id,node_id,number,title,user,state,locked,assignee,milestone,comments,created_at,updated_at,closed_at,author_association,active_lock_reason,draft,pull_request,body,reactions,performed_via_github_app,state_reason,repo,type
1044693438,I_kwDOAMm_X84-RMG-,5937,DataArray.dt.seconds returns incorrect value for negative `timedelta64[ns]`,2405019,closed,0,,,4,2021-11-04T12:05:24Z,2023-11-10T00:39:17Z,2023-11-10T00:39:17Z,CONTRIBUTOR,,,,"**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**:

<details><summary>Output of <tt>xr.show_versions()</tt></summary>

```
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.7 (default, May  6 2020, 04:59:01)
[Clang 4.0.1 (tags/RELEASE_401/final)]
python-bits: 64
OS: Darwin
OS-release: 19.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_GB.UTF-8
LANG: None
LOCALE: ('en_GB', 'UTF-8')
libhdf5: 1.10.4
libnetcdf: 4.6.2

xarray: 0.18.2
pandas: 1.3.4
numpy: 1.19.1
scipy: 1.5.0
netCDF4: 1.4.2
pydap: installed
h5netcdf: None
h5py: 2.9.0
Nio: None
zarr: 2.10.1
cftime: 1.5.1.1
nc_time_axis: None
PseudoNetCDF: None
rasterio: None
cfgrib: None
iris: None
bottleneck: 1.3.2
dask: 2021.09.1
distributed: 2021.09.1
matplotlib: 3.2.2
cartopy: 0.18.0
seaborn: 0.10.1
numbagg: None
fsspec: 2021.06.1
cupy: None
pint: 0.18
sparse: None
setuptools: 46.4.0.post20200518
pip: 21.1.2
conda: None
pytest: 6.0.1
IPython: 7.16.1
sphinx: None
```
</details>
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5937/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
798676024,MDU6SXNzdWU3OTg2NzYwMjQ=,4854,`bounds_error=True` ignored in 1D interpolation,2405019,closed,0,,,0,2021-02-01T20:17:34Z,2021-02-10T21:42:06Z,2021-02-10T21:42:06Z,CONTRIBUTOR,,,,"**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**:

<details><summary>Output of <tt>xr.show_versions()</tt></summary>

INSTALLED VERSIONS
------------------
commit: None
python: 2.7.15.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-957.27.2.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: None.None

xarray: 0.10.7
pandas: 0.23.0
numpy: 1.14.3
scipy: 1.1.0
netCDF4: None
h5netcdf: None
h5py: 2.7.1
Nio: None
zarr: None
bottleneck: 1.2.1
cyordereddict: None
dask: 0.17.5
distributed: 1.21.8
matplotlib: None
cartopy: None
seaborn: None
setuptools: 39.1.0
pip: 10.0.1
conda: 4.6.4
pytest: 3.5.1
IPython: 5.7.0
sphinx: 1.7.4



</details>
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4854/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
672662079,MDU6SXNzdWU2NzI2NjIwNzk=,4306,Indexing datetime broken with pandas 1.1.0,2405019,closed,0,,,2,2020-08-04T09:50:59Z,2020-08-04T09:54:46Z,2020-08-04T09:54:46Z,CONTRIBUTOR,,,,"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])
```

<details><summary>The `.sel` operation produces a KeyError in `pandas/core/indexes/datetimes.py`: </summary>
<pre>
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
</pre>
</details>

**Environment**:

<details><summary>Output of <tt>xr.show_versions()</tt></summary>
INSTALLED VERSIONS
------------------
commit: None
python: 3.8.5 | packaged by conda-forge | (default, Jul 24 2020, 01:06:20)
[Clang 10.0.1 ]
python-bits: 64
OS: Darwin
OS-release: 18.0.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_GB.UTF-8
LANG: None
LOCALE: en_GB.UTF-8
libhdf5: 1.10.5
libnetcdf: 4.6.3

xarray: 0.16.0
pandas: 1.1.0
numpy: 1.19.1
scipy: 1.5.2
netCDF4: 1.5.4
pydap: None
h5netcdf: None
h5py: None
Nio: None
zarr: None
cftime: 1.2.1
nc_time_axis: None
PseudoNetCDF: None
rasterio: None
cfgrib: None
iris: None
bottleneck: None
dask: 2.22.0
distributed: None
matplotlib: 3.3.0
cartopy: None
seaborn: None
numbagg: None
pint: None
setuptools: 49.2.0.post20200712
pip: 20.1.1
conda: None
pytest: 6.0.1
IPython: 7.16.1
sphinx: None
</details>

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.","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4306/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
607678694,MDU6SXNzdWU2MDc2Nzg2OTQ=,4010,Issue indexing by xarray's own time values + offset,2405019,closed,0,,,2,2020-04-27T16:20:34Z,2020-04-28T11:03:06Z,2020-04-28T08:20:16Z,CONTRIBUTOR,,,,"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
<!-- In order for the maintainers to efficiently understand and prioritize issues, we ask you post a ""Minimal, Complete and Verifiable Example"" (MCVE): http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports -->

```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](http://xarray.pydata.org/en/stable/time-series.html#datetime-indexing)?

#### Versions

<details><summary>Output of <tt>xr.show_versions()</tt></summary>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7 |Anaconda, Inc.| (default, Oct 23 2018, 19:16:44) 
[GCC 7.3.0]
python-bits: 64
OS: Linux
OS-release: 3.10.0-957.27.2.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8
libhdf5: 1.10.1
libnetcdf: 4.5.0

xarray: 0.15.1
pandas: 0.25.3
numpy: 1.15.4
scipy: 1.1.0
netCDF4: 1.4.0
pydap: None
h5netcdf: 0.7.4
h5py: 2.10.0
Nio: None
zarr: None
cftime: 1.0.2.1
nc_time_axis: None
PseudoNetCDF: None
rasterio: None
cfgrib: None
iris: 2.2.0
bottleneck: None
dask: 0.20.0
distributed: 1.24.0
matplotlib: 2.2.3
cartopy: 0.16.0
seaborn: 0.9.0
numbagg: None
setuptools: 46.1.3
pip: 10.0.1
conda: None
pytest: 5.3.2
IPython: 7.1.1
sphinx: None

</details>
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