Handling pandas Series and single column DataFrames

holoviews/core/data/dictionary.py

@@ -107,7 +107,7 @@ def init(cls, eltype, data, kdims, vdims):
 
     @classmethod
     def validate(cls, dataset, vdims=True):
-        dim_types = 'key' if vdims else 'all'
+        dim_types = 'all' if vdims else 'key'
         dimensions = dataset.dimensions(dim_types, label='name')
         not_found = [d for d in dimensions if d not in dataset.data]
         if not_found:

holoviews/core/data/pandas.py

@@ -35,7 +35,11 @@ def init(cls, eltype, data, kdims, vdims):
         element_params = eltype.params()
         kdim_param = element_params['kdims']
         vdim_param = element_params['vdims']
+        if util.is_series(data):
+            data = data.to_frame()
         if util.is_dataframe(data):
+            if eltype._auto_indexable_1d and len(data.columns) == 1:
+                data = data.reset_index()
             if isinstance(kdim_param.bounds[1], int):
                 ndim = min([kdim_param.bounds[1], len(kdim_param.default)])
             else:
@@ -108,7 +112,7 @@ def isscalar(cls, dataset, dim):
 
     @classmethod
     def validate(cls, dataset, vdims=True):
-        dim_types = 'key' if vdims else 'all'
+        dim_types = 'all' if vdims else 'key'
         dimensions = dataset.dimensions(dim_types, label='name')
         not_found = [d for d in dimensions if d not in dataset.data.columns]
         if not_found:

holoviews/core/util.py

@@ -1134,12 +1134,20 @@ def find_file(folder, filename):
 
 def is_dataframe(data):
     """
-    Checks whether the supplied data is DataFrame type.
+    Checks whether the supplied data is of DataFrame type.
     """
     return((pd is not None and isinstance(data, pd.DataFrame)) or
           (dd is not None and isinstance(data, dd.DataFrame)))
 
 
+def is_series(data):
+    """
+    Checks whether the supplied data is of Series type.
+    """
+    return((pd is not None and isinstance(data, pd.Series)) or
+          (dd is not None and isinstance(data, dd.Series)))
+
+
 def get_param_values(data):
     params = dict(kdims=data.kdims, vdims=data.vdims,
                   label=data.label)

tests/testdataset.py

@@ -863,6 +863,14 @@ def setUp(self):
         self.data_instance_type = pd.DataFrame
         self.init_column_data()
 
+    def test_dataset_series_construct(self):
+        ds = Dataset(pd.Series([1, 2, 3], name='A'))
+        self.assertEqual(ds, Dataset(([0, 1, 2], [1, 2, 3]), ['index', 'A']))
+
+    def test_dataset_single_column_construct(self):
+        ds = Dataset(pd.DataFrame([1, 2, 3], columns=['A']))
+        self.assertEqual(ds, Dataset(([0, 1, 2], [1, 2, 3]), ['index', 'A']))
+
     def test_dataset_extract_vdims(self):
         df = pd.DataFrame({'x': [1, 2, 3], 'y': [1, 2, 3], 'z': [1, 2, 3]},
                           columns=['x', 'y', 'z'])

tests/teststatselements.py

@@ -1,7 +1,9 @@
-import numpy as np
+from unittest import SkipTest
 
+import numpy as np
 import holoviews
 from holoviews.core.dimension import Dimension
+from holoviews.core.util import pd
 from holoviews.core.options import Compositor, Store
 from holoviews.element import (Distribution, Bivariate, Points, Image,
                                Curve, Area, Contours, Polygons)
@@ -15,6 +17,20 @@ def test_distribution_array_constructor(self):
         self.assertEqual(dist.kdims, [Dimension('Value')])
         self.assertEqual(dist.vdims, [Dimension('Density')])
 
+    def test_distribution_series_constructor(self):
+        if pd is None:
+            raise SkipTest("Test requires pandas")
+        dist = Distribution(pd.Series([0, 1, 2], name='Value'))
+        self.assertEqual(dist.kdims, [Dimension('Value')])
+        self.assertEqual(dist.vdims, [Dimension('Density')])
+
+    def test_distribution_dframe_constructor(self):
+        if pd is None:
+            raise SkipTest("Test requires pandas")
+        dist = Distribution(pd.DataFrame({'Value': [0, 1, 2]}))
+        self.assertEqual(dist.kdims, [Dimension('Value')])
+        self.assertEqual(dist.vdims, [Dimension('Density')])
+
     def test_distribution_array_constructor_custom_vdim(self):
         dist = Distribution(np.array([0, 1, 2]), vdims=['Test'])
         self.assertEqual(dist.kdims, [Dimension('Value')])