Test failures with pandas 0.24.0

[shoyer]

From a recent build on Travis-CI:

=================================== FAILURES ===================================
___________________ test_cf_timedelta[timedeltas7-days-nan] ____________________
timedeltas = numpy.datetime64('NaT'), units = 'days', numbers = array(nan)
    @pytest.mark.parametrize(
        ['timedeltas', 'units', 'numbers'],
        [('1D', 'days', np.int64(1)),
         (['1D', '2D', '3D'], 'days', np.array([1, 2, 3], 'int64')),
         ('1h', 'hours', np.int64(1)),
         ('1ms', 'milliseconds', np.int64(1)),
         ('1us', 'microseconds', np.int64(1)),
         (['NaT', '0s', '1s'], None, [np.nan, 0, 1]),
         (['30m', '60m'], 'hours', [0.5, 1.0]),
         (np.timedelta64('NaT', 'ns'), 'days', np.nan),
         (['NaT', 'NaT'], 'days', [np.nan, np.nan])])
    def test_cf_timedelta(timedeltas, units, numbers):
        timedeltas = pd.to_timedelta(timedeltas, box=False)
        numbers = np.array(numbers)
    
        expected = numbers
>       actual, _ = coding.times.encode_cf_timedelta(timedeltas, units)
xarray/tests/test_coding_times.py:550: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
timedeltas = numpy.datetime64('NaT'), units = 'days'
    def encode_cf_timedelta(timedeltas, units=None):
        if units is None:
            units = infer_timedelta_units(timedeltas)
    
        np_unit = _netcdf_to_numpy_timeunit(units)
>       num = 1.0 * timedeltas / np.timedelta64(1, np_unit)
E       TypeError: ufunc multiply cannot use operands with types dtype('float64') and dtype('<M8[ns]')
xarray/coding/times.py:379: TypeError
_____________________ TestDataArray.test_struct_array_dims _____________________
self = <xarray.tests.test_dataarray.TestDataArray object at 0x7fb508944a90>
    def test_struct_array_dims(self):
        """
        This test checks subraction of two DataArrays for the case
        when dimension is a structured array.
        """
        # GH837, GH861
        # checking array subraction when dims are the same
        p_data = np.array([('John', 180), ('Stacy', 150), ('Dick', 200)],
                          dtype=[('name', '|S256'), ('height', object)])
    
        p_data_1 = np.array([('John', 180), ('Stacy', 150), ('Dick', 200)],
                            dtype=[('name', '|S256'), ('height', object)])
    
        p_data_2 = np.array([('John', 180), ('Dick', 200)],
                            dtype=[('name', '|S256'), ('height', object)])
    
        weights_0 = DataArray([80, 56, 120], dims=['participant'],
                              coords={'participant': p_data})
    
        weights_1 = DataArray([81, 52, 115], dims=['participant'],
                              coords={'participant': p_data_1})
    
        actual = weights_1 - weights_0
    
        expected = DataArray([1, -4, -5], dims=['participant'],
                             coords={'participant': p_data})
    
        assert_identical(actual, expected)
    
        # checking array subraction when dims are not the same
        p_data_1 = np.array([('John', 180), ('Stacy', 151), ('Dick', 200)],
                            dtype=[('name', '|S256'), ('height', object)])
    
        weights_1 = DataArray([81, 52, 115], dims=['participant'],
                              coords={'participant': p_data_1})
    
        actual = weights_1 - weights_0
    
        expected = DataArray([1, -5], dims=['participant'],
                             coords={'participant': p_data_2})
    
>       assert_identical(actual, expected)
E       AssertionError: Left and right DataArray objects are not identical
E       
E       Differing values:
E       L
E           array([-5,  1])
E       R
E           array([ 1, -5])
E       Differing coordinates:
E       L * participant  (participant) object (b'Dick', 200) (b'John', 180)
E       R * participant  (participant) [('name', 'S256'), ('height', 'O')] (b'John', 180) (b'Dick', 200)