issues: 483280810

Problem Description

While digging for #2511 I found that da.argmax() causes compute on a dask array in nanargmax(a, axis=None): https://github.com/pydata/xarray/blob/131f6022763d35edf461b11857d7c4ec6630b19d/xarray/core/nanops.py#L120 I feel like this shouldn't be the case as da.max() and da.data.argmax() don't compute and it renders the laziness useless.

MCVE Code Sample

```python In [1]: import numpy as np
...: import dask
...: import xarray as xr

In [2]: class Scheduler:
...: """ From: https://stackoverflow.com/questions/53289286/ """
...:
...: def init(self, max_computes=0):
...: self.max_computes = max_computes
...: self.total_computes = 0
...:
...: def call(self, dsk, keys, kwargs):
...: self.total_computes += 1
...: if self.total_computes > self.max_computes:
...: raise RuntimeError(
...: "Too many dask computations were scheduled: {}".format(
...: self.total_computes
...: )
...: )
...: return dask.get(dsk, keys, kwargs)
...:

In [3]: scheduler = Scheduler()

In [4]: with dask.config.set(scheduler=scheduler):
...: da = xr.DataArray(
...: np.random.rand(234).reshape((2, 3, 4)),
...: ).chunk(dict(dim_0=1))
...:
...: dim = da.dims[-1]
...:
...: dask_idcs = da.data.argmax(axis=-1) # Computes 0 times
...: print("Total number of computes: %d" % scheduler.total_computes)
...:
...: da.max(dim) # Computes 0 times
...: print("Total number of computes: %d" % scheduler.total_computes)
...:
...: da.argmax(dim) # Does compute
...: print("Total number of computes: %d" % scheduler.total_computes)
...:
Total number of computes: 0
Total number of computes: 0

---

RuntimeError Traceback (most recent call last)
<ipython-input-4-f95c8753dbe6> in <module>
12 print("Total number of computes: %d" % scheduler.total_computes)
13
---> 14 da.argmax(dim) # Does compute
15 print("Total number of computes: %d" % scheduler.total_computes)
16

~/src/xarray/xarray/core/common.py in wrapped_func(self, dim, axis, skipna, kwargs)
42 def wrapped_func(self, dim=None, axis=None, skipna=None, kwargs):
43 return self.reduce(
---> 44 func, dim, axis, skipna=skipna, allow_lazy=True, **kwargs
45 )
46

~/src/xarray/xarray/core/dataarray.py in reduce(self, func, dim, axis, keep_attrs, keepdims, kwargs)
2120 """
2121
-> 2122 var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, kwargs)
2123 return self._replace_maybe_drop_dims(var)
2124

~/src/xarray/xarray/core/variable.py in reduce(self, func, dim, axis, keep_attrs, keepdims, allow_lazy, kwargs)
1456 input_data = self.data if allow_lazy else self.values
1457 if axis is not None:
-> 1458 data = func(input_data, axis=axis, kwargs)
1459 else:
1460 data = func(input_data, **kwargs)

~/src/xarray/xarray/core/duck_array_ops.py in f(values, axis, skipna, kwargs)
279
280 try:
--> 281 return func(values, axis=axis, kwargs)
282 except AttributeError:
283 if isinstance(values, dask_array_type):

~/src/xarray/xarray/core/nanops.py in nanargmax(a, axis)
118 if mask is not None:
119 mask = mask.all(axis=axis)
--> 120 if mask.any():
121 raise ValueError("All-NaN slice encountered")
122 return res

/usr/lib/python3.7/site-packages/dask/array/core.py in bool(self)
1370 )
1371 else:
-> 1372 return bool(self.compute())
1373
1374 nonzero = bool # python 2

/usr/lib/python3.7/site-packages/dask/base.py in compute(self, kwargs)
173 dask.base.compute
174 """
--> 175 (result,) = compute(self, traverse=False, kwargs) 176 return result
177

/usr/lib/python3.7/site-packages/dask/base.py in compute(args, kwargs)
444 keys = [x.dask_keys() for x in collections]
445 postcomputes = [x.dask_postcompute() for x in collections]
--> 446 results = schedule(dsk, keys, kwargs)
447 return repack([f(r, a) for r, (f, a) in zip(results, postcomputes)])
448
```

Expected Output

None of the methods should actually compute: ``` python Total number of computes: 0
Total number of computes: 0
Total number of computes: 0
````

Output of xr.show_versions()

INSTALLED VERSIONS ------------------ commit: None python: 3.7.4 (default, Jul 16 2019, 07:12:58) [GCC 9.1.0] python-bits: 64 OS: Linux OS-release: 5.2.9-arch1-1-ARCH machine: x86_64 processor: byteorder: little LC_ALL: None LANG: de_DE.utf8 LOCALE: de_DE.UTF-8 libhdf5: 1.10.5 libnetcdf: 4.7.0 xarray: 0.12.3+63.g131f6022 pandas: 0.25.0 numpy: 1.17.0 scipy: 1.3.1 netCDF4: 1.5.1.2 pydap: None h5netcdf: 0.7.4 h5py: 2.9.0 Nio: None zarr: None cftime: 1.0.3.4 nc_time_axis: None PseudoNetCDF: None rasterio: 1.0.25 cfgrib: None iris: None bottleneck: 1.2.1 dask: 2.1.0 distributed: 1.27.1 matplotlib: 3.1.1 cartopy: 0.17.0 seaborn: 0.9.0 numbagg: None setuptools: 41.0.1 pip: 19.0.3 conda: None pytest: 5.0.1 IPython: 7.6.1 sphinx: 2.2.0