What's the best way to assert DataArray's dtype?

[ahuang11]

import numpy as np
import xarray as xr

ds = xr.tutorial.open_dataset('air_temperature')
print(ds)
assert isinstance(ds['time'].dtype, np.datetime64)

yields False so I was wondering what's the proper way to check?

<xarray.Dataset>
Dimensions:  (lat: 25, lon: 53, time: 2920)
Coordinates:
  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0
  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0
  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00
Data variables:
    air      (time, lat, lon) float32 ...
Attributes:
    Conventions:  COARDS
    title:        4x daily NMC reanalysis (1948)
    description:  Data is from NMC initialized reanalysis\n(4x/day).  These a...
    platform:     Model
    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...

[andersy005]

It's my understanding that due to the complexity of hierarchy of type objects in Numpy, using np.issubdtype() is more reliable/robust than .isinstance():

In [18]: np.issubdtype(ds.air.dtype, np.float32)
Out[18]: True

In [19]: np.issubdtype(ds.time.dtype, np.datetime64)
Out[19]: True

[keewis]

this also works:

>>> ds.time.dtype.kind == "M"
True
>>> ds.time.dtype == np.datetime64
True

[shoyer]

np.datetime64 is actually a NumPy "scalar" type object, not a dtype. (Yes, it's confusing that these are different!)

So if another option is to pull out the NumPy "scalar type" object, and use issubclass for checking the relationship, e.g., assert issubclass(ds['time'].dtype.type, np.datetime64)

[ahuang11]

There's also

from pandas.api.types import (
    is_datetime64_any_dtype,
    is_timedelta64_dtype,
    is_numeric_dtype,
    is_string_dtype,
)

which works for xarray