What happened:

For a negative timedelta64[ns] of 42 nanoseconds DataArray.dt.seconds returned a non-zero value (the returned value was 86399). When I pass in a positive 42 nanosecond timedelta64[ns] with the the TimeDeltaAccessor correctly returns zero. I would have expected both assertions in the example below to have passed, but the second fails. This seems to be a general issue with negative timedelta64[ns].

bash <xarray.DataArray 'seconds' (dim_0: 1)> array([0]) Dimensions without coordinates: dim_0 <xarray.DataArray 'seconds' (dim_0: 1)> array([86399]) Dimensions without coordinates: dim_0 Traceback (most recent call last): File "bug_dt_seconds.py", line 15, in <module> assert da.dt.seconds == 0 AssertionError

What you expected to happen: bash <xarray.DataArray 'seconds' (dim_0: 1)> array([0]) Dimensions without coordinates: dim_0 <xarray.DataArray 'seconds' (dim_0: 1)> array([0]) Dimensions without coordinates: dim_0

Minimal Complete Verifiable Example:

```python

coding: utf-8

import xarray as xr import numpy as np

number of nanoseconds

value = 42

da = xr.DataArray([np.timedelta64(value, "ns")]) print(da.dt.seconds) assert da.dt.seconds == 0

da = xr.DataArray([np.timedelta64(-value, "ns")]) print(da.dt.seconds) assert da.dt.seconds == 0 ```

Anything else we need to know?:

I've narrowed this down to the call to pd.Series(values.ravel()) in xarray.core.accessor_dt._access_through_series:

python ipdb> pd.Series(values.ravel()) 0 -1 days +23:59:59.999999958 dtype: timedelta64[ns]

I think the issue arises because pandas turns the numpy timedelta64 into a "minus one day plus a time". This actually does have a number of "seconds" in it, but the "total_seconds" has the expected value:

python ipdb> pd.Series(values.ravel()).dt.total_seconds() 0 -4.200000e-08 dtype: float64

Which would correctly round to zero.

I don't think the issue is in pandas, although the output from pandas is counter-intuitive:

python ipdb> pd.Series(values.ravel()).dt.seconds 0 86399 dtype: int64

Maybe we should handle this as a special case by taking the absolute value before passing the values to pandas (and then applying the original sign again afterwards)?

Environment:

Previously bounds_error was always passed as False to scipy.interpolate.interp1d and the value in kwargs was ignored making it impossible to get bounds errors when doing 1D interpolation.

• [x] Closes #4854
• [x] Tests added
• [ ] Passes pre-commit run --all-files
• [ ] User visible changes (including notable bug fixes) are documented in whats-new.rst
• [ ] New functions/methods are listed in api.rst

What happened:

Attempted to interpolate outside coordinate range while passing bounds_error=True through kwargs dictionary (which internally gets passed to scipy.interpolate.interp1d). Instead if interp function raising a ValueError nans are returned.

What you expected to happen:

I expected a ValueError exception to be raised when I've passed in the kwargs bounds_error=True.

Minimal Complete Verifiable Example:

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

da = xr.DataArray( np.sin(0.3 * np.arange(12).reshape(4, 3)), [("time", np.arange(4)), ("space", [0.1, 0.2, 0.3])], )

this should return nans, as the default is to fill with nans

da.interp(time=3.5)

<xarray.DataArray (space: 3)> array([nan, nan, nan]) Coordinates: * space (space) float64 0.1 0.2 0.3 time float64 3.5

this should raise ValueError, but bounds_error is ignored

da.interp(time=3.5, kwargs=dict(bounds_error=True))

<xarray.DataArray (space: 3)> array([nan, nan, nan]) Coordinates: * space (space) float64 0.1 0.2 0.3 time float64 3.5

```

Anything else we need to know?:

I've made a pull-request to fix the issue

Environment:

Code below works with pandas<=1.0.5 and is broken with the most recent version (pandas==1.1.0) independent of xarray version (tried 0.16.0 and 0.15.0)

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

dates = pd.date_range("2000-01-01", periods=5) ds = xr.Dataset(coords=dict(dates=dates)) ds['v'] = ("dates"), np.arange(ds.dates.count())

ds.sel(dates=ds.dates.values[2]) ```

Traceback (most recent call last):
  File "datetime_problem.py", line 11, in <module>
    ds.sel(dates=ds.dates.values[2])
  File "/Users/leifdenby/miniconda3/envs/lagtraj/lib/python3.8/site-packages/xarray/core/dataset.py", line 2101, in sel
    pos_indexers, new_indexes = remap_label_indexers(
  File "/Users/leifdenby/miniconda3/envs/lagtraj/lib/python3.8/site-packages/xarray/core/coordinates.py", line 396, in remap_label_indexers
    pos_indexers, new_indexes = indexing.remap_label_indexers(
  File "/Users/leifdenby/miniconda3/envs/lagtraj/lib/python3.8/site-packages/xarray/core/indexing.py", line 270, in remap_label_indexers
    idxr, new_idx = convert_label_indexer(index, label, dim, method, tolerance)
  File "/Users/leifdenby/miniconda3/envs/lagtraj/lib/python3.8/site-packages/xarray/core/indexing.py", line 189, in convert_label_indexer
    indexer = index.get_loc(
  File "/Users/leifdenby/miniconda3/envs/lagtraj/lib/python3.8/site-packages/pandas/core/indexes/datetimes.py", line 622, in get_loc
    raise KeyError(key)
KeyError: 946857600000000000

Environment:

Apologies if this is a know issue. I tried to work out whether this is an issue with pandas or xarray (I assume it is with pandas), but couldn't find the right piece of code. Happy to fix the issue if someone could show me what needs to change.

I'm struggling to work out how to index by a xarray time value + an offset (either created using np.timedelta64 or datetime.timedelta). I read through https://github.com/pydata/xarray/issues/1240 and https://github.com/pydata/xarray/issues/1240 because they appear related, but I'm not sure how to correctly achieve this.

MCVE Code Sample

```python import xarray as xr import numpy as np import datetime as dt

now = dt.datetime.now() dt_array = xr.DataArray( range(10), dims=('time', ), coords=dict(time=[now + dt.timedelta(seconds=i) for i in range(10)]) )

this works

dt_array.loc[dt_array.time.min():dt_array.time.max()].count() == 10

this fails, only the first value is returned (adding

the time delta appears to have no effect)

dt_array.loc[dt_array.time.min():dt_array.time.min() + np.timedelta64(seconds=4)].count() == 4

this fails, an exception is raised when trying to add

a datetime.timedelta to the xarray value

dt_array.loc[dt_array.time.min():dt_array.time.max() + dt.timedelta(seconds=4)].count() == 4

also fails, I got the impression from issue #1240

that .loc[...] should work for indexing too, but just to double-check

dt_array.sel(time=slice(dt_array.time.min(), dt_array.time.min() + np.timedelta64(seconds=4))).count() == 4

fails, showing that adding a time increment has no effect

dt_array.time.min() + np.timedelta64(seconds=10) != dt_array.time.min() ```

Expected Output

Where I am indexing by the minimum time plus a np.timedelta64 offset of 4 seconds I would expect a DataArray of length 4 to be return. It would be nice if it was possible to add an increment with a native datetime.timedelta object.

Problem Description

I can't work out how to correctly add an increment to a time value in an xarray DataArray. It would be nice if one of the above approaches worked. Or maybe if I'm missing something obvious I could add an example to the documentation on datetime-indexing?

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