates: time datetime64[ns] 2014-11-01 ```

Expected Output

Expected output is that seltime and ds.sel(time=seltime).time are the same

Output of xr.show_versions()

files.zip Hi, I really like Xarray because it is doing a lot of work me. However, maybe in this case it was overdoing it a bit. When making a field average of some data (data.nc) I use the grid area as weights (gridarea.nc). However, after multiplying the data with the gridarea the grid changed without warning. The gridarea is constructed using 'cdo gridarea other_data.nc gridarea.nc'. The 'other_data.nc' in this case is from the same climate model and grid as data.nc, though from different simulations (and possibly post-processing). Below a reproducable example, with the nc files attached.

MCVE Code Sample

python import xarray as xr data = xr.open_dataset('data.nc') gridarea = xr.open_dataset('gridarea.nc') Then I check whether the grids are the same python print((data.lat == gridarea.lat).all()) print((data.lon == gridarea.lon).all()) python <xarray.DataArray 'lat' ()> array(True) <xarray.DataArray 'lon' ()> array(True) Then I multiply the data with the weights python new_data = data * gridarea ```python print(data)

<xarray.Dataset> Dimensions: (lat: 16, lon: 30, time: 1) Coordinates: * time (time) datetime64[ns] 1995-01-01T12:00:00 * lon (lon) float64 0.0 1.125 2.25 3.375 4.5 ... 29.25 30.38 31.5 32.62 * lat (lat) float64 71.21 70.09 68.97 67.85 ... 57.76 56.64 55.51 54.39 Data variables: U10M (time, lat, lon) float32 ... python print(new_data)

<xarray.Dataset> Dimensions: (lat: 12, lon: 30, time: 1) Coordinates: * lat (lat) float64 71.21 70.09 68.97 65.61 ... 58.88 57.76 56.64 55.51 * time (time) datetime64[ns] 1995-01-01T12:00:00 * lon (lon) float64 0.0 1.125 2.25 3.375 4.5 ... 29.25 30.38 31.5 32.62 Data variables: empty ``` Note the different latitude for both datasets

Expected Output

The expected output is to get a dataset with the same grid as the original dataset, however, the latitude has now length 12 i.s.o. length 16

Problem Description

After digging a bit more into this problem it turns out the grids are not identical. python print(data.lat == gridarea.lat) <xarray.DataArray 'lat' (lat: 12)> array([ True, True, True, True, True, True, True, True, True, True, True, True]) Coordinates: * lat (lat) float64 71.21 70.09 68.97 65.61 ... 58.88 57.76 56.64 55.51 whilst python print(data.lat.values == gridarea.lat.values) [ True True True False False True False True True True True True True True True False] So the 'problem' is that xarray identifies the values which are not identical (e.g. 67.84978441466984 i.s.o. 67.84978441466983) and throws them out. Hence my original check if the grids are the same returns 'True' because all the values not equal are already thrown out. For me this introduced a 'conclusion rewriting error', because I averaged the data straight after multiplying with the weights. Hence, I didn't catch it at first. I'm not sure if this is 'wanted behavior'. For me it isn't at least ;-)

Could it be possible to either - make xarray not throw out the unequal values when comparing data, or
- get a warning when you multiply two fields with the same dimensions (e.g ['time','lat','lon] and ['lat','lon'] and then it returns a different sized ['lat','lon']?

Output of xr.show_versions()