Merge pull request #1355 from ioam/empty_dataset_support

Added support for constructing empty Dataset types

holoviews/core/data/array.py

@@ -45,6 +45,8 @@ def init(cls, eltype, data, kdims, vdims):
                 data = np.column_stack(data)
             else:
                 raise ValueError('ArrayInterface expects data to be of uniform shape.')
+        elif isinstance(data, list) and data == []:
+            data = np.empty((0,len(dimensions)))
         elif not isinstance(data, np.ndarray):
             data = np.array([], ndmin=2).T if data is None else list(data)
             try:

holoviews/core/data/dask.py

@@ -178,7 +178,7 @@ def aggregate(cls, columns, dimensions, function, **kwargs):
         inbuilts = {'amin': 'min', 'amax': 'max', 'mean': 'mean',
                     'std': 'std', 'sum': 'sum', 'var': 'var'}
         if len(dimensions):
-            groups = reindexed.groupby(cols, sort=False)
+            groups = reindexed.groupby(cols)
             if (function.__name__ in inbuilts):
                 agg = getattr(groups, inbuilts[function.__name__])()
             else:

holoviews/core/data/dictionary.py

@@ -54,6 +54,8 @@ def init(cls, eltype, data, kdims, vdims):
                 else:
                     data = np.atleast_2d(data).T
             data = {k: data[:,i] for i,k in enumerate(dimensions)}
+        elif isinstance(data, list) and data == []:
+            data = OrderedDict([(d, []) for d in dimensions])
         elif isinstance(data, list) and np.isscalar(data[0]):
             data = {dimensions[0]: np.arange(len(data)), dimensions[1]: data}
         elif (isinstance(data, list) and isinstance(data[0], tuple) and len(data[0]) == 2

holoviews/core/data/grid.py

@@ -50,10 +50,14 @@ def init(cls, eltype, data, kdims, vdims):
             raise ValueError('GridInterface interface requires at least '
                              'one value dimension.')
 
+        ndims = len(kdims)
         dimensions = [d.name if isinstance(d, Dimension) else
                       d for d in kdims + vdims]
         if isinstance(data, tuple):
             data = {d: v for d, v in zip(dimensions, data)}
+        elif isinstance(data, list) and data == []:
+            data = {d: np.array([]) for d in dimensions[:ndims]}
+            data.update({d: np.empty((0,) * ndims) for d in dimensions[ndims:]})
         elif not isinstance(data, dict):
             raise TypeError('GridInterface must be instantiated as a '
                             'dictionary or tuple')

holoviews/core/data/iris.py

@@ -87,7 +87,14 @@ def init(cls, eltype, data, kdims, vdims):
             if isinstance(data, tuple):
                 value_array = data[-1]
                 data = {d: vals for d, vals in zip(kdim_names + [vdim.name], data)}
-            elif isinstance(data, dict):
+            elif isinstance(data, list) and data == []:
+                ndims = len(kdims)
+                dimensions = [d.name if isinstance(d, Dimension) else
+                              d for d in kdims + vdims]
+                data = {d: np.array([]) for d in dimensions[:ndims]}
+                data.update({d: np.empty((0,) * ndims) for d in dimensions[ndims:]})
+
+            if isinstance(data, dict):
                 value_array = data[vdim.name]
             coords = [(iris.coords.DimCoord(data[kd.name], long_name=kd.name,
                                             units=kd.unit), ndims-n-1)

holoviews/core/data/pandas.py

@@ -67,6 +67,8 @@ def init(cls, eltype, data, kdims, vdims):
                         data = np.atleast_2d(data).T
                 else:
                     data = tuple(data[:, i] for i in range(data.shape[1]))
+            elif isinstance(data, list) and data == []:
+                data = None
 
             if isinstance(data, tuple):
                 data = [np.array(d) if not isinstance(d, np.ndarray) else d for d in data]

holoviews/core/data/xarray.py

@@ -58,6 +58,12 @@ def init(cls, eltype, data, kdims, vdims):
                      for vd in vdims]
             if isinstance(data, tuple):
                 data = {d.name: vals for d, vals in zip(kdims + vdims, data)}
+            elif isinstance(data, list) and data == []:
+                ndims = len(kdims)
+                dimensions = [d.name if isinstance(d, Dimension) else
+                              d for d in kdims + vdims]
+                data = {d: np.array([]) for d in dimensions[:ndims]}
+                data.update({d: np.empty((0,) * ndims) for d in dimensions[ndims:]})
             if not isinstance(data, dict):
                 raise TypeError('XArrayInterface could not interpret data type')
             coords = [(kd.name, data[kd.name]) for kd in kdims][::-1]

tests/testdataset.py

@@ -101,6 +101,11 @@ def test_dataset_dataframe_init_hm_alias(self):
                           kdims=[('x', 'X-label')], vdims=[('x2', 'X2-label')])
         self.assertTrue(isinstance(dataset.data, self.data_instance_type))
 
+    def test_dataset_empty_list_init(self):
+        dataset = Dataset([], kdims=['x'], vdims=['y'])
+        for d in 'xy':
+            self.assertEqual(dataset.dimension_values(d), np.array([]))
+
     # Properties and information
 
     def test_dataset_shape(self):
@@ -613,6 +618,12 @@ def setUp(self):
     # Literal formats that have been previously been supported but
     # currently are only supported via NdElement.
 
+    def test_dataset_empty_list_init(self):
+        """
+        Will soon be deprecated, new features not supported
+        """
+        raise SkipTest("Not supported")
+
     def test_dataset_double_zip_init(self):
         dataset = Dataset(zip(zip(self.gender, self.age),
                               zip(self.weight, self.height)),