Remove .T as shortcut for transpose() (#2509)

* Remove .T as shortcut for transpose()

* fix whats-new

* remove Dataset.__dir__

* Update whats-new.rst

* Update whats-new.rst

Author: dcherian

doc/whats-new.rst

@@ -33,6 +33,8 @@ v0.11.0 (unreleased)
 Breaking changes
 ~~~~~~~~~~~~~~~~
 
+- ``Dataset.T`` has been removed as a shortcut for :py:meth:`Dataset.transpose`.
+  Call :py:meth:`Dataset.transpose` directly instead.
 - Iterating over a ``Dataset`` now includes only data variables, not coordinates.
   Similarily, calling ``len`` and ``bool`` on a ``Dataset`` now  
   includes only data variables

xarray/core/dataset.py

@@ -916,13 +916,6 @@ def _item_sources(self):
         return [self.data_vars, self.coords, {d: self[d] for d in self.dims},
                 LevelCoordinatesSource(self)]
 
-    def __dir__(self):
-        # In order to suppress a deprecation warning in Ipython autocompletion
-        # .T is explicitly removed from __dir__. GH: issue 1675
-        d = super(Dataset, self).__dir__()
-        d.remove('T')
-        return d
-
     def __contains__(self, key):
         """The 'in' operator will return true or false depending on whether
         'key' is an array in the dataset or not.
@@ -2647,13 +2640,6 @@ def transpose(self, *dims):
             ds._variables[name] = var.transpose(*var_dims)
         return ds
 
-    @property
-    def T(self):
-        warnings.warn('xarray.Dataset.T has been deprecated as an alias for '
-                      '`.transpose()`. It will be removed in xarray v0.11.',
-                      FutureWarning, stacklevel=2)
-        return self.transpose()
-
     def dropna(self, dim, how='any', thresh=None, subset=None):
         """Returns a new dataset with dropped labels for missing values along
         the provided dimension.

xarray/tests/test_dataset.py

@@ -3801,10 +3801,6 @@ def test_dataset_transpose(self):
         expected = ds.apply(lambda x: x.transpose())
         assert_identical(expected, actual)
 
-        with pytest.warns(FutureWarning):
-            actual = ds.T
-        assert_identical(expected, actual)
-
         actual = ds.transpose('x', 'y')
         expected = ds.apply(lambda x: x.transpose('x', 'y'))
         assert_identical(expected, actual)